These design patterns are useful for building reliable, scalable, secure applications in the cloud.
Each pattern describes the problem that the pattern addresses, considerations for applying the pattern. However, most of the patterns are relevant to any distributed system, whether hosted on Azure or on other cloud platforms.
Availability is the proportion of time that the system is functional and working, usually measured as a percentage of uptime. It can be affected by system errors, infrastructure problems, malicious attacks, and system load. Cloud applications typically provide users with a service level agreement (SLA), so applications must be designed to maximize availability.
Data management is the key element of cloud applications, and influences most of the quality attributes. Data is typically hosted in different locations and across multiple servers for reasons such as performance, scalability or availability, and this can present a range of challenges. For example, data consistency must be maintained, and data will typically need to be synchronized across different locations.
Good design encompasses factors such as consistency and coherence in component design and deployment, maintainability to simplify administration and development, and reusability to allow components and subsystems to be used in other applications and in other scenarios. Decisions made during the design and implementation phase have a huge impact on the quality and the total cost of ownership of cloud hosted applications and services.
The distributed nature of cloud applications requires a messaging infrastructure that connects the components and services, ideally in a loosely coupled manner in order to maximize scalability. Asynchronous messaging is widely used, and provides many benefits, but also brings challenges such as the ordering of messages, poison message management, idempotency, and more.
Cloud applications run in a remote datacenter where you do not have full control of the infrastructure or, in some cases, the operating system. This can make management and monitoring more difficult than an on-premises deployment. Applications must expose runtime information that administrators and operators can use to manage and monitor the system, as well as supporting changing business requirements and customization without requiring the application to be stopped or redeployed.
Performance is an indication of the responsiveness of a system to execute any action within a given time interval, while scalability is ability of a system either to handle increases in load without impact on performance or for the available resources to be readily increased. Cloud applications typically encounter variable workloads and peaks in activity. Predicting these, especially in a multitenant scenario, is almost impossible. Instead, applications should be able to scale out within limits to meet peaks in demand, and scale in when demand decreases. Scalability concerns not just compute instances, but other elements such as data storage, messaging infrastructure, and more.
Resiliency is the ability of a system to gracefully handle and recover from failures. The nature of cloud hosting, where applications are often multitenant, use shared platform services, compete for resources and bandwidth, communicate over the Internet, and run on commodity hardware means there is an increased likelihood that both transient and more permanent faults will arise. Detecting failures, and recovering quickly and efficiently, is necessary to maintain resiliency.
Security is the capability of a system to prevent malicious or accidental actions outside of the designed usage, and to prevent disclosure or loss of information. Cloud applications are exposed on the Internet outside trusted on-premises boundaries, are often open to the public, and may serve untrusted users. Applications must be designed and deployed in a way that protects them from malicious attacks, restricts access to only approved users, and protects sensitive data.
| Category | Pattern | Summary |
|---|---|---|
| Availability | Geodes | Deploy backend services into a set of geographical nodes, each of which can service any client request in any region. |
| Availability | Health Endpoint Monitoring Pattern | Implement functional checks in an application that external tools can access through exposed endpoints at regular intervals. |
| Availability | Queue-Based Load Leveling Pattern | Use a queue that acts as a buffer between a task and a service that it invokes, to smooth intermittent heavy loads. |
| Availability | Throttling Pattern | Control the consumption of resources by an instance of an application, an individual tenant, or an entire service. |
| Data Management | Cache-Aside Pattern | Load data on demand into a cache from a data store |
| Data Management | Command and Query Responsibility Segregation (CQRS) Pattern | Segregate operations that read data from operations that update data by using separate interfaces. |
| Data Management | Event Sourcing Pattern | Use an append-only store to record the full series of events that describe actions taken on data in a domain |
| Data Management | Index Table Pattern | Create indexes over the fields in data stores that are frequently referenced by queries. |
| Data Management | Materialized View Pattern | Generate prepopulated views over the data in one or more data stores when the data isn't ideally formatted for required query operations. |
| Data Management | Sharding Pattern | Divide a data store into a set of horizontal partitions or shards. |
| Data Management | Static Content Hosting Pattern | Deploy static content to a cloud-based storage service that can deliver them directly to the client. |
| Data Management | Valet Key Pattern | Use a token or key that provides clients with restricted direct access to a specific resource or service. |
|
Design and Implementation |
Ambassador Pattern | Create helper services that send network requests on behalf of a consumer service or application. |
| Design and Implementation | Anti-Corruption Layer Pattern | Implement a façade or adapter layer between a modern application and a legacy system. |
| Design and Implementation | Backends for Frontends Pattern | Create separate backend services to be consumed by specific frontend applications or interfaces. |
| Design and Implementation | Command and Query Responsibility Segregation (CQRS) Pattern | Segregate operations that read data from operations that update data by using separate interfaces |
| Compute Resource Consolidation Pattern | ||
| External Configuration Store Pattern | ||
| Gateway Aggregation Pattern | ||
| Gateway Offloading Pattern | ||
| Gateway Routing Pattern | ||
| Leader Election Pattern | ||
| Pipes and Filters Pattern | ||
| Sidecar Pattern | ||
| Static Content Hosting Pattern | ||
| Strangler Pattern | ||
| Ambassador Pattern | ||
| Anti-Corruption Layer Pattern | ||
| External Configuration Store Pattern | ||
| Gateway Aggregation Pattern | ||
| Gateway Offloading Pattern | ||
| Gateway Routing Pattern | ||
| Health Endpoint Monitoring Pattern | ||
| Sidecar Pattern | ||
| Strangler Pattern | ||
| Asynchronous Request-Reply Pattern | ||
| Claim-Check Pattern | ||
| Choreography Pattern | ||
| Competing Consumers Pattern | ||
| Pipes and Filters Pattern | ||
| Priority Queue Pattern | ||
| Publisher-Subscriber Pattern | ||
| Queue-Based Load Leveling Pattern | ||
| Scheduler Agent Supervisor Pattern | ||
| Sequential Convoy Pattern | ||
| Cache-Aside Pattern | ||
| Choreography Pattern | ||
| Command and Query Responsibility Segregation (CQRS) Pattern | ||
| Event Sourcing Pattern | ||
| Geodes | ||
| Index Table Pattern | ||
| Materialized View Pattern | ||
| Priority Queue Pattern | ||
| Queue-Based Load Leveling Pattern | ||
| Sharding Pattern | ||
| Static Content Hosting Pattern | ||
| Throttling Pattern | ||
| Bulkhead Pattern | ||
| Circuit Breaker Pattern | ||
| Compensating Transaction Pattern | ||
| Health Endpoint Monitoring Pattern | ||
| Leader Election Pattern | ||
| Queue-Based Load Leveling Pattern | ||
| Retry Pattern | ||
| Scheduler Agent Supervisor Pattern | ||
| Federated Identity Pattern | ||
| Gatekeeper Pattern | ||
| Valet Key Pattern |
Create helper services that send network requests on behalf of a consumer service or application. An ambassador service can be thought of as an out-of-process proxy that is co-located with the client.
This pattern can be useful for offloading common client connectivity tasks such as monitoring, logging, routing, security (such as TLS), and resiliency patterns in a language agnostic way. It is often used with legacy applications, or other applications that are difficult to modify, in order to extend their networking capabilities. It can also enable a specialized team to implement those features.
Resilient cloud-based applications require features such as circuit breaking, routing, metering and monitoring, and the ability to make network-related configuration updates. It may be difficult or impossible to update legacy applications or existing code libraries to add these features, because the code is no longer maintained or can't be easily modified by the development team.
Network calls may also require substantial configuration for connection, authentication, and authorization. If these calls are used across multiple applications, built using multiple languages and frameworks, the calls must be configured for each of these instances. In addition, network and security functionality may need to be managed by a central team within your organization. With a large code base, it can be risky for that team to update application code they aren't familiar with.
Put client frameworks and libraries into an external process that acts as a proxy between your application and external services. Deploy the proxy on the same host environment as your application to allow control over routing, resiliency, security features, and to avoid any host-related access restrictions. You can also use the ambassador pattern to standardize and extend instrumentation. The proxy can monitor performance metrics such as latency or resource usage, and this monitoring happens in the same host environment as the application.
Features that are offloaded to the ambassador can be managed independently of the application. You can update and modify the ambassador without disturbing the application's legacy functionality. It also allows for separate, specialized teams to implement and maintain security, networking, or authentication features that have been moved to the ambassador.
Ambassador services can be deployed as a sidecar to accompany the lifecycle of a consuming application or service. Alternatively, if an ambassador is shared by multiple separate processes on a common host, it can be deployed as a daemon or Windows service. If the consuming service is containerized, the ambassador should be created as a separate container on the same host, with the appropriate links configured for communication.
- The proxy adds some latency overhead. Consider whether a client library, invoked directly by the application, is a better approach.
- Consider the possible impact of including generalized features in the proxy. For example, the ambassador could handle retries, but that might not be safe unless all operations are idempotent.
- Consider a mechanism to allow the client to pass some context to the proxy, as well as back to the client. For example, include HTTP request headers to opt out of retry or specify the maximum number of times to retry.
- Consider how you will package and deploy the proxy.
- Consider whether to use a single shared instance for all clients or an instance for each client.
Use this pattern when you:
- Need to build a common set of client connectivity features for multiple languages or frameworks.
- Need to offload cross-cutting client connectivity concerns to infrastructure developers or other more specialized teams.
- Need to support cloud or cluster connectivity requirements in a legacy application or an application that is difficult to modify.
This pattern may not be suitable:
- When network request latency is critical. A proxy will introduce some overhead, although minimal, and in some cases this may affect the application.
- When client connectivity features are consumed by a single language. In that case, a better option might be a client library that is distributed to the development teams as a package.
- When connectivity features cannot be generalized and require deeper integration with the client application.
Create helper services that send network requests on behalf of a consumer service or application. An ambassador service can be thought of as an out-of-process proxy that is co-located with the client.
This pattern can be useful for offloading common client connectivity tasks such as monitoring, logging, routing, security (such as TLS), and resiliency patterns in a language agnostic way. It is often used with legacy applications, or other applications that are difficult to modify, in order to extend their networking capabilities. It can also enable a specialized team to implement those features.
Resilient cloud-based applications require features such as circuit breaking, routing, metering and monitoring, and the ability to make network-related configuration updates. It may be difficult or impossible to update legacy applications or existing code libraries to add these features, because the code is no longer maintained or can't be easily modified by the development team.
Network calls may also require substantial configuration for connection, authentication, and authorization. If these calls are used across multiple applications, built using multiple languages and frameworks, the calls must be configured for each of these instances. In addition, network and security functionality may need to be managed by a central team within your organization. With a large code base, it can be risky for that team to update application code they aren't familiar with.
Put client frameworks and libraries into an external process that acts as a proxy between your application and external services. Deploy the proxy on the same host environment as your application to allow control over routing, resiliency, security features, and to avoid any host-related access restrictions. You can also use the ambassador pattern to standardize and extend instrumentation. The proxy can monitor performance metrics such as latency or resource usage, and this monitoring happens in the same host environment as the application.
Features that are offloaded to the ambassador can be managed independently of the application. You can update and modify the ambassador without disturbing the application's legacy functionality. It also allows for separate, specialized teams to implement and maintain security, networking, or authentication features that have been moved to the ambassador.
Ambassador services can be deployed as a sidecar to accompany the lifecycle of a consuming application or service. Alternatively, if an ambassador is shared by multiple separate processes on a common host, it can be deployed as a daemon or Windows service. If the consuming service is containerized, the ambassador should be created as a separate container on the same host, with the appropriate links configured for communication.
- The proxy adds some latency overhead. Consider whether a client library, invoked directly by the application, is a better approach.
- Consider the possible impact of including generalized features in the proxy. For example, the ambassador could handle retries, but that might not be safe unless all operations are idempotent.
- Consider a mechanism to allow the client to pass some context to the proxy, as well as back to the client. For example, include HTTP request headers to opt out of retry or specify the maximum number of times to retry.
- Consider how you will package and deploy the proxy.
- Consider whether to use a single shared instance for all clients or an instance for each client.
Use this pattern when you:
- Need to build a common set of client connectivity features for multiple languages or frameworks.
- Need to offload cross-cutting client connectivity concerns to infrastructure developers or other more specialized teams.
- Need to support cloud or cluster connectivity requirements in a legacy application or an application that is difficult to modify.
This pattern may not be suitable:
- When network request latency is critical. A proxy will introduce some overhead, although minimal, and in some cases this may affect the application.
- When client connectivity features are consumed by a single language. In that case, a better option might be a client library that is distributed to the development teams as a package.
- When connectivity features cannot be generalized and require deeper integration with the client application.
Decouple backend processing from a frontend host, where backend processing needs to be asynchronous, but the frontend still needs a clear response.
In modern application development, it's normal for client applications --- often code running in a web-client (browser) --- to depend on remote APIs to provide business logic and compose functionality. These APIs may be directly related to the application or may be shared services provided by a third party. Commonly these API calls take place over the HTTP(S) protocol and follow REST semantics.
In most cases, APIs for a client application are designed to respond quickly, on the order of 100 ms or less. Many factors can affect the response latency, including:
- An application's hosting stack
- Security components
- The relative geographic location of the caller and the backend
- Network infrastructure
- Current load
- The size of the request payload
- Processing queue length
- The time for the backend to process the request
Any of these factors can add latency to the response. Some can be mitigated by scaling out the backend. Others, such as network infrastructure, are largely out of the control of the application developer. Most APIs can respond quickly enough for responses to arrive back over the same connection. Application code can make a synchronous API call in a non-blocking way, giving the appearance of asynchronous processing, which is recommended for I/O-bound operations.
In some scenarios, however, the work done by backend may be long-running, on the order of seconds, or might be a background process that is executed in minutes or even hours. In that case, it isn't feasible to wait for the work to complete before responding to the request. This situation is a potential problem for any synchronous request-reply pattern.
Some architectures solve this problem by using a message broker to separate the request and response stages. This separation is often achieved by use of the Queue Based Load Leveling Pattern. This separation can allow the client process and the backend API to scale independently. But this separation also brings additional complexity when the client requires success notification, as this step needs to become asynchronous.
Many of the same considerations discussed for client applications also apply for server-to-server REST API calls in distributed systems --- for example, in a microservices architecture.
One solution to this problem is to use HTTP polling. Polling is useful to client-side code, as it can be hard to provide call-back endpoints or use long running connections. Even when callbacks are possible, the extra libraries and services that are required can sometimes add too much extra complexity.
- The client application makes a synchronous call to the API, triggering a long-running operation on the backend.
- The API responds synchronously as quickly as possible. It returns an HTTP 202 (Accepted) status code, acknowledging that the request has been received for processing. Note: The API should validate both the request and the action to be performed before starting the long running process. If the request is invalid, reply immediately with an error code such as HTTP 400 (Bad Request).
- The response holds a location reference pointing to an endpoint that the client can poll to check for the result of the long running operation.
- The API offloads processing to another component, such as a message queue.
- While the work is still pending, the status endpoint returns HTTP 202. Once the work is complete, the status endpoint can either return a resource that indicates completion, or redirect to another resource URL. For example, if the asynchronous operation creates a new resource, the status endpoint would redirect to the URL for that resource.
The following diagram shows a typical flow:
- The client sends a request and receives an HTTP 202 (Accepted) response.
- The client sends an HTTP GET request to the status endpoint. The work is still pending, so this call also returns HTTP 202.
- At some point, the work is complete and the status endpoint returns 302 (Found) redirecting to the resource.
- The client fetches the resource at the specified URL.
- There are a number of possible ways to implement this pattern over HTTP and not all upstream services have the same semantics. For example, most services won't return an HTTP 202 response back from a GET method when a remote process hasn't finished. Following pure REST semantics, they should return HTTP 404 (Not Found). This response makes sense when you consider the result of the call isn't present yet.
- An HTTP 202 response should indicate the location and frequency that the client should poll for the response. It should have the following additional headers:
| Header | Description | Notes |
|---|---|---|
| Location | A URL the client should poll for a response status. | This URL could be a SAS token with the Valet Key Pattern being appropriate if this location needs access control. The valet key pattern is also valid when response polling needs offloading to another backend |
| Retry-After | An estimate of when processing will complete | This header is designed to prevent polling clients from overwhelming the back-end with retries. |
- You may need to use a processing proxy or facade to manipulate the response headers or payload depending on the underlying services used.
- If the status endpoint redirects on completion, either HTTP 302 or HTTP 303 are appropriate return codes, depending on the exact semantics you support.
- Upon successful processing, the resource specified by the Location header should return an appropriate HTTP response code such as 200 (OK), 201 (Created), or 204 (No Content).
- If an error occurs during processing, persist the error at the resource URL described in the Location header and ideally return an appropriate response code to the client from that resource (4xx code).
- Not all solutions will implement this pattern in the same way and some services will include additional or alternate headers. For example, Azure Resource Manager uses a modified variant of this pattern. For more information, see Azure Resource Manager Async Operations.
- Legacy clients might not support this pattern. In that case, you might need to place a facade over the asynchronous API to hide the asynchronous processing from the original client. For example, Azure Logic Apps supports this pattern natively can be used as an integration layer between an asynchronous API and a client that makes synchronous calls. See Perform long-running tasks with the webhook action pattern.
- In some scenarios, you might want to provide a way for clients to cancel a long-running request. In that case, the backend service must support some form of cancellation instruction.
Use this pattern for:
- Client-side code, such as browser applications, where it's difficult to provide call-back endpoints, or the use of long-running connections adds too much additional complexity.
- Service calls where only the HTTP protocol is available and the return service can't fire callbacks because of firewall restrictions on the client-side.
- Service calls that need to be integrated with legacy architectures that don't support modern callback technologies such as WebSockets or webhooks.
This pattern might not be suitable when:
- You can use a service built for asynchronous notifications instead, such as Azure Event Grid.
- Responses must stream in real time to the client.
- The client needs to collect many results, and received latency of those results is important. Consider a service bus pattern instead.
- You can use server-side persistent network connections such as WebSockets or SignalR. These services can be used to notify the caller of the result.
- The network design allows you to open up ports to receive asynchronous callbacks or webhooks.
Create separate backend services to be consumed by specific frontend applications or interfaces. This pattern is useful when you want to avoid customizing a single backend for multiple interfaces. This pattern was first described by Sam Newman.
An application may initially be targeted at a desktop web UI. Typically, a backend service is developed in parallel that provides the features needed for that UI. As the application's user base grows, a mobile application is developed that must interact with the same backend. The backend service becomes a general-purpose backend, serving the requirements of both the desktop and mobile interfaces.
But the capabilities of a mobile device differ significantly from a desktop browser, in terms of screen size, performance, and display limitations. As a result, the requirements for a mobile application backend differ from the desktop web UI.
These differences result in competing requirements for the backend. The backend requires regular and significant changes to serve both the desktop web UI and the mobile application. Often, separate interface teams work on each frontend, causing the backend to become a bottleneck in the development process. Conflicting update requirements, and the need to keep the service working for both frontends, can result in spending a lot of effort on a single deployable resource.
As the development activity focuses on the backend service, a separate team may be created to manage and maintain the backend. Ultimately, this results in a disconnect between the interface and backend development teams, placing a burden on the backend team to balance the competing requirements of the different UI teams. When one interface team requires changes to the backend, those changes must be validated with other interface teams before they can be integrated into the backend.
Create one backend per user interface. Fine-tune the behavior and performance of each backend to best match the needs of the frontend environment, without worrying about affecting other frontend experiences.
Because each backend is specific to one interface, it can be optimized for that interface. As a result, it will be smaller, less complex, and likely faster than a generic backend that tries to satisfy the requirements for all interfaces. Each interface team has autonomy to control their own backend and doesn't rely on a centralized backend development team. This gives the interface team flexibility in language selection, release cadence, prioritization of workload, and feature integration in their backend.
For more information, see Pattern: Backends For Frontends.
- onsider how many backends to deploy.
- If different interfaces (such as mobile clients) will make the same requests, consider whether it is necessary to implement a backend for each interface, or if a single backend will suffice.
- Code duplication across services is highly likely when implementing this pattern.
- Frontend-focused backend services should only contain client-specific logic and behavior. General business logic and other global features should be managed elsewhere in your application.
- Think about how this pattern might be reflected in the responsibilities of a development team.
- Consider how long it will take to implement this pattern. Will the effort of building the new backends incur technical debt, while you continue to support the existing generic backend?
Use this pattern when:
- A shared or general purpose backend service must be maintained with significant development overhead.
- You want to optimize the backend for the requirements of specific client interfaces.
- Customizations are made to a general-purpose backend to accommodate multiple interfaces.
- An alternative language is better suited for the backend of a different user interface.
This pattern may not be suitable:
- When interfaces make the same or similar requests to the backend.
- When only one interface is used to interact with the backend.
The Bulkhead pattern is a type of application design that is tolerant of failure. In a bulkhead architecture, elements of an application are isolated into pools so that if one fails, the others will continue to function. It's named after the sectioned partitions (bulkheads) of a ship's hull. If the hull of a ship is compromised, only the damaged section fills with water, which prevents the ship from sinking.
A cloud-based application may include multiple services, with each service having one or more consumers. Excessive load or failure in a service will impact all consumers of the service.
Moreover, a consumer may send requests to multiple services simultaneously, using resources for each request. When the consumer sends a request to a service that is misconfigured or not responding, the resources used by the client's request may not be freed in a timely manner. As requests to the service continue, those resources may be exhausted. For example, the client's connection pool may be exhausted. At that point, requests by the consumer to other services are affected. Eventually the consumer can no longer send requests to other services, not just the original unresponsive service.
The same issue of resource exhaustion affects services with multiple consumers. A large number of requests originating from one client may exhaust available resources in the service. Other consumers are no longer able to consume the service, causing a cascading failure effect.
Partition service instances into different groups, based on consumer load and availability requirements. This design helps to isolate failures, and allows you to sustain service functionality for some consumers, even during a failure.
A consumer can also partition resources, to ensure that resources used to call one service don't affect the resources used to call another service. For example, a consumer that calls multiple services may be assigned a connection pool for each service. If a service begins to fail, it only affects the connection pool assigned for that service, allowing the consumer to continue using the other services.
The benefits of this pattern include:
- Isolates consumers and services from cascading failures. An issue affecting a consumer or service can be isolated within its own bulkhead, preventing the entire solution from failing.
- Allows you to preserve some functionality in the event of a service failure. Other services and features of the application will continue to work.
- Allows you to deploy services that offer a different quality of service for consuming applications. A high-priority consumer pool can be configured to use high-priority services.
The following diagram shows bulkheads structured around connection pools that call individual services. If Service A fails or causes some other issue, the connection pool is isolated, so only workloads using the thread pool assigned to Service A are affected. Workloads that use Service B and C are not affected and can continue working without interruption.
The next diagram shows multiple clients calling a single service. Each client is assigned a separate service instance. Client 1 has made too many requests and overwhelmed its instance. Because each service instance is isolated from the others, the other clients can continue making calls.
- Define partitions around the business and technical requirements of the application.
- When partitioning services or consumers into bulkheads, consider the level of isolation offered by the technology as well as the overhead in terms of cost, performance and manageability.
- Consider combining bulkheads with retry, circuit breaker, and throttling patterns to provide more sophisticated fault handling.
- When partitioning consumers into bulkheads, consider using processes, thread pools, and semaphores. Projects like resilience4j and Polly offer a framework for creating consumer bulkheads.
- When partitioning services into bulkheads, consider deploying them into separate virtual machines, containers, or processes. Containers offer a good balance of resource isolation with fairly low overhead.
- Services that communicate using asynchronous messages can be isolated through different sets of queues. Each queue can have a dedicated set of instances processing messages on the queue, or a single group of instances using an algorithm to dequeue and dispatch processing.
- Determine the level of granularity for the bulkheads. For example, if you want to distribute tenants across partitions, you could place each tenant into a separate partition, or put several tenants into one partition.
- Monitor each partition's performance and SLA.
- Isolate resources used to consume a set of backend services, especially if the application can provide some level of functionality even when one of the services is not responding.
- Isolate critical consumers from standard consumers.
- Protect the application from cascading failures.
This pattern may not be suitable when:
- Less efficient use of resources may not be acceptable in the project.
- The added complexity is not necessary
Load data on demand into a cache from a data store. This can improve performance and also helps to maintain consistency between data held in the cache and data in the underlying data store.
Applications use a cache to improve repeated access to information held in a data store. However, it's impractical to expect that cached data will always be completely consistent with the data in the data store. Applications should implement a strategy that helps to ensure that the data in the cache is as up-to-date as possible, but can also detect and handle situations that arise when the data in the cache has become stale.
Many commercial caching systems provide read-through and write-through/write-behind operations. In these systems, an application retrieves data by referencing the cache. If the data isn't in the cache, it's retrieved from the data store and added to the cache. Any modifications to data held in the cache are automatically written back to the data store as well.
For caches that don't provide this functionality, it's the responsibility of the applications that use the cache to maintain the data.
An application can emulate the functionality of read-through caching by implementing the cache-aside strategy. This strategy loads data into the cache on demand. The figure illustrates using the Cache-Aside pattern to store data in the cache.
If an application updates information, it can follow the write-through strategy by making the modification to the data store, and by invalidating the corresponding item in the cache.
When the item is next required, using the cache-aside strategy will cause the updated data to be retrieved from the data store and added back into the cache.
Consider the following points when deciding how to implement this pattern:
Lifetime of cached data. Many caches implement an expiration policy that invalidates data and removes it from the cache if it's not accessed for a specified period. For cache-aside to be effective, ensure that the expiration policy matches the pattern of access for applications that use the data. Don't make the expiration period too short because this can cause applications to continually retrieve data from the data store and add it to the cache. Similarly, don't make the expiration period so long that the cached data is likely to become stale. Remember that caching is most effective for relatively static data, or data that is read frequently.
Evicting data. Most caches have a limited size compared to the data store where the data originates, and they'll evict data if necessary. Most caches adopt a least-recently-used policy for selecting items to evict, but this might be customizable. Configure the global expiration property and other properties of the cache, and the expiration property of each cached item, to ensure that the cache is cost effective. It isn't always appropriate to apply a global eviction policy to every item in the cache. For example, if a cached item is very expensive to retrieve from the data store, it can be beneficial to keep this item in the cache at the expense of more frequently accessed but less costly items.
Priming the cache. Many solutions prepopulate the cache with the data that an application is likely to need as part of the startup processing. The Cache-Aside pattern can still be useful if some of this data expires or is evicted.
Consistency. Implementing the Cache-Aside pattern doesn't guarantee consistency between the data store and the cache. An item in the data store can be changed at any time by an external process, and this change might not be reflected in the cache until the next time the item is loaded. In a system that replicates data across data stores, this problem can become serious if synchronization occurs frequently.
Local (in-memory) caching. A cache could be local to an application instance and stored in-memory. Cache-aside can be useful in this environment if an application repeatedly accesses the same data. However, a local cache is private and so different application instances could each have a copy of the same cached data. This data could quickly become inconsistent between caches, so it might be necessary to expire data held in a private cache and refresh it more frequently. In these scenarios, consider investigating the use of a shared or a distributed caching mechanism.
Use this pattern when:
- A cache doesn't provide native read-through and write-through operations.
- Resource demand is unpredictable. This pattern enables applications to load data on demand. It makes no assumptions about which data an application will require in advance.
This pattern might not be suitable:
- When the cached data set is static. If the data will fit into the available cache space, prime the cache with the data on startup and apply a policy that prevents the data from expiring.
- For caching session state information in a web application hosted in a web farm. In this environment, you should avoid introducing dependencies based on client-server affinity.
Have each component of the system participate in the decision-making process about the workflow of a business transaction, instead of relying on a central point of control.
In microservices architecture, it's often the case that a cloud-based application is divided into several small services that work together to process a business transaction end-to-end. To lower coupling between services, each service is responsible for a single business operation. Some benefits include faster development, smaller code base, and scalability. However, designing an efficient and scalable workflow is a challenge and often requires complex interservice communication.
The services communicate with each other by using well-defined APIs. Even a single business operation can result in multiple point-to-point calls amongst all services. A common pattern for communication is to use a centralized service that acts as the orchestrator. It acknowledges all incoming requests and delegates operations to the respective services. In doing so, it also manages the workflow of the entire business transaction. Each service just completes an operation and is not aware of the overall workflow.
The orchestrator pattern reduces point-to-point communication between services but has some drawbacks because of the tight coupling between the orchestrator and other services that participate in processing of the business transaction. To execute tasks in a sequence, the orchestrator needs to have some domain knowledge about the responsibilities of those services. If you want to add or remove services, existing logic will break, and you'll need to rewire portions of the communication path. While you can configure the workflow, add or remove services easily with a well-designed orchestrator, such an implementation is complex hard to maintain.
Let each service decide when and how a business operation is processed, instead of depending on a central orchestrator.
One way to implement choreography is to use the asynchronous messaging pattern to coordinate the business operations.
A client request publishes messages to a message queue. As messages arrive, they are pushed to subscribers, or services, interested in that message. Each subscribed service does their operation as indicated by the message and responds to the message queue with success or failure of the operation. In case of success, the service can push a message back to the same queue or a different message queue so that another service can continue the workflow if needed. If an operation fails, the message bus can retry that operation.
This way, the services choreograph the workflow among themselves without depending on an orchestrator or having direct communication between them.
Because there isn't point-to-point communication, this pattern helps reduce coupling between services. Also, it can remove the performance bottleneck caused by the orchestrator when it has to deal with all transactions.
Decentralizing the orchestrator can cause issues while managing the workflow.
If a service fails to complete a business operation, it can be difficult to recover from that failure. One way is to have the service indicate failure by firing an event. Another service subscribes to those failed events takes necessary actions such as applying compensating transactions to undo successful operations in a request. The failed service might also fail to fire an event for the failure. In that case, consider using a retry and, or time out mechanism to recognize that operation as a failure. For an example, see the Example section.
It's simple to implement a workflow when you want to process independent business operations in parallel. You can use a single message bus. However, the workflow can become complicated when choreography needs to occur in a sequence. For instance, Service C can start its operation only after Service A and Service B have completed their operations with success. One approach is to have multiple message buses that get messages in the required order. For more information, see the Example section.
The choreography pattern becomes a challenge if the number of services grow rapidly. Given the high number of independent moving parts, the workflow between services tends to get complex. Also, distributed tracing becomes difficult.
The orchestrator centrally manages the resiliency of the workflow and it can become a single point of failure. On the other hand, for choreography, the role is distributed between all services and resiliency becomes less robust.
Each service isn't only responsible for the resiliency of its operation but also the workflow. This responsibility can be burdensome for the service and hard to implement. Each service must retry transient, non-transient, and time-out failures, so that the request terminates gracefully, if needed. Also, the service must be diligent about communicating the success or failure of the operation so that other services can act accordingly.
Use the choreography pattern if you expect to update, remove, or add new services frequently. The entire app can be modified with lesser effort and minimal disruption to existing services.
Consider this pattern if you experience performance bottlenecks in the central orchestrator.
This pattern is a natural model for the serverless architecture where all services can be short lived, or event driven. Services can spin up because of an event, do their task, and are removed when the task is finished.
Handle faults that might take a variable amount of time to recover from, when connecting to a remote service or resource. This can improve the stability and resiliency of an application.
In a distributed environment, calls to remote resources and services can fail due to transient faults, such as slow network connections, timeouts, or the resources being overcommitted or temporarily unavailable. These faults typically correct themselves after a short period of time, and a robust cloud application should be prepared to handle them by using a strategy such as the Retry pattern.
However, there can also be situations where faults are due to unanticipated events, and that might take much longer to fix. These faults can range in severity from a partial loss of connectivity to the complete failure of a service. In these situations it might be pointless for an application to continually retry an operation that is unlikely to succeed, and instead the application should quickly accept that the operation has failed and handle this failure accordingly.
Additionally, if a service is very busy, failure in one part of the system might lead to cascading failures. For example, an operation that invokes a service could be configured to implement a timeout, and reply with a failure message if the service fails to respond within this period. However, this strategy could cause many concurrent requests to the same operation to be blocked until the timeout period expires. These blocked requests might hold critical system resources such as memory, threads, database connections, and so on. Consequently, these resources could become exhausted, causing failure of other possibly unrelated parts of the system that need to use the same resources. In these situations, it would be preferable for the operation to fail immediately, and only attempt to invoke the service if it's likely to succeed. Note that setting a shorter timeout might help to resolve this problem, but the timeout shouldn't be so short that the operation fails most of the time, even if the request to the service would eventually succeed.
The Circuit Breaker pattern, popularized by Michael Nygard in his book, Release It!, can prevent an application from repeatedly trying to execute an operation that's likely to fail. Allowing it to continue without waiting for the fault to be fixed or wasting CPU cycles while it determines that the fault is long lasting. The Circuit Breaker pattern also enables an application to detect whether the fault has been resolved. If the problem appears to have been fixed, the application can try to invoke the operation.
The purpose of the Circuit Breaker pattern is different than the Retry pattern. The Retry pattern enables an application to retry an operation in the expectation that it'll succeed. The Circuit Breaker pattern prevents an application from performing an operation that is likely to fail. An application can combine these two patterns by using the Retry pattern to invoke an operation through a circuit breaker. However, the retry logic should be sensitive to any exceptions returned by the circuit breaker and abandon retry attempts if the circuit breaker indicates that a fault is not transient.
A circuit breaker acts as a proxy for operations that might fail. The proxy should monitor the number of recent failures that have occurred, and use this information to decide whether to allow the operation to proceed, or simply return an exception immediately.
The proxy can be implemented as a state machine with the following states that mimic the functionality of an electrical circuit breaker:
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Closed: The request from the application is routed to the operation. The proxy maintains a count of the number of recent failures, and if the call to the operation is unsuccessful the proxy increments this count. If the number of recent failures exceeds a specified threshold within a given time period, the proxy is placed into the Open state. At this point the proxy starts a timeout timer, and when this timer expires the proxy is placed into the Half-Open state.
The purpose of the timeout timer is to give the system time to fix the problem that caused the failure before allowing the application to try to perform the operation again.
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Open: The request from the application fails immediately and an exception is returned to the application.
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Half-Open: A limited number of requests from the application are allowed to pass through and invoke the operation. If these requests are successful, it's assumed that the fault that was previously causing the failure has been fixed and the circuit breaker switches to the Closed state (the failure counter is reset). If any request fails, the circuit breaker assumes that the fault is still present so it reverts back to the Open state and restarts the timeout timer to give the system a further period of time to recover from the failure.
The Half-Openstate is useful to prevent a recovering service from suddenly being flooded with requests. As a service recovers, it might be able to support a limited volume of requests until the recovery is complete, but while recovery is in progress a flood of work can cause the service to time out or fail again.
In the figure, the failure counter used by the Closed state is time based. It's automatically reset at periodic intervals. This helps to prevent the circuit breaker from entering the Open state if it experiences occasional failures. The failure threshold that trips the circuit breaker into the Open state is only reached when a specified number of failures have occurred during a specified interval. The counter used by the Half-Open state records the number of successful attempts to invoke the operation. The circuit breaker reverts to the Closed state after a specified number of consecutive operation invocations have been successful. If any invocation fails, the circuit breaker enters the Open state immediately and the success counter will be reset the next time it enters the Half-Open state.
How the system recovers is handled externally, possibly by restoring > or restarting a failed component or repairing a network connection.
The Circuit Breaker pattern provides stability while the system recovers from a failure and minimizes the impact on performance. It can help to maintain the response time of the system by quickly rejecting a request for an operation that's likely to fail, rather than waiting for the operation to time out, or never return. If the circuit breaker raises an event each time it changes state, this information can be used to monitor the health of the part of the system protected by the circuit breaker, or to alert an administrator when a circuit breaker trips to the Open state.
The pattern is customizable and can be adapted according to the type of the possible failure. For example, you can apply an increasing timeout timer to a circuit breaker. You could place the circuit breaker in the Open state for a few seconds initially, and then if the failure hasn't been resolved increase the timeout to a few minutes, and so on. In some cases, rather than the Open state returning failure and raising an exception, it could be useful to return a default value that is meaningful to the application.
You should consider the following points when deciding how to implement this pattern:
Exception Handling. An application invoking an operation through a circuit breaker must be prepared to handle the exceptions raised if the operation is unavailable. The way exceptions are handled will be application specific. For example, an application could temporarily degrade its functionality, invoke an alternative operation to try to perform the same task or obtain the same data, or report the exception to the user and ask them to try again later.
Types of Exceptions. A request might fail for many reasons, some of which might indicate a more severe type of failure than others. For example, a request might fail because a remote service has crashed and will take several minutes to recover, or because of a timeout due to the service being temporarily overloaded. A circuit breaker might be able to examine the types of exceptions that occur and adjust its strategy depending on the nature of these exceptions. For example, it might require a larger number of timeout exceptions to trip the circuit breaker to the Open state compared to the number of failures due to the service being completely unavailable.
Logging. A circuit breaker should log all failed requests (and possibly successful requests) to enable an administrator to monitor the health of the operation.
Recoverability. You should configure the circuit breaker to match the likely recovery pattern of the operation it's protecting. For example, if the circuit breaker remains in the Open state for a long period, it could raise exceptions even if the reason for the failure has been resolved. Similarly, a circuit breaker could fluctuate and reduce the response times of applications if it switches from the Open state to the Half-Open state too quickly.
Testing Failed Operations. In the Open state, rather than using a timer to determine when to switch to the Half-Open state, a circuit breaker can instead periodically ping the remote service or resource to determine whether it's become available again. This ping could take the form of an attempt to invoke an operation that had previously failed, or it could use a special operation provided by the remote service specifically for testing the health of the service, as described by the Health Endpoint Monitoring pattern.
Manual Override. In a system where the recovery time for a failing operation is extremely variable, it's beneficial to provide a manual reset option that enables an administrator to close a circuit breaker (and reset the failure counter). Similarly, an administrator could force a circuit breaker into the Open state (and restart the timeout timer) if the operation protected by the circuit breaker is temporarily unavailable.
Concurrency. The same circuit breaker could be accessed by a large number of concurrent instances of an application. The implementation shouldn't block concurrent requests or add excessive overhead to each call to an operation.
Resource Differentiation. Be careful when using a single circuit breaker for one type of resource if there might be multiple underlying independent providers. For example, in a data store that contains multiple shards, one shard might be fully accessible while another is experiencing a temporary issue. If the error responses in these scenarios are merged, an application might try to access some shards even when failure is highly likely, while access to other shards might be blocked even though it's likely to succeed.
Accelerated Circuit Breaking. Sometimes a failure response can contain enough information for the circuit breaker to trip immediately and stay tripped for a minimum amount of time. For example, the error response from a shared resource that's overloaded could indicate that an immediate retry isn't recommended and that the application should instead try again in a few minutes.
A service can return HTTP 429 (Too Many Requests) if it is throttling the client, or HTTP 503 (Service Unavailable) if the service is not currently available. The response can include additional information, such as the anticipated duration of the delay.
Replaying Failed Requests. In the Open state, rather than simply failing quickly, a circuit breaker could also record the details of each request to a journal and arrange for these requests to be replayed when the remote resource or service becomes available.
Inappropriate Timeouts on External Services. A circuit breaker might not be able to fully protect applications from operations that fail in external services that are configured with a lengthy timeout period. If the timeout is too long, a thread running a circuit breaker might be blocked for an extended period before the circuit breaker indicates that the operation has failed. In this time, many other application instances might also try to invoke the service through the circuit breaker and tie up a significant number of threads before they all fail.
Use this pattern:
- To prevent an application from trying to invoke a remote service or access a shared resource if this operation is highly likely to fail.
This pattern isn't recommended:
- For handling access to local private resources in an application, such as in-memory data structure. In this environment, using a circuit breaker would add overhead to your system.
- As a substitute for handling exceptions in the business logic of your applications.
Split a large message into a claim check and a payload. Send the claim check to the messaging platform and store the payload to an external service. This pattern allows large messages to be processed, while protecting the message bus and the client from being overwhelmed or slowed down. This pattern also helps to reduce costs, as storage is usually cheaper than resource units used by the messaging platform.
This pattern is also known as Reference-Based Messaging, and was originally described in the book Enterprise Integration Patterns, by Gregor Hohpe and Bobby Woolf.
A messaging-based architecture at some point must be able to send, receive, and manipulate large messages. Such messages may contain anything, including images (for example, MRI scans), sound files (for example, call-center calls), text documents, or any kind of binary data of arbitrary size.
Sending such large messages to the message bus directly is not recommended, because they require more resources and bandwidth to be consumed. Large messages can also slow down the entire solution, because messaging platforms are usually fine-tuned to handle huge quantities of small messages. Also, most messaging platforms have limits on message size, so you may need to work around these limits for large messages.
Store the entire message payload into an external service, such as a database. Get the reference to the stored payload, and send just that reference to the message bus. The reference acts like a claim check used to retrieve a piece of luggage, hence the name of the pattern. Clients interested in processing that specific message can use the obtained reference to retrieve the payload, if needed.
Consider the following points when deciding how to implement this pattern:
- Consider deleting the message data after consuming it, if you don't need to archive the messages. Although blob storage is relatively cheap, it costs some money in the long run, especially if there is a lot of data. Deleting the message can be done synchronously by the application that receives and processes the message, or asynchronously by a separate dedicated process. The asynchronous approach removes old data with no impact on the throughput and message processing performance of the receiving application.
- Storing and retrieving the message causes some additional overhead and latency. You may want to implement logic in the sending application to use this pattern only when the message size exceeds the data limit of the message bus. The pattern would be skipped for smaller messages. This approach would result in a conditional claim-check pattern.
This pattern should be used whenever a message cannot fit the supported message limit of the chosen message bus technology. For example, Event Hubs currently has a limit of 256 KB (Basic Tier), while Event Grid supports only 64-KB messages.
The pattern can also be used if the payload should be accessed only by services that are authorized to see it. By offloading the payload to an external resource, stricter authentication and authorization rules can be put in place, to ensure that security is enforced when sensitive data is stored in the payload.
The Command and Query Responsibility Segregation (CQRS) pattern separates read and update operations for a data store. Implementing CQRS in your application can maximize its performance, scalability, and security. The flexibility created by migrating to CQRS allows a system to better evolve over time and prevents update commands from causing merge conflicts at the domain level.
In traditional architectures, the same data model is used to query and update a database. That's simple and works well for basic CRUD operations. In more complex applications, however, this approach can become unwieldy. For example, on the read side, the application may perform many different queries, returning data transfer objects (DTOs) with different shapes. Object mapping can become complicated. On the write side, the model may implement complex validation and business logic. As a result, you can end up with an overly complex model that does too much.
Read and write workloads are often asymmetrical, with very different performance and scale requirements.
- There is often a mismatch between the read and write representations of the data, such as additional columns or properties that must be updated correctly even though they aren't required as part of an operation.
- Data contention can occur when operations are performed in parallel on the same set of data.
- The traditional approach can have a negative effect on performance due to load on the data store and data access layer, and the complexity of queries required to retrieve information.
- Managing security and permissions can become complex, because each entity is subject to both read and write operations, which might expose data in the wrong context.
CQRS separates reads and writes into different models, using commands to update data, and queries to read data.
- Commands should be task based, rather than data centric. ("Book hotel room", not "set ReservationStatus to Reserved").
- Commands may be placed on a queue for asynchronous processing, rather than being processed synchronously.
- Queries never modify the database. A query returns a DTO that does not encapsulate any domain knowledge.
The models can then be isolated, as shown in the following diagram, although that's not an absolute requirement.
Having separate query and update models simplifies the design and implementation. However, one disadvantage is that CQRS code can't automatically be generated from a database schema using scaffolding mechanisms such as O/RM tools.
For greater isolation, you can physically separate the read data from the write data. In that case, the read database can use its own data schema that is optimized for queries. For example, it can store a materialized view of the data, in order to avoid complex joins or complex O/RM mappings. It might even use a different type of data store. For example, the write database might be relational, while the read database is a document database.
If separate read and write databases are used, they must be kept in sync. Typically this is accomplished by having the write model publish an event whenever it updates the database. Updating the database and publishing the event must occur in a single transaction.
The read store can be a read-only replica of the write store, or the read and write stores can have a different structure altogether. Using multiple read-only replicas can increase query performance, especially in distributed scenarios where read-only replicas are located close to the application instances.
Separation of the read and write stores also allows each to be scaled appropriately to match the load. For example, read stores typically encounter a much higher load than write stores.
Some implementations of CQRS use the Event Sourcing pattern. With this pattern, application state is stored as a sequence of events. Each event represents a set of changes to the data. The current state is constructed by replaying the events. In a CQRS context, one benefit of Event Sourcing is that the same events can be used to notify other components --- in particular, to notify the read model. The read model uses the events to create a snapshot of the current state, which is more efficient for queries. However, Event Sourcing adds complexity to the design.
Benefits of CQRS include:
- Independent scaling. CQRS allows the read and write workloads to scale independently, and may result in fewer lock contentions.
- Optimized data schemas. The read side can use a schema that is optimized for queries, while the write side uses a schema that is optimized for updates.
- Security. It's easier to ensure that only the right domain entities are performing writes on the data.
- Separation of concerns. Segregating the read and write sides can result in models that are more maintainable and flexible. Most of the complex business logic goes into the write model. The read model can be relatively simple.
- Simpler queries. By storing a materialized view in the read database, the application can avoid complex joins when querying.
Some challenges of implementing this pattern include:
- Complexity. The basic idea of CQRS is simple. But it can lead to a more complex application design, especially if they include the Event Sourcing pattern.
- Messaging. Although CQRS does not require messaging, it's common to use messaging to process commands and publish update events. In that case, the application must handle message failures or duplicate messages.
- Eventual consistency. If you separate the read and write databases, the read data may be stale. The read model store must be updated to reflect changes to the write model store, and it can be difficult to detect when a user has issued a request based on stale read data.
Consider CQRS for the following scenarios:
- Collaborative domains where many users access the same data in parallel. CQRS allows you to define commands with enough granularity to minimize merge conflicts at the domain level, and conflicts that do arise can be merged by the command.
- Task-based user interfaces where users are guided through a complex process as a series of steps or with complex domain models. The write model has a full command-processing stack with business logic, input validation, and business validation. The write model may treat a set of associated objects as a single unit for data changes (an aggregate, in DDD terminology) and ensure that these objects are always in a consistent state. The read model has no business logic or validation stack, and just returns a DTO for use in a view model. The read model is eventually consistent with the write model.
- Scenarios where performance of data reads must be fine tuned separately from performance of data writes, especially when the number of reads is much greater than the number of writes. In this scenario, you can scale out the read model, but run the write model on just a few instances. A small number of write model instances also helps to minimize the occurrence of merge conflicts.
- Scenarios where one team of developers can focus on the complex domain model that is part of the write model, and another team can focus on the read model and the user interfaces.
- Scenarios where the system is expected to evolve over time and might contain multiple versions of the model, or where business rules change regularly.
- Integration with other systems, especially in combination with event sourcing, where the temporal failure of one subsystem shouldn't affect the availability of the others.
This pattern isn't recommended when:
- The domain or the business rules are simple.
- A simple CRUD-style user interface and data access operations are sufficient.
Consider applying CQRS to limited sections of your system where it will be most valuable.
Undo the work performed by a series of steps, which together define an eventually consistent operation, if one or more of the steps fail. Operations that follow the eventual consistency model are commonly found in cloud-hosted applications that implement complex business processes and workflows.
Applications running in the cloud frequently modify data. This data might be spread across various data sources held in different geographic locations. To avoid contention and improve performance in a distributed environment, an application shouldn't try to provide strong transactional consistency. Rather, the application should implement eventual consistency. In this model, a typical business operation consists of a series of separate steps. While these steps are being performed, the overall view of the system state might be inconsistent, but when the operation has completed and all of the steps have been executed the system should become consistent again.
The Data Consistency Primer provides information about why distributed transactions don't scale well, and the principles of the eventual consistency model.
A challenge in the eventual consistency model is how to handle a step that has failed. In this case it might be necessary to undo all of the work completed by the previous steps in the operation. However, the data can't simply be rolled back because other concurrent instances of the application might have changed it. Even in cases where the data hasn't been changed by a concurrent instance, undoing a step might not simply be a matter of restoring the original state. It might be necessary to apply various business-specific rules (see the travel website described in the Example section).
If an operation that implements eventual consistency spans several heterogeneous data stores, undoing the steps in the operation will require visiting each data store in turn. The work performed in every data store must be undone reliably to prevent the system from remaining inconsistent.
Not all data affected by an operation that implements eventual consistency might be held in a database. In a service oriented architecture (SOA) environment an operation could invoke an action in a service, and cause a change in the state held by that service. To undo the operation, this state change must also be undone. This can involve invoking the service again and performing another action that reverses the effects of the first.
The solution is to implement a compensating transaction. The steps in a compensating transaction must undo the effects of the steps in the original operation. A compensating transaction might not be able to simply replace the current state with the state the system was in at the start of the operation because this approach could overwrite changes made by other concurrent instances of an application. Instead, it must be an intelligent process that takes into account any work done by concurrent instances. This process will usually be application specific, driven by the nature of the work performed by the original operation.
A common approach is to use a workflow to implement an eventually consistent operation that requires compensation. As the original operation proceeds, the system records information about each step and how the work performed by that step can be undone. If the operation fails at any point, the workflow rewinds back through the steps it's completed and performs the work that reverses each step. Note that a compensating transaction might not have to undo the work in the exact reverse order of the original operation, and it might be possible to perform some of the undo steps in parallel.
This approach is similar to the Sagas strategy discussed in Clemens Vasters' blog.
A compensating transaction is also an eventually consistent operation and it could also fail. The system should be able to resume the compensating transaction at the point of failure and continue. It might be necessary to repeat a step that's failed, so the steps in a compensating transaction should be defined as idempotent commands. For more information, see Idempotency Patterns on Jonathan Oliver's blog.
In some cases it might not be possible to recover from a step that has failed except through manual intervention. In these situations the system should raise an alert and provide as much information as possible about the reason for the failure.
Consider the following points when deciding how to implement this pattern:
It might not be easy to determine when a step in an operation that implements eventual consistency has failed. A step might not fail immediately, but instead could block. It might be necessary to implement some form of time-out mechanism.
-Compensation logic isn't easily generalized. A compensating transaction is application specific. It relies on the application having sufficient information to be able to undo the effects of each step in a failed operation.
You should define the steps in a compensating transaction as idempotent commands. This enables the steps to be repeated if the compensating transaction itself fails.
The infrastructure that handles the steps in the original operation, and the compensating transaction, must be resilient. It must not lose the information required to compensate for a failing step, and it must be able to reliably monitor the progress of the compensation logic.
A compensating transaction doesn't necessarily return the data in the system to the state it was in at the start of the original operation. Instead, it compensates for the work performed by the steps that completed successfully before the operation failed.
The order of the steps in the compensating transaction doesn't necessarily have to be the exact opposite of the steps in the original operation. For example, one data store might be more sensitive to inconsistencies than another, and so the steps in the compensating transaction that undo the changes to this store should occur first.
Placing a short-term timeout-based lock on each resource that's required to complete an operation, and obtaining these resources in advance, can help increase the likelihood that the overall activity will succeed. The work should be performed only after all the resources have been acquired. All actions must be finalized before the locks expire.
Consider using retry logic that is more forgiving than usual to minimize failures that trigger a compensating transaction. If a step in an operation that implements eventual consistency fails, try handling the failure as a transient exception and repeat the step. Only stop the operation and initiate a compensating transaction if a step fails repeatedly or irrecoverably.
Many of the challenges of implementing a compensating transaction are the same as those with implementing eventual consistency. See the section Considerations for Implementing Eventual Consistency in the Data Consistency Primer for more information.
Use this pattern only for operations that must be undone if they fail. If possible, design solutions to avoid the complexity of requiring compensating transactions.
Enable multiple concurrent consumers to process messages received on the same messaging channel. This enables a system to process multiple messages concurrently to optimize throughput, to improve scalability and availability, and to balance the workload.
An application running in the cloud is expected to handle a large number of requests. Rather than process each request synchronously, a common technique is for the application to pass them through a messaging system to another service (a consumer service) that handles them asynchronously. This strategy helps to ensure that the business logic in the application isn't blocked while the requests are being processed.
The number of requests can vary significantly over time for many reasons. A sudden increase in user activity or aggregated requests coming from multiple tenants can cause an unpredictable workload. At peak hours a system might need to process many hundreds of requests per second, while at other times the number could be very small. Additionally, the nature of the work performed to handle these requests might be highly variable. Using a single instance of the consumer service can cause that instance to become flooded with requests, or the messaging system might be overloaded by an influx of messages coming from the application. To handle this fluctuating workload, the system can run multiple instances of the consumer service. However, these consumers must be coordinated to ensure that each message is only delivered to a single consumer. The workload also needs to be load balanced across consumers to prevent an instance from becoming a bottleneck.
Use a message queue to implement the communication channel between the application and the instances of the consumer service. The application posts requests in the form of messages to the queue, and the consumer service instances receive messages from the queue and process them. This approach enables the same pool of consumer service instances to handle messages from any instance of the application. The figure illustrates using a message queue to distribute work to instances of a service.
This solution has the following benefits:
- It provides a load-leveled system that can handle wide variations in the volume of requests sent by application instances. The queue acts as a buffer between the application instances and the consumer service instances. This can help to minimize the impact on availability and responsiveness for both the application and the service instances, as described by the Queue-based Load Leveling pattern. Handling a message that requires some long-running processing doesn't prevent other messages from being handled concurrently by other instances of the consumer service.
- It improves reliability. If a producer communicates directly with a consumer instead of using this pattern, but doesn't monitor the consumer, there's a high probability that messages could be lost or fail to be processed if the consumer fails. In this pattern, messages aren't sent to a specific service instance. A failed service instance won't block a producer, and messages can be processed by any working service instance.
- It doesn't require complex coordination between the consumers, or between the producer and the consumer instances. The message queue ensures that each message is delivered at least once.
- It's scalable. The system can dynamically increase or decrease the number of instances of the consumer service as the volume of messages fluctuates.
- It can improve resiliency if the message queue provides transactional read operations. If a consumer service instance reads and processes the message as part of a transactional operation, and the consumer service instance fails, this pattern can ensure that the message will be returned to the queue to be picked up and handled by another instance of the consumer service.
Consider the following points when deciding how to implement this pattern:
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Message ordering. The order in which consumer service instances receive messages isn't guaranteed, and doesn't necessarily reflect the order in which the messages were created. Design the system to ensure that message processing is idempotent because this will help to eliminate any dependency on the order in which messages are handled. For more information, see Idempotency Patterns on Jonathon Oliver's blog.
Microsoft Azure Service Bus Queues can implement guaranteed first-in-first-out ordering of messages by using message sessions. For more information, see Messaging Patterns Using Sessions.
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Designing services for resiliency. If the system is designed to detect and restart failed service instances, it might be necessary to implement the processing performed by the service instances as idempotent operations to minimize the effects of a single message being retrieved and processed more than once.
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Detecting poison messages. A malformed message, or a task that requires access to resources that aren't available, can cause a service instance to fail. The system should prevent such messages being returned to the queue, and instead capture and store the details of these messages elsewhere so that they can be analyzed if necessary.
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Handling results. The service instance handling a message is fully decoupled from the application logic that generates the message, and they might not be able to communicate directly. If the service instance generates results that must be passed back to the application logic, this information must be stored in a location that's accessible to both. In order to prevent the application logic from retrieving incomplete data the system must indicate when processing is complete.
If you're using Azure, a worker process can pass results back to the application logic by using a dedicated message reply queue. The application logic must be able to correlate these results with the original message. This scenario is described in more detail in the Asynchronous Messaging Primer.
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Scaling the messaging system. In a large-scale solution, a single message queue could be overwhelmed by the number of messages and become a bottleneck in the system. In this situation, consider partitioning the messaging system to send messages from specific producers to a particular queue, or use load balancing to distribute messages across multiple message queues.
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Ensuring reliability of the messaging system. A reliable messaging system is needed to guarantee that after the application enqueues a message it won't be lost. This is essential for ensuring that all messages are delivered at least once.
Use this pattern when:
- The workload for an application is divided into tasks that can run asynchronously.
- Tasks are independent and can run in parallel.
- The volume of work is highly variable, requiring a scalable solution.
- The solution must provide high availability, and must be resilient if the processing for a task fails.
This pattern might not be useful when:
- It's not easy to separate the application workload into discrete tasks, or there's a high degree of dependence between tasks.
- Tasks must be performed synchronously, and the application logic must wait for a task to complete before continuing.
- Tasks must be performed in a specific sequence.
Some messaging systems support sessions that enable a producer to group messages together and ensure that they're all handled by the same consumer. This mechanism can be used with prioritized messages (if they are supported) to implement a form of message ordering that delivers messages in sequence from a producer to a single consumer.
Consolidate multiple tasks or operations into a single computational unit. This can increase compute resource utilization, and reduce the costs and management overhead associated with performing compute processing in cloud-hosted applications.
A cloud application often implements a variety of operations. In some solutions it makes sense to follow the design principle of separation of concerns initially, and divide these operations into separate computational units that are hosted and deployed individually (for example, as separate App Service web apps, separate Virtual Machines, or separate Cloud Service roles). However, although this strategy can help simplify the logical design of the solution, deploying a large number of computational units as part of the same application can increase runtime hosting costs and make management of the system more complex.
As an example, the figure shows the simplified structure of a cloud-hosted solution that is implemented using more than one computational unit. Each computational unit runs in its own virtual environment. Each function has been implemented as a separate task (labeled Task A through Task E) running in its own computational unit.
Each computational unit consumes chargeable resources, even when it's idle or lightly used. Therefore, this isn't always the most cost-effective solution.
In Azure, this concern applies to roles in a Cloud Service, App Services, and Virtual Machines. These items run in their own virtual environment. Running a collection of separate roles, websites, or virtual machines that are designed to perform a set of well-defined operations, but that need to communicate and cooperate as part of a single solution, can be an inefficient use of resources.
To help reduce costs, increase utilization, improve communication speed, and reduce management it's possible to consolidate multiple tasks or operations into a single computational unit.
Tasks can be grouped according to criteria based on the features provided by the environment and the costs associated with these features. A common approach is to look for tasks that have a similar profile concerning their scalability, lifetime, and processing requirements. Grouping these together allows them to scale as a unit. The elasticity provided by many cloud environments enables additional instances of a computational unit to be started and stopped according to the workload. For example, Azure provides autoscaling that you can apply to roles in a Cloud Service, App Services, and Virtual Machines. For more information, see Autoscaling Guidance.
As a counter example to show how scalability can be used to determine which operations shouldn't be grouped together, consider the following two tasks:
- Task 1 polls for infrequent, time-insensitive messages sent to a queue.
- Task 2 handles high-volume bursts of network traffic.
The second task requires elasticity that can involve starting and stopping a large number of instances of the computational unit. Applying the same scaling to the first task would simply result in more tasks listening for infrequent messages on the same queue, and is a waste of resources.
In many cloud environments it's possible to specify the resources available to a computational unit in terms of the number of CPU cores, memory, disk space, and so on. Generally, the more resources specified, the greater the cost. To save money, it's important to maximize the work an expensive computational unit performs, and not let it become inactive for an extended period.
If there are tasks that require a great deal of CPU power in short bursts, consider consolidating these into a single computational unit that provides the necessary power. However, it's important to balance this need to keep expensive resources busy against the contention that could occur if they are over stressed. Long-running, compute-intensive tasks shouldn't share the same computational unit, for example.
Consider the following points when implementing this pattern:
Scalability and elasticity. Many cloud solutions implement scalability and elasticity at the level of the computational unit by starting and stopping instances of units. Avoid grouping tasks that have conflicting scalability requirements in the same computational unit.
Lifetime. The cloud infrastructure periodically recycles the virtual environment that hosts a computational unit. When there are many long-running tasks inside a computational unit, it might be necessary to configure the unit to prevent it from being recycled until these tasks have finished. Alternatively, design the tasks by using a check-pointing approach that enables them to stop cleanly, and continue at the point they were interrupted when the computational unit is restarted.
Release cadence. If the implementation or configuration of a task changes frequently, it might be necessary to stop the computational unit hosting the updated code, reconfigure and redeploy the unit, and then restart it. This process will also require that all other tasks within the same computational unit are stopped, redeployed, and restarted.
Security. Tasks in the same computational unit might share the same security context and be able to access the same resources. There must be a high degree of trust between the tasks, and confidence that one task isn't going to corrupt or adversely affect another. Additionally, increasing the number of tasks running in a computational unit increases the attack surface of the unit. Each task is only as secure as the one with the most vulnerabilities.
Fault tolerance. If one task in a computational unit fails or behaves abnormally, it can affect the other tasks running within the same unit. For example, if one task fails to start correctly it can cause the entire startup logic for the computational unit to fail, and prevent other tasks in the same unit from running.
Contention. Avoid introducing contention between tasks that compete for resources in the same computational unit. Ideally, tasks that share the same computational unit should exhibit different resource utilization characteristics. For example, two compute-intensive tasks should probably not reside in the same computational unit, and neither should two tasks that consume large amounts of memory. However, mixing a compute-intensive task with a task that requires a large amount of memory is a workable combination.
Consider consolidating compute resources only for a system that's been in production for a period of time so that operators and developers can monitor the system and create a heat map that identifies how each task uses differing resources. This map can be used to determine which tasks are good candidates for sharing compute resources.
Complexity. Combining multiple tasks into a single computational unit adds complexity to the code in the unit, possibly making it more difficult to test, debug, and maintain.
Stable logical architecture. Design and implement the code in each task so that it shouldn't need to change, even if the physical environment the task runs in does change.
Other strategies. Consolidating compute resources is only one way to help reduce costs associated with running multiple tasks concurrently. It requires careful planning and monitoring to ensure that it remains an effective approach. Other strategies might be more appropriate, depending on the nature of the work and where the users these tasks are running are located. For example, functional decomposition of the workload (as described by the Compute Partitioning Guidance) might be a better option.
Use this pattern for tasks that are not cost effective if they run in their own computational units. If a task spends much of its time idle, running this task in a dedicated unit can be expensive.
This pattern might not be suitable for tasks that perform critical fault-tolerant operations, or tasks that process highly sensitive or private data and require their own security context. These tasks should run in their own isolated environment, in a separate computational unit.
Instead of storing just the current state of the data in a domain, use an append-only store to record the full series of actions taken on that data. The store acts as the system of record and can be used to materialize the domain objects. This can simplify tasks in complex domains, by avoiding the need to synchronize the data model and the business domain, while improving performance, scalability, and responsiveness. It can also provide consistency for transactional data, and maintain full audit trails and history that can enable compensating actions.
Most applications work with data, and the typical approach is for the application to maintain the current state of the data by updating it as users work with it. For example, in the traditional create, read, update, and delete (CRUD) model a typical data process is to read data from the store, make some modifications to it, and update the current state of the data with the new values---often by using transactions that lock the data.
The CRUD approach has some limitations:
- CRUD systems perform update operations directly against a data store, which can slow down performance and responsiveness, and limit scalability, due to the processing overhead it requires.
- In a collaborative domain with many concurrent users, data update conflicts are more likely because the update operations take place on a single item of data.
- Unless there's an additional auditing mechanism that records the details of each operation in a separate log, history is lost.
For a deeper understanding of the limits of the CRUD approach, see CRUD, Only When You Can Afford It.
The Event Sourcing pattern defines an approach to handling operations on data that's driven by a sequence of events, each of which is recorded in an append-only store. Application code sends a series of events that imperatively describe each action that has occurred on the data to the event store, where they're persisted. Each event represents a set of changes to the data (such as AddedItemToOrder).
The events are persisted in an event store that acts as the system of record (the authoritative data source) about the current state of the data. The event store typically publishes these events so that consumers can be notified and can handle them if needed. Consumers could, for example, initiate tasks that apply the operations in the events to other systems, or perform any other associated action that's required to complete the operation. Notice that the application code that generates the events is decoupled from the systems that subscribe to the events.
Typical uses of the events published by the event store are to maintain materialized views of entities as actions in the application change them, and for integration with external systems. For example, a system can maintain a materialized view of all customer orders that's used to populate parts of the UI. As the application adds new orders, adds or removes items on the order, and adds shipping information, the events that describe these changes can be handled and used to update the materialized view.
In addition, at any point it's possible for applications to read the history of events, and use it to materialize the current state of an entity by playing back and consuming all the events related to that entity. This can occur on demand to materialize a domain object when handling a request, or through a scheduled task so that the state of the entity can be stored as a materialized view to support the presentation layer.
The figure shows an overview of the pattern, including some of the options for using the event stream such as creating a materialized view, integrating events with external applications and systems, and replaying events to create projections of the current state of specific entities.
The Event Sourcing pattern provides the following advantages:
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Events are immutable and can be stored using an append-only operation. The user interface, workflow, or process that initiated an event can continue, and tasks that handle the events can run in the background. This, combined with the fact that there's no contention during the processing of transactions, can vastly improve performance and scalability for applications, especially for the presentation level or user interface.
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Events are simple objects that describe some action that occurred, together with any associated data required to describe the action represented by the event. Events don't directly update a data store. They're simply recorded for handling at the appropriate time. This can simplify implementation and management.
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Events typically have meaning for a domain expert, whereas object-relational impedance mismatch can make complex database tables hard to understand. Tables are artificial constructs that represent the current state of the system, not the events that occurred.
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Event sourcing can help prevent concurrent updates from causing conflicts because it avoids the requirement to directly update objects in the data store. However, the domain model must still be designed to protect itself from requests that might result in an inconsistent state.
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The append-only storage of events provides an audit trail that can be used to monitor actions taken against a data store, regenerate the current state as materialized views or projections by replaying the events at any time, and assist in testing and debugging the system. In addition, the requirement to use compensating events to cancel changes provides a history of changes that were reversed, which wouldn't be the case if the model simply stored the current state. The list of events can also be used to analyze application performance and detect user behavior trends, or to obtain other useful business information.
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The event store raises events, and tasks perform operations in response to those events. This decoupling of the tasks from the events provides flexibility and extensibility. Tasks know about the type of event and the event data, but not about the operation that triggered the event. In addition, multiple tasks can handle each event. This enables easy integration with other services and systems that only listen for new events raised by the event store. However, the event sourcing events tend to be very low level, and it might be necessary to generate specific integration events instead.
Event sourcing is commonly combined with the CQRS pattern by performing the data management tasks in response to the events, and by materializing views from the stored events.
Consider the following points when deciding how to implement this pattern:
The system will only be eventually consistent when creating materialized views or generating projections of data by replaying events. There's some delay between an application adding events to the event store as the result of handling a request, the events being published, and consumers of the events handling them. During this period, new events that describe further changes to entities might have arrived at the event store.
See the Data Consistency Primer for information about eventual consistency.
The event store is the permanent source of information, and so the event data should never be updated. The only way to update an entity to undo a change is to add a compensating event to the event store. If the format (rather than the data) of the persisted events needs to change, perhaps during a migration, it can be difficult to combine existing events in the store with the new version. It might be necessary to iterate through all the events making changes so they're compliant with the new format, or add new events that use the new format. Consider using a version stamp on each version of the event schema to maintain both the old and the new event formats.
Multi-threaded applications and multiple instances of applications might be storing events in the event store. The consistency of events in the event store is vital, as is the order of events that affect a specific entity (the order that changes occur to an entity affects its current state). Adding a timestamp to every event can help to avoid issues. Another common practice is to annotate each event resulting from a request with an incremental identifier. If two actions attempt to add events for the same entity at the same time, the event store can reject an event that matches an existing entity identifier and event identifier.
There's no standard approach, or existing mechanisms such as SQL queries, for reading the events to obtain information. The only data that can be extracted is a stream of events using an event identifier as the criteria. The event ID typically maps to individual entities. The current state of an entity can be determined only by replaying all of the events that relate to it against the original state of that entity.
The length of each event stream affects managing and updating the system. If the streams are large, consider creating snapshots at specific intervals such as a specified number of events. The current state of the entity can be obtained from the snapshot and by replaying any events that occurred after that point in time. For more information about creating snapshots of data, see Snapshot on Martin Fowler's Enterprise Application Architecture website and Master-Subordinate Snapshot Replication.
Even though event sourcing minimizes the chance of conflicting updates to the data, the application must still be able to deal with inconsistencies that result from eventual consistency and the lack of transactions. For example, an event that indicates a reduction in stock inventory might arrive in the data store while an order for that item is being placed, resulting in a requirement to reconcile the two operations either by advising the customer or creating a back order.
Event publication might be "at least once," and so consumers of the events must be idempotent. They must not reapply the update described in an event if the event is handled more than once. For example, if multiple instances of a consumer maintain an aggregate an entity's property, such as the total number of orders placed, only one must succeed in incrementing the aggregate when an order placed event occurs. While this isn't a key characteristic of event sourcing, it's the usual implementation decision.
Use this pattern in the following scenarios:
- When you want to capture intent, purpose, or reason in the data. For example, changes to a customer entity can be captured as a series of specific event types such as Moved home, Closed account, or Deceased.
- When it's vital to minimize or completely avoid the occurrence of conflicting updates to data.
- When you want to record events that occur, and be able to replay them to restore the state of a system, roll back changes, or keep a history and audit log. For example, when a task involves multiple steps you might need to execute actions to revert updates and then replay some steps to bring the data back into a consistent state.
- When using events is a natural feature of the operation of the application, and requires little additional development or implementation effort
- When you need to decouple the process of inputting or updating data from the tasks required to apply these actions. This might be to improve UI performance, or to distribute events to other listeners that take action when the events occur. For example, integrating a payroll system with an expense submission website so that events raised by the event store in response to data updates made in the website are consumed by both the website and the payroll system.
- When you want flexibility to be able to change the format of materialized models and entity data if requirements change, or---when used in conjunction with CQRS---you need to adapt a read model or the views that expose the data.
- When used in conjunction with CQRS, and eventual consistency is acceptable while a read model is updated, or the performance impact of rehydrating entities and data from an event stream is acceptable.
This pattern might not be useful in the following situations:
- Small or simple domains, systems that have little or no business logic, or nondomain systems that naturally work well with traditional CRUD data management mechanisms.
- Systems where consistency and real-time updates to the views of the data are required.
- Systems where audit trails, history, and capabilities to roll back and replay actions are not required.
- Systems where there's only a very low occurrence of conflicting updates to the underlying data. For example, systems that predominantly add data rather than updating it.
Move configuration information out of the application deployment package to a centralized location. This can provide opportunities for easier management and control of configuration data, and for sharing configuration data across applications and application instances.
The majority of application runtime environments include configuration information that's held in files deployed with the application. In some cases, it's possible to edit these files to change the application behavior after it's been deployed. However, changes to the configuration require the application be redeployed, often resulting in unacceptable downtime and other administrative overhead.
Local configuration files also limit the configuration to a single application, but sometimes it would be useful to share configuration settings across multiple applications. Examples include database connection strings, UI theme information, or the URLs of queues and storage used by a related set of applications.
It's challenging to manage changes to local configurations across multiple running instances of the application, especially in a cloud-hosted scenario. It can result in instances using different configuration settings while the update is being deployed.
In addition, updates to applications and components might require changes to configuration schemas. Many configuration systems don't support different versions of configuration information.
Store the configuration information in external storage, and provide an interface that can be used to quickly and efficiently read and update configuration settings. The type of external store depends on the hosting and runtime environment of the application. In a cloud-hosted scenario it's typically a cloud-based storage service, but could be a hosted database or other system.
The backing store you choose for configuration information should have an interface that provides consistent and easy-to-use access. It should expose the information in a correctly typed and structured format. The implementation might also need to authorize users' access in order to protect configuration data, and be flexible enough to allow storage of multiple versions of the configuration (such as development, staging, or production, including multiple release versions of each one).
Many built-in configuration systems read the data when the application starts up, and cache the data in memory to provide fast access and minimize the impact on application performance. Depending on the type of backing store used, and the latency of this store, it might be helpful to implement a caching mechanism within the external configuration store. For more information, see the Caching Guidance. The figure illustrates an overview of the External Configuration Store pattern with optional local cache.
Consider the following points when deciding how to implement this pattern:
Choose a backing store that offers acceptable performance, high availability, robustness, and can be backed up as part of the application maintenance and administration process. In a cloud-hosted application, using a cloud storage mechanism is usually a good choice to meet these requirements.
Design the schema of the backing store to allow flexibility in the types of information it can hold. Ensure that it provides for all configuration requirements such as typed data, collections of settings, multiple versions of settings, and any other features that the applications using it require. The schema should be easy to extend to support additional settings as requirements change.
Consider the physical capabilities of the backing store, how it relates to the way configuration information is stored, and the effects on performance. For example, storing an XML document containing configuration information will require either the configuration interface or the application to parse the document in order to read individual settings. It'll make updating a setting more complicated, though caching the settings can help to offset slower read performance.
Consider how the configuration interface will permit control of the scope and inheritance of configuration settings. For example, it might be a requirement to scope configuration settings at the organization, application, and the machine level. It might need to support delegation of control over access to different scopes, and to prevent or allow individual applications to override settings.
Ensure that the configuration interface can expose the configuration data in the required formats such as typed values, collections, key/value pairs, or property bags.
Consider how the configuration store interface will behave when settings contain errors, or don't exist in the backing store. It might be appropriate to return default settings and log errors. Also consider aspects such as the case sensitivity of configuration setting keys or names, the storage and handling of binary data, and the ways that null or empty values are handled.
Consider how to protect the configuration data to allow access to only the appropriate users and applications. This is likely a feature of the configuration store interface, but it's also necessary to ensure that the data in the backing store can't be accessed directly without the appropriate permission. Ensure strict separation between the permissions required to read and to write configuration data. Also consider whether you need to encrypt some or all of the configuration settings, and how this'll be implemented in the configuration store interface.
Centrally stored configurations, which change application behavior during runtime, are critically important and should be deployed, updated, and managed using the same mechanisms as deploying application code. For example, changes that can affect more than one application must be carried out using a full test and staged deployment approach to ensure that the change is appropriate for all applications that use this configuration. If an administrator edits a setting to update one application, it could adversely impact other applications that use the same setting.
If an application caches configuration information, the application needs to be alerted if the configuration changes. It might be possible to implement an expiration policy over cached configuration data so that this information is automatically refreshed periodically and any changes picked up (and acted on).
This pattern is useful for:
- Configuration settings that are shared between multiple applications and application instances, or where a standard configuration must be enforced across multiple applications and application instances.
- A standard configuration system that doesn't support all of the required configuration settings, such as storing images or complex data types.
- As a complementary store for some of the settings for applications, perhaps allowing applications to override some or all of the centrally-stored settings.
- As a way to simplify administration of multiple applications, and optionally for monitoring use of configuration settings by logging some or all types of access to the configuration store.
Delegate authentication to an external identity provider. This can simplify development, minimize the requirement for user administration, and improve the user experience of the application.
Users typically need to work with multiple applications provided and hosted by different organizations they have a business relationship with. These users might be required to use specific (and different) credentials for each one. This can:
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Cause a disjointed user experience. Users often forget sign-in credentials when they have many different ones.
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Expose security vulnerabilities. When a user leaves the company the account must immediately be deprovisioned. It's easy to overlook this in large organizations.
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Complicate user management. Administrators must manage credentials for all of the users, and perform additional tasks such as providing password reminders.
Users typically prefer to use the same credentials for all these applications.
Implement an authentication mechanism that can use federated identity. Separate user authentication from the application code, and delegate authentication to a trusted identity provider. This can simplify development and allow users to authenticate using a wider range of identity providers (IdP) while minimizing the administrative overhead. It also allows you to clearly decouple authentication from authorization.
The trusted identity providers include corporate directories, on-premises federation services, other security token services (STS) provided by business partners, or social identity providers that can authenticate users who have, for example, a Microsoft, Google, Yahoo!, or Facebook account.
The figure illustrates the Federated Identity pattern when a client application needs to access a service that requires authentication. The authentication is performed by an IdP that works in concert with an STS. The IdP issues security tokens that provide information about the authenticated user. This information, referred to as claims, includes the user's identity, and might also include other information such as role membership and more granular access rights.
This model is often called claims-based access control. Applications and services authorize access to features and functionality based on the claims contained in the token. The service that requires authentication must trust the IdP. The client application contacts the IdP that performs the authentication. If the authentication is successful, the IdP returns a token containing the claims that identify the user to the STS (note that the IdP and STS can be the same service). The STS can transform and augment the claims in the token based on predefined rules, before returning it to the client. The client application can then pass this token to the service as proof of its identity.
There might be additional STSs in the chain of trust. For example, in the scenario described later, an on-premises STS trusts another STS that is responsible for accessing an identity provider to authenticate the user. This approach is common in enterprise scenarios where there's an on-premises STS and directory.
Federated authentication provides a standards-based solution to the issue of trusting identities across diverse domains, and can support single sign-on. It's becoming more common across all types of applications, especially cloud-hosted applications, because it supports single sign-on without requiring a direct network connection to identity providers. The user doesn't have to enter credentials for every application. This increases security because it prevents the creation of credentials required to access many different applications, and it also hides the user's credentials from all but the original identity provider. Applications see just the authenticated identity information contained within the token.
Federated identity also has the major advantage that management of the identity and credentials is the responsibility of the identity provider. The application or service doesn't need to provide identity management features. In addition, in corporate scenarios, the corporate directory doesn't need to know about the user if it trusts the identity provider. This removes all the administrative overhead of managing the user identity within the directory.
Consider the following when designing applications that implement federated authentication:
- Authentication can be a single point of failure. If you deploy your application to multiple datacenters, consider deploying your identity management mechanism to the same datacenters to maintain application reliability and availability.
- Authentication tools make it possible to configure access control based on role claims contained in the authentication token. This is often referred to as role-based access control (RBAC), and it can allow a more granular level of control over access to features and resources.
- Unlike a corporate directory, claims-based authentication using social identity providers doesn't usually provide information about the authenticated user other than an email address, and perhaps a name. Some social identity providers, such as a Microsoft account, provide only a unique identifier. The application usually needs to maintain some information on registered users, and be able to match this information to the identifier contained in the claims in the token. Typically this is done through registration when the user first accesses the application, and information is then injected into the token as additional claims after each authentication.
- If there's more than one identity provider configured for the STS, it must detect which identity provider the user should be redirected to for authentication. This process is called home realm discovery. The STS might be able to do this automatically based on an email address or user name that the user provides, a subdomain of the application that the user is accessing, the user's IP address scope, or on the contents of a cookie stored in the user's browser. For example, if the user entered an email address in the Microsoft domain, such as [email protected], the STS will redirect the user to the Microsoft account sign-in page. On later visits, the STS could use a cookie to indicate that the last sign in was with a Microsoft account. If automatic discovery can't determine the home realm, the STS will display a home realm discovery page that lists the trusted identity providers, and the user must select the one they want to use.
This pattern is useful for scenarios such as:
- Single sign-on in the enterprise. In this scenario you need to authenticate employees for corporate applications that are hosted in the cloud outside the corporate security boundary, without requiring them to sign in every time they visit an application. The user experience is the same as when using on-premises applications where they're authenticated when signing in to a corporate network, and from then on have access to all relevant applications without needing to sign in again.
- Federated identity with multiple partners. In this scenario you need to authenticate both corporate employees and business partners who don't have accounts in the corporate directory. This is common in business-to-business applications, applications that integrate with third-party services, and where companies with different IT systems have merged or shared resources.
- Federated identity in SaaS applications. In this scenario independent software vendors provide a ready-to-use service for multiple clients or tenants. Each tenant authenticates using a suitable identity provider. For example, business users will use their corporate credentials, while consumers and clients of the tenant will use their social identity credentials.
This pattern might not be useful in the following situations:
- All users of the application can be authenticated by one identity provider, and there's no requirement to authenticate using any other identity provider. This is typical in business applications that use a corporate directory (accessible within the application) for authentication, by using a VPN, or (in a cloud-hosted scenario) through a virtual network connection between the on-premises directory and the application.
- The application was originally built using a different authentication mechanism, perhaps with custom user stores, or doesn't have the capability to handle the negotiation standards used by claims-based technologies. Retrofitting claims-based authentication and access control into existing applications can be complex, and probably not cost effective.
Protect applications and services by using a dedicated host instance that acts as a broker between clients and the application or service, validates and sanitizes requests, and passes requests and data between them. This can provide an additional layer of security, and limit the attack surface of the system.
Applications expose their functionality to clients by accepting and processing requests. In cloud-hosted scenarios, applications expose endpoints clients connect to, and typically include the code to handle the requests from clients. This code performs authentication and validation, some or all request processing, and is likely to accesses storage and other services on behalf of the client.
If a malicious user is able to compromise the system and gain access to the application's hosting environment, the security mechanisms it uses such as credentials and storage keys, and the services and data it accesses, are exposed. As a result, the malicious user can gain unrestrained access to sensitive information and other services.
To minimize the risk of clients gaining access to sensitive information and services, decouple hosts or tasks that expose public endpoints from the code that processes requests and accesses storage. You can achieve this by using a façade or a dedicated task that interacts with clients and then hands off the request---perhaps through a decoupled interface---to the hosts or tasks that'll handle the request. The figure provides a high-level overview of this pattern.
The gatekeeper pattern can be used to simply protect storage, or it can be used as a more comprehensive façade to protect all of the functions of the application. The important factors are:
- Controlled validation. The gatekeeper validates all requests, and rejects those that don't meet validation requirements.
- Limited risk and exposure. The gatekeeper doesn't have access to the credentials or keys used by the trusted host to access storage and services. If the gatekeeper is compromised, the attacker doesn't get access to these credentials or keys.
- Appropriate security. The gatekeeper runs in a limited privilege mode, while the rest of the application runs in the full trust mode required to access storage and services. If the gatekeeper is compromised, it can't directly access the application services or data.
This pattern acts like a firewall in a typical network topography. It allows the gatekeeper to examine requests and make a decision about whether to pass the request on to the trusted host (sometimes called the keymaster) that performs the required tasks. This decision typically requires the gatekeeper to validate and sanitize the request content before passing it on to the trusted host.
Consider the following points when deciding how to implement this pattern:
- Ensure that the trusted hosts the gatekeeper passes requests to expose only internal or protected endpoints, and connect only to the gatekeeper. The trusted hosts shouldn't expose any external endpoints or interfaces.
- The gatekeeper must run in a limited privilege mode. Typically this means running the gatekeeper and the trusted host in separate hosted services or virtual machines.
- The gatekeeper shouldn't perform any processing related to the application or services, or access any data. Its function is purely to validate and sanitize requests. The trusted hosts might need to perform additional validation of requests, but the core validation should be performed by the gatekeeper.
- Use a secure communication channel (HTTPS, SSL, or TLS) between the gatekeeper and the trusted hosts or tasks where this is possible. However, some hosting environments don't support HTTPS on internal endpoints.
- Adding the extra layer to the application to implement the gatekeeper pattern is likely to have some impact on performance due to the additional processing and network communication it requires.
- The gatekeeper instance could be a single point of failure. To minimize the impact of a failure, consider deploying additional instances and using an autoscaling mechanism to ensure capacity to maintain availability.
This pattern is useful for:
- Applications that handle sensitive information, expose services that must have a high degree of protection from malicious attacks, or perform mission-critical operations that shouldn't be disrupted.
- Distributed applications where it's necessary to perform request validation separately from the main tasks, or to centralize this validation to simplify maintenance and administration.
Use a gateway to aggregate multiple individual requests into a single request. This pattern is useful when a client must make multiple calls to different backend systems to perform an operation.
To perform a single task, a client may have to make multiple calls to various backend services. An application that relies on many services to perform a task must expend resources on each request. When any new feature or service is added to the application, additional requests are needed, further increasing resource requirements and network calls. This chattiness between a client and a backend can adversely impact the performance and scale of the application. Microservice architectures have made this problem more common, as applications built around many smaller services naturally have a higher amount of cross-service calls.
In the following diagram, the client sends requests to each service (1,2,3). Each service processes the request and sends the response back to the application (4,5,6). Over a cellular network with typically high latency, using individual requests in this manner is inefficient and could result in broken connectivity or incomplete requests. While each request may be done in parallel, the application must send, wait, and process data for each request, all on separate connections, increasing the chance of failure.
Use a gateway to reduce chattiness between the client and the services. The gateway receives client requests, dispatches requests to the various backend systems, and then aggregates the results and sends them back to the requesting client.
This pattern can reduce the number of requests that the application makes to backend services, and improve application performance over high-latency networks.
In the following diagram, the application sends a request to the gateway (1). The request contains a package of additional requests. The gateway decomposes these and processes each request by sending it to the relevant service (2). Each service returns a response to the gateway (3). The gateway combines the responses from each service and sends the response to the application (4). The application makes a single request and receives only a single response from the gateway.
- Insulates the clients from how the application is partitioned into microservices
- Insulates the clients from the problem of determining the locations of service instances
- Provides the optimal API for each client
- Reduces the number of requests/roundtrips. For example, the API gateway enables clients to retrieve data from multiple services with a single round-trip. Fewer requests also means less overhead and improves the user experience. An API gateway is essential for mobile applications.
- Simplifies the client by moving logic for calling multiple services from the client to API gateway
- Translates from a "standard" public web-friendly API protocol to whatever protocols are used internally
- The gateway should not introduce service coupling across the backend services.
- The gateway should be located near the backend services to reduce latency as much as possible.
- The gateway service may introduce a single point of failure. Ensure the gateway is properly designed to meet your application's availability requirements.
- The gateway may introduce a bottleneck. Ensure the gateway has adequate performance to handle load and can be scaled to meet your anticipated growth.
- Perform load testing against the gateway to ensure you don't introduce cascading failures for services.
- Implement a resilient design, using techniques such as bulkheads, circuit breaking, retry, and timeouts.
- If one or more service calls takes too long, it may be acceptable to timeout and return a partial set of data. Consider how your application will handle this scenario.
- Use asynchronous I/O to ensure that a delay at the backend doesn't cause performance issues in the application.
- Implement distributed tracing using correlation IDs to track each individual call.
- Monitor request metrics and response sizes.
- Consider returning cached data as a failover strategy to handle failures.
- Instead of building aggregation into the gateway, consider placing an aggregation service behind the gateway. Request aggregation will likely have different resource requirements than other services in the gateway and may impact the gateway's routing and offloading functionality.
- How implement the API gateway? An event-driven/reactive approach is best if it must scale to scale to handle high loads. On the JVM, NIO-based libraries such as Netty, Spring Reactor, etc. make sense. NodeJS is another option.
Use this pattern when:
- A client needs to communicate with multiple backend services to perform an operation.
- The client may use networks with significant latency, such as cellular networks.
This pattern may not be suitable when:
- You want to reduce the number of calls between a client and a single service across multiple operations. In that scenario, it may be better to add a batch operation to the service.
- The client or application is located near the backend services and latency is not a significant factor.
Offload shared or specialized service functionality to a gateway proxy. This pattern can simplify application development by moving shared service functionality, such as the use of SSL certificates, from other parts of the application into the gateway.
Some features are commonly used across multiple services, and these features require configuration, management, and maintenance. A shared or specialized service that is distributed with every application deployment increases the administrative overhead and increases the likelihood of deployment error. Any updates to a shared feature must be deployed across all services that share that feature.
Properly handling security issues (token validation, encryption, SSL certificate management) and other complex tasks can require team members to have highly specialized skills. For example, a certificate needed by an application must be configured and deployed on all application instances. With each new deployment, the certificate must be managed to ensure that it does not expire. Any common certificate that is due to expire must be updated, tested, and verified on every application deployment.
Other common services such as authentication, authorization, logging, monitoring, or throttling can be difficult to implement and manage across a large number of deployments. It may be better to consolidate this type of functionality, in order to reduce overhead and the chance of errors.
Offload some features into an API gateway, particularly cross-cutting concerns such as certificate management, authentication, SSL termination, monitoring, protocol translation, or throttling.
The following diagram shows an API gateway that terminates inbound SSL connections. It requests data on behalf of the original requestor from any HTTP server upstream of the API gateway.
Benefits of this pattern include:
- Simplify the development of services by removing the need to distribute and maintain supporting resources, such as web server certificates and configuration for secure websites. Simpler configuration results in easier management and scalability and makes service upgrades simpler.
- Allow dedicated teams to implement features that require specialized expertise, such as security. This allows your core team to focus on the application functionality, leaving these specialized but cross-cutting concerns to the relevant experts.
- Provide some consistency for request and response logging and monitoring. Even if a service is not correctly instrumented, the gateway can be configured to ensure a minimum level of monitoring and logging.
- Ensure the API gateway is highly available and resilient to failure. Avoid single points of failure by running multiple instances of your API gateway.
- Ensure the gateway is designed for the capacity and scaling requirements of your application and endpoints. Make sure the gateway does not become a bottleneck for the application and is sufficiently scalable.
- Only offload features that are used by the entire application, such as security or data transfer.
- Business logic should never be offloaded to the API gateway.
- If you need to track transactions, consider generating correlation IDs for logging purposes.
Use this pattern when:
- An application deployment has a shared concern such as SSL certificates or encryption.
- A feature that is common across application deployments that may have different resource requirements, such as memory resources, storage capacity or network connections.
- You wish to move the responsibility for issues such as network security, throttling, or other network boundary concerns to a more specialized team.
This pattern may not be suitable if it introduces coupling across services.
Route requests to multiple services using a single endpoint. This pattern is useful when you wish to expose multiple services on a single endpoint and route to the appropriate service based on the request.
When a client needs to consume multiple services, setting up a separate endpoint for each service and having the client manage each endpoint can be challenging. For example, an e-commerce application might provide services such as search, reviews, cart, checkout, and order history. Each service has a different API that the client must interact with, and the client must know about each endpoint in order to connect to the services. If an API changes, the client must be updated as well. If you refactor a service into two or more separate services, the code must change in both the service and the client.
Place a gateway in front of a set of applications, services, or deployments. Use application Layer 7 routing to route the request to the appropriate instances.
With this pattern, the client application only needs to know about and communicate with a single endpoint. If a service is consolidated or decomposed, the client does not necessarily require updating. It can continue making requests to the gateway, and only the routing changes.
A gateway also lets you abstract backend services from the clients, allowing you to keep client calls simple while enabling changes in the backend services behind the gateway. Client calls can be routed to whatever service or services need to handle the expected client behavior, allowing you to add, split, and reorganize services behind the gateway without changing the client.
This pattern can also help with deployment, by allowing you to manage how updates are rolled out to users. When a new version of your service is deployed, it can be deployed in parallel with the existing version. Routing lets you control what version of the service is presented to the clients, giving you the flexibility to use various release strategies, whether incremental, parallel, or complete rollouts of updates. Any issues discovered after the new service is deployed can be quickly reverted by making a configuration change at the gateway, without affecting clients.
- The gateway service may introduce a single point of failure. Ensure it is properly designed to meet your availability requirements. Consider resiliency and fault tolerance capabilities when implementing.
- The gateway service may introduce a bottleneck. Ensure the gateway has adequate performance to handle load and can easily scale in line with your growth expectations.
- Perform load testing against the gateway to ensure you don't introduce cascading failures for services.
- Gateway routing is level 7. It can be based on IP, port, header, or URL.
Use this pattern when:
- A client needs to consume multiple services that can be accessed behind a gateway.
- You wish to simplify client applications by using a single endpoint.
- You need to route requests from externally addressable endpoints to internal virtual endpoints, such as exposing ports on a VM to cluster virtual IP addresses.
This pattern may not be suitable when you have a simple application that uses only one or two services.
The geode pattern involves deploying a collection of backend services into a set of geographical n odes, each of which can service any request for any client in any region. This pattern allows serving requests in an active-active style, improving latency and increasing availability by distributing request processing around the globe.
Many large-scale services have specific challenges around geo-availability and scale. Classic designs often bring the data to the compute by storing data in a remote SQL server that serves as the compute tier for that data, relying on scale-up for growth.
The classic approach may present a number of challenges:
- Network latency issues for users coming from the other side of the globe to connect to the hosting endpoint
- Traffic management for demand bursts that can overwhelm the services in a single region
- Cost-prohibitive complexity of deploying copies of app infrastructure into multiple regions for a 24x7 service
Modern cloud infrastructure has evolved to enable geographic load balancing of front-end services, while allowing for geographic replication of backend services. For availability and performance, getting data closer to the user is good. When data is geo-distributed across a far-flung user base, the geo-distributed datastores should also be colocated with the compute resources that process the data. The geode pattern brings the compute to the data.
Deploy the service into a number of satellite deployments spread around the globe, each of which is called a geode. The geode pattern harnesses key features of Azure to route traffic via the shortest path to a nearby geode, which improves latency and performance. Each geode is behind a global load balancer, and uses a geo-replicated read-write service to host the data plane, ensuring cross-geode data consistency. Data replication services ensure that data stores are identical across geodes, so all requests can be served from all geodes.
The key difference between a deployment stamp and a geode is that geodes never exist in isolation. There should always be more than one geode in a production platform.
Geodes have the following characteristics:
- Consist of a collection of disparate types of resources, often defined in a template.
- Have no dependencies outside of the geode footprint and are self-contained. No geode is dependent on another to operate, and if one dies, the others continue to operate.
- Are loosely coupled via an edge network and replication backplane. For example, you can use Azure Traffic Manager or Azure Front Door for fronting the geodes, while Azure Cosmos DB can act as the replication backplane. Geodes are not the same as clusters because they share a replication backplane, so the platform takes care of quorum issues.
The geode pattern occurs in big data architectures that use commodity hardware to process data colocated on the same machine, and MapReduce to consolidate results across machines. Another usage is near-edge compute, which brings compute closer to the intelligent edge of the network to reduce response time.
Services can use this pattern over dozens or hundreds of geodes. Furthermore, the resiliency of the whole solution increases with each added geode, since any geodes can take over if a regional outage takes one or more geodes offline.
It's also possible to augment local availability techniques, such as availability zones or paired regions, with the geode pattern for global availability. This increases complexity, but is useful if your architecture is underpinned by a storage engine such as blob storage that can only replicate to a paired region. You can deploy geodes into an intra-zone, zonal, or regional footprint, with a mind to regulatory or latency constraints on location.
Use the following techniques and technologies to implement this pattern:
- Modern DevOps practices and tools to produce and rapidly deploy identical geodes across a large number of regions or instances.
- Autoscaling to scale out compute and database throughput instances within a geode. Each geode individually scales out, within the common backplane constraints.
- A front-end service like Azure Front Door that does dynamic content acceleration, split TCP, and Anycast routing.
- A replicating data store like Azure Cosmos DB to control data consistency.
- Serverless technologies where possible, to reduce always-on deployment cost, especially when load is frequently rebalanced around the globe. This strategy allows for many geodes to be deployed with minimal additional investment. Serverless and consumption-based billing technologies reduce waste and cost from duplicate geo-distributed deployments.
Consider the following points when deciding how to implement this pattern:
- Choose whether to process data locally in each region, or to distribute aggregations in a single geode and replicate the result across the globe. The Azure Cosmos DB change feed processor offers this granular control using its lease container concept, and the leasecollectionprefix in the corresponding Azure Functions binding. Each approach has distinct advantages and drawbacks.
- Geodes can work in tandem, using the Azure Cosmos DB change feed and a real-time communication platform like SignalR. Geodes can communicate with remote users via other geodes in a mesh pattern, without knowing or caring where the remote user is located.
- This design pattern implicitly decouples everything, resulting in an ultra-highly distributed and decoupled architecture. Decoupling is a good thing, but consider how to track different components of the same request as they might execute asynchronously on different instances. Get a good monitoring strategy in place.
Use this pattern:
- To implement a high-scale platform that has users distributed over a wide area.
- For any service that requires extreme availability and resilience characteristics, because services based on the geode pattern can survive the loss of multiple service regions at the same time.
This pattern might not be suitable for
- Architectures that have constraints so that all geodes can't be equal for data storage. For example, there may be data residency requirements, an application that needs to maintain temporary state for a particular session, or a heavy weighting of requests towards a single region. In this case, consider using deployment stamps in combination with a global routing plane that is aware of where a user's data sits.
- Situations where there's no geographical distribution required. Instead, consider availability zones and paired regions for clustering.
- Situations where a legacy platform needs to be retrofitted. This pattern works for cloud-native development only, and can be difficult to retrofit.
- Simple architectures and requirements, where geo-redundancy and geo-distribution aren't required or advantageous.
Implement functional checks in an application that external tools can access through exposed endpoints at regular intervals. This can help to verify that applications and services are performing correctly.
It's a good practice, and often a business requirement, to monitor web applications and back-end services, to ensure they're available and performing correctly. However, it's more difficult to monitor services running in the cloud than it is to monitor on-premises services. For example, you don't have full control of the hosting environment, and the services typically depend on other services provided by platform vendors and others.
There are many factors that affect cloud-hosted applications such as network latency, the performance and availability of the underlying compute and storage systems, and the network bandwidth between them. The service can fail entirely or partially due to any of these factors. Therefore, you must verify at regular intervals that the service is performing correctly to ensure the required level of availability, which might be part of your service level agreement (SLA).
Implement health monitoring by sending requests to an endpoint on the application. The application should perform the necessary checks, and return an indication of its status.
A health monitoring check typically combines two factors:
- The checks (if any) performed by the application or service in response to the request to the health verification endpoint.
- Analysis of the results by the tool or framework that performs the health verification check.
The response code indicates the status of the application and, optionally, any components or services it uses. The latency or response time check is performed by the monitoring tool or framework. The figure provides an overview of the pattern.
Other checks that might be carried out by the health monitoring code in the application include:
- Checking cloud storage or a database for availability and response time.
- Checking other resources or services located in the application, or located elsewhere but used by the application.
Services and tools are available that monitor web applications by submitting a request to a configurable set of endpoints, and evaluating the results against a set of configurable rules. It's relatively easy to create a service endpoint whose sole purpose is to perform some functional tests on the system.
Typical checks that can be performed by the monitoring tools include:
- Validating the response code. For example, an HTTP response of 200 (OK) indicates that the application responded without error. The monitoring system might also check for other response codes to give more comprehensive results.
- Checking the content of the response to detect errors, even when a 200 (OK) status code is returned. This can detect errors that affect only a section of the returned web page or service response. For example, checking the title of a page or looking for a specific phrase that indicates the correct page was returned.
- Measuring the response time, which indicates a combination of the network latency and the time that the application took to execute the request. An increasing value can indicate an emerging problem with the application or network.
- Checking resources or services located outside the application, such as a content delivery network used by the application to deliver content from global caches.
- Checking for expiration of SSL certificates.
- Measuring the response time of a DNS lookup for the URL of the application to measure DNS latency and DNS failures.
- Validating the URL returned by the DNS lookup to ensure correct entries. This can help to avoid malicious request redirection through a successful attack on the DNS server.
It's also useful, where possible, to run these checks from different on-premises or hosted locations to measure and compare response times. Ideally you should monitor applications from locations that are close to customers to get an accurate view of the performance from each location. In addition to providing a more robust checking mechanism, the results can help you decide on the deployment location for the application---and whether to deploy it in more than one datacenter.
Tests should also be run against all the service instances that customers use to ensure the application is working correctly for all customers. For example, if customer storage is spread across more than one storage account, the monitoring process should check all of these.
Consider the following points when deciding how to implement this pattern:
How to validate the response. For example, is just a single 200 (OK) status code sufficient to verify the application is working correctly? While this provides the most basic measure of application availability, and is the minimum implementation of this pattern, it provides little information about the operations, trends, and possible upcoming issues in the application.
Make sure that the application correctly returns a 200 (OK) only when the target resource is found and processed. In some scenarios, such as when using a master page to host the target web page, the server sends back a 200 (OK) status code instead of a 404 (Not Found) code, even when the target content page was not found.
The number of endpoints to expose for an application. One approach is to expose at least one endpoint for the core services that the application uses and another for lower priority services, allowing different levels of importance to be assigned to each monitoring result. Also consider exposing more endpoints, such as one for each core service, for additional monitoring granularity. For example, a health verification check might check the database, storage, and an external geocoding service that an application uses, with each requiring a different level of uptime and response time. The application could still be healthy if the geocoding service, or some other background task, is unavailable for a few minutes.
Whether to use the same endpoint for monitoring as is used for general access, but to a specific path designed for health verification checks, for example, /HealthCheck/{GUID}/ on the general access endpoint. This allows some functional tests in the application to be run by the monitoring tools, such as adding a new user registration, signing in, and placing a test order, while also verifying that the general access endpoint is available.
The type of information to collect in the service in response to monitoring requests, and how to return this information. Most existing tools and frameworks look only at the HTTP status code that the endpoint returns. To return and validate additional information, you might have to create a custom monitoring utility or service.
How much information to collect. Performing excessive processing during the check can overload the application and impact other users. The time it takes might exceed the timeout of the monitoring system so it marks the application as unavailable. Most applications include instrumentation such as error handlers and performance counters that log performance and detailed error information, this might be sufficient instead of returning additional information from a health verification check.
Caching the endpoint status. It could be expensive to run the health check too frequently. If the health status is reported through a dashboard, for example, you don't want every request from the dashboard to trigger a health check. Instead, periodically check the system health and cache the status. Expose an endpoint that returns the cached status.
How to configure security for the monitoring endpoints to protect them from public access, which might expose the application to malicious attacks, risk the exposure of sensitive information, or attract denial of service (DoS) attacks. Typically this should be done in the application configuration so that it can be updated easily without restarting the application. Consider using one or more of the following techniques:
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Secure the endpoint by requiring authentication. You can do this by using an authentication security key in the request header or by passing credentials with the request, provided that the monitoring service or tool supports authentication.
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Use an obscure or hidden endpoint. For example, expose the endpoint on a different IP address to that used by the default application URL, configure the endpoint on a nonstandard HTTP port, and/or use a complex path to the test page. You can usually specify additional endpoint addresses and ports in the application configuration, and add entries for these endpoints to the DNS server if required to avoid having to specify the IP address directly.
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Expose a method on an endpoint that accepts a parameter such as a key value or an operation mode value. Depending on the value supplied for this parameter, when a request is received the code can perform a specific test or set of tests, or return a 404 (Not Found) error if the parameter value isn't recognized. The recognized parameter values could be set in the application configuration.
DoS attacks are likely to have less impact on a separate endpoint that performs basic functional tests without compromising the operation of the application. Ideally, avoid using a test that might expose sensitive information. If you must return information that might be useful to an attacker, consider how you'll protect the endpoint and the data from unauthorized access. In this case just relying on obscurity isn't enough. You should also consider using an HTTPS connection and encrypting any sensitive data, although this will increase the load on the server.
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How to access an endpoint that's secured using authentication. Not all tools and frameworks can be configured to include credentials with the health verification request. For example, Microsoft Azure built-in health verification features can't provide authentication credentials. Some third-party alternatives are Pingdom, Panopta, NewRelic, and Statuscake.
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How to ensure that the monitoring agent is performing correctly. One approach is to expose an endpoint that simply returns a value from the application configuration or a random value that can be used to test the agent.
Also ensure that the monitoring system performs checks on itself, such as a self-test and built-in test, to avoid it issuing false positive results.
This pattern is useful for:
- Monitoring websites and web applications to verify availability.
- Monitoring websites and web applications to check for correct operation.
- Monitoring middle-tier or shared services to detect and isolate a failure that could disrupt other applications.
- Complementing existing instrumentation in the application, such as performance counters and error handlers. Health verification checking doesn't replace the requirement for logging and auditing in the application. Instrumentation can provide valuable information for an existing framework that monitors counters and error logs to detect failures or other issues. However, it can't provide information if the application is unavailable.
Create indexes over the fields in data stores that are frequently referenced by queries. This pattern can improve query performance by allowing applications to more quickly locate the data to retrieve from a data store.
Many data stores organize the data for a collection of entities using the primary key. An application can use this key to locate and retrieve data. The figure shows an example of a data store holding customer information. The primary key is the Customer ID. The figure shows customer information organized by the primary key (Customer ID).
While the primary key is valuable for queries that fetch data based on the value of this key, an application might not be able to use the primary key if it needs to retrieve data based on some other field. In the customers example, an application can't use the Customer ID primary key to retrieve customers if it queries data solely by referencing the value of some other attribute, such as the town in which the customer is located. To perform a query such as this, the application might have to fetch and examine every customer record, which could be a slow process.
Many relational database management systems support secondary indexes. A secondary index is a separate data structure that's organized by one or more nonprimary (secondary) key fields, and it indicates where the data for each indexed value is stored. The items in a secondary index are typically sorted by the value of the secondary keys to enable fast lookup of data. These indexes are usually maintained automatically by the database management system.
You can create as many secondary indexes as you need to support the different queries that your application performs. For example, in a Customers table in a relational database where the Customer ID is the primary key, it's beneficial to add a secondary index over the town field if the application frequently looks up customers by the town where they reside.
However, although secondary indexes are common in relational systems, most NoSQL data stores used by cloud applications don't provide an equivalent feature.
If the data store doesn't support secondary indexes, you can emulate them manually by creating your own index tables. An index table organizes the data by a specified key. Three strategies are commonly used for structuring an index table, depending on the number of secondary indexes that are required and the nature of the queries that an application performs.
The first strategy is to duplicate the data in each index table but organize it by different keys (complete denormalization). The next figure shows index tables that organize the same customer information by Town and LastName.
This strategy is appropriate if the data is relatively static compared to the number of times it's queried using each key. If the data is more dynamic, the processing overhead of maintaining each index table becomes too large for this approach to be useful. Also, if the volume of data is very large, the amount of space required to store the duplicate data is significant.
The second strategy is to create normalized index tables organized by different keys and reference the original data by using the primary key rather than duplicating it, as shown in the following figure. The original data is called a fact table.
This technique saves space and reduces the overhead of maintaining duplicate data. The disadvantage is that an application has to perform two lookup operations to find data using a secondary key. It has to find the primary key for the data in the index table, and then use the primary key to look up the data in the fact table.
The third strategy is to create partially normalized index tables organized by different keys that duplicate frequently retrieved fields. Reference the fact table to access less frequently accessed fields. The next figure shows how commonly accessed data is duplicated in each index table.
With this strategy, you can strike a balance between the first two approaches. The data for common queries can be retrieved quickly by using a single lookup, while the space and maintenance overhead isn't as significant as duplicating the entire data set.
If an application frequently queries data by specifying a combination of values (for example, "Find all customers that live in Redmond and that have a last name of Smith"), you could implement the keys to the items in the index table as a concatenation of the Town attribute and the LastName attribute. The next figure shows an index table based on composite keys. The keys are sorted by Town, and then by LastName for records that have the same value for Town.
Index tables can speed up query operations over sharded data, and are especially useful where the shard key is hashed. The next figure shows an example where the shard key is a hash of the Customer ID. The index table can organize data by the nonhashed value (Town and LastName), and provide the hashed shard key as the lookup data. This can save the application from repeatedly calculating hash keys (an expensive operation) if it needs to retrieve data that falls within a range, or it needs to fetch data in order of the nonhashed key. For example, a query such as "Find all customers that live in Redmond" can be quickly resolved by locating the matching items in the index table, where they're all stored in a contiguous block. Then, follow the references to the customer data using the shard keys stored in the index table.
Consider the following points when deciding how to implement this pattern:
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The overhead of maintaining secondary indexes can be significant. You must analyze and understand the queries that your application uses. Only create index tables when they're likely to be used regularly. Don't create speculative index tables to support queries that an application doesn't perform, or performs only occasionally.
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Duplicating data in an index table can add significant overhead in storage costs and the effort required to maintain multiple copies of data.
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Implementing an index table as a normalized structure that references the original data requires an application to perform two lookup operations to find data. The first operation searches the index table to retrieve the primary key, and the second uses the primary key to fetch the data.
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If a system incorporates a number of index tables over very large data sets, it can be difficult to maintain consistency between index tables and the original data. It might be possible to design the application around the eventual consistency model. For example, to insert, update, or delete data, an application could post a message to a queue and let a separate task perform the operation and maintain the index tables that reference this data asynchronously. For more information about implementing eventual consistency, see the Data Consistency Primer.
Microsoft Azure storage tables support transactional updates for changes made to data held in the same partition (referred to as entity group transactions). If you can store the data for a fact table and one or more index tables in the same partition, you can use this feature to help ensure consistency.
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Index tables might themselves be partitioned or sharded.
Use this pattern to improve query performance when an application frequently needs to retrieve data by using a key other than the primary (or shard) key.
This pattern might not be useful when:
- Data is volatile. An index table can become out of date very quickly, making it ineffective or making the overhead of maintaining the index table greater than any savings made by using it.
- A field selected as the secondary key for an index table is nondiscriminating and can only have a small set of values (for example, gender).
- The balance of the data values for a field selected as the secondary key for an index table are highly skewed. For example, if 90% of the records contain the same value in a field, then creating and maintaining an index table to look up data based on this field might create more overhead than scanning sequentially through the data. However, if queries very frequently target values that lie in the remaining 10%, this index can be useful. You should understand the queries that your application is performing, and how frequently they're performed.
Coordinate the actions performed by a collection of collaborating instances in a distributed application by electing one instance as the leader that assumes responsibility for managing the others. This can help to ensure that instances don't conflict with each other, cause contention for shared resources, or inadvertently interfere with the work that other instances are performing.
A typical cloud application has many tasks acting in a coordinated manner. These tasks could all be instances running the same code and requiring access to the same resources, or they might be working together in parallel to perform the individual parts of a complex calculation.
The task instances might run separately for much of the time, but it might also be necessary to coordinate the actions of each instance to ensure that they don't conflict, cause contention for shared resources, or accidentally interfere with the work that other task instances are performing.
For example:
- In a cloud-based system that implements horizontal scaling, multiple instances of the same task could be running at the same time with each instance serving a different user. If these instances write to a shared resource, it's necessary to coordinate their actions to prevent each instance from overwriting the changes made by the others.
- If the tasks are performing individual elements of a complex calculation in parallel, the results need to be aggregated when they all complete.
The task instances are all peers, so there isn't a natural leader that can act as the coordinator or aggregator.
A single task instance should be elected to act as the leader, and this instance should coordinate the actions of the other subordinate task instances. If all of the task instances are running the same code, they are each capable of acting as the leader. Therefore, the election process must be managed carefully to prevent two or more instances taking over the leader role at the same time.
The system must provide a robust mechanism for selecting the leader. This method has to cope with events such as network outages or process failures. In many solutions, the subordinate task instances monitor the leader through some type of heartbeat method, or by polling. If the designated leader terminates unexpectedly, or a network failure makes the leader unavailable to the subordinate task instances, it's necessary for them to elect a new leader.
There are several strategies for electing a leader among a set of tasks in a distributed environment, including:
- Selecting the task instance with the lowest-ranked instance or process ID.
- Racing to acquire a shared, distributed mutex. The first task instance that acquires the mutex is the leader. However, the system must ensure that, if the leader terminates or becomes disconnected from the rest of the system, the mutex is released to allow another task instance to become the leader.
- Implementing one of the common leader election algorithms such as the Bully Algorithm or the Ring Algorithm. These algorithms assume that each candidate in the election has a unique ID, and that it can communicate with the other candidates reliably.
Consider the following points when deciding how to implement this pattern:
- The process of electing a leader should be resilient to transient and persistent failures.
- It must be possible to detect when the leader has failed or has become otherwise unavailable (such as due to a communications failure). How quickly detection is needed is system dependent. Some systems might be able to function for a short time without a leader, during which a transient fault might be fixed. In other cases, it might be necessary to detect leader failure immediately and trigger a new election.
- In a system that implements horizontal autoscaling, the leader could be terminated if the system scales back and shuts down some of the computing resources.
- Using a shared, distributed mutex introduces a dependency on the external service that provides the mutex. The service constitutes a single point of failure. If it becomes unavailable for any reason, the system won't be able to elect a leader.
- Using a single dedicated process as the leader is a straightforward approach. However, if the process fails there could be a significant delay while it's restarted. The resulting latency can affect the performance and response times of other processes if they're waiting for the leader to coordinate an operation.
- Implementing one of the leader election algorithms manually provides the greatest flexibility for tuning and optimizing the code.
Use this pattern when the tasks in a distributed application, such as a cloud-hosted solution, need careful coordination and there's no natural leader.
Avoid making the leader a bottleneck in the system. The purpose of the leader is to coordinate the work of the subordinate tasks, and it doesn't necessarily have to participate in this work itself---although it should be able to do so if the task isn't elected as the leader.
This pattern might not be useful if:
- There's a natural leader or dedicated process that can always act as the leader. For example, it might be possible to implement a singleton process that coordinates the task instances. If this process fails or becomes unhealthy, the system can shut it down and restart it.
- The coordination between tasks can be achieved using a more lightweight method. For example, if several task instances simply need coordinated access to a shared resource, a better solution is to use optimistic or pessimistic locking to control access.
- A third-party solution is more appropriate. For example, the Microsoft Azure HDInsight service (based on Apache Hadoop) uses the services provided by Apache Zookeeper to coordinate the map and reduce tasks that collect and summarize data.
Generate prepopulated views over the data in one or more data stores when the data isn't ideally formatted for required query operations. This can help support efficient querying and data extraction, and improve application performance.
When storing data, the priority for developers and data administrators is often focused on how the data is stored, as opposed to how it's read. The chosen storage format is usually closely related to the format of the data, requirements for managing data size and data integrity, and the kind of store in use. For example, when using NoSQL document store, the data is often represented as a series of aggregates, each containing all of the information for that entity.
However, this can have a negative effect on queries. When a query only needs a subset of the data from some entities, such as a summary of orders for several customers without all of the order details, it must extract all of the data for the relevant entities in order to obtain the required information.
To support efficient querying, a common solution is to generate, in advance, a view that materializes the data in a format suited to the required results set. The Materialized View pattern describes generating prepopulated views of data in environments where the source data isn't in a suitable format for querying, where generating a suitable query is difficult, or where query performance is poor due to the nature of the data or the data store.
These materialized views, which only contain data required by a query, allow applications to quickly obtain the information they need. In addition to joining tables or combining data entities, materialized views can include the current values of calculated columns or data items, the results of combining values or executing transformations on the data items, and values specified as part of the query. A materialized view can even be optimized for just a single query.
A key point is that a materialized view and the data it contains is completely disposable because it can be entirely rebuilt from the source data stores. A materialized view is never updated directly by an application, and so it's a specialized cache.
When the source data for the view changes, the view must be updated to include the new information. You can schedule this to happen automatically, or when the system detects a change to the original data. In some cases it might be necessary to regenerate the view manually. The figure shows an example of how the Materialized View pattern might be used.
Consider the following points when deciding how to implement this pattern:
How and when the view will be updated. Ideally it'll regenerate in response to an event indicating a change to the source data, although this can lead to excessive overhead if the source data changes rapidly. Alternatively, consider using a scheduled task, an external trigger, or a manual action to regenerate the view.
In some systems, such as when using the Event Sourcing pattern to maintain a store of only the events that modified the data, materialized views are necessary. Prepopulating views by examining all events to determine the current state might be the only way to obtain information from the event store. If you're not using Event Sourcing, you need to consider whether a materialized view is helpful or not. Materialized views tend to be specifically tailored to one, or a small number of queries. If many queries are used, materialized views can result in unacceptable storage capacity requirements and storage cost.
Consider the impact on data consistency when generating the view, and when updating the view if this occurs on a schedule. If the source data is changing at the point when the view is generated, the copy of the data in the view won't be fully consistent with the original data.
Consider where you'll store the view. The view doesn't have to be located in the same store or partition as the original data. It can be a subset from a few different partitions combined.
A view can be rebuilt if lost. Because of that, if the view is transient and is only used to improve query performance by reflecting the current state of the data, or to improve scalability, it can be stored in a cache or in a less reliable location.
When defining a materialized view, maximize its value by adding data items or columns to it based on computation or transformation of existing data items, on values passed in the query, or on combinations of these values when appropriate.
Where the storage mechanism supports it, consider indexing the materialized view to further increase performance. Most relational databases support indexing for views, as do big data solutions based on Apache Hadoop.
This pattern is useful when:
- Creating materialized views over data that's difficult to query directly, or where queries must be very complex to extract data that's stored in a normalized, semi-structured, or unstructured way.
- Creating temporary views that can dramatically improve query performance, or can act directly as source views or data transfer objects for the UI, for reporting, or for display.
- Supporting occasionally connected or disconnected scenarios where connection to the data store isn't always available. The view can be cached locally in this case.
- Simplifying queries and exposing data for experimentation in a way that doesn't require knowledge of the source data format. For example, by joining different tables in one or more databases, or one or more domains in NoSQL stores, and then formatting the data to fit its eventual use.
- Providing access to specific subsets of the source data that, for security or privacy reasons, shouldn't be generally accessible, open to modification, or fully exposed to users.
- Bridging different data stores, to take advantage of their individual capabilities. For example, using a cloud store that's efficient for writing as the reference data store, and a relational database that offers good query and read performance to hold the materialized views.
This pattern isn't useful in the following situations:
- The source data is simple and easy to query.
- The source data changes very quickly, or can be accessed without using a view. In these cases, you should avoid the processing overhead of creating views.
- Consistency is a high priority. The views might not always be fully consistent with the original data.
Decompose a task that performs complex processing into a series of separate elements that can be reused. This can improve performance, scalability, and reusability by allowing task elements that perform the processing to be deployed and scaled independently.
An application is required to perform a variety of tasks of varying complexity on the information that it processes. A straightforward but inflexible approach to implementing an application is to perform this processing as a monolithic module. However, this approach is likely to reduce the opportunities for refactoring the code, optimizing it, or reusing it if parts of the same processing are required elsewhere within the application.
The figure illustrates the issues with processing data using the monolithic approach. An application receives and processes data from two sources. The data from each source is processed by a separate module that performs a series of tasks to transform this data, before passing the result to the business logic of the application.
Some of the tasks that the monolithic modules perform are functionally very similar, but the modules have been designed separately. The code that implements the tasks is closely coupled in a module, and has been developed with little or no thought given to reuse or scalability.
However, the processing tasks performed by each module, or the deployment requirements for each task, could change as business requirements are updated. Some tasks might be compute intensive and could benefit from running on powerful hardware, while others might not require such expensive resources. Also, additional processing might be required in the future, or the order in which the tasks performed by the processing could change. A solution is required that addresses these issues, and increases the possibilities for code reuse.
Break down the processing required for each stream into a set of separate components (or filters), each performing a single task. By standardizing the format of the data that each component receives and sends, these filters can be combined together into a pipeline. This helps to avoid duplicating code, and makes it easy to remove, replace, or integrate additional components if the processing requirements change. The next figure shows a solution implemented using pipes and filters.
The time it takes to process a single request depends on the speed of the slowest filter in the pipeline. One or more filters could be a bottleneck, especially if a large number of requests appear in a stream from a particular data source. A key advantage of the pipeline structure is that it provides opportunities for running parallel instances of slow filters, enabling the system to spread the load and improve throughput.
The filters that make up a pipeline can run on different machines, enabling them to be scaled independently and take advantage of the elasticity that many cloud environments provide. A filter that is computationally intensive can run on high-performance hardware, while other less demanding filters can be hosted on less expensive commodity hardware. The filters don't even have to be in the same datacenter or geographic location, which allows each element in a pipeline to run in an environment close to the resources it requires. The next figure shows an example applied to the pipeline for the data from Source 1.
If the input and output of a filter are structured as a stream, it's possible to perform the processing for each filter in parallel. The first filter in the pipeline can start its work and output its results, which are passed directly on to the next filter in the sequence before the first filter has completed its work.
Another benefit is the resiliency that this model can provide. If a filter fails or the machine it's running on is no longer available, the pipeline can reschedule the work that the filter was performing and direct this work to another instance of the component. Failure of a single filter doesn't necessarily result in failure of the entire pipeline.
Using the Pipes and Filters pattern in conjunction with the Compensating Transaction pattern is an alternative approach to implementing distributed transactions. A distributed transaction can be broken down into separate, compensable tasks, each of which can be implemented by using a filter that also implements the Compensating Transaction pattern. The filters in a pipeline can be implemented as separate hosted tasks running close to the data that they maintain.
You should consider the following points when deciding how to implement this pattern:
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Complexity. The increased flexibility that this pattern provides can also introduce complexity, especially if the filters in a pipeline are distributed across different servers.
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Reliability. Use an infrastructure that ensures that data flowing between filters in a pipeline won't be lost.
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Idempotency. If a filter in a pipeline fails after receiving a message and the work is rescheduled to another instance of the filter, part of the work might have already been completed. If this work updates some aspect of the global state (such as information stored in a database), the same update could be repeated. A similar issue might occur if a filter fails after posting its results to the next filter in the pipeline, but before indicating that it's completed its work successfully. In these cases, the same work could be repeated by another instance of the filter, causing the same results to be posted twice. This could result in subsequent filters in the pipeline processing the same data twice. Therefore filters in a pipeline should be designed to be idempotent. For more information, see Idempotency Patterns on Jonathan Oliver's blog.
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Repeated messages. If a filter in a pipeline fails after posting a message to the next stage of the pipeline, another instance of the filter might be run, and it'll post a copy of the same message to the pipeline. This could cause two instances of the same message to be passed to the next filter. To avoid this, the pipeline should detect and eliminate duplicate messages.
If you're implementing the pipeline by using message queues (such as Microsoft Azure Service Bus queues), the message queuing infrastructure might provide automatic duplicate message detection and removal.
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Context and state. In a pipeline, each filter essentially runs in isolation and shouldn't make any assumptions about how it was invoked. This means that each filter should be provided with sufficient context to perform its work. This context could include a large amount of state information.
Use this pattern when:
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The processing required by an application can easily be broken down into a set of independent steps.
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The processing steps performed by an application have different scalability requirements.
It's possible to group filters that should scale together in the same process. For more information, see the Compute Resource Consolidation pattern.
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Flexibility is required to allow reordering of the processing steps performed by an application, or the capability to add and remove steps.
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The system can benefit from distributing the processing for steps across different servers.
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A reliable solution is required that minimizes the effects of failure in a step while data is being processed.
This pattern might not be useful when:
- The processing steps performed by an application aren't independent, or they have to be performed together as part of the same transaction.
- The amount of context or state information required by a step makes this approach inefficient. It might be possible to persist state information to a database instead, but don't use this strategy if the additional load on the database causes excessive contention.
Prioritize requests sent to services so that requests with a higher priority are received and processed more quickly than those with a lower priority. This pattern is useful in applications that offer different service level guarantees to individual clients.
Applications can delegate specific tasks to other services, for example, to perform background processing or to integrate with other applications or services. In the cloud, a message queue is typically used to delegate tasks to background processing. In many cases the order requests are received in by a service isn't important. In some cases, though, it's necessary to prioritize specific requests. These requests should be processed earlier than lower priority requests that were sent previously by the application.
A queue is usually a first-in, first-out (FIFO) structure, and consumers typically receive messages in the same order that they were posted to the queue. However, some message queues support priority messaging. The application posting a message can assign a priority and the messages in the queue are automatically reordered so that those with a higher priority will be received before those with a lower priority. The figure illustrates a queue with priority messaging.
Most message queue implementations support multiple consumers (following the Competing Consumers pattern), and the number of consumer processes can be scaled up or down depending on demand.
In systems that don't support priority-based message queues, an alternative solution is to maintain a separate queue for each priority. The application is responsible for posting messages to the appropriate queue. Each queue can have a separate pool of consumers. Higher priority queues can have a larger pool of consumers running on faster hardware than lower priority queues. The next figure illustrates using separate message queues for each priority.
A variation on this strategy is to have a single pool of consumers that check for messages on high priority queues first, and only then start to fetch messages from lower priority queues. There are some semantic differences between a solution that uses a single pool of consumer processes (either with a single queue that supports messages with different priorities or with multiple queues that each handle messages of a single priority), and a solution that uses multiple queues with a separate pool for each queue.
In the single pool approach, higher priority messages are always received and processed before lower priority messages. In theory, messages that have a very low priority could be continually superseded and might never be processed. In the multiple pool approach, lower priority messages will always be processed, just not as quickly as those of a higher priority (depending on the relative size of the pools and the resources that they have available).
Using a priority queuing mechanism can provide the following advantages:
- It allows applications to meet business requirements that require prioritization of availability or performance, such as offering different levels of service to specific groups of customers.
- It can help to minimize operational costs. In the single queue approach, you can scale back the number of consumers if necessary. High priority messages will still be processed first (although possibly more slowly), and lower priority messages might be delayed for longer. If you've implemented the multiple message queue approach with separate pools of consumers for each queue, you can reduce the pool of consumers for lower priority queues, or even suspend processing for some very low priority queues by stopping all the consumers that listen for messages on those queues.
- The multiple message queue approach can help maximize application performance and scalability by partitioning messages based on processing requirements. For example, vital tasks can be prioritized to be handled by receivers that run immediately while less important background tasks can be handled by receivers that are scheduled to run at less busy periods.
Consider the following points when deciding how to implement this pattern:
Define the priorities in the context of the solution. For example, high priority could mean that messages should be processed within ten seconds. Identify the requirements for handling high priority items, and the other resources that should be allocated to meet these criteria.
Decide if all high priority items must be processed before any lower priority items. If the messages are being processed by a single pool of consumers, you have to provide a mechanism that can preempt and suspend a task that's handling a low priority message if a higher priority message becomes available.
In the multiple queue approach, when using a single pool of consumer processes that listen on all queues rather than a dedicated consumer pool for each queue, the consumer must apply an algorithm that ensures it always services messages from higher priority queues before those from lower priority queues.
Monitor the processing speed on high and low priority queues to ensure that messages in these queues are processed at the expected rates.
If you need to guarantee that low priority messages will be processed, it's necessary to implement the multiple message queue approach with multiple pools of consumers. Alternatively, in a queue that supports message prioritization, it's possible to dynamically increase the priority of a queued message as it ages. However, this approach depends on the message queue providing this feature.
Using a separate queue for each message priority works best for systems that have a small number of well-defined priorities.
Message priorities can be determined logically by the system. For example, rather than having explicit high and low priority messages, they could be designated as "fee paying customer," or "non-fee paying customer." Depending on your business model, your system can allocate more resources to processing messages from fee paying customers than non-fee paying ones.
There might be a financial and processing cost associated with checking a queue for a message (some commercial messaging systems charge a small fee each time a message is posted or retrieved, and each time a queue is queried for messages). This cost increases when checking multiple queues.
It's possible to dynamically adjust the size of a pool of consumers based on the length of the queue that the pool is servicing. For more information, see the Autoscaling Guidance.
This pattern is useful in scenarios where:
- The system must handle multiple tasks that have different priorities.
- Different users or tenants should be served with different priority.
Enable an application to announce events to multiple interested consumers asynchronously, without coupling the senders to the receivers.
Also called: Pub/sub messaging
In cloud-based and distributed applications, components of the system often need to provide information to other components as events happen.
Asynchronous messaging is an effective way to decouple senders from consumers, and avoid blocking the sender to wait for a response. However, using a dedicated message queue for each consumer does not effectively scale to many consumers. Also, some of the consumers might be interested in only a subset of the information. How can the sender announce events to all interested consumers without knowing their identities?
Introduce an asynchronous messaging subsystem that includes the following:
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An input messaging channel used by the sender. The sender packages events into messages, using a known message format, and sends these messages via the input channel. The sender in this pattern is also called the publisher.
Note
A message is a packet of data. An event is a message that notifies other components about a change or an action that has taken place.
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One output messaging channel per consumer. The consumers are known as subscribers.
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A mechanism for copying each message from the input channel to the output channels for all subscribers interested in that message. This operation is typically handled by an intermediary such as a message broker or event bus.
The following diagram shows the logical components of this pattern:
Pub/sub messaging has the following benefits:
- It decouples subsystems that still need to communicate. Subsystems can be managed independently, and messages can be properly managed even if one or more receivers are offline.
- It increases scalability and improves responsiveness of the sender. The sender can quickly send a single message to the input channel, then return to its core processing responsibilities. The messaging infrastructure is responsible for ensuring messages are delivered to interested subscribers.
- It improves reliability. Asynchronous messaging helps applications continue to run smoothly under increased loads and handle intermittent failures more effectively.
- It allows for deferred or scheduled processing. Subscribers can wait to pick up messages until off-peak hours, or messages can be routed or processed according to a specific schedule.
- It enables simpler integration between systems using different platforms, programming languages, or communication protocols, as well as between on-premises systems and applications running in the cloud.
- It facilitates asynchronous workflows across an enterprise.
- It improves testability. Channels can be monitored and messages can be inspected or logged as part of an overall integration test strategy.
- It provides separation of concerns for your applications. Each application can focus on its core capabilities, while the messaging infrastructure handles everything required to reliably route messages to multiple consumers.
Consider the following points when deciding how to implement this pattern:
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Existing technologies. It is strongly recommended to use available messaging products and services that support a publish-subscribe model, rather than building your own. In Azure, consider using Service Bus or Event Grid. Other technologies that can be used for pub/sub messaging include Redis, RabbitMQ, and Apache Kafka.
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Subscription handling. The messaging infrastructure must provide mechanisms that consumers can use to subscribe to or unsubscribe from available channels.
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Security. Connecting to any message channel must be restricted by security policy to prevent eavesdropping by unauthorized users or applications.
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Subsets of messages. Subscribers are usually only interested in subset of the messages distributed by a publisher. Messaging services often allow subscribers to narrow the set of messages received by:
- Topics. Each topic has a dedicated output channel, and each consumer can subscribe to all relevant topics.
- Content filtering. Messages are inspected and distributed based on the content of each message. Each subscriber can specify the content it is interested in.
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Wildcard subscribers. Consider allowing subscribers to subscribe to multiple topics via wildcards.
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Bi-directional communication. The channels in a publish-subscribe system are treated as unidirectional. If a specific subscriber needs to send acknowledgment or communicate status back to the publisher, consider using the Request/Reply Pattern. This pattern uses one channel to send a message to the subscriber, and a separate reply channel for communicating back to the publisher.
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Message ordering. The order in which consumer instances receive messages isn't guaranteed, and doesn't necessarily reflect the order in which the messages were created. Design the system to ensure that message processing is idempotent to help eliminate any dependency on the order of message handling.
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Message priority. Some solutions may require that messages are processed in a specific order. The Priority Queue pattern provides a mechanism for ensuring specific messages are delivered before others.
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Poison messages. A malformed message, or a task that requires access to resources that aren't available, can cause a service instance to fail. The system should prevent such messages being returned to the queue. Instead, capture and store the details of these messages elsewhere so that they can be analyzed if necessary.
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Repeated messages. The same message might be sent more than once. For example, the sender might fail after posting a message. Then a new instance of the sender might start up and repeat the message. The messaging infrastructure should implement duplicate message detection and removal (also known as de-duping) based on message IDs in order to provide at-most-once delivery of messages.
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Message expiration. A message might have a limited lifetime. If it isn't processed within this period, it might no longer be relevant and should be discarded. A sender can specify an expiration time as part of the data in the message. A receiver can examine this information before deciding whether to perform the business logic associated with the message.
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Message scheduling. A message might be temporarily embargoed and should not be processed until a specific date and time. The message should not be available to a receiver until this time.
Use this pattern when:
- An application needs to broadcast information to a significant number of consumers.
- An application needs to communicate with one or more independently-developed applications or services, which may use different platforms, programming languages, and communication protocols.
- An application can send information to consumers without requiring real-time responses from the consumers.
- The systems being integrated are designed to support an eventual consistency model for their data.
- An application needs to communicate information to multiple consumers, which may have different availability requirements or uptime schedules than the sender.
This pattern might not be useful when:
- An application has only a few consumers who need significantly different information from the producing application.
- An application requires near real-time interaction with consumers.
Use a queue that acts as a buffer between a task and a service it invokes in order to smooth intermittent heavy loads that can cause the service to fail or the task to time out. This can help to minimize the impact of peaks in demand on availability and responsiveness for both the task and the service.
Many solutions in the cloud involve running tasks that invoke services. In this environment, if a service is subjected to intermittent heavy loads, it can cause performance or reliability issues.
A service could be part of the same solution as the tasks that use it, or it could be a third-party service providing access to frequently used resources such as a cache or a storage service. If the same service is used by a number of tasks running concurrently, it can be difficult to predict the volume of requests to the service at any time.
A service might experience peaks in demand that cause it to overload and be unable to respond to requests in a timely manner. Flooding a service with a large number of concurrent requests can also result in the service failing if it's unable to handle the contention these requests cause.
Refactor the solution and introduce a queue between the task and the service. The task and the service run asynchronously. The task posts a message containing the data required by the service to a queue. The queue acts as a buffer, storing the message until it's retrieved by the service. The service retrieves the messages from the queue and processes them. Requests from a number of tasks, which can be generated at a highly variable rate, can be passed to the service through the same message queue. This figure shows using a queue to level the load on a service.
The queue decouples the tasks from the service, and the service can handle the messages at its own pace regardless of the volume of requests from concurrent tasks. Additionally, there's no delay to a task if the service isn't available at the time it posts a message to the queue.
This pattern provides the following benefits:
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It can help to maximize availability because delays arising in services won't have an immediate and direct impact on the application, which can continue to post messages to the queue even when the service isn't available or isn't currently processing messages.
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It can help to maximize scalability because both the number of queues and the number of services can be varied to meet demand.
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It can help to control costs because the number of service instances deployed only have to be adequate to meet average load rather than the peak load.
Some services implement throttling when demand reaches a threshold beyond which the system could fail. Throttling can reduce the functionality available. You can implement load leveling with these services to ensure that this threshold isn't reached.
Consider the following points when deciding how to implement this pattern:
- It's necessary to implement application logic that controls the rate at which services handle messages to avoid overwhelming the target resource. Avoid passing spikes in demand to the next stage of the system. Test the system under load to ensure that it provides the required leveling, and adjust the number of queues and the number of service instances that handle messages to achieve this.
- Message queues are a one-way communication mechanism. If a task expects a reply from a service, it might be necessary to implement a mechanism that the service can use to send a response. For more information, see the Asynchronous Messaging Primer.
- Be careful if you apply autoscaling to services that are listening for requests on the queue. This can result in increased contention for any resources that these services share and diminish the effectiveness of using the queue to level the load.
This pattern is useful to any application that uses services that are subject to overloading.
This pattern isn't useful if the application expects a response from the service with minimal latency.
Enable an application to handle transient failures when it tries to connect to a service or network resource, by transparently retrying a failed operation. This can improve the stability of the application.
An application that communicates with elements running in the cloud has to be sensitive to the transient faults that can occur in this environment. Faults include the momentary loss of network connectivity to components and services, the temporary unavailability of a service, or timeouts that occur when a service is busy.
These faults are typically self-correcting, and if the action that triggered a fault is repeated after a suitable delay it's likely to be successful. For example, a database service that's processing a large number of concurrent requests can implement a throttling strategy that temporarily rejects any further requests until its workload has eased. An application trying to access the database might fail to connect, but if it tries again after a delay it might succeed.
In the cloud, transient faults aren't uncommon and an application should be designed to handle them elegantly and transparently. This minimizes the effects faults can have on the business tasks the application is performing.
If an application detects a failure when it tries to send a request to a remote service, it can handle the failure using the following strategies:
- Cancel. If the fault indicates that the failure isn't transient or is unlikely to be successful if repeated, the application should cancel the operation and report an exception. For example, an authentication failure caused by providing invalid credentials is not likely to succeed no matter how many times it's attempted.
- Retry. If the specific fault reported is unusual or rare, it might have been caused by unusual circumstances such as a network packet becoming corrupted while it was being transmitted. In this case, the application could retry the failing request again immediately because the same failure is unlikely to be repeated and the request will probably be successful.
- Retry after delay. If the fault is caused by one of the more commonplace connectivity or busy failures, the network or service might need a short period while the connectivity issues are corrected or the backlog of work is cleared. The application should wait for a suitable time before retrying the request.
For the more common transient failures, the period between retries should be chosen to spread requests from multiple instances of the application as evenly as possible. This reduces the chance of a busy service continuing to be overloaded. If many instances of an application are continually overwhelming a service with retry requests, it'll take the service longer to recover.
If the request still fails, the application can wait and make another attempt. If necessary, this process can be repeated with increasing delays between retry attempts, until some maximum number of requests have been attempted. The delay can be increased incrementally or exponentially, depending on the type of failure and the probability that it'll be corrected during this time.
The following diagram illustrates invoking an operation in a hosted service using this pattern. If the request is unsuccessful after a predefined number of attempts, the application should treat the fault as an exception and handle it accordingly.
The application should wrap all attempts to access a remote service in code that implements a retry policy matching one of the strategies listed above. Requests sent to different services can be subject to different policies. Some vendors provide libraries that implement retry policies, where the application can specify the maximum number of retries, the time between retry attempts, and other parameters.
An application should log the details of faults and failing operations. This information is useful to operators. If a service is frequently unavailable or busy, it's often because the service has exhausted its resources. You can reduce the frequency of these faults by scaling out the service. For example, if a database service is continually overloaded, it might be beneficial to partition the database and spread the load across multiple servers.
Microsoft Entity Framework provides facilities for retrying database operations. Also, most Azure services and client SDKs include a retry mechanism. For more information, see Retry guidance for specific services.
You should consider the following points when deciding how to implement this pattern.
The retry policy should be tuned to match the business requirements of the application and the nature of the failure. For some noncritical operations, it's better to fail fast rather than retry several times and impact the throughput of the application. For example, in an interactive web application accessing a remote service, it's better to fail after a smaller number of retries with only a short delay between retry attempts, and display a suitable message to the user (for example, "please try again later"). For a batch application, it might be more appropriate to increase the number of retry attempts with an exponentially increasing delay between attempts.
An aggressive retry policy with minimal delay between attempts, and a large number of retries, could further degrade a busy service that's running close to or at capacity. This retry policy could also affect the responsiveness of the application if it's continually trying to perform a failing operation.
If a request still fails after a significant number of retries, it's better for the application to prevent further requests going to the same resource and simply report a failure immediately. When the period expires, the application can tentatively allow one or more requests through to see whether they're successful. For more details of this strategy, see the Circuit Breaker pattern.
Consider whether the operation is idempotent. If so, it's inherently safe to retry. Otherwise, retries could cause the operation to be executed more than once, with unintended side effects. For example, a service might receive the request, process the request successfully, but fail to send a response. At that point, the retry logic might re-send the request, assuming that the first request wasn't received.
A request to a service can fail for a variety of reasons raising different exceptions depending on the nature of the failure. Some exceptions indicate a failure that can be resolved quickly, while others indicate that the failure is longer lasting. It's useful for the retry policy to adjust the time between retry attempts based on the type of the exception.
Consider how retrying an operation that's part of a transaction will affect the overall transaction consistency. Fine tune the retry policy for transactional operations to maximize the chance of success and reduce the need to undo all the transaction steps.
Ensure that all retry code is fully tested against a variety of failure conditions. Check that it doesn't severely impact the performance or reliability of the application, cause excessive load on services and resources, or generate race conditions or bottlenecks.
Implement retry logic only where the full context of a failing operation is understood. For example, if a task that contains a retry policy invokes another task that also contains a retry policy, this extra layer of retries can add long delays to the processing. It might be better to configure the lower-level task to fail fast and report the reason for the failure back to the task that invoked it. This higher-level task can then handle the failure based on its own policy.
It's important to log all connectivity failures that cause a retry so that underlying problems with the application, services, or resources can be identified.
Investigate the faults that are most likely to occur for a service or a resource to discover if they're likely to be long lasting or terminal. If they are, it's better to handle the fault as an exception. The application can report or log the exception, and then try to continue either by invoking an alternative service (if one is available), or by offering degraded functionality. For more information on how to detect and handle long-lasting faults, see the Circuit Breaker pattern.
Use this pattern when an application could experience transient faults as it interacts with a remote service or accesses a remote resource. These faults are expected to be short lived, and repeating a request that has previously failed could succeed on a subsequent attempt.
This pattern might not be useful:
- When a fault is likely to be long lasting, because this can affect the responsiveness of an application. The application might be wasting time and resources trying to repeat a request that's likely to fail.
- For handling failures that aren't due to transient faults, such as internal exceptions caused by errors in the business logic of an application.
- As an alternative to addressing scalability issues in a system. If an application experiences frequent busy faults, it's often a sign that the service or resource being accessed should be scaled up.
Coordinate a set of distributed actions as a single operation. If any of the actions fail, try to handle the failures transparently, or else undo the work that was performed, so the entire operation succeeds or fails as a whole. This can add resiliency to a distributed system, by enabling it to recover and retry actions that fail due to transient exceptions, long-lasting faults, and process failures.
An application performs tasks that include a number of steps, some of which might invoke remote services or access remote resources. The individual steps might be independent of each other, but they are orchestrated by the application logic that implements the task.
Whenever possible, the application should ensure that the task runs to completion and resolve any failures that might occur when accessing remote services or resources. Failures can occur for many reasons. For example, the network might be down, communications could be interrupted, a remote service might be unresponsive or in an unstable state, or a remote resource might be temporarily inaccessible, perhaps due to resource constraints. In many cases the failures will be transient and can be handled by using the Retry pattern.
If the application detects a more permanent fault it can't easily recover from, it must be able to restore the system to a consistent state and ensure integrity of the entire operation.
The Scheduler Agent Supervisor pattern defines the following actors. These actors orchestrate the steps to be performed as part of the overall task.
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The Scheduler arranges for the steps that make up the task to be executed and orchestrates their operation. These steps can be combined into a pipeline or workflow. The Scheduler is responsible for ensuring that the steps in this workflow are performed in the right order. As each step is performed, the Scheduler records the state of the workflow, such as "step not yet started," "step running," or "step completed." The state information should also include an upper limit of the time allowed for the step to finish, called the complete-by time. If a step requires access to a remote service or resource, the Scheduler invokes the appropriate Agent, passing it the details of the work to be performed. The Scheduler typically communicates with an Agent using asynchronous request/response messaging. This can be implemented using queues, although other distributed messaging technologies could be used instead.
The Scheduler performs a similar function to the Process Manager in the Process Manager pattern. The actual workflow is typically defined and implemented by a workflow engine that's controlled by the Scheduler. This approach decouples the business logic in the workflow from the Scheduler.
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The Agent contains logic that encapsulates a call to a remote service, or access to a remote resource referenced by a step in a task. Each Agent typically wraps calls to a single service or resource, implementing the appropriate error handling and retry logic (subject to a timeout constraint, described later). If the steps in the workflow being run by the Scheduler use several services and resources across different steps, each step might reference a different Agent (this is an implementation detail of the pattern).
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The Supervisor monitors the status of the steps in the task being performed by the Scheduler. It runs periodically (the frequency will be system-specific), and examines the status of steps maintained by the Scheduler. If it detects any that have timed out or failed, it arranges for the appropriate Agent to recover the step or execute the appropriate remedial action (this might involve modifying the status of a step). Note that the recovery or remedial actions are implemented by the Scheduler and Agents. The Supervisor should simply request that these actions be performed.
The Scheduler, Agent, and Supervisor are logical components and their physical implementation depends on the technology being used. For example, several logical agents might be implemented as part of a single web service.
The Scheduler maintains information about the progress of the task and the state of each step in a durable data store, called the state store. The Supervisor can use this information to help determine whether a step has failed. The figure illustrates the relationship between the Scheduler, the Agents, the Supervisor, and the state store.
This diagram shows a simplified version of the pattern. In a real implementation, there might be many instances of the Scheduler running concurrently, each a subset of tasks. Similarly, the system could run multiple instances of each Agent, or even multiple Supervisors. In this case, Supervisors must coordinate their work with each other carefully to ensure that they don't compete to recover the same failed steps and tasks. The Leader Election pattern provides one possible solution to this problem.
When the application is ready to run a task, it submits a request to the Scheduler. The Scheduler records initial state information about the task and its steps (for example, step not yet started) in the state store and then starts performing the operations defined by the workflow. As the Scheduler starts each step, it updates the information about the state of that step in the state store (for example, step running).
If a step references a remote service or resource, the Scheduler sends a message to the appropriate Agent. The message contains the information that the Agent needs to pass to the service or access the resource, in addition to the complete-by time for the operation. If the Agent completes its operation successfully, it returns a response to the Scheduler. The Scheduler can then update the state information in the state store (for example, step completed) and perform the next step. This process continues until the entire task is complete.
An Agent can implement any retry logic that's necessary to perform its work. However, if the Agent doesn't complete its work before the complete-by period expires, the Scheduler will assume that the operation has failed. In this case, the Agent should stop its work and not try to return anything to the Scheduler (not even an error message), or try any form of recovery. The reason for this restriction is that, after a step has timed out or failed, another instance of the Agent might be scheduled to run the failing step (this process is described later).
If the Agent fails, the Scheduler won't receive a response. The pattern doesn't make a distinction between a step that has timed out and one that has genuinely failed.
If a step times out or fails, the state store will contain a record that indicates that the step is running, but the complete-by time will have passed. The Supervisor looks for steps like this and tries to recover them. One possible strategy is for the Supervisor to update the complete-by value to extend the time available to complete the step, and then send a message to the Scheduler identifying the step that has timed out. The Scheduler can then try to repeat this step. However, this design requires the tasks to be idempotent.
The Supervisor might need to prevent the same step from being retried if it continually fails or times out. To do this, the Supervisor could maintain a retry count for each step, along with the state information, in the state store. If this count exceeds a predefined threshold the Supervisor can adopt a strategy of waiting for an extended period before notifying the Scheduler that it should retry the step, in the expectation that the fault will be resolved during this period. Alternatively, the Supervisor can send a message to the Scheduler to request the entire task be undone by implementing a Compensating Transaction pattern. This approach will depend on the Scheduler and Agents providing the information necessary to implement the compensating operations for each step that completed successfully.
It isn't the purpose of the Supervisor to monitor the Scheduler and Agents, and restart them if they fail. This aspect of the system should be handled by the infrastructure these components are running in. Similarly, the Supervisor shouldn't have knowledge of the actual business operations that the tasks being performed by the Scheduler are running (including how to compensate should these tasks fail). This is the purpose of the workflow logic implemented by the Scheduler. The sole responsibility of the Supervisor is to determine whether a step has failed and arrange either for it to be repeated or for the entire task containing the failed step to be undone.
If the Scheduler is restarted after a failure, or the workflow being performed by the Scheduler terminates unexpectedly, the Scheduler should be able to determine the status of any inflight task that it was handling when it failed, and be prepared to resume this task from that point. The implementation details of this process are likely to be system-specific. If the task can't be recovered, it might be necessary to undo the work already performed by the task. This might also require implementing a compensating transaction.
The key advantage of this pattern is that the system is resilient in the event of unexpected temporary or unrecoverable failures. The system can be constructed to be self-healing. For example, if an Agent or the Scheduler fails, a new one can be started and the Supervisor can arrange for a task to be resumed. If the Supervisor fails, another instance can be started and can take over from where the failure occurred. If the Supervisor is scheduled to run periodically, a new instance can be automatically started after a predefined interval. The state store can be replicated to reach an even greater degree of resiliency.
You should consider the following points when deciding how to implement this pattern:
- This pattern can be difficult to implement and requires thorough testing of each possible failure mode of the system.
- The recovery/retry logic implemented by the Scheduler is complex and dependent on state information held in the state store. It might also be necessary to record the information required to implement a compensating transaction in a durable data store.
- How often the Supervisor runs will be important. It should run often enough to prevent any failed steps from blocking an application for an extended period, but it shouldn't run so often that it becomes an overhead.
- The steps performed by an Agent could be run more than once. The logic that implements these steps should be idempotent.
Use this pattern when a process that runs in a distributed environment, such as the cloud, must be resilient to communications failure and/or operational failure.
This pattern might not be suitable for tasks that don't invoke remote services or access remote resources.
Coordinate a set of distributed actions as a single operation. If any of the actions fail, try to handle the failures transparently, or else undo the work that was performed, so the entire operation succeeds or fails as a whole. This can add resiliency to a distributed system, by enabling it to recover and retry actions that fail due to transient exceptions, long-lasting faults, and process failures.
An application performs tasks that include a number of steps, some of which might invoke remote services or access remote resources. The individual steps might be independent of each other, but they are orchestrated by the application logic that implements the task.
Whenever possible, the application should ensure that the task runs to completion and resolve any failures that might occur when accessing remote services or resources. Failures can occur for many reasons. For example, the network might be down, communications could be interrupted, a remote service might be unresponsive or in an unstable state, or a remote resource might be temporarily inaccessible, perhaps due to resource constraints. In many cases the failures will be transient and can be handled by using the Retry pattern.
If the application detects a more permanent fault it can't easily recover from, it must be able to restore the system to a consistent state and ensure integrity of the entire operation.
The Scheduler Agent Supervisor pattern defines the following actors. These actors orchestrate the steps to be performed as part of the overall task.
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The Scheduler arranges for the steps that make up the task to be executed and orchestrates their operation. These steps can be combined into a pipeline or workflow. The Scheduler is responsible for ensuring that the steps in this workflow are performed in the right order. As each step is performed, the Scheduler records the state of the workflow, such as "step not yet started," "step running," or "step completed." The state information should also include an upper limit of the time allowed for the step to finish, called the complete-by time. If a step requires access to a remote service or resource, the Scheduler invokes the appropriate Agent, passing it the details of the work to be performed. The Scheduler typically communicates with an Agent using asynchronous request/response messaging. This can be implemented using queues, although other distributed messaging technologies could be used instead.
The Scheduler performs a similar function to the Process Manager in the Process Manager pattern. The actual workflow is typically defined and implemented by a workflow engine that's controlled by the Scheduler. This approach decouples the business logic in the workflow from the Scheduler.
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The Agent contains logic that encapsulates a call to a remote service, or access to a remote resource referenced by a step in a task. Each Agent typically wraps calls to a single service or resource, implementing the appropriate error handling and retry logic (subject to a timeout constraint, described later). If the steps in the workflow being run by the Scheduler use several services and resources across different steps, each step might reference a different Agent (this is an implementation detail of the pattern).
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The Supervisor monitors the status of the steps in the task being performed by the Scheduler. It runs periodically (the frequency will be system-specific), and examines the status of steps maintained by the Scheduler. If it detects any that have timed out or failed, it arranges for the appropriate Agent to recover the step or execute the appropriate remedial action (this might involve modifying the status of a step). Note that the recovery or remedial actions are implemented by the Scheduler and Agents. The Supervisor should simply request that these actions be performed.
The Scheduler, Agent, and Supervisor are logical components and their physical implementation depends on the technology being used. For example, several logical agents might be implemented as part of a single web service.
The Scheduler maintains information about the progress of the task and the state of each step in a durable data store, called the state store. The Supervisor can use this information to help determine whether a step has failed. The figure illustrates the relationship between the Scheduler, the Agents, the Supervisor, and the state store.
This diagram shows a simplified version of the pattern. In a real implementation, there might be many instances of the Scheduler running concurrently, each a subset of tasks. Similarly, the system could run multiple instances of each Agent, or even multiple Supervisors. In this case, Supervisors must coordinate their work with each other carefully to ensure that they don't compete to recover the same failed steps and tasks. The Leader Election pattern provides one possible solution to this problem.
When the application is ready to run a task, it submits a request to the Scheduler. The Scheduler records initial state information about the task and its steps (for example, step not yet started) in the state store and then starts performing the operations defined by the workflow. As the Scheduler starts each step, it updates the information about the state of that step in the state store (for example, step running).
If a step references a remote service or resource, the Scheduler sends a message to the appropriate Agent. The message contains the information that the Agent needs to pass to the service or access the resource, in addition to the complete-by time for the operation. If the Agent completes its operation successfully, it returns a response to the Scheduler. The Scheduler can then update the state information in the state store (for example, step completed) and perform the next step. This process continues until the entire task is complete.
An Agent can implement any retry logic that's necessary to perform its work. However, if the Agent doesn't complete its work before the complete-by period expires, the Scheduler will assume that the operation has failed. In this case, the Agent should stop its work and not try to return anything to the Scheduler (not even an error message), or try any form of recovery. The reason for this restriction is that, after a step has timed out or failed, another instance of the Agent might be scheduled to run the failing step (this process is described later).
If the Agent fails, the Scheduler won't receive a response. The pattern doesn't make a distinction between a step that has timed out and one that has genuinely failed.
If a step times out or fails, the state store will contain a record that indicates that the step is running, but the complete-by time will have passed. The Supervisor looks for steps like this and tries to recover them. One possible strategy is for the Supervisor to update the complete-by value to extend the time available to complete the step, and then send a message to the Scheduler identifying the step that has timed out. The Scheduler can then try to repeat this step. However, this design requires the tasks to be idempotent.
The Supervisor might need to prevent the same step from being retried if it continually fails or times out. To do this, the Supervisor could maintain a retry count for each step, along with the state information, in the state store. If this count exceeds a predefined threshold the Supervisor can adopt a strategy of waiting for an extended period before notifying the Scheduler that it should retry the step, in the expectation that the fault will be resolved during this period. Alternatively, the Supervisor can send a message to the Scheduler to request the entire task be undone by implementing a Compensating Transaction pattern. This approach will depend on the Scheduler and Agents providing the information necessary to implement the compensating operations for each step that completed successfully.
It isn't the purpose of the Supervisor to monitor the Scheduler and Agents, and restart them if they fail. This aspect of the system should be handled by the infrastructure these components are running in. Similarly, the Supervisor shouldn't have knowledge of the actual business operations that the tasks being performed by the Scheduler are running (including how to compensate should these tasks fail). This is the purpose of the workflow logic implemented by the Scheduler. The sole responsibility of the Supervisor is to determine whether a step has failed and arrange either for it to be repeated or for the entire task containing the failed step to be undone.
If the Scheduler is restarted after a failure, or the workflow being performed by the Scheduler terminates unexpectedly, the Scheduler should be able to determine the status of any inflight task that it was handling when it failed, and be prepared to resume this task from that point. The implementation details of this process are likely to be system-specific. If the task can't be recovered, it might be necessary to undo the work already performed by the task. This might also require implementing a compensating transaction.
The key advantage of this pattern is that the system is resilient in the event of unexpected temporary or unrecoverable failures. The system can be constructed to be self-healing. For example, if an Agent or the Scheduler fails, a new one can be started and the Supervisor can arrange for a task to be resumed. If the Supervisor fails, another instance can be started and can take over from where the failure occurred. If the Supervisor is scheduled to run periodically, a new instance can be automatically started after a predefined interval. The state store can be replicated to reach an even greater degree of resiliency.
You should consider the following points when deciding how to implement this pattern:
- This pattern can be difficult to implement and requires thorough testing of each possible failure mode of the system.
- The recovery/retry logic implemented by the Scheduler is complex and dependent on state information held in the state store. It might also be necessary to record the information required to implement a compensating transaction in a durable data store.
- How often the Supervisor runs will be important. It should run often enough to prevent any failed steps from blocking an application for an extended period, but it shouldn't run so often that it becomes an overhead.
- The steps performed by an Agent could be run more than once. The logic that implements these steps should be idempotent.
Use this pattern when a process that runs in a distributed environment, such as the cloud, must be resilient to communications failure and/or operational failure.
This pattern might not be suitable for tasks that don't invoke remote services or access remote resources.
Process a set of related messages in a defined order, without blocking processing of other groups of messages.
Applications often need to process a sequence of messages in the order they arrive, while still being able to scale out to handle increased load. In a distributed architecture, processing these messages in order is not straightforward, because the workers can scale independently and often pull messages independently, using a Competing Consumers pattern.
For example, an order tracking system receives a ledger containing orders and the relevant operations on those orders. These operations could be to create an order, add a transaction to the order, modify a past transaction, or delete an order. In this system, operations must be performed in a first-in-first-out (FIFO) manner, but only at the order level. However, the initial queue receives a ledger containing transactions for many orders, which may be interleaved.
Push related messages into categories within the queuing system, and have the queue listeners lock and pull only from one category, one message at a time.
Here's what the general Sequential Convoy pattern looks like:
In the queue, messages for different categories may be interleaved, as shown in the following diagram:
Divide a data store into a set of horizontal partitions or shards. This can improve scalability when storing and accessing large volumes of data.
A data store hosted by a single server might be subject to the following limitations:
- Storage space. A data store for a large-scale cloud application is expected to contain a huge volume of data that could increase significantly over time. A server typically provides only a finite amount of disk storage, but you can replace existing disks with larger ones, or add further disks to a machine as data volumes grow. However, the system will eventually reach a limit where it isn't possible to easily increase the storage capacity on a given server.
- Computing resources. A cloud application is required to support a large number of concurrent users, each of which run queries that retrieve information from the data store. A single server hosting the data store might not be able to provide the necessary computing power to support this load, resulting in extended response times for users and frequent failures as applications attempting to store and retrieve data time out. It might be possible to add memory or upgrade processors, but the system will reach a limit when it isn't possible to increase the compute resources any further.
- Network bandwidth. Ultimately, the performance of a data store running on a single server is governed by the rate the server can receive requests and send replies. It's possible that the volume of network traffic might exceed the capacity of the network used to connect to the server, resulting in failed requests.
- Geography. It might be necessary to store data generated by specific users in the same region as those users for legal, compliance, or performance reasons, or to reduce latency of data access. If the users are dispersed across different countries or regions, it might not be possible to store the entire data for the application in a single data store.
Scaling vertically by adding more disk capacity, processing power, memory, and network connections can postpone the effects of some of these limitations, but it's likely to only be a temporary solution. A commercial cloud application capable of supporting large numbers of users and high volumes of data must be able to scale almost indefinitely, so vertical scaling isn't necessarily the best solution.
Divide the data store into horizontal partitions or shards. Each shard has the same schema, but holds its own distinct subset of the data. A shard is a data store in its own right (it can contain the data for many entities of different types), running on a server acting as a storage node.
This pattern has the following benefits:
- You can scale the system out by adding further shards running on additional storage nodes.
- A system can use off-the-shelf hardware rather than specialized and expensive computers for each storage node.
- You can reduce contention and improve performance by balancing the workload across shards.
- In the cloud, shards can be located physically close to the users that'll access the data.
When dividing a data store up into shards, decide which data should be placed in each shard. A shard typically contains items that fall within a specified range determined by one or more attributes of the data. These attributes form the shard key (sometimes referred to as the partition key). The shard key should be static. It shouldn't be based on data that might change.
Sharding physically organizes the data. When an application stores and retrieves data, the sharding logic directs the application to the appropriate shard. This sharding logic can be implemented as part of the data access code in the application, or it could be implemented by the data storage system if it transparently supports sharding.
Abstracting the physical location of the data in the sharding logic provides a high level of control over which shards contain which data. It also enables data to migrate between shards without reworking the business logic of an application if the data in the shards need to be redistributed later (for example, if the shards become unbalanced). The tradeoff is the additional data access overhead required in determining the location of each data item as it's retrieved.
To ensure optimal performance and scalability, it's important to split the data in a way that's appropriate for the types of queries that the application performs. In many cases, it's unlikely that the sharding scheme will exactly match the requirements of every query. For example, in a multi-tenant system an application might need to retrieve tenant data using the tenant ID, but it might also need to look up this data based on some other attribute such as the tenant's name or location. To handle these situations, implement a sharding strategy with a shard key that supports the most commonly performed queries.
If queries regularly retrieve data using a combination of attribute values, you can likely define a composite shard key by linking attributes together. Alternatively, use a pattern such as Index Table to provide fast lookup to data based on attributes that aren't covered by the shard key.
Three strategies are commonly used when selecting the shard key and deciding how to distribute data across shards. Note that there doesn't have to be a one-to-one correspondence between shards and the servers that host them---a single server can host multiple shards. The strategies are:
The Lookup strategy. In this strategy the sharding logic implements a map that routes a request for data to the shard that contains that data using the shard key. In a multi-tenant application all the data for a tenant might be stored together in a shard using the tenant ID as the shard key. Multiple tenants might share the same shard, but the data for a single tenant won't be spread across multiple shards. The figure illustrates sharding tenant data based on tenant IDs.
The mapping between the shard key and the physical storage can be based on physical shards where each shard key maps to a physical partition. Alternatively, a more flexible technique for rebalancing shards is virtual partitioning, where shard keys map to the same number of virtual shards, which in turn map to fewer physical partitions. In this approach, an application locates data using a shard key that refers to a virtual shard, and the system transparently maps virtual shards to physical partitions. The mapping between a virtual shard and a physical partition can change without requiring the application code be modified to use a different set of shard keys.
The Range strategy. This strategy groups related items together in the same shard, and orders them by shard key---the shard keys are sequential. It's useful for applications that frequently retrieve sets of items using range queries (queries that return a set of data items for a shard key that falls within a given range). For example, if an application regularly needs to find all orders placed in a given month this data can be retrieved more quickly if all orders for a month are stored in date and time order in the same shard. If each order was stored in a different shard, they'd have to be fetched individually by performing a large number of point queries (queries that return a single data item). The next figure illustrates storing sequential sets (ranges) of data in shard.
In this example, the shard key is a composite key containing the order month as the most significant element, followed by the order day and the time. The data for orders is naturally sorted when new orders are created and added to a shard. Some data stores support two-part shard keys containing a partition key element that identifies the shard and a row key that uniquely identifies an item in the shard. Data is usually held in row key order in the shard. Items that are subject to range queries and need to be grouped together can use a shard key that has the same value for the partition key but a unique value for the row key.
The Hash strategy. The purpose of this strategy is to reduce the chance of hotspots (shards that receive a disproportionate amount of load). It distributes the data across the shards in a way that achieves a balance between the size of each shard and the average load that each shard will encounter. The sharding logic computes the shard to store an item in based on a hash of one or more attributes of the data. The chosen hashing function should distribute data evenly across the shards, possibly by introducing some random element into the computation. The next figure illustrates sharding tenant data based on a hash of tenant IDs.
To understand the advantage of the Hash strategy over other sharding strategies, consider how a multi-tenant application that enrolls new tenants sequentially might assign the tenants to shards in the data store. When using the Range strategy, the data for tenants 1 to n will all be stored in shard A, the data for tenants n+1 to m will all be stored in shard B, and so on. If the most recently registered tenants are also the most active, most data activity will occur in a small number of shards, which could cause hotspots. In contrast, the Hash strategy allocates tenants to shards based on a hash of their tenant ID. This means that sequential tenants are most likely to be allocated to different shards, which will distribute the load across them. The previous figure shows this for tenants 55 and 56.
The three sharding strategies have the following advantages and considerations:
- Lookup. This offers more control over the way that shards are configured and used. Using virtual shards reduces the impact when rebalancing data because new physical partitions can be added to even out the workload. The mapping between a virtual shard and the physical partitions that implement the shard can be modified without affecting application code that uses a shard key to store and retrieve data. Looking up shard locations can impose an additional overhead.
- Range. This is easy to implement and works well with range queries because they can often fetch multiple data items from a single shard in a single operation. This strategy offers easier data management. For example, if users in the same region are in the same shard, updates can be scheduled in each time zone based on the local load and demand pattern. However, this strategy doesn't provide optimal balancing between shards. Rebalancing shards is difficult and might not resolve the problem of uneven load if the majority of activity is for adjacent shard keys.
- Hash. This strategy offers a better chance of more even data and load distribution. Request routing can be accomplished directly by using the hash function. There's no need to maintain a map. Note: that computing the hash might impose an additional overhead. Also, rebalancing shards is difficult.
Most common sharding systems implement one of the approaches described above, but you should also consider the business requirements of your applications and their patterns of data usage. For example, in a multi-tenant application:
- You can shard data based on workload. You could segregate the data for highly volatile tenants in separate shards. The speed of data access for other tenants might be improved as a result.
- You can shard data based on the location of tenants. You can take the data for tenants in a specific geographic region offline for backup and maintenance during off-peak hours in that region, while the data for tenants in other regions remains online and accessible during their business hours.
- High-value tenants could be assigned their own private, high performing, lightly loaded shards, whereas lower-value tenants might be expected to share more densely-packed, busy shards.
- The data for tenants that need a high degree of data isolation and privacy can be stored on a completely separate server.
Each of the sharding strategies implies different capabilities and levels of complexity for managing scale in, scale out, data movement, and maintaining state.
The Lookup strategy permits scaling and data movement operations to be carried out at the user level, either online or offline. The technique is to suspend some or all user activity (perhaps during off-peak periods), move the data to the new virtual partition or physical shard, change the mappings, invalidate or refresh any caches that hold this data, and then allow user activity to resume. Often this type of operation can be centrally managed. The Lookup strategy requires state to be highly cacheable and replica friendly.
The Range strategy imposes some limitations on scaling and data movement operations, which must typically be carried out when a part or all of the data store is offline because the data must be split and merged across the shards. Moving the data to rebalance shards might not resolve the problem of uneven load if the majority of activity is for adjacent shard keys or data identifiers that are within the same range. The Range strategy might also require some state to be maintained in order to map ranges to the physical partitions.
The Hash strategy makes scaling and data movement operations more complex because the partition keys are hashes of the shard keys or data identifiers. The new location of each shard must be determined from the hash function, or the function modified to provide the correct mappings. However, the Hash strategy doesn't require maintenance of state.
Consider the following points when deciding how to implement this pattern:
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Sharding is complementary to other forms of partitioning, such as vertical partitioning and functional partitioning. For example, a single shard can contain entities that have been partitioned vertically, and a functional partition can be implemented as multiple shards. For more information about partitioning, see the Data Partitioning Guidance.
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Keep shards balanced so they all handle a similar volume of I/O. As data is inserted and deleted, it's necessary to periodically rebalance the shards to guarantee an even distribution and to reduce the chance of hotspots. Rebalancing can be an expensive operation. To reduce the necessity of rebalancing, plan for growth by ensuring that each shard contains sufficient free space to handle the expected volume of changes. You should also develop strategies and scripts you can use to quickly rebalance shards if this becomes necessary.
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Use stable data for the shard key. If the shard key changes, the corresponding data item might have to move between shards, increasing the amount of work performed by update operations. For this reason, avoid basing the shard key on potentially volatile information. Instead, look for attributes that are invariant or that naturally form a key.
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Ensure that shard keys are unique. For example, avoid using autoincrementing fields as the shard key. Is some systems, autoincremented fields can't be coordinated across shards, possibly resulting in items in different shards having the same shard key.
Autoincremented values in other fields that are not shard keys can also cause problems. For example, if you use autoincremented fields to generate unique IDs, then two different items located in different shards might be assigned the same ID.
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It might not be possible to design a shard key that matches the requirements of every possible query against the data. Shard the data to support the most frequently performed queries, and if necessary create secondary index tables to support queries that retrieve data using criteria based on attributes that aren't part of the shard key. For more information, see the Index Table pattern.
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Queries that access only a single shard are more efficient than those that retrieve data from multiple shards, so avoid implementing a sharding system that results in applications performing large numbers of queries that join data held in different shards. Remember that a single shard can contain the data for multiple types of entities. Consider denormalizing your data to keep related entities that are commonly queried together (such as the details of customers and the orders that they have placed) in the same shard to reduce the number of separate reads that an application performs.
If an entity in one shard references an entity stored in another shard, include the shard key for the second entity as part of the schema for the first entity. This can help to improve the performance of queries that reference related data across shards.
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If an application must perform queries that retrieve data from multiple shards, it might be possible to fetch this data by using parallel tasks. Examples include fan-out queries, where data from multiple shards is retrieved in parallel and then aggregated into a single result. However, this approach inevitably adds some complexity to the data access logic of a solution.
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For many applications, creating a larger number of small shards can be more efficient than having a small number of large shards because they can offer increased opportunities for load balancing. This can also be useful if you anticipate the need to migrate shards from one physical location to another. Moving a small shard is quicker than moving a large one.
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Make sure the resources available to each shard storage node are sufficient to handle the scalability requirements in terms of data size and throughput. For more information, see the section "Designing Partitions for Scalability" in the Data Partitioning Guidance.
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Consider replicating reference data to all shards. If an operation that retrieves data from a shard also references static or slow-moving data as part of the same query, add this data to the shard. The application can then fetch all of the data for the query easily, without having to make an additional round trip to a separate data store.
If reference data held in multiple shards changes, the system must synchronize these changes across all shards. The system can experience a degree of inconsistency while this synchronization occurs. If you do this, you should design your applications to be able to handle it.
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It can be difficult to maintain referential integrity and consistency between shards, so you should minimize operations that affect data in multiple shards. If an application must modify data across shards, evaluate whether complete data consistency is actually required. Instead, a common approach in the cloud is to implement eventual consistency. The data in each partition is updated separately, and the application logic must take responsibility for ensuring that the updates all complete successfully, as well as handling the inconsistencies that can arise from querying data while an eventually consistent operation is running. For more information about implementing eventual consistency, see the Data Consistency Primer.
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Configuring and managing a large number of shards can be a challenge. Tasks such as monitoring, backing up, checking for consistency, and logging or auditing must be accomplished on multiple shards and servers, possibly held in multiple locations. These tasks are likely to be implemented using scripts or other automation solutions, but that might not completely eliminate the additional administrative requirements.
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Shards can be geolocated so that the data that they contain is close to the instances of an application that use it. This approach can considerably improve performance, but requires additional consideration for tasks that must access multiple shards in different locations.
Use this pattern when a data store is likely to need to scale beyond the resources available to a single storage node, or to improve performance by reducing contention in a data store.
The primary focus of sharding is to improve the performance and scalability of a system, but as a by-product it can also improve availability due to how the data is divided into separate partitions. A failure in one partition doesn't necessarily prevent an application from accessing data held in other partitions, and an operator can perform maintenance or recovery of one or more partitions without making the entire data for an application inaccessible. For more information, see the Data Partitioning Guidance.
Deploy components of an application into a separate process or container to provide isolation and encapsulation. This pattern can also enable applications to be composed of heterogeneous components and technologies.
This pattern is named Sidecar because it resembles a sidecar attached to a motorcycle. In the pattern, the sidecar is attached to a parent application and provides supporting features for the application. The sidecar also shares the same lifecycle as the parent application, being created and retired alongside the parent. The sidecar pattern is sometimes referred to as the sidekick pattern and is a decomposition pattern.
Applications and services often require related functionality, such as monitoring, logging, configuration, and networking services. These peripheral tasks can be implemented as separate components or services.
If they are tightly integrated into the application, they can run in the same process as the application, making efficient use of shared resources. However, this also means they are not well isolated, and an outage in one of these components can affect other components or the entire application. Also, they usually need to be implemented using the same language as the parent application. As a result, the component and the application have close interdependence on each other.
If the application is decomposed into services, then each service can be built using different languages and technologies. While this gives more flexibility, it means that each component has its own dependencies and requires language-specific libraries to access the underlying platform and any resources shared with the parent application. In addition, deploying these features as separate services can add latency to the application. Managing the code and dependencies for these language-specific interfaces can also add considerable complexity, especially for hosting, deployment, and management.
Co-locate a cohesive set of tasks with the primary application, but place them inside their own process or container, providing a homogeneous interface for platform services across languages.
A sidecar service is not necessarily part of the application, but is connected to it. It goes wherever the parent application goes. Sidecars are supporting processes or services that are deployed with the primary application. On a motorcycle, the sidecar is attached to one motorcycle, and each motorcycle can have its own sidecar. In the same way, a sidecar service shares the fate of its parent application. For each instance of the application, an instance of the sidecar is deployed and hosted alongside it.
Advantages of using a sidecar pattern include:
- A sidecar is independent from its primary application in terms of runtime environment and programming language, so you don't need to develop one sidecar per language.
- The sidecar can access the same resources as the primary application. For example, a sidecar can monitor system resources used by both the sidecar and the primary application.
- Because of its proximity to the primary application, there's no significant latency when communicating between them.
- Even for applications that don't provide an extensibility mechanism, you can use a sidecar to extend functionality by attaching it as its own process in the same host or sub-container as the primary application.
The sidecar pattern is often used with containers and referred to as a sidecar container or sidekick container.
- Consider the deployment and packaging format you will use to deploy services, processes, or containers. Containers are particularly well suited to the sidecar pattern.
- When designing a sidecar service, carefully decide on the interprocess communication mechanism. Try to use language- or framework-agnostic technologies unless performance requirements make that impractical.
- Before putting functionality into a sidecar, consider whether it would work better as a separate service or a more traditional daemon.
- Also consider whether the functionality could be implemented as a library or using a traditional extension mechanism. Language-specific libraries may have a deeper level of integration and less network overhead.
Use this pattern when:
- Your primary application uses a heterogeneous set of languages and frameworks. A component located in a sidecar service can be consumed by applications written in different languages using different frameworks.
- A component is owned by a remote team or a different organization.
- A component or feature must be co-located on the same host as the application
- You need a service that shares the overall lifecycle of your main application, but can be independently updated.
- You need fine-grained control over resource limits for a particular resource or component. For example, you may want to restrict the amount of memory a specific component uses. You can deploy the component as a sidecar and manage memory usage independently of the main application.
This pattern may not be suitable:
- When interprocess communication needs to be optimized. Communication between a parent application and sidecar services includes some overhead, notably latency in the calls. This may not be an acceptable trade-off for chatty interfaces.
- For small applications where the resource cost of deploying a sidecar service for each instance is not worth the advantage of isolation.
- When the service needs to scale differently than or independently from the main applications. If so, it may be better to deploy the feature as a separate service.
Deploy static content to a cloud-based storage service that can deliver them directly to the client. This can reduce the need for potentially expensive compute instances.
Web applications typically include some elements of static content. This static content might include HTML pages and other resources such as images and documents that are available to the client, either as part of an HTML page (such as inline images, style sheets, and client-side JavaScript files) or as separate downloads (such as PDF documents).
Although web servers are optimized for dynamic rendering and output caching, they still have to handle requests to download static content. This consumes processing cycles that could often be put to better use.
In most cloud hosting environments, you can put some of an application's resources and static pages in a storage service. The storage service can serve requests for these resources, reducing load on the compute resources that handle other web requests. The cost for cloud-hosted storage is typically much less than for compute instances.
When hosting some parts of an application in a storage service, the main considerations are related to deployment of the application and to securing resources that aren't intended to be available to anonymous users.
Consider the following points when deciding how to implement this pattern:
- The hosted storage service must expose an HTTP endpoint that users can access to download the static resources. Some storage services also support HTTPS, so it's possible to host resources in storage services that require SSL.
- For maximum performance and availability, consider using a content delivery network (CDN) to cache the contents of the storage container in multiple datacenters around the world. However, you'll likely have to pay for using the CDN.
- Storage accounts are often geo-replicated by default to provide resiliency against events that might affect a datacenter. This means that the IP address might change, but the URL will remain the same.
- When some content is located in a storage account and other content is in a hosted compute instance, it becomes more challenging to deploy and update the application. You might have to perform separate deployments, and version the application and content to manage it more easily---especially when the static content includes script files or UI components. However, if only static resources have to be updated, they can simply be uploaded to the storage account without needing to redeploy the application package.
- Storage services might not support the use of custom domain names. In this case it's necessary to specify the full URL of the resources in links because they'll be in a different domain from the dynamically-generated content containing the links.
- The storage containers must be configured for public read access, but it's vital to ensure that they aren't configured for public write access to prevent users being able to upload content.
- Consider using a valet key or token to control access to resources that shouldn't be available anonymously. See the Valet Key pattern for more information.
This pattern is useful for:
- Minimizing the hosting cost for websites and applications that contain some static resources.
- Minimizing the hosting cost for websites that consist of only static content and resources. Depending on the capabilities of the hosting provider's storage system, it might be possible to entirely host a fully static website in a storage account.
- Exposing static resources and content for applications running in other hosting environments or on-premises servers.
- Locating content in more than one geographical area using a content delivery network that caches the contents of the storage account in multiple datacenters around the world.
- Monitoring costs and bandwidth usage. Using a separate storage account for some or all of the static content allows the costs to be more easily separated from hosting and runtime costs.
This pattern might not be useful in the following situations:
- The application needs to perform some processing on the static content before delivering it to the client. For example, it might be necessary to add a timestamp to a document.
- The volume of static content is very small. The overhead of retrieving this content from separate storage can outweigh the cost benefit of separating it out from the compute resource.
Incrementally migrate a legacy system by gradually replacing specific pieces of functionality with new applications and services. As features from the legacy system are replaced, the new system eventually replaces all of the old system's features, strangling the old system and allowing you to decommission it.
As systems age, the development tools, hosting technology, and even system architectures they were built on can become increasingly obsolete. As new features and functionality are added, the complexity of these applications can increase dramatically, making them harder to maintain or add new features to.
Completely replacing a complex system can be a huge undertaking. Often, you will need a gradual migration to a new system, while keeping the old system to handle features that haven't been migrated yet. However, running two separate versions of an application means that clients have to know where particular features are located. Every time a feature or service is migrated, clients need to be updated to point to the new location.
Incrementally replace specific pieces of functionality with new applications and services. Create a façade that intercepts requests going to the backend legacy system. The façade routes these requests either to the legacy application or the new services. Existing features can be migrated to the new system gradually, and consumers can continue using the same interface, unaware that any migration has taken place.
This pattern helps to minimize risk from the migration, and spread the development effort over time. With the façade safely routing users to the correct application, you can add functionality to the new system at whatever pace you like, while ensuring the legacy application continues to function. Over time, as features are migrated to the new system, the legacy system is eventually "strangled" and is no longer necessary. Once this process is complete, the legacy system can safely be retired.
- Consider how to handle services and data stores that are potentially used by both new and legacy systems. Make sure both can access these resources side-by-side.
- Structure new applications and services in a way that they can easily be intercepted and replaced in future strangler migrations.
- At some point, when the migration is complete, the strangler façade will either go away or evolve into an adaptor for legacy clients.
- Make sure the façade keeps up with the migration.
- Make sure the façade doesn't become a single point of failure or a performance bottleneck.
Use this pattern when gradually migrating a back-end application to a new architecture.
This pattern may not be suitable:
- When requests to the back-end system cannot be intercepted.
- For smaller systems where the complexity of wholesale replacement is low.
Control the consumption of resources used by an instance of an application, an individual tenant, or an entire service. This can allow the system to continue to function and meet service level agreements, even when an increase in demand places an extreme load on resources.
The load on a cloud application typically varies over time based on the number of active users or the types of activities they are performing. For example, more users are likely to be active during business hours, or the system might be required to perform computationally expensive analytics at the end of each month. There might also be sudden and unanticipated bursts in activity. If the processing requirements of the system exceed the capacity of the resources that are available, it'll suffer from poor performance and can even fail. If the system has to meet an agreed level of service, such failure could be unacceptable.
There're many strategies available for handling varying load in the cloud, depending on the business goals for the application. One strategy is to use autoscaling to match the provisioned resources to the user needs at any given time. This has the potential to consistently meet user demand, while optimizing running costs. However, while autoscaling can trigger the provisioning of additional resources, this provisioning isn't immediate. If demand grows quickly, there can be a window of time where there's a resource deficit.
An alternative strategy to autoscaling is to allow applications to use resources only up to a limit, and then throttle them when this limit is reached. The system should monitor how it's using resources so that, when usage exceeds the threshold, it can throttle requests from one or more users. This will enable the system to continue functioning and meet any service level agreements (SLAs) that are in place. For more information on monitoring resource usage, see the Instrumentation and Telemetry Guidance.
The system could implement several throttling strategies, including:
- Rejecting requests from an individual user who's already accessed system APIs more than n times per second over a given period of time. This requires the system to meter the use of resources for each tenant or user running an application. For more information, see the Service Metering Guidance.
- Disabling or degrading the functionality of selected nonessential services so that essential services can run unimpeded with sufficient resources. For example, if the application is streaming video output, it could switch to a lower resolution.
- Using load leveling to smooth the volume of activity (this approach is covered in more detail by the Queue-based Load Leveling pattern). In a multi-tenant environment, this approach will reduce the performance for every tenant. If the system must support a mix of tenants with different SLAs, the work for high-value tenants might be performed immediately. Requests for other tenants can be held back, and handled when the backlog has eased. The Priority Queue pattern could be used to help implement this approach.
- Deferring operations being performed on behalf of lower priority applications or tenants. These operations can be suspended or limited, with an exception generated to inform the tenant that the system is busy and that the operation should be retried later.
The figure shows an area graph for resource use (a combination of memory, CPU, bandwidth, and other factors) against time for applications that are making use of three features. A feature is an area of functionality, such as a component that performs a specific set of tasks, a piece of code that performs a complex calculation, or an element that provides a service such as an in-memory cache. These features are labeled A, B, and C.
The area immediately below the line for a feature indicates the resources that are used by applications when they invoke this feature. For example, the area below the line for Feature A shows the resources used by applications that are making use of Feature A, and the area between the lines for Feature A and Feature B indicates the resources used by applications invoking Feature B. Aggregating the areas for each feature shows the total resource use of the system.
The previous figure illustrates the effects of deferring operations. Just prior to time T1, the total resources allocated to all applications using these features reach a threshold (the limit of resource use). At this point, the applications are in danger of exhausting the resources available. In this system, Feature B is less critical than Feature A or Feature C, so it's temporarily disabled and the resources that it was using are released. Between times T1 and T2, the applications using Feature A and Feature C continue running as normal. Eventually, the resource use of these two features diminishes to the point when, at time T2, there is sufficient capacity to enable Feature B again.
The autoscaling and throttling approaches can also be combined to help keep the applications responsive and within SLAs. If the demand is expected to remain high, throttling provides a temporary solution while the system scales out. At this point, the full functionality of the system can be restored.
The next figure shows an area graph of the overall resource use by all applications running in a system against time, and illustrates how throttling can be combined with autoscaling.
At time T1, the threshold specifying the soft limit of resource use is reached. At this point, the system can start to scale out. However, if the new resources don't become available quickly enough, then the existing resources might be exhausted and the system could fail. To prevent this from occurring, the system is temporarily throttled, as described earlier. When autoscaling has completed and the additional resources are available, throttling can be relaxed.
You should consider the following points when deciding how to implement this pattern:
- Throttling an application, and the strategy to use, is an architectural decision that impacts the entire design of a system. Throttling should be considered early in the application design process because it isn't easy to add once a system has been implemented.
- Throttling must be performed quickly. The system must be capable of detecting an increase in activity and react accordingly. The system must also be able to revert to its original state quickly after the load has eased. This requires that the appropriate performance data is continually captured and monitored.
- If a service needs to temporarily deny a user request, it should return a specific error code so the client application understands that the reason for the refusal to perform an operation is due to throttling. The client application can wait for a period before retrying the request.
- Throttling can be used as a temporary measure while a system autoscales. In some cases it's better to simply throttle, rather than to scale, if a burst in activity is sudden and isn't expected to be long lived because scaling can add considerably to running costs.
- If throttling is being used as a temporary measure while a system autoscales, and if resource demands grow very quickly, the system might not be able to continue functioning---even when operating in a throttled mode. If this isn't acceptable, consider maintaining larger capacity reserves and configuring more aggressive autoscaling.
Use this pattern:
- To ensure that a system continues to meet service level agreements.
- To prevent a single tenant from monopolizing the resources provided by an application.
- To handle bursts in activity.
- To help cost-optimize a system by limiting the maximum resource levels needed to keep it functioning.
Use a token that provides clients with restricted direct access to a specific resource, in order to offload data transfer from the application. This is particularly useful in applications that use cloud-hosted storage systems or queues, and can minimize cost and maximize scalability and performance.
Client programs and web browsers often need to read and write files or data streams to and from an application's storage. Typically, the application will handle the movement of the data --- either by fetching it from storage and streaming it to the client, or by reading the uploaded stream from the client and storing it in the data store. However, this approach absorbs valuable resources such as compute, memory, and bandwidth.
Data stores have the ability to handle upload and download of data directly, without requiring that the application perform any processing to move this data. But, this typically requires the client to have access to the security credentials for the store. This can be a useful technique to minimize data transfer costs and the requirement to scale out the application, and to maximize performance. It means, though, that the application is no longer able to manage the security of the data. After the client has a connection to the data store for direct access, the application can't act as the gatekeeper. It's no longer in control of the process and can't prevent subsequent uploads or downloads from the data store.
This isn't a realistic approach in distributed systems that need to serve untrusted clients. Instead, applications must be able to securely control access to data in a granular way, but still reduce the load on the server by setting up this connection and then allowing the client to communicate directly with the data store to perform the required read or write operations.
You need to resolve the problem of controlling access to a data store where the store can't manage authentication and authorization of clients. One typical solution is to restrict access to the data store's public connection and provide the client with a key or token that the data store can validate.
This key or token is usually referred to as a valet key. It provides time-limited access to specific resources and allows only predefined operations such as reading and writing to storage or queues, or uploading and downloading in a web browser. Applications can create and issue valet keys to client devices and web browsers quickly and easily, allowing clients to perform the required operations without requiring the application to directly handle the data transfer. This removes the processing overhead, and the impact on performance and scalability, from the application and the server.
The client uses this token to access a specific resource in the data store for only a specific period, and with specific restrictions on access permissions, as shown in the figure. After the specified period, the key becomes invalid and won't allow access to the resource.
It's also possible to configure a key that has other dependencies, such as the scope of the data. For example, depending on the data store capabilities, the key can specify a complete table in a data store, or only specific rows in a table. In cloud storage systems the key can specify a container, or just a specific item within a container.
The key can also be invalidated by the application. This is a useful approach if the client notifies the server that the data transfer operation is complete. The server can then invalidate that key to prevent further access.
Using this pattern can simplify managing access to resources because there's no requirement to create and authenticate a user, grant permissions, and then remove the user again. It also makes it easy to limit the location, the permission, and the validity period---all by simply generating a key at runtime. The important factors are to limit the validity period, and especially the location of the resource, as tightly as possible so that the recipient can only use it for the intended purpose.
Consider the following points when deciding how to implement this pattern:
Manage the validity status and period of the key. If leaked or compromised, the key effectively unlocks the target item and makes it available for malicious use during the validity period. A key can usually be revoked or disabled, depending on how it was issued. Server-side policies can be changed or, the server key it was signed with can be invalidated. Specify a short validity period to minimize the risk of allowing unauthorized operations to take place against the data store. However, if the validity period is too short, the client might not be able to complete the operation before the key expires. Allow authorized users to renew the key before the validity period expires if multiple accesses to the protected resource are required.
Control the level of access the key will provide. Typically, the key should allow the user to only perform the actions necessary to complete the operation, such as read-only access if the client shouldn't be able to upload data to the data store. For file uploads, it's common to specify a key that provides write-only permission, as well as the location and the validity period. It's critical to accurately specify the resource or the set of resources to which the key applies.
Consider how to control users' behavior. Implementing this pattern means some loss of control over the resources users are granted access to. The level of control that can be exerted is limited by the capabilities of the policies and permissions available for the service or the target data store. For example, it's usually not possible to create a key that limits the size of the data to be written to storage, or the number of times the key can be used to access a file. This can result in huge unexpected costs for data transfer, even when used by the intended client, and might be caused by an error in the code that causes repeated upload or download. To limit the number of times a file can be uploaded, where possible, force the client to notify the application when one operation has completed. For example, some data stores raise events the application code can use to monitor operations and control user behavior. However, it's hard to enforce quotas for individual users in a multi-tenant scenario where the same key is used by all the users from one tenant.
Validate, and optionally sanitize, all uploaded data. A malicious user that gains access to the key could upload data designed to compromise the system. Alternatively, authorized users might upload data that's invalid and, when processed, could result in an error or system failure. To protect against this, ensure that all uploaded data is validated and checked for malicious content before use.
Audit all operations. Many key-based mechanisms can log operations such as uploads, downloads, and failures. These logs can usually be incorporated into an audit process, and also used for billing if the user is charged based on file size or data volume. Use the logs to detect authentication failures that might be caused by issues with the key provider, or accidental removal of a stored access policy.
Deliver the key securely. It can be embedded in a URL that the user activates in a web page, or it can be used in a server redirection operation so that the download occurs automatically. Always use HTTPS to deliver the key over a secure channel.
Protect sensitive data in transit. Sensitive data delivered through the application will usually take place using SSL or TLS, and this should be enforced for clients accessing the data store directly.
Other issues to be aware of when implementing this pattern are:
- If the client doesn't, or can't, notify the server of completion of the operation, and the only limit is the expiration period of the key, the application won't be able to perform auditing operations such as counting the number of uploads or downloads, or preventing multiple uploads or downloads.
- The flexibility of key policies that can be generated might be limited. For example, some mechanisms only allow the use of a timed expiration period. Others aren't able to specify a sufficient granularity of read/write permissions.
- If the start time for the key or token validity period is specified, ensure that it's a little earlier than the current server time to allow for client clocks that might be slightly out of synchronization. The default, if not specified, is usually the current server time.
- The URL containing the key will be recorded in server log files. While the key will typically have expired before the log files are used for analysis, ensure that you limit access to them. If log data is transmitted to a monitoring system or stored in another location, consider implementing a delay to prevent leakage of keys until after their validity period has expired.
- If the client code runs in a web browser, the browser might need to support cross-origin resource sharing (CORS) to enable code that executes within the web browser to access data in a different domain from the one that served the page. Some older browsers and some data stores don't support CORS, and code that runs in these browsers might not be able to use a valet key to provide access to data in a different domain, such as a cloud storage account.
This pattern is useful for the following situations:
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To minimize resource loading and maximize performance and scalability. Using a valet key doesn't require the resource to be locked, no remote server call is required, there's no limit on the number of valet keys that can be issued, and it avoids a single point of failure resulting from performing the data transfer through the application code. Creating a valet key is typically a simple cryptographic operation of signing a string with a key.
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To minimize operational cost. Enabling direct access to stores and queues is resource and cost efficient, can result in fewer network round trips, and might allow for a reduction in the number of compute resources required.
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When clients regularly upload or download data, particularly where there's a large volume or when each operation involves large files.
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When the application has limited compute resources available, either due to hosting limitations or cost considerations. In this scenario, the pattern is even more helpful if there are many concurrent data uploads or downloads because it relieves the application from handling the data transfer.
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When the data is stored in a remote data store or a different datacenter. If the application was required to act as a gatekeeper, there might be a charge for the additional bandwidth of transferring the data between datacenters, or across public or private networks between the client and the application, and then between the application and the data store.
This pattern might not be useful in the following situations:
- If the application must perform some task on the data before it's stored or before it's sent to the client. For example, if the application needs to perform validation, log access success, or execute a transformation on the data. However, some data stores and clients are able to negotiate and carry out simple transformations such as compression and decompression (for example, a web browser can usually handle GZip formats).
- If the design of an existing application makes it difficult to incorporate the pattern. Using this pattern typically requires a different architectural approach for delivering and receiving data.
- If it's necessary to maintain audit trails or control the number of times a data transfer operation is executed, and the valet key mechanism in use doesn't support notifications that the server can use to manage these operations.
- If it's necessary to limit the size of the data, especially during upload operations. The only solution to this is for the application to check the data size after the operation is complete, or check the size of uploads after a specified period or on a scheduled basis.
References