From d407726cb25dcd224483c806528a921220f48587 Mon Sep 17 00:00:00 2001 From: Ellis Tarn Date: Wed, 14 Jul 2021 13:21:19 -0700 Subject: [PATCH] Added docs website scaffolding --- .gitignore | 2 + .gitmodules | 3 + docs/README.md => API.md | 2 +- docs/CODE_OF_CONDUCT.md => CODE_OF_CONDUCT.md | 0 docs/CONTRIBUTING.md => CONTRIBUTING.md | 0 docs/DEVELOPER_GUIDE.md => DEVELOPER_GUIDE.md | 10 +- docs/FAQs.md => FAQs.md | 0 Makefile | 15 +- README.md | 26 +- docs/ROADMAP.md => ROADMAP.md | 0 docs/TROUBLESHOOTING.md => TROUBLESHOOTING.md | 0 .../README.md => WORKING_GROUP.md | 0 _config.yaml | 2 + .../aws-launch-templates-options.md | 0 {docs/designs => designs}/bin-packing.md | 0 {docs/designs => designs}/termination.md | 0 docs/404.html | 128 + docs/categories/index.html | 135 + docs/categories/index.xml | 18 + docs/css/prism.css | 208 + docs/css/shortcodes.css | 2 + docs/css/shortcodes/cards-pane.css | 21 + docs/css/shortcodes/tabbed-pane.css | 18 + docs/css/swagger-ui.css | 4 + 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pkg/cloudprovider/aws/docs}/karpenter.cloudformation.yaml (100%) create mode 100644 website/archetypes/default.md create mode 100644 website/config.toml create mode 100644 website/package-lock.json create mode 100644 website/package.json create mode 160000 website/themes/docsy diff --git a/.gitignore b/.gitignore index 7970236360ae..f02e9286818a 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,5 @@ coverage.out coverage.html *.test +website/node_modules +website/resources diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 000000000000..4a9058b790fd --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "website/themes/docsy"] + path = website/themes/docsy + url = https://github.com/google/docsy.git diff --git a/docs/README.md b/API.md similarity index 99% rename from docs/README.md rename to API.md index 1a7e8fb6bf81..1a7c9319632f 100644 --- a/docs/README.md +++ b/API.md @@ -427,5 +427,5 @@ its target, and indicates whether or not those conditions are met.

Generated with gen-crd-api-reference-docs -on git commit b4e9691. +on git commit 5e4fe45.

diff --git a/docs/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md similarity index 100% rename from docs/CODE_OF_CONDUCT.md rename to CODE_OF_CONDUCT.md diff --git a/docs/CONTRIBUTING.md b/CONTRIBUTING.md similarity index 100% rename from docs/CONTRIBUTING.md rename to CONTRIBUTING.md diff --git a/docs/DEVELOPER_GUIDE.md b/DEVELOPER_GUIDE.md similarity index 89% rename from docs/DEVELOPER_GUIDE.md rename to DEVELOPER_GUIDE.md index fdf73dbf2662..c3691c25e9b9 100644 --- a/docs/DEVELOPER_GUIDE.md +++ b/DEVELOPER_GUIDE.md @@ -1,8 +1,8 @@ -# Contributing +# Developer Guide ## Dependencies -The following tools are required for doing development on Karpenter. +The following tools are required for developing the Karpenter operator. | Package | Version | Install | | ------------------------------------------------------------------ | -------- | ---------------------- | @@ -11,6 +11,12 @@ The following tools are required for doing development on Karpenter. | [helm](https://helm.sh/docs/intro/install/) | | `brew install helm` | | Other tools | | `make toolchain` | +The following tools are required to developing the Karpenter website.. +| Package | Version | Install | +| ----------------------------- | ------- | ------------------- | +| [hugo](https://gohugo.io/) | | `brew install hugo` | +| [npm](https://www.npmjs.com/) | | `brew install node` | + ## Developing ### Setup / Teardown diff --git a/docs/FAQs.md b/FAQs.md similarity index 100% rename from docs/FAQs.md rename to FAQs.md diff --git a/Makefile b/Makefile index adb58de5a271..f94022e27389 100644 --- a/Makefile +++ b/Makefile @@ -66,19 +66,22 @@ codegen: ## Generate code. Must be run if changes are made to ./pkg/apis/... gen-crd-api-reference-docs \ -api-dir ./pkg/apis/provisioning/v1alpha2 \ -config $(shell go env GOMODCACHE)/github.com/ahmetb/gen-crd-api-reference-docs@v0.3.0/example-config.json \ - -out-file docs/README.md \ + -out-file API.md \ -template-dir $(shell go env GOMODCACHE)/github.com/ahmetb/gen-crd-api-reference-docs@v0.3.0/template publish: ## Generate release manifests and publish a versioned container image. @aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin $(RELEASE_REPO) - yq e -i ".controller.image = \"$$($(WITH_RELEASE_REPO) $(WITH_GOFLAGS) ko publish -B -t $(RELEASE_VERSION) --platform all ./cmd/controller)\"" ./charts/karpenter/values.yaml - yq e -i ".webhook.image = \"$$($(WITH_RELEASE_REPO) $(WITH_GOFLAGS) ko publish -B -t $(RELEASE_VERSION) --platform all ./cmd/webhook)\"" ./charts/karpenter/values.yaml - yq e -i '.version = "$(RELEASE_VERSION)"' ./charts/karpenter/Chart.yaml + yq e -i ".controller.image = \"$$($(WITH_RELEASE_REPO) $(WITH_GOFLAGS) ko publish -B -t $(RELEASE_VERSION) --platform all ./cmd/controller)\"" charts/karpenter/values.yaml + yq e -i ".webhook.image = \"$$($(WITH_RELEASE_REPO) $(WITH_GOFLAGS) ko publish -B -t $(RELEASE_VERSION) --platform all ./cmd/webhook)\"" charts/karpenter/values.yaml + yq e -i '.version = "$(RELEASE_VERSION)"' charts/karpenter/Chart.yaml -helm: ## Generate Helm Chart +helm: ## Generate Helm Chart cd charts;helm lint karpenter;helm package karpenter;helm repo index . +website: ## Generate Docs Website + cd website;npm install;hugo -d ../docs + toolchain: ## Install developer toolchain ./hack/toolchain.sh -.PHONY: help dev ci release test battletest verify codegen apply delete publish helm toolchain licenses +.PHONY: help dev ci release test battletest verify codegen apply delete publish helm website toolchain licenses diff --git a/README.md b/README.md index c675aea55116..7d9270d46b3f 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ [![GitHub License](https://img.shields.io/badge/License-Apache%202.0-ff69b4.svg)](https://github.com/awslabs/karpenter/blob/main/LICENSE) [![contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/awslabs/karpenter/issues) -![](docs/images/karpenter-banner.png) +![](images/karpenter-banner.png) Karpenter is a node lifecycle management solution. It observes incoming pods and launches the right instances for the situation. Instance selection decisions are intent based and driven by the specification of incoming pods, including resource requests and scheduling constraints. @@ -14,20 +14,20 @@ It's responsible for: - **Terminating** nodes if outdated or no longer needed - **Draining** nodes gracefully before preemption -For most use cases, the entirety of a cluster’s capacity can be managed by a single Karpenter [Provisioner](./docs/README.md). Availability zone, instance type, capacity type, machine image, and scheduling constraints are automatically determined by the controller using a combination of defaults and overrides. Additionally, you can define multiple Provisioners, enabling use cases like isolation, entitlements, and sharding. +For most use cases, the entirety of a cluster’s capacity can be managed by a single Karpenter [Provisioner](README.md). Availability zone, instance type, capacity type, machine image, and scheduling constraints are automatically determined by the controller using a combination of defaults and overrides. Additionally, you can define multiple Provisioners, enabling use cases like isolation, entitlements, and sharding. Karpenter optimizes for scheduling latency and utilization efficiency using two complementary control loops. First, is the allocator, a fast-acting latency-sensitive controller responsible for ensuring that incoming pods are scheduled as quickly as possible. Second, is the reallocator, a slow-acting cost-sensitive controller that replaces nodes as pods requests and capacity prices shift over time. Together, they maximize the availability and efficiency of your cluster. Come discuss Karpenter in the [#provider-aws channel](https://kubernetes.slack.com/archives/C0LRMHZ1T) in the [Kubernetes slack](https://slack.k8s.io/)! -*Note: Reallocation is still in development. Check out the [FAQs](docs/FAQs.md) and [Roadmap](docs/ROADMAP.md) to learn more.* +*Note: Reallocation is still in development. Check out the [FAQs](FAQs.md) and [Roadmap](ROADMAP.md) to learn more.* - + ## Installation Follow the setup recommendations of your cloud provider. -- [AWS](docs/aws) +- [AWS](pkg/cloudprovider/aws/docs) > ❗ Note: There may be backwards incompatible changes between versions when upgrading before v0.3.0. Karpenter follows [Kubernetes versioning guidelines](https://kubernetes.io/docs/concepts/overview/kubernetes-api/#api-changes). Before upgrading, we recommend: > - Check the [release notes](https://github.com/awslabs/karpenter/releases) @@ -35,14 +35,14 @@ Follow the setup recommendations of your cloud provider. > - Remove all nodes launched by karpenter > - Reinstall Karpenter -## Docs -- [API](docs/README.md) -- [FAQs](docs/FAQs.md) -- [Roadmap](docs/ROADMAP.md) -- [Examples](docs/aws/examples) -- [Working Group](docs/working-group) -- [Developer Guide](docs/DEVELOPER_GUIDE.md) -- [Contributing](docs/CONTRIBUTING.md) +## References +- [Docs](https://awslabs.github.io/karpenter/docs) +- [API](README.md) +- [FAQs](FAQs.md) +- [Roadmap](ROADMAP.md) +- [Working Group](WORKING_GROUP.md) +- [Developer Guide](DEVELOPER_GUIDE.md) +- [Contributing](CONTRIBUTING.md) ## Talks - [Karpenter @ Container Day, May 2021](https://www.twitch.tv/videos/1010593737?t=141m50s) diff --git a/docs/ROADMAP.md b/ROADMAP.md similarity index 100% rename from docs/ROADMAP.md rename to ROADMAP.md diff --git a/docs/TROUBLESHOOTING.md b/TROUBLESHOOTING.md similarity index 100% rename from docs/TROUBLESHOOTING.md rename to TROUBLESHOOTING.md diff --git a/docs/working-group/README.md b/WORKING_GROUP.md similarity index 100% rename from docs/working-group/README.md rename to WORKING_GROUP.md diff --git a/_config.yaml b/_config.yaml new file mode 100644 index 000000000000..1b1846f3df5a --- /dev/null +++ b/_config.yaml @@ -0,0 +1,2 @@ +# Jekyll config +exclude: ['./website'] diff --git a/docs/designs/aws-launch-templates-options.md b/designs/aws-launch-templates-options.md similarity index 100% rename from docs/designs/aws-launch-templates-options.md rename to designs/aws-launch-templates-options.md diff --git a/docs/designs/bin-packing.md b/designs/bin-packing.md similarity index 100% rename from docs/designs/bin-packing.md rename to designs/bin-packing.md diff --git a/docs/designs/termination.md b/designs/termination.md similarity index 100% rename from docs/designs/termination.md rename to designs/termination.md diff --git a/docs/404.html b/docs/404.html new file mode 100644 index 000000000000..acec8f723295 --- /dev/null +++ b/docs/404.html @@ -0,0 +1,128 @@ + + + + + + + + + + + + + + + + + + + +404 Page not found | My New Hugo Site + + + + + + + + + + + + + + + + + + + + + + + + + +
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+pre[class*="language-"] { + color: black; + background: none; + text-shadow: 0 1px white; + font-family: Consolas, Monaco, 'Andale Mono', 'Ubuntu Mono', monospace; + font-size: 1em; + text-align: left; + white-space: pre; + word-spacing: normal; + word-break: normal; + word-wrap: normal; + line-height: 1.5; + + -moz-tab-size: 4; + -o-tab-size: 4; + tab-size: 4; + + -webkit-hyphens: none; + -moz-hyphens: none; + -ms-hyphens: none; + hyphens: none; +} + +pre[class*="language-"]::-moz-selection, pre[class*="language-"] ::-moz-selection, +code[class*="language-"]::-moz-selection, code[class*="language-"] ::-moz-selection { + text-shadow: none; + background: #b3d4fc; +} + +pre[class*="language-"]::selection, pre[class*="language-"] ::selection, +code[class*="language-"]::selection, code[class*="language-"] ::selection { + text-shadow: none; + background: #b3d4fc; +} + +@media print { + code[class*="language-"], + pre[class*="language-"] { + text-shadow: none; + } +} + +/* Code blocks */ +pre[class*="language-"] { + padding: 1em; + margin: .5em 0; + overflow: auto; +} + +:not(pre) > code[class*="language-"], +pre[class*="language-"] { + background: #f5f2f0; +} + +/* Inline code */ +:not(pre) > code[class*="language-"] { + padding: .1em; + border-radius: .3em; + white-space: normal; +} + +.token.comment, +.token.prolog, +.token.doctype, +.token.cdata { + color: slategray; +} + +.token.punctuation { + color: #999; +} + +.token.namespace { + opacity: .7; +} + +.token.property, +.token.tag, +.token.boolean, +.token.number, +.token.constant, +.token.symbol, +.token.deleted { + color: #905; +} + +.token.selector, +.token.attr-name, +.token.string, +.token.char, +.token.builtin, +.token.inserted { + color: #690; +} + +.token.operator, +.token.entity, +.token.url, +.language-css .token.string, +.style .token.string { + color: #9a6e3a; + /* This background color was intended by the author of this theme. */ + background: hsla(0, 0%, 100%, .5); +} + +.token.atrule, 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inherit; + line-height: normal; + overflow: visible; + padding: 0; + -webkit-user-select: none; /* for button */ + -moz-user-select: none; + -ms-user-select: none; +} + +div.code-toolbar > .toolbar a, +div.code-toolbar > .toolbar button, +div.code-toolbar > .toolbar span { + color: #bbb; + font-size: .8em; + padding: 0 .5em; + background: #f5f2f0; + background: rgba(224, 224, 224, 0.2); + box-shadow: 0 2px 0 0 rgba(0,0,0,0.2); + border-radius: .5em; +} + +div.code-toolbar > .toolbar a:hover, +div.code-toolbar > .toolbar a:focus, +div.code-toolbar > .toolbar button:hover, +div.code-toolbar > .toolbar button:focus, +div.code-toolbar > .toolbar span:hover, +div.code-toolbar > .toolbar span:focus { + color: inherit; + text-decoration: none; +} + diff --git a/docs/css/shortcodes.css b/docs/css/shortcodes.css new file mode 100644 index 000000000000..0aa1c0f8304c --- /dev/null +++ b/docs/css/shortcodes.css @@ -0,0 +1,2 @@ +@import "shortcodes/tabbed-pane.css"; +@import "shortcodes/cards-pane.css"; diff --git a/docs/css/shortcodes/cards-pane.css b/docs/css/shortcodes/cards-pane.css new file mode 100644 index 000000000000..34c85450dd11 --- /dev/null +++ b/docs/css/shortcodes/cards-pane.css @@ -0,0 +1,21 @@ +.card-deck { + max-width: 83%; +} + +.card { + max-width: 80%; +} + +.card-body.code { + background-color: #f8f9fa; + padding: 0 0 0 1ex; +} + +.card-body pre { + margin: 0; + padding: 0 1rem 1rem 1rem; +} + +.card .highlight { + border: none; +} diff --git a/docs/css/shortcodes/tabbed-pane.css b/docs/css/shortcodes/tabbed-pane.css new file mode 100644 index 000000000000..30163987280b --- /dev/null +++ b/docs/css/shortcodes/tabbed-pane.css @@ -0,0 +1,18 @@ +.td-content .highlight { + margin: 0rem 0 2rem 0; +} + +.tab-content .highlight { + border: none; +} + +.tab-content { + margin: 0rem; + max-width: 80%; +} + +.tab-content pre { + border-left: 1px solid rgba(0, 0, 0, 0.125); + border-right: 1px solid rgba(0, 0, 0, 0.125); + border-bottom: 1px 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code{font-size:14px;padding:5px 7px;border-radius:4px;background:rgba(0,0,0,.05);font-family:monospace;font-weight:600;color:#9012fe}.swagger-ui .markdown pre>code,.swagger-ui .renderedMarkdown pre>code{display:block} + +/*# sourceMappingURL=swagger-ui.css.map*/ \ No newline at end of file diff --git a/docs/deprecated/FAQs.md b/docs/deprecated/FAQs.md deleted file mode 100644 index 0e3bc6e97a8f..000000000000 --- a/docs/deprecated/FAQs.md +++ /dev/null @@ -1,58 +0,0 @@ -# Frequently Asked Questions - -1. **Why should I use Karpenter?** -Karpenter enables Kubernetes users to maximize resource utilization and improve availability for their clusters without requiring them to manually allocate or over-provision resources. Users can choose the metrics they want to drive the amount of compute resources allocated for their cluster, letting them scale their clusters independently, ahead-of, or in-concert with the scale of their applications. Users can configure scaling across multiple compute options and Karpenter offers straightforward and highly customizable options for scaling that are defined with configuration files, so they can be easily shared and implemented across multiple clusters. Karpenter runs as a set of linked components within a Kubernetes cluster, which allows the system to make fast, linear time scaling decisions for any size of cluster. - -2. **How do I start using Karpenter?** -Check out our [Getting Started documentation](/README.md#installation) for installation instructions and common usage patterns. - -3. **How does Karpenter compare to the Kubernetes cluster autoscaler?** -The Kubernetes cluster autoscaler reacts to the number of pods running on the cluster and tightly coupled to the type of compute used as well as the number of pods running. This means that cluster autoscaler will not scale a cluster until more pods are running and means that for workloads where you desire to scale on a schedule or over-provision, you need to implement work arounds such as ‘Hollow Pods’ that essentially hack cluster autoscaler. Karpenter is more flexible. You can apply a traditional pending pods metric to some of your node groups, while using different metrics like reserved capacity to scale other node groups. This gives Karpenter the flexibility to implement sophisticated scaling behavior to accommodate a wide variety of use cases. - -4. **Where can I use Karpenter?** -Karpenter works with any Kubernetes cluster running in any environment. You can use Karpenter with Kubernetes clusters in the cloud and on premises. - -5. **How does Karpenter work?** -Karpenter is an open, metrics-driven autoscaling system. There are four logical components: 1/ metrics producer which outputs metrics that can be used to drive scaling, 2/ metrics server which aggregates and stores scaling metric data from each producer, 3/ autoscaler which contains the logic for scaling including metric tracking strategies and scaling policies, and 4/ replica controller which changes the number of desired replicas for a unit of compute. These components are able to be implemented together or flexibly in combination with other systems such as KEDA. See appendix for more information about the Karpenter system architecture. - -6. **Is Karpenter a node autoscaler only? Are there plans to make it extensible for workloads as well?** -At launch, we plan to build replica controllers to drive node scaling for Kubernetes clusters. That said, Karpenter has an open design can be used to scale anything that implements the scale sub-resource. This includes multiple Kubernetes resource controllers and even cloud provider resources that are controlled from the Kuberentes API (such as with [ACK](https://github.com/aws/aws-controllers-k8s)). - -7. **Can I define my own metrics?** -Yes! You can write your own metrics producers for any metric you want to use to scale your cluster. - -8. **How does scaling work in Karpenter?** -Karpenter manages scaling based on the principals of proportional control. This means that Karpenter attempts to maintain a desired number of compute resources in proportion to a scaling metric that you define. Similar to how you set a minimum and maximum temperature for your house’s thermostat, you can set separate scaling rules for these proportions with relation to both scaling up and scaling down. Each scaling rule includes a scaling policy that allows you to define which metric to scale off of and how to scale based on that metric. In Karpenter you can define multiple metrics, and multiple scaling policies and apply these to separate node groups. In this way, Karpenter can be as simple, or as complex, as your use case dictates. If you want to simply scale up all nodes based on the number of items in a queue or inbound connection requests at a load balancer, you can do that. If you want to scale up certain node groups based on CPU utilization and only scale down if traffic to the website drops below a particular threshold, you can do that also. - -9. **Some cloud providers have existing managed scaling systems, such as predictive scaling. Can Karpenter use those?** -Yes. Each component of Karpenter is decoupled so that any can be swapped out to take advantage of existing managed systems. This means that any existing predictive scaling system can be integrated into Karpenter by using that scaling system as a metrics source. Additionally, Karpenter can feed data to these systems by sending them scale decisions as a replica target. At the extreme, this could mean using Karpenter as an API standard and having all functionality fulfilled by other systems. - -10. **What kinds of metric targets can I use to setup scaling policies?** -Karpenter can scale using three types of metric targets: value, average-value, and utilization. Value and utilization let you drive scaling based on the metric signal and existing replicas. Average value lets you track scaling to a desired input metric value and is independent of replicas. You can use these interchangeably with metrics signals and Karpenter scaling policies to drive different scaling behavior. - -11. **Can Karpenter scale from/to 0?** -Because average value metric target type is independent of the number of existing replicas in a cluster, you can use this metric target type to drive scaling decisions when replicas are 0. Furthermore, Karpenter lets you define multiple metrics signals in a single policy, allowing you to scale from zero using an average value metric and then beyond that using a completely different metric. - -12. **Does Karpenter work with multiple types of nodes?** -Karpenter allows you to target specific policies for specific node groups. For example, you can scale one group by one node for every 1k inbound connection requests and another group by two nodes for every 1k inbound connection requests. When Karpenter sees the inbound connections increase, it will request the appropriate node group to scale by the relative amount you define. This pattern works with different sizes of node groups, but also can be extended to different types of nodes like ARM and Spot where you need the control over which applications and scenarios scale these groups. Karpenter lets you directly define the relationship between the scaling metric and what is scaled. This gives you fine-grained control in how to scale different types of nodes across your cluster and still allows you to apply globally computed metrics to a set of node groups. - -13. **Does Karpenter work with EC2 Spot?** -Yes. Karpenter allows you to specify fine grained policies on any node group. You can configure multiple node groups to scale with different or the same metrics, depending on your use case. One way to do this with spot is to configure a `capacityReservation` metric for two node groups with spot instances of different instance types. As the scheduler fills the nodes with incoming pods, the node groups will scale out. If one of the instances types becomes unavailable, the other node group will continue to scale. - -14. **Does Karpenter respect pod disruption budgets?** -Karpenter does not include an integration to Kubernetes lifecycle hooks in order to drain nodes during scaling. However, Karpenter does allow you to connect resources like an EKS managed node group (MNG) to the replica controller. Resources such as MNG have existing integrations to Kubernetes lifecycle events to ensure graceful scale down events. - -15. **How does Karpenter work with Prometheus?** -Karpenter uses promql queries for its HorizontalAutoscaler. Any metrics available in Prometheus can be used to autoscale a resource. For example, you can use Karpenter's MetricsProducer resource, kube-state-metrics, or any custom code that exposes metrics to Prometheus in your scaling policies. - -16. **Metrics-driven open source autoscaling systems like HPA and KEDA exist today. How is Karpenter different?** -Systems including HPA are similar to Karpenter, but designed to manage pod scaling with the assumption that a reactive node scaling system will ensure enough compute is available for the cluster. Karpenter takes a very similar approach to these exiting metrics-driven systems, and uses many of the same principals, applying them to node scaling. - -17. **Cluster Autoscaler has worked just fine for my clusters, why should I use Karpenter?** -Cluster autoscaler works well for a number of common use cases. However, some use cases such as scheduled scaling or batch processing, you have to do a lot of extra work or accept the performance and resource inefficiencies created by the architecture of Cluster Autoscaler reacting to pending pods. If you have a mix of use cases in your organization, this means that some users have a fundamentally different architecture and approach to scaling their clusters based on what they are doing. Karpenter allows standardization in how scaling works across your entire organization and for all use cases. Its flexibility lets you optimize your cluster scaling to meet any Kubernetes application use case while reducing implementation complexities and differences that cause delays and take significant extra work on behalf of some teams. - -18. **Metrics will be polled periodically to calculate the desired replicas. Is it possible to configure the polling period?** -The default polling period is 10 seconds, though the user can configure this in their HorizontalAutoscaler policy. - -19. **Is this just an AWS project?** -This is an AWS initiated project, but we intend our working group to grow to members across the Kubernetes community. We welcome and encourage anyone to join us! See [contributing](CONTRIBUTING.md). diff --git a/docs/deprecated/README.md b/docs/deprecated/README.md deleted file mode 100644 index 937a36b584ed..000000000000 --- a/docs/deprecated/README.md +++ /dev/null @@ -1,2 +0,0 @@ -# Deprecated -This folder contains docs that are no longer applicable to the project, but kept for posterity. diff --git a/docs/deprecated/designs/metrics-driven-autoscaling.md b/docs/deprecated/designs/metrics-driven-autoscaling.md deleted file mode 100644 index a2da23a97400..000000000000 --- a/docs/deprecated/designs/metrics-driven-autoscaling.md +++ /dev/null @@ -1,487 +0,0 @@ -# Metrics Driven Autoscaling - -*Authors: ellistarn@* - -Node Autoscaling (a.k.a. Cluster Autoscaling) is the process of continually adding and removing a cluster’s nodes to meet the resource demands of its pods. As users scale to increasingly large clusters, autoscaling becomes necessary for both practicality and cost reasons. While overprovisioning is a viable approach at smaller scales, it becomes prohibitively expensive as organizations grow. In response to increasing infrastructure costs, some users create manual processes to scale node groups, but this approach yields inefficient resource utilization and is error prone. Node autoscaling replaces these manual processes with automation. - -## Overview - -Metrics driven autoscaling architectures are widespread in the Kubernetes ecosystem, including Kubernetes Horizontal Pod Autoscaler, Knative, and KEDA. Public clouds also use metrics driven architectures, such as [EC2 Autoscaling](https://docs.aws.amazon.com/autoscaling/ec2/userguide/as-scaling-target-tracking.html) and [ECS Autoscaling](https://docs.aws.amazon.com/AmazonECS/latest/developerguide/service-auto-scaling.html). This approach has been attempted for node autoscaling in Kubernetes by project [Cerebral](https://github.com/containership/cerebral), although it is [no longer actively maintained](https://github.com/containership/cerebral/issues/124#issuecomment-679363530). Existing node autoscaling solutions suffer from complexity, inflexibility, performance, and scalability issues. The extensibility of these systems is limited by both configuration mechanism as well as fundamental architectural constraints, preventing users from applying multiple scaling policies to their cluster or using arbitrary signals to control scaling actions. - -We will first discuss metrics driven autoscaling in the abstract, before applying the techniques to the domain of node autoscaling. We will also evaluate the landscape of existing Kubernetes ecosystem projects that will be either used to rapidly implement this approach or be aligned with in the long term. - -Many aspects of this design contain large subproblems that are beyond the scope of what can be effectively tackled here. The specific component implementations proposed (e.g. metrics producers, autoscaler algorithms) should be viewed as a naive first iteration. The metrics driven architecture for node autoscaling is the basis for future improvements. - -## Goals - -* Create a flexible and extensible architecture for Node Autoscaling. -* Focus on performance; autoscaling should introduce minimal overhead and not limit cluster size or shape. -* Target the common case with sane defaults; for most use cases “it should just work” with default configuration. -* Provide straightforward tradeoffs for Scalability, Performance, Availability, and Cost. -* Maximize the compatibility with existing solutions within the Kubernetes ecosystem. - -## Critical User Journeys - -* As a user, I am able to scale up or down on a single or combination of multiple signals. - * e.g. Capacity Reservations, Scheduled Capacity, Pending Pods, Queue Length -* As a user, I am able to author my own custom metrics to plug into the autoscaling architecture. -* As a user, I am able to autoscale multiple node group implementations from different providers. - * e.g. EC2 Autoscaling Groups, EKS Managed Node Groups, Kops Instance Groups, etc. -* As a user, I am able to provision and autoscale capacity in the same cluster with disjoint scheduling properties. - * e.g. GPUs, HPC, Spot Instances, node labels and taints. - -## Metrics Driven Autoscaling Architecture - -Metrics Driven Autoscaling is broken down into four logical components. - -![](../images/overview.jpeg) - -These components are able to be implemented flexibly, either combined into a single system (e.g. Kubernetes Cluster Autoscaler), using one system per component (e.g. Horizontal Pod Autoscaler), or some combination (e.g. KEDA, which implements a Metrics Producer/Metrics Server; Knative, which implements a Metrics Producer/Metrics Server/Autoscaler) - -### 1. Metrics Producer - -Metrics need to come from somewhere. It is the responsibility of a Metrics Producer to provide an accurate, up to date signal to be consumed by the autoscaler. Some metrics producers require explicit configuration, while others are natural to the cluster and are natively available (e.g. CPU utilization/requests). Metrics Producers are pluggable and novel metrics are expected to emerge over time. - -### 2. Metrics Server - -Once generated, metrics must be stored somewhere. Some metrics server implementations store a long history in a timeseries database (e.g. Prometheus), while others keep a short ephemeral history in memory (e.g. [MetricsServer](https://github.com/kubernetes-sigs/metrics-server)/[KEDA](https://github.com/kedacore/keda)/[Knative](https://github.com/knative/serving)). Often, metrics server implementations implement a pull-based architecture, where the server is responsible for discovering and calling known metrics producers to populate its metrics store. Push based alternatives are also possible, where metrics producers publish their metrics to a metrics server. - -### 3. Autoscaler - -Metrics values are periodically polled and used to calculate desiredReplicas for the autoscaled resource. The autoscaler contains a generic, black-box autoscaling function that can be parameterized by users. - -`replicas = f(currentReplicas, currentMetricValue, desiredMetricValue, params...)` - -This implementation of the function can be a proportional controller ([see HPA](https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/#algorithm-details)), a [PID controller](https://en.wikipedia.org/wiki/PID_controller), a [predictive controller](https://netflixtechblog.com/scryer-netflixs-predictive-auto-scaling-engine-part-2-bb9c4f9b9385), or something else entirely. These functions are generic such that users should be able experiment with different autoscaling functions using the same underlying metrics. Input metrics can be any signal. For example, users could use a raw signal or transform their metric with some (e.g. step) function before it is input into the autoscaler. - -### 4. Replica Controller - -The replica controller is responsible for the actual actuation of desiredReplicas. Common examples of this for Kubernetes pods include Deployment, ReplicaSet, and ReplicationController. Kubernetes has the concept of a [scale subresource](https://kubernetes.io/docs/tasks/extend-kubernetes/custom-resources/custom-resource-definitions/#subresources), which are generically targetable by autoscalers. This component has analogues in Node Group implementations like EC2 Auto Scaling Groups, EKS Managed Node Groups, and GKE Node Pools. - -## Node Autoscaling - -For node autoscaling, configurations are applied to node groups, drawing parallels to how pod autoscaling applies to deployments. This deviates from existing solutions like the Kubernetes Cluster Autoscaler or Escalator, which globally consider all node groups in their scaling decisions. It gives users the flexibility to configure policies on different capacity types, but does not limit users from applying globally computed metrics to a set of node groups. - -Assuming that a metrics driven approach results in significantly improved performance, flexibility, and functionality, the following questions emerge. - -* What existing systems can be leveraged as part of the metrics pipeline? -* What is the best interface to model autoscaling policies? -* Which autoscaling algorithm(s) should be implemented? -* How can we generically interface with different node group providers? -* What is the set of commonly used metrics producer implementations that should be supported? - -While we will strive to leverage existing systems where it makes sense, there will inevitably be component gaps that must be filled in both the short and long term. These components will live under an umbrella project named “Karpenter”. - -### Karpenter - -Karpenter is a metrics driven node autoscaler. It’s implemented as a Kubernetes controller and defines three custom resources: MetricsProducer, HorizontalAutoscaler, and ScalableNodeGroup. It aligns its interfaces as closely as possible to the Horizontal Pod Autoscaler’s interface, with a long term goal of providing a universal HorizontalAutoscaler definition in upstream Kubernetes. It takes a dependency on [Prometheus](https://prometheus.io/) and provides out-of-the-box support for commonly used metrics producers for Capacity Reservations, Scheduled Capacity, Pending Pods, and Queue Length. - -![](../images/design.jpeg) - -Before deep diving the design questions, we’ll cover some examples to see how Karpenter works for some common cases. - -### Example: Scaling with a Queue - -Alice wants to run weekly machine learning jobs using GPUs on her team’s cluster. The resources are expensive, so she wants her workloads to run on capacity that scales up when needed and back down when she’s done. The tasks are enqueued in an AWS SQS Queue, and she has configured a pod [autoscaler](https://keda.sh/docs/1.5/scalers/aws-sqs/) to scale up a pod for each queue message. She needs a node autoscaling solution to provision nodes so that the pods can be scheduled. In the past, she’s used the Kubernetes Cluster Autoscaler, but due to its reactive architecture, [she found that it was too slow](https://github.com/kedacore/keda/issues/637). - -She creates three Karpenter resources and applies them with kubectl apply: -```yaml -apiVersion: karpenter.sh/v1alpha2 -kind: MetricsProducer -metadata: - name: ml-training-queue - namespace: alice -spec: - queue: - provider: AWSSQSQueue - id: arn:aws:sqs:us-west-2:1234567890:alice-ml-training-queue -``` - -Her “ml-training-queue” configures Karpenter to periodically monitor queue metrics, such as the length of her AWS SQS Queue. The monitoring process has a Prometheus metrics endpoint at /metrics that returns the a set of metrics about the queue, including queue length. Alice has a Prometheus Server installed in her cluster, which dynamically discovers and periodically scrapes the queue length from the metrics producer and stores it in a timeseries database. Alice queries this data manually using karpenter:metrics_producer:queue-length{name="ml-training-queue", namespace="alice"} to make sure that everything is working smoothly. - -```yaml -apiVersion: karpenter.sh/v1alpha2 -kind: ScalableNodeGroup -metadata: - name: ml-training-capacity -spec: - type: AWSEKSNodeGroup - id: arn:aws:eks:us-west-2:1234567890:node-group:training-capacity - replicas: 0 # Will be updated by the HorizontalAutoscaler - ---- - -apiVersion: karpenter.sh/v1alpha2 -kind: HorizontalAutoscaler -metadata: - name: ml-training-capacity-autoscaler - namespace: alice -spec: - scaleTargetRef: - apiVersion: karpenter.sh/v1alpha2 - kind: ScalableNodeGroup - name: ml-training-capacity - metrics: - - type: Prometheus - prometheus: - query: karpenter:metrics_producer:queue_length{name="ml-training-queue", namespace="alice"} - target: - type: AverageValue - value: 4 -``` -Her “ml-training-capacity-autoscaler” defines autoscaling policy for “ml-training-capacity” which manages the replicas in her EKS Node Group. The autoscaler consumes the metric defined by the MetricsProducer resource, karpenter:metrics_producer:queue-length{name="ml-training-queue", namespace="alice"}, and targets an AverageValue of 4 sampled over a default of a 1 minute window. This means that the autoscaler will attempt to maintain a target of 1 node per 4 messages. Once these resources are created, Karpenter will periodically query the metric, compare it to the current number of replicas in the node group, and set the computed value on the ScalableNodeGroup. In response to a watch event from the ScalableNodeGroup’s update, Karpenter’s controller will set the desiredReplicas on the underlying EKS Node Group. - -Alice enqueues 2400 tasks into her queue, Karpenter’s PID algorithm quickly converges the node group on a value of 600 desired replicas. At the same time, Alice’s Horizontal Pod Autoscaler responds to the queue length by creating pods. The pods schedule onto the newly created nodes and remove the tasks from the queue. Once the queue is empty, Karpenter’s autoscaling algorithm returns the node group back down to zero. - -### Example: Reserving Capacity - -Bob is a coworker of Alice and their teams share the same cluster. His team hosts a set of microservices for a product that is gaining new customers every day. Users choose Bob’s product since it has much lower latency than alternatives. Bob is working with a limited infrastructure budget and needs to minimize costs while making sure his applications are scaling with user demands. He configures a pod autoscaler for each microservice, which will scale up to maintain low latency as long as capacity is available. Bob’s nodes have 16 cores and 20gb of memory each, and each microservice pod has a resource request of 1 core and 1 gb memory. He is willing to pay for 40% capacity overhead to minimize the chance that a pod will be unschedulable due to unavailable capacity. - -He creates two Karpenter resources and applies them with kubectl apply: -```yaml -apiVersion: karpenter.sh/v1alpha2 -kind: ScalableNodeGroup -metadata: - name: bobs-microservices -spec: - type: AWSEKSNodeGroup - id: arn:aws:eks:us-west-2:1234567890:node-group:default-capacity - ---- - -apiVersion: karpenter.sh/v1alpha2 -kind: HorizontalAutoscaler -metadata: - name: bobs-microservices-autoscaler - namespace: bob -spec: - scaleTargetRef: - apiVersion: karpenter.sh/v1alpha2 - kind: ScalableNodeGroup - name: bobs-capacity - metrics: - - type: Prometheus - prometheus: - query: karpenter:metrics_producer:reserved_capacity{node_group="bobs-microservices", type="cpu"} - target: - type: AverageUtilization - value: 60 - - type: Prometheus - prometheus: - query: karpenter:metrics_producer:reserved_capacity{node_group="bobs-microservices", type="memory"} - target: - type: AverageUtilization - value: 60 -``` - -His autoscaler is configured to target two metrics: one for 60% CPU and one for 60% memory. Reservations refers to the resource requests of the pods that are scheduled to the node. Unlike Alice, Bob didn’t need to configure a MetricsProducer. Reservation metrics are cheap to calculate, so Karpenter’s controller automatically produces them for all ScalableNodeGroup resources and exposes them to Prometheus with its /metrics endpoint. - -Bob starts out with 9 pods, resulting in (9/16)=.5625 CPU and (9/20)=.45 memory reservations. As his customers’ demand increases, 2 of his microservice pods scale up at the same time. The reservations are now (11/16)=.6875 CPU and 11/20=.55 memory, triggering a scale up. By default, the autoscaler uses largest value in its calculations for scale up and scale down and rounds up to the nearest integer. This conservative approach favors resource availability over cost and Bob can [override](https://godoc.org/k8s.io/api/autoscaling/v2beta2#ScalingPolicySelect) them if necessary. - -### Prometheus vs Kubernetes Metrics API - -Which metrics technology stack should Karpenter leverage? - -Kubernetes has an established landscape for metrics-driven autoscaling. This work was [motivated by and evolved alongside](https://github.com/kubernetes/community/blob/master/contributors/design-proposals/instrumentation/custom-metrics-api.md) the Horizontal Pod Autoscaler. The [Kubernetes monitoring architecture](https://kubernetes.io/docs/tasks/debug-application-cluster/resource-metrics-pipeline/) defines three metrics APIs that are implemented as [Kubernetes aggregated APIs](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/apiserver-aggregation/). - -* metrics.k8s.io provides metrics for pod and node resources like cpu and memory. -* custom.metrics.k8s.io provides metrics for arbitrary Kubernetes objects like an Ingress’s qps. -* external.metrics.k8s.io. provides metrics from systems outside of the cluster. - -Each API must be implemented by a service in the cluster. Implementations include the [metrics-server](https://github.com/kubernetes-sigs/metrics-server) for metrics.k8s.io, [k8s-prometheus-adapter](https://github.com/DirectXMan12/k8s-prometheus-adapter) for custom.metrics.k8s.io, and [KEDA](https://github.com/kedacore/keda) for external.metrics.k8s.io. - -For example, here’s how the Horizontal Pod Autoscaler uses [k8s-prometheus-adapter](https://github.com/DirectXMan12/k8s-prometheus-adapter) and custom.metrics.k8s.io. - -![](../images/hpa.png) -Source: https://towardsdatascience.com/kubernetes-hpa-with-custom-metrics-from-prometheus-9ffc201991e - -The metrics API is an attractive dependency for several reasons. It uses Kubernetes API semantics, bringing popular Kubernetes features (e.g. kubectl, API standardization) to the domain of metrics. It also enables users to control access using RBAC, though this isn’t hugely compelling as autoscalers typically operate globally on the cluster and have full permissions to the metrics API (see HPA). - -The metrics API has drawbacks. Each API can only have [one implementation](https://github.com/kubernetes/community/blob/master/contributors/design-proposals/instrumentation/monitoring_architecture.md), which creates compatibility challenges when trying to deploy multiple applications that attempt to implement the same metrics API. It’s tempting to make Karpenter an external metrics API implementation by leveraging [existing open source libraries](https://github.com/kubernetes-sigs/custom-metrics-apiserver). In fact, this is exactly how KEDA (a popular metrics driven pod autoscaler) implements its metrics stack. However, this approach would mean that Karpenter could not be deployed to any cluster that uses KEDA or any other external metrics API implementation. This “single-implementation“ design decision has led other autoscaling solutions like [Knative Serving](https://github.com/knative/serving) to avoid dependency on these [metrics APIs](https://github.com/knative/serving/issues/9087#issuecomment-675138178). - -Given this constraint, something generic and universal could implement the metrics APIs and then allow systems like Karpenter to feed metrics into it. The [k8s-prometheus-adapter](https://github.com/DirectXMan12/k8s-prometheus-adapter) is a community solution which attempts to be this solution and uses Prometheus as an intermediary, but the adapter must be [manually configured for each metric it exposes](https://github.com/DirectXMan12/k8s-prometheus-adapter/blob/master/docs/config.md). This is a nontrivial user burden that requires deep knowledge of Prometheus, the k8s-prometheus-adapter, the metrics producer, and Kubernetes metrics APIs. We could explore building a convention for naming external metrics such that the adapter can automatically translate metrics API resources into their external counterparts, removing the need for additional configuration. This [used to be supported](https://github.com/DirectXMan12/k8s-prometheus-adapter#presentation) by k8s-prometheus adapter, but was deprecated in favor of explicit configuration and a configuration generator. - -It’s also possible to closely align with KEDA and share an external metrics API server for both pod and node autoscaling. This introduces a project alignment challenge, but it is not insurmountable. Even if this could work, it’s not a perfect solution, as there will continue to be compatibility issues with other Kubernetes metrics API implementations. - -Another alternative is to use a metrics solution that doesn’t have the limitations mentioned above; we can build our own ([see Knative](https://github.com/knative/serving/issues/9087#issuecomment-675205480)) or rely on a strong open source candidate. - -Prometheus is ubiquitous throughout the Kubernetes ecosystem. It was the second project to graduate from the CNCF after Kubernetes. Both Kubelets and Kubernetes master components expose their metrics through a Prometheus-formatted /metrics endpoint. Controllers built with the Kubebuilder project come with built-in Prometheus metrics support and those that predate Kubebuilder [typically provide integrations](https://istio.io/latest/docs/ops/integrations/prometheus/). There are [well supported client libraries](https://prometheus.io/docs/instrumenting/clientlibs/) that make it straightforward for any process on Kubernetes to become a Prometheus metrics provider. [Grafana](https://grafana.com/), a widely used monitoring dashboard, integrates deeply with Prometheus and can provide visualizations for metrics driven node autoscaling. Another compelling feature of Prometheus is its [discovery API](https://prometheus.io/docs/prometheus/latest/configuration/configuration/#scrape_config), which automatically exposes metrics from providers without user configuration. Finally, it’s worth noting that KEDA has built first class [Prometheus integration](https://keda.sh/docs/1.4/scalers/prometheus/). - -There are a few drawbacks to diverging from the existing Kubernetes Metrics APIs. It forces divergence from the Horizontal Pod Autoscaler’s architecture (see next section), which may cause alignment challenges in the future. Kubernetes metrics APIs also come with RBAC support, but Prometheus does not have per-metric authorization. There are also tools like kubectl top which rely on the metrics API, but this command is specific to pod metrics and not useful for metrics used by node autoscaling. - -Direct Prometheus integration appears to be the best option. It avoids compatibility issues with other metrics providers. Generic metrics API adapters like k8s-prometheus-adapter create a domain knowledge and configuration burden for users. This decision has cascading effects to the rest of the design and should be considered very carefully. However, it is a two way door. The API can be flexible to arbitrary metrics stacks, including non-Prometheus alternatives. Prometheus will be considered a soft dependency; it will serve as our reference metrics implementation for Karpenter’s MVP. - -### Alignment with the Horizontal Pod Autoscaler API - -Is it possible or worthwhile to align with the Horizontal Pod Autoscaler? - -The Horizontal Pod Autoscaler (HPA) is a metrics driven pod autoscaling solution in upstream Kubernetes. It’s maintained by SIG Autoscaling and is the canonical solution for the Kubernetes community. Its API has undergone significant changes as Kubernetes has evolved. It initially provided support for scaling a deployment against the average CPU of its Pods, but has since expanded its flexibility in the [v2beta2 API](https://godoc.org/k8s.io/api/autoscaling/v2beta2#HorizontalPodAutoscalerSpec) to support arbitrary resource targets and custom metrics. It can target any Kubernetes resource that implements the [scale subresource](https://kubernetes.io/docs/tasks/extend-kubernetes/custom-resources/custom-resource-definitions/#subresources). Today, the existing HPA API is even able to target a Kubernetes resource representing a node group; the only gap is to implement metrics for the domain of node autoscaling. - -Unified autoscaling is a powerful concept, as it means that the same implementation can be shared for all autoscaled resources within a cluster. We want to avoid forcing premature alignment, but as long as it doesn’t compromise the design, there is value in keeping these interfaces as similar as possible. Users need only learn a single architecture for autoscaling, reducing complexity and cognitive load. - -There are a couple drawbacks to using the HPA’s API directly. The most obvious is the name, which would be more aptly called HorizontalAutoscaler. Most of its abstractions extend cleanly to Node Groups (e.g. [ScaleTargetRef](https://godoc.org/k8s.io/api/autoscaling/v2beta2#HorizontalPodAutoscalerSpec), [MetricTarget](https://godoc.org/k8s.io/api/autoscaling/v2beta2#MetricTarget), [ScalingPolicy](https://godoc.org/k8s.io/api/autoscaling/v2beta2#HPAScalingPolicy), [MinReplicas](https://godoc.org/k8s.io/api/autoscaling/v2beta2#HorizontalPodAutoscalerSpec), [MaxReplicas](https://godoc.org/k8s.io/api/autoscaling/v2beta2#HorizontalPodAutoscalerSpec), [Behavior](https://godoc.org/k8s.io/api/autoscaling/v2beta2#HorizontalPodAutoscalerBehavior), [StabilizationWindowSeconds](https://godoc.org/k8s.io/api/autoscaling/v2beta2#HPAScalingRules)). Others require slight adjustments (e.g. [ScalingPolicyType](https://godoc.org/k8s.io/api/autoscaling/v2beta2#HPAScalingPolicyType) needs to be tweaked to refer to “replicas” instead of “pods”). However, [MetricSpec](https://godoc.org/k8s.io/api/autoscaling/v2beta2#MetricSpec) is specific to pods and requires changes if relied upon. MetricsSpec has four subfields corresponding to different metrics sources. [ResourceMetricSource](https://godoc.org/k8s.io/api/autoscaling/v2beta2#ResourceMetricSource), which uses the [Resource Metrics API](https://github.com/kubernetes/community/blob/master/contributors/design-proposals/instrumentation/resource-metrics-api.md) and provides CPU and memory for pods and nodes. [PodsMetricSource](https://godoc.org/k8s.io/api/autoscaling/v2beta2#PodsMetricSource), which is syntactic sugar for [ObjectMetricSource](https://godoc.org/k8s.io/api/autoscaling/v2beta2#ObjectMetricSource), each of which each retrieve metrics from the [Custom Metrics API](https://github.com/kubernetes/community/blob/master/contributors/design-proposals/instrumentation/custom-metrics-api.md). [ExternalMetricSource](https://godoc.org/k8s.io/api/autoscaling/v2beta2#ExternalMetricSource), which uses the [External Metrics API](https://github.com/kubernetes/community/blob/master/contributors/design-proposals/instrumentation/external-metrics-api.md) to map metric name and namespace to an external object like an AWS SQS Queue. - -One approach would be to use the MetricsSpec and its four sources as-is. This requires sourcing all metrics from the Kubernetes metrics APIs (see limitations above). It’s also somewhat awkward, as users would likely never use the PodsMetricSpec or ResourceMetricsSpec to scale their node groups. The primary reason to go this route is alignment with the HorizontalPodAutoscaler and existing Kubernetes metrics APIs. The current Kubernetes metrics architecture is arguably too pod specific and could be changed to be more generic, but we consider engagement with SIG Instrumentation to be out of scope for the short term. - -Another option would be use ObjectMetricsSpec and ExternalMetricsSpec and omit pod-specific metrics APIs. This generically covers metrics for both in-cluster and external objects (i.e. custom.metrics.k8s.io and external.metrics.k8s.io). This approach is cleaner from the perspective of a node autoscaler, but makes future alignment with the HPA more challenging. Pod metrics could still specified, but this removes the syntactic sugar that simplifies the most common use cases for pod autoscaling. - -If we choose to integrate directly with Prometheus metrics (discussed above), there will need to be a new option in the MetricsSpec to specify it as a metrics source (e.g PrometheusMetricSource). Users would specify a [promql query](https://prometheus.io/docs/prometheus/latest/querying/basics/) to retrieve the metric. The decision to create a PrometheusMetricSource is orthogonal from whether or not we keep existing HPA metrics sources. Either way requires changes to the MetricsSpec; Prometheus support can be built alongside or replace existing metrics sources. - -We could also completely diverge from the HPA and start with a minimal autoscaler definition that covers initial node autoscaling use cases. This avoids premature abstraction of a generic autoscaling definition. However, we’re cautious to start from scratch, as it presumes we can design autoscaling APIs better than the HPA. It also makes alignment more challenging in the future. - -We will develop a new custom resource that is aligned with the HPA on all fields except for MetricsSpec, which will be tailored specifically to Prometheus. We will vet our theories for metrics driven node autoscaling and revisit upstreaming once the project matures. There are other benefits to owning a custom resource in the short term, like rapid iteration on the API and decoupling the release process from Kubernetes. The decision to directly rely on Prometheus (see previous section) forces divergence from the existing MetricsSpec. If the specs are already diverged, it makes less sense to provide support for metrics sources that aren’t useful for node autoscaling (e.g. ResourceMetricsSpec, PodMetricsSpec). There is still value in using the spec as a starting point, as the concepts are generic to autoscaling and familiar to users. - -### Autoscaling Algorithm - -Which autoscaling algorithm(s) should be implemented by the horizontal autoscaler? - -The HPA implements a [proportional algorithm](https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/#algorithm-details) that scales linearly with the number of replicas. When combined with rounding and windowing logic, this approach is good enough to be used widely by the Kubernetes community. It suffers from a drawback known as [proportional droop](https://www.yld.io/blog/kubernetes-piloting-the-cybernetic-dreamboat/) where the decision fails to take into account the rate of change of the metric, causing it to over or undershoot optimal scale decisions. - -A more advanced algorithm called [Proportional Integral Derivative](https://en.wikipedia.org/wiki/PID_controller) (PID) is widely used to solve this problem for thermostats, cruise control, and other control systems. In addition to a proportional term, this approach includes an integral term to capture historical changes and a derivative term to extrapolate from the current rate of change. With well tuned coefficients, this approach can accurately model arbitrary traffic curves for arbitrary use cases. - -Predictive autoscaling is an experimental field that leverages machine learning to make scale decisions. This approach is used at [Netflix](https://netflixtechblog.com/scryer-netflixs-predictive-auto-scaling-engine-part-2-bb9c4f9b9385) to learn periodic traffic patterns by analyzing metrics like request per second. Theoretically, deep learning could be used in combination with a rich cluster metrics dataset (e.g. Prometheus) to produce high accuracy black box scale decisions. - -The question of which algorithm to use is difficult to answer without deep research and experimentation with real user workloads. Rather than staking a claim on any particular algorithm, we will leave the door open to iterate and improve options and the default for Karpenter’s autoscaling algorithm. We will initially implement a proportional algorithm. - -## APIs - -All APIs for Node Autoscaling will be modeled with the [Kubernetes Resource Model](https://kubernetes.io/docs/concepts/overview/working-with-objects/kubernetes-objects/#kubernetes-objects) (KRM) using [Custom Resource Definitions](https://kubernetes.io/docs/tasks/extend-kubernetes/custom-resources/custom-resource-definitions/) and a long term goal of upstreaming them into the [Kubernetes autoscaling API group](https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/#api-object). Leveraging the KRM for autoscaling configuration provides myriad benefits including abstraction of interface from implementation, RBAC support, and a familiar user experience with kubectl. - -There are three things that need to be modeled: - -* a *Metrics Producer* defines a controller that outputs a metric (e.g. Queue Monitor) -* a *Horizontal Autoscaler* associates a scalable resource with a list of target metrics (e.g. HPA) -* a *Scalable Node Group* is able to scale the number of nodes using a “replicas” field - -It’s important to note that these APIs should be considered as primitives that can be used to build higher level concepts. For example, this is similar to how KEDA’s [ScaledObject](https://keda.sh/docs/1.4/concepts/scaling-deployments/#scaledobject-spec) resource provides a single abstraction over a HorizontalAutoscaler (HPA) and a MetricsProducer (Queue Length). For this design, description of higher level abstractions for node group autoscaling are considered out of scope. These primitives alone yield a better user experience than existing solutions. - -### Metrics Producer - -Many metrics (e.g. node capacity utilization) are implicit to the cluster and can made available for autoscaling purposes by Karpenter without explicit user configuration. Other metrics come from other systems and can be plugged directly into the metrics server implementation. However, some metrics require explicit user configuration. Karpenter will provide first-class support for common use cases by generating metrics implicitly where possible and otherwise explicitly configured and generated by a MetricsProducer resource. - -We expect this definition to evolve over time (see: Metrics Producer Implementations). -```yaml -apiVersion: karpenter.sh/v1alpha2 -kind: MetricsProducer -metadata: - name: foo-queue-length -spec: - queue: - type: AWSSQSQueue - id: arn:aws:sqs:us-west-2:1234567890:foo-queue -``` - -### Horizontal Autoscaler - -As discussed above, we will align as much as possible with the HPA API. An explicit long term goal for this project is to upstream an abstraction that unifies horizontal autoscaling in Kubernetes. We will mirror the Horizontal Pod Autoscaler API, but will rename references from “pod“ to ”replica“ in API, code comments, and go structs. The only exception to this is the MetricsSpec, which is the only reference to ”pod“ that cannot be generalized to ”replica“. - -We expect this definition to evolve over time and intend to eventually upstream it into the core autoscaling API group. -```yaml -apiVersion: karpenter.sh/v1alpha2 -kind: HorizontalAutoscaler -metadata: - name: my-capacity -spec: - scaleTargetRef: - apiVersion: karpenter.sh/v1alpha2 - kind: ScalableNodeGroup - name: my-capacity - minReplicas: 1 - maxReplicas: 10 - metrics: - - type: Prometheus - prometheus: - query: karpenter:metrics_producer:queue-length{name="foo-queue", namespace="default"} - target: - type: AverageValue - value: 3 # messages per node -``` - -### Scalable Node Group - -The decision to use the HPA’s scaleTargetRef concept creates two requirements for this resource. The API must represent a node group and must implement the [scale subresource](https://kubernetes.io/docs/tasks/extend-kubernetes/custom-resources/custom-resource-definitions/#subresources). The only responsibility of this resource is to control the replicas field, but this doesn’t preclude targeting a resource that is a full representation of the node group. There currently isn’t a Kubernetes native resource for node group, but there are a number of Kubernetes ecosystem projects that model node groups including [Kops Instance Groups](https://kops.sigs.k8s.io/tutorial/working-with-instancegroups/), [Cluster API Machine Pool](https://github.com/kubernetes-sigs/cluster-api/blob/master/docs/proposals/20190919-machinepool-api.md), [Amazon Controllers for Kubernetes (asg implementation tbd)](https://aws.amazon.com/blogs/containers/aws-controllers-for-kubernetes-ack/), [Anthos Config Management (node pool implementation tbd)](https://cloud.google.com/anthos-config-management/docs/overview), and others. - -The Horizontal Autoscaler API is flexible to any and all of these options. However, many Kubernetes users don’t rely on one of these mechanisms for node group management, instead relying on cloud provider specific abstractions (e.g. ASG). Therefore, we will introduce a resource that can be optionally targeted by HorizontalAutoscaler’s scaleTargetRef for users who don’t have a KRM node group concept. This is a stop gap until other node group representations become more widespread. This object will follow a cloud provider model to provide implementations for different cloud provider solutions such as EKS Managed Node Groups, EC2 Auto Scaling Groups, and others. The only field supported by this resource is replicas, leaving management responsibilities like upgrade up to some other controller. - -```yaml -apiVersion: karpenter.sh/v1alpha2 -kind: ScalableNodeGroup -metadata: - name: default-capacity -spec: - type: AWSEC2AutoScalingGroup - replicas: 1 -``` - -## Appendix A: Metrics Producer Implementations - -Metric driven autoscaling is extensible to as-of-yet unknown use cases. Users will face novel problems and will invent new metrics on which to scale their clusters. As a starting point, we will implement a small set of metrics to support the majority of known use cases. We expect these to evolve significantly with user feedback. As new commonly used metric sources are identified, they will be upstreamed into Karpenter. - -### Capacity Reservation - -Capacity reservation is perhaps the most straightforward approach to node autoscaling. It’s similar to the commonly used utilization metric for horizontal pod autoscaling. However, instead of scaling on current resource utilization, nodes are scaled on the resource requests (a.k.a. reserved capacity) of existing pods. This approach is used in other container orchestration systems like [AWS ECS](https://aws.amazon.com/blogs/aws/aws-ecs-cluster-auto-scaling-is-now-generally-available/). Note that due to its reliance on current usage of nodes, scale to zero is not supported. - -Karpenter can automatically output capacity reservation metrics as they’re cheap to compute. This creates a zero-config starting point for users. As user requirements become more complex, capacity reservations can be used in conjunction with other signals. For example, capacity reservations can be used to drive scale down, while scale up is driven by pending pods. This mimics the Kubernetes Cluster Autoscaler’s algorithm. - -Users will be able to configure this as follows: - -### Percentage overprovisioning - -```yaml -prometheus: - query: karpenter:metrics_producer:capacity_reservation{node_group="name", type="cpu"} - target: - type: AverageUtilization - value: 20 -``` - -There are important edge cases with capacity reservations. It’s possible for a node to be unschedulable and for the capacity reservation to not trigger a not scale up. For example, a pod will fail to schedule if it requires 55% of a node’s CPU, all nodes are at 50% reservation, and the reservation is set to 50%. This edge case occurs most frequently when nodes are small, pods are large, and reservations are low. This can be resolved by pairing capacity reservations with other metrics like pending pods, which will ensure that reservations are kept whenever possible, but edge cases will still trigger a scale up. - -A similar edge case exists for scale down, where a node could be deleted that holds a pod which is unable to fit in another other node. For further discussion of this case and other scale down edge cases, see Scale Down Edge Cases below. - -### Scheduled Capacity - -Scheduled capacity is another approach. Many use cases have oscillating traffic patterns such as lower traffic on weekends. Users can used scheduled capacity as a signal to scale up or down by defining a metrics producer with scheduled behavior. Users can layer many crontabs on top of each other to produce a more complex signal. - -This signal can be used in combination with other metrics in cases where demand is unexpected. For example, if scheduled capacity recommends that scale down should occur on a Friday afternoon, but due to a traffic spike a capacity reservation disagrees, the replica count will remain high. - -```yaml -apiVersion: karpenter.sh/v1alpha2 -kind: MetricsProducer -metadata: - name: foo-queue -spec: - scheduledCapacity: - nodeGroup: ... - behaviors: - - crontab: ... - replicas: 4 # Set replicas to 4 at time of cron - -prometheus: - query: karpenter:metrics_producer:scheduled_capacity{node_group="name", type="cpu"} - target: - type: AverageValue - averageValue: 1 -``` - -### Pending Pods - -Pending pods operates across multiple node groups. When a pod becomes unschedulable, the algorithm attempts to find a node group which if scaled up, would cause the pod to be scheduled. The MetricsProducer emits a signal per node group that corresponds to whether or not a scale up should occur. The MetricsProducer doesn’t necessarily apply to all node groups in the cluster. This allows some capacity to be scaled using pending pods and others to rely on different metrics producers, which is common for large and diverse clusters. - -```yaml -apiVersion: karpenter.sh/v1alpha2 -kind: MetricsProducer -metadata: - name: -spec: - pendingPods: - provider: EKSManagedNodeGroups - nodeGroup: ... - -prometheus: - query: karpenter:metrics_producer:pending_capacity{node_group="name", type="cpu"} - target: - type: AverageValue - value: 1 -``` - -### Queue Length - -Queue Length is a metric optimized for event driven and batch computing workloads. Users push messages into a provider like AWS SQS, Apache Kafka, or a large number of alternatives. The length of the queue is periodically monitored by the metrics producer, which is then acted on by the autoscaler. Queue Length is often used for pod autoscaling (see [KEDA](https://github.com/kedacore/keda)), but can also be used to drive node autoscaling. It’s important to distinguish two separate ways this can work, and the tradeoffs for when to use each technique. - -The first is to drive pod autoscaling with a queue metric and node autoscaling with pending pods. This is currently how KEDA integrates with the Cluster Autoscaler for node autoscaling. The team has seen some [challenges with this approach](https://github.com/kedacore/keda/issues/637), where reactive autoscaling results in high scheduling latency. Additionally, because the Cluster Autoscaler scales linearly with node groups and unschedulable pods, this approach does not scale to clusters with many thousands of nodes. - -The second approach is to drive both pods and nodes using the same queue metric. This can be configured on a 1:1 basis, or as a ratio of n pods per node. The benefit of this approach over pending pods is in both latency and scalability. Pods and nodes are actively scaled up and the decision to scale up a node group is vastly simplified — it’s explicit, rather than implicit. - -Queue-based node autoscaling and pending pods are both viable approaches with different trade offs. Pending pods is aligned with Kubernetes bin-packing principles, and yields increased capacity utilization for clusters that host diverse workloads and are overprovisioned. In this case, new pods will first schedule into existing capacity before forcing node group scale up via a pending pod autoscaler. However, if users are looking for a simple batch processing workflow of scaleup → do work → scale down, the bin-packing benefits must be weighed against pending pods’ complexity, scalability, and scheduling latency tradeoffs. - -```yaml -apiVersion: karpenter.sh/v1alpha2 -kind: MetricsProducer -metadata: - name: foo-queue -spec: - queue: - provider: AWSSQSQueue - id: arn:aws:sqs:us-west-2:1234567890:foo-queue - -prometheus: - query: karpenter:metrics_producer:queueLength{name="foo-queue", namespace="default"} - target: - type: AverageValue - value: 3 # messages per node -``` - -## Appendix B: Additional Considerations - -### Preemptable Nodes - -Metrics driven autoscaling supports preemptable node architectures like AWS Spot. It’s incorrect to state that all metrics producers work flawlessly with preemptable nodes, but Karpenter’s flexibility gives users the ability to apply preemption optimized metrics to preemptable node groups. - -Preemptable nodes introduce new requirements; capacity is unavailable more frequently, and it can be be reclaimed by the cloud provider at any time. In autoscaling terms, this results in two cases: failure to scale up and forced scale down. A common solution to mitigate these problems is to rely on multiple preemptable instance types; if one becomes unavailable or removed, the autoscaler can scale up a new instance type that is available. Autoscaling algorithms require that instance types in the same node group are of the same shape (https://aws.github.io/aws-eks-best-practices/cluster-autoscaling/#configuring-your-node-groups) (CPU, memory, etc). This limits the number of instance types that can be used in any given group, increasing the likelihood of insufficient capacity errors. Users combat this by creating multiple preemptable node groups, each of a different shape. - -One way to coordinate scaling across multiple node groups is to let the scheduler drive pod placement and use a capacity reservation metric for each node group. The horizontal autoscalers for each node group are not aware of each other, but they are aware of the pods that the scheduler has assigned to their nodes. As the scheduler adds pods to nodes, the corresponding node group will expand to maintain its reservation. If capacity for any node group becomes unavailable, the node group will fill up until the scheduler is forced to schedule elsewhere. This will gracefully fall back to node groups that have capacity and will continue to scale based off of their reservation metrics. - -Alternatively, it’s possible to implement a metrics producer that is purpose built for scaling multiple node groups with respect to the cloud provider’s preemptable node economy. Design of this specific metrics producer is out of scope, but we’ll briefly explore what it might look in order to validate metrics driven autoscaling’s extensibility. This metric would operate on a set of preemptable node groups with deep knowledge of underlying capacity availability and pricing. It would raise or lower the current metric value for each node group, causing the node groups’ horizontal autoscalers to make scaling decisions. It would not interfere with non-preemptable node groups, which would scale on alternative metrics. - -### Scale Down Edge Cases - -Scale down is a much higher risk operation than scale up. Stateless workloads are typically more tolerant to disruption than stateful or batch computing jobs, but the cost is use case specific. This problem extends beyond autoscaling. For example, nodes must be terminated when system components like the Kubelet are upgraded. They must also be terminated when capacity is migrated from one instance type to another. - -Some node autoscalers (e.g. Kubernetes Cluster Autoscaler, Escalator) attempt to deeply understand the pods and nodes they manage in order to intelligently pick which node to delete. This tightly couples the scale down decision to the autoscaler. Metrics driven autoscaling limits itself to controlling only the replicas field of a node group. This means that the scale down decision is delegated to the node group controller. This is critically important to avoid conflict between the controllers. If the autoscaler were to attempt to scale down during a rolling node group upgrade, disagreements can occur causing unintended behavior (https://github.com/aws/containers-roadmap/issues/916). - -### Scalability - -Each autoscaling component scales differently. However, as the system as a whole reaches scalability bottlenecks, autoscaler responsiveness will be delayed until autoscaling is non-responsive. It’s also possible for components to overwhelm the Kubernetes API server, Prometheus metrics server, or the node group’s underlying API server. - -ScalableNodeGroup’s controller leverages the Kubernetes watch API to ensure that only one downstream request is made to the cloud provider for each change in replicas. We don’t anticipate any scalability challenges here. - -HorizontalAutoscaler logic varies with the underlying algorithm. Proportional autoscaling decisions are calculated in constant time, while PID autoscaling decisions are polynomial with respect to its time window. Predictive autoscalers can be arbitrarily complex. Across a cluster, the autoscaler scales linearly with the number of node groups that have an autoscaler. If scalability becomes a concern for this component, this can be solved using sharding, as decisions for each node group’s autoscaler is independent. - -Prometheus’s scalability is a [large topic](https://improbable.io/blog/thanos-prometheus-at-scale) beyond the scope of Karpenter. We don’t anticipate challenges here, as autoscaling decisions require less history than Prometheus’ other use cases. We will treat Prometheus’ scalability as out of scope. - -MetricsProducer logic varies with the underlying algorithm, similar to the HorizontalAutoscaler’s scalability characteristics. Similarly, these processes can be sharded per producer if scalability challenges arise. Some MetricsProducers will break down as the cluster scales, such as PendingCapacity, which requires global analysis of the cluster’s node groups and pending pods. We consider the scalability concerns of any given MetricsProducer out of scope of the metrics driven autoscaling architecture. - -### KEDA - -There is potential for collaboration between KEDA and Karpenter to share an API and implementation for the MetricsProducer resource for both pod and node autoscaling. The responsibility of this component is identical in both systems, and we could benefit from the velocity gained by sharing metrics implementations. However, KEDA’s current design needs some tweaks to support this. - -Firstly, KEDA’s [scaler](https://keda.sh/docs/1.5/scalers/) concept needs to be separated from the [ScaledObject](https://keda.sh/docs/1.4/concepts/scaling-deployments/#scaledobject-spec) resource. Today, ScaledObjects are responsible for defining both MetricsProducer behavior (e.g. queue monitoring) and HorizontalAutoscaler behavior in a single resource. Under the hood, KEDA creates an underlying HorizontalPodAutoscaler resource as part of its implementation. Similarly, if KEDA separated out a MetricsProducer resource, this resource could be be used as part of ScaledObject’s implementation without changes to the ScaledObject API. This factored out MetricsProducer resource could be leveraged by both Karpenter and KEDA. This code could also be shared via libraries, but Karpenter would still need to implement the MetricsProducer custom resource to expose the functionality to users. - -There is an additional complication in that KEDA exposes metrics via Kubernetes metrics APIs. If shared, and if Karpenter chooses to rely directly on Prometheus (see above), MetricsProducers will need to expose metrics both as a Prometheus endpoint and to KEDA’s metrics API server. This is backwards compatible and straight forward to implement using [Prometheus client libraries](https://prometheus.io/docs/instrumenting/clientlibs/). - -## Appendix C: Future Areas for Exploration - -### Autogenerating Horizontal Autoscalers - -We recognize that for clusters with many node groups, defining autoscaling configurations will become a burden. We will explore building automated solutions like higher level custom resource abstractions or configuration generators (e.g. [CDK8s](https://github.com/awslabs/cdk8s)). - -### Vertical Node Autoscaling / Auto Provisioning - -Horizontal Autoscaling limits itself to creating replicas of existing instance types. It’s possible that existing or future workloads could be run more efficiently on new instance types. We will explore systems to modify the instance type of existing node groups (vertical autoscaling) or create and delete node groups (auto provisioning). - -## Appendix D: FAQ - -### Q: Should MetricsProducer, HorizontalAutoscaler, ScalableNodeGroup be namespaced resources? - -Should any or all of our CRDs be namespaced? Namespacing has the benefit of more RBAC control and allows hiding resources from multiple users in the same cluster. - -It’s clear that MetricsProducers should be namespaced. Under the hood, they correspond to pods, and should follow similar scoping rules. - -Namespacing the HorizontalAutoscaler is nuanced. If we aspire to a global autoscaling definition for Pods and Nodes, this object must be able to be applied to namespaced resources (e.g. deployments) as well as global resources (e.g. ScalableNodeGroups). By this line of reasoning, it would make sense to make the HorizontalAutoscaler a namespaced resource, but it would be somewhat awkward to apply a namespaced Horizontal Autoscaler to a global resource. Future node group representations (ACK/Cluster API) will be namespaced, but it isn’t clear if this will be true for all implementations. It always possible to create a “ClusterHorizontalAutoscaler” that could apply to globally scoped resources. - -The Node resource is not namespaced, so it might make sense to do the same for ScalableNodeGroup. Multiple ScalableNodeGroups pointing to the same cloud provider node group will result in undesired behavior. This could still happen if multiple conflicting resources were applied to the same namespace, but this scenario is much less likely. Given that MetricsProducer and HorizontalAutoscaler are both namespaced, it will provide a more intuitive user experience to namespace all three resources. - -### Q: Should users be able to apply multiple HorizontalAutoscaler configurations to the same scaleTargetRef? - -The Horizontal Autoscaler API has a []metrics field that lets users pass in multiple metrics. This allows users to specify an OR semantic to scale off of multiple signals. What about multiple Horizontal Autoscaler resources pointing to the same scaleTargetRef? For the HorizontalPodAutoscaler, this results in undesired behavior. For HorizontalAutoscaler, it’s possible to extend the OR semantic across multiple resources. The benefit would be that multiple application developers sharing a node group could scale the node group off of separate policies without being aware of each other. - -It’s not clear whether or not this is an intuitive user experience. It’s arguable that this will lead to more confusion and questions of “why did my node group scale unexpectedly?”. We will await user requests for this feature before considering it further. - -### Q: How can we make sure that Karpenter is horizontally scalable? - -Karpenter will reconcile resources, automatically produce node group metrics, and it’s likely that these responsibilities will increase over time. There are several paths forward to ensure that the Karpenter controller scales to large clusters. Individual resources are not tightly coupled to each other, so they can be arbitrarily sharded into multiple Karpenter replicas. Initially, metrics producers will be implemented as a goroutine in the Karpenter controller, but can easily be separated out into individual pods. - -### Q: Is it possible to abstract away Prometheus in favor of Open Telemetry? - -Yes — it may be possible to replace the Prometheus metrics layer with https://opentelemetry.io/ in the future. This project is currently incubating, so we’ll keep a close eye on this project and its roadmap. diff --git a/docs/deprecated/designs/scheduled_capacity.md b/docs/deprecated/designs/scheduled_capacity.md deleted file mode 100644 index feeae268599d..000000000000 --- a/docs/deprecated/designs/scheduled_capacity.md +++ /dev/null @@ -1,331 +0,0 @@ -# Scheduled Capacity Design -*Authors: njtran@* -## Introduction -Today, some Kubernetes users handle their workloads by scaling up and down in a recurring pattern. These patterns are -often indicative of some change in operational load and can come in the form of anything from a series of complex -scaling decisions to a one-off scale decision. - -## User Stories -* As a user I can periodically scale up and scale down my resources -* As a user I can schedule a special one-off scale request for my resources -* As a user I can utilize this metric in combination with others to schedule complex scaling decisions -* As a user I can see the current and future recommended states of my resources - -## Background -The important parts of Karpenter to take note of will be the HorizontalAutoscaler and the MetricsProducer. For any -user-specified resource, the MetricsProducer will be responsible for parsing the user input, calculating the metric -recommendation, and exposing it to the metrics endpoint. The HorizontalAutoscaler will be responsible for sending the -signals to scale the resource by using a `promql` query to grab the metric that the MetricsProducer has created. - -The core of each MetricsProducer is a reconcile loop, which runs at a pre-configured interval of time, and a record -function. The reconciliation ensures the metric is always being calculated, while the record function makes the data -available to the Prometheus server at every iteration of the loop. - -![](../images/scheduled-capacity-dataflow-diagram.png) - -While a HorizontalAutoscaler can only scale one resource, the metric that a MetricsProducer makes available can be used -by any amount of HorizontalAutoscalers. In addition, with a more complex `promql` -[query](https://prometheus.io/docs/prometheus/latest/querying/basics/), a user can also use a HorizontalAutoscaler to -scale based off multiple MetricsProducers. - -For more details, refer to [Karpenter’s design doc](DESIGN.md). - -## Design -This design encompasses the `ScheduleSpec` and `ScheduledCapacityStatus` structs. The spec corresponds to the user -input specifying the scheduled behaviors. The status will be used as a way for the user to check the state of the -metric through `kubectl` commands. - -### Metrics Producer Spec -The `ScheduleSpec` is where the user will specify the times in which a schedule will activate and recommend what the -value of the metric should be. - -```go -type Timezone string - -type ScheduleSpec struct { - // Behaviors may be layered to achieve complex scheduling autoscaling logic - Behaviors []ScheduledBehavior `json:"behaviors"` - // Defaults to UTC. Users will specify their schedules assuming this is their timezone - // ref: https://en.wikipedia.org/wiki/List_of_tz_database_time_zones - // +optional - Timezone *Timezone `json:"timezone,omitempty"` - // A schedule defaults to this value when no behaviors are active - DefaultReplicas int32 `json:"defaultReplicas"` -} - -// ScheduledBehavior sets the metric to a replica value based on a start and end pattern. -type ScheduledBehavior struct { - // The value the MetricsProducer will emit when the current time is within start and end - Replicas int32 `json:"replicas"` - Start *Pattern `json:"start"` - End *Pattern `json:"end"` -} - -// Pattern is a strongly-typed version of crontabs -type Pattern struct { - // When minutes or hours are left out, they are assumed to match to 0 - Minutes *string `json:"minutes,omitempty"` - Hours *string `json:"hours,omitempty"` - // When Days, Months, or Weekdays are left out, - // they are represented by wildcards, meaning any time matches - Days *string `json:"days,omitempty"` - // List of 3-letter abbreviations i.e. Jan, Feb, Mar - Months *string `json:"months,omitempty"` - // List of 3-letter abbreviations i.e. "Mon, Tue, Wed" - Weekdays *string `json:"weekdays,omitempty"` -} -``` - -The spec below details how a user might configure their scheduled behaviors. The picture to the right corresponds to -the configuration. - -This configuration is scaling up for 9-5 on weekdays (red), scaling down a little at night (green), and then scaling -down almost fully for the weekends (blue). -![](../images/scheduled-capacity-example-schedule-graphic.png) -```yaml -apiVersion: autoscaling.karpenter.sh/v1alpha2 -kind: MetricsProducer -metadata: - name: scheduling -spec: - schedule: - timezone: America/Los_Angeles - defaultReplicas: 2 - behaviors: - // Scale way down on Friday evening for the weekend - - replicas: 1 - start: - weekdays: Fri - hours: 17 - end: - weekdays: Mon - hours: 9 - // Scale up on Weekdays for usual traffic - - replicas: 3 - start: - weekdays: Mon,Tue,Wed,Thu,Fri - hours: 9 - end: - weekdays: Mon,Tue,Wed,Thu,Fri - hours: 17 - // Scale down on weekday evenings but not as much as on weekends - - replicas: 2 - start: - weekdays: Mon,Tue,Wed,Thu,Fri - hours: 17 - end: - weekdays: Mon,Tue,Wed,Thu,Fri - hours: 9 -``` - -### Metrics Producer Status Struct -The `ScheduledCapacityStatus` can be used to monitor the MetricsProducer. The results of the algorithm will populate -this struct at every iteration of the reconcile loop. A user can see the values of this struct with -`kubectl get metricsproducers -oyaml`. -```go -type ScheduledCapacityStatus struct { - // The current recommendation - the metric the MetricsProducer is emitting - CurrentValue *int32 `json:"currentValue,omitempty"` - - // The time where CurrentValue will switch to NextValue - NextValueTime *apis.VolatileTime `json:"nextValueTime,omitempty"` - - // The next recommendation for the metric - NextValue *int32 `json:"nextValue,omitempty"` -} -``` - -## Algorithm Design -The algorithm will parse all behaviors and the start and end schedule formats. We find the `nextStartTime` and -`nextEndTime` for each of the schedules. These will be the times they next match in the future. - -We say a schedule matches if the following are all true: - -* The current time is before or equal to the `nextEndTime` -* The `nextStartTime` is after or equal to the `nextEndTime` - -Based on how many schedules match: - -* If there is no match, we set the metric to the `defaultReplicas` -* If there is only one match, we set the metric to that behavior’s value -* If there are multiple matches, we set the metric to the value that is specified first in the spec - -This algorithm and API choice are very similar to [KEDA’s Cron Scaler](https://keda.sh/docs/2.0/scalers/cron/). - -## Strongly-Typed vs Crontabs -Most other time-based schedulers use Crontabs as their API. This section discusses why we chose against Crontabs and -how the two choices are similar. - -* The [Cron library](https://github.com/robfig/cron) captures too broad of a scope for our use-case. - * In planning critical scaling decisions, freedom can hurt more than help. One malformed scale signal can cost the - user a lot more money, or even scale down unexpectedly. - * While our implementation will use the Cron library, picking a strongly-typed API will allows us to decide which - portions of the library we want to allow the users to configure. -* The wide range of functionality Cron provides is sometimes misunderstood -(e.g. [Crontab Pitfalls](#crontab-pitfalls)). - * Adopting Crontab syntax adopts its pitfalls, which can be hard to fix in the future. - * If users have common problems involving Cron, it is more difficult to fix than if they were problems specific to - Karpenter. -* Karpenter’s metrics signals are best described as level-triggered. Crontabs were created to describe when to trigger -Cronjobs, which is best described as edge-triggered. - * If a user sees Crontabs, they may assume that Karpenter is edge-triggered behind the scenes, which - implies certain [problems](https://hackernoon.com/level-triggering-and-reconciliation-in-kubernetes-1f17fe30333d) - with availability. - * We want our users to infer correctly what is happening behind the scenes. - -## Field Plurality and Configuration Bloat -While Crontabs allow a user to specify **ranges** and **lists** of numbers/strings, we chose to **only** allow a **list** of -numbers/strings. Having a start and stop configuration in the form of Crontabs can confuse the user if they use overly -complex configurations. Reducing the scope of their choices to just a list of values can make it clearer. - -It is important to allow a user to specify multiple values to ease configuration load. While simpler cases like below -are easier to understand, adding more Crontab aspects like skip values and ranges can be much harder to mentally parse -at more complex levels of planning. We want to keep the tool intuitive, precise, and understandable, so that users who -understand their workloads can easily schedule them. - -```yaml -apiVersion: autoscaling.karpenter.sh/v1alpha2 -kind: MetricsProducer -metadata: - name: FakeScheduling -spec: - schedule: - timezone: America/Los_Angeles - defaultReplicas: 2 - behaviors: - // This spec WILL NOT work according to the design. - // Scale up on Weekdays for usual traffic - - replicas: 7 - start: - weekdays: Mon-Fri - hours: 9 - months: Jan-Mar - end: - weekdays: Mon-Fri - hours: 17 - months: Feb-Apr - // This spec WILL work according to the design. - // Scale down on weekday evenings - - replicas: 7 - start: - weekdays: Mon,Tue,Wed,Thu,Fri - hours: 9 - months: Jan,Feb,Mar - end: - weekdays: Mon,Tue,Wed,Thu,Fri - hours: 17 - months: Feb,Mar,Apr -``` - -## FAQ -### How does this design handle collisions right now? - -* In the MVP, if a schedule ends when another starts, it will select the schedule that is starting. If more than one -are starting/valid, then it will use the schedule that comes first in the spec. -* Look at the Out of Scope https://quip-amazon.com/zQ7mAxg0wNDC/Karpenter-Periodic-Autoscaling#ANY9CAbqSLH below for -more details. - -### How would a priority system work for collisions? - -* Essentially, every schedule would have some associated Priority. If multiple schedules match to the same time, the -one with the higher priority will win. In the event of a tie, we resort to position in the spec. Whichever schedule is -configured first will win. - -### How can I leverage this tool to work with other metrics? - -* Using this metric in tandem with others is a part of the Karpenter HorizontalAutoscaler. There are many possibilities, - and it’s possible to do so with all metrics in prometheus, as long as they return an instant vector (a singular value). -* Let’s say a user is scaling based-off a queue (a metric currently supported by Karpenter). If they’d like to keep a -healthy minimum value regardless of the size of the queue to stay ready for an abnormally large batch of jobs, they can -configure their HorizontalAutoscaler’s Spec.Metrics.Prometheus.Query field to be the line below. - -`max(karpenter_queue_length{name="ml-training-queue"},karpenter_scheduled_capacity{name="schedules"})` - -### Is it required to use Prometheus? - -* Currently, Karpenter’s design has a dependency on Prometheus. We use Prometheus to store the data that the core design -components (MetricsProducer, HorizontalAutoscaler, ScalableNodeGroup) use to communicate with each other. - -### Why Karpenter HorizontalAutoscaler and MetricsProducer? Why not use the HPA? - -* Karpenter’s design details why we have a CRD called HorizontalAutoscaler, and how our MetricsProducers complement -them. While there are a lot of similarities, there are key differences as detailed in the design -[here](../designs/DESIGN.md#alignment-with-the-horizontal-pod-autoscaler-api). - -## Out of Scope - Additional Future Features -Our current design currently does not have a robust way to handle collisions and help visualize how the metric will look -over time. While these are important issues, their implementations will not be included in the MVP. - -### Collisions -Collisions occur when more than one schedule matches to the current time. When this happens, the MetricsProducer cannot -emit more than one value at a time, so it must choose one value. - -* When could collisions happen past user error? - * When a user wants a special one-off scale up request - * e.g. MyCompany normally has `x` replicas on all Fridays at 06:00 and `y` replicas on Fridays at 20:00, but - wants `z` replicas on Black Friday at 06:00 - * For only this Friday, the normal Friday schedule and this special one-off request will conflict -* Solutions for collision handling - * Create a warning with the first times that a collision could happen - * Doesn’t decide functionality for users - * Does not guarantee it will be resolved - * Associate each schedule with a priority which will be used in comparison to other colliding schedules - * Requires users to rank each of their schedules, which they may want to change based on the time they collide - * Ties in priority - * Use the order in which they’re specified in the spec **OR** - * Default to the defaultReplicas - -The only change to the structs from the initial design would be to add a Priority field in the ScheduledBehavior struct -as below. -```go -type ScheduledBehavior struct { - Replicas int32 `json:"replicas"` - Start *Pattern `json:"start"` - End *Pattern `json:"end"` - Priority *int32 `json:"priority,omitempty"` -} -``` - -### Configuration Complexity -When a user is configuring their resources, it’s easy to lose track of how the metric will look over time, especially -if a user may want to plan far into the future with many complex behaviors. Creating a tool to visualize schedules will -not only help users understand how their schedules will match up, but can ease concerns during the configuration -process. This can empower users to create even more complex schedules to match their needs. - -Possible Designs: - -* A dual standalone tool/UI that users can use to either validate or create their YAML - * Pros - * Allows check-as-you-go for configuration purposes - * Auto creates their YAML with a recommendation based - * Cons - * Requires users to manually use it - * Requires a lot more work to implement a UI to help create the YAML as opposed to just a tool to validate -* An extra function to be included as part of the MetricsProducerStatus - * Pros - * Always available to see the visualization with kubectl commands - * Cons - * Will use some compute power to keep running (may be trivial amount of compute) - * Cannot use to check-as-you-go for configuration purposes - -## Crontab Pitfalls -This design includes the choice to use a strongly-typed API due to cases where Crontabs do not act as expected. Below -is the most common misunderstanding. - -* Let's say I have a schedule to trigger on the following dates: - * Schedule A: First 3 Thursdays of January - * Schedule B: The Friday of the last week of January and the first 2 weeks of February - * Schedule C: Tuesday for every week until the end of March after Schedule B -* This is how someone might do it - * Schedule A - "* * 1-21 1 4" for the first three Thursdays of January - * Schedule B - "* * 22-31 1 5" for the last week of January and "* * 1-14 2 5" for the first two weeks of February - * Schedule C - "* * 15-31 2 2" for the last Tuesdays in February and "* * * 3 2" for the Tuesdays in March -* Problems with the above approach - * Schedule A will match to any day in January that is in 1/1 to 1/21 or is a Thursday - * Schedule B’s first crontab will match to any day in January that is in 1/22 to 1/31 or is a Friday - * Schedule B’s second crontab will match to any day in February that is in 2/1 to 2/14 or is a Friday - * Schedule C’s first crontab will match to any day in February that is in 2/15 to 2/31 or is a Tuesday - * Schedule C’s second crontab is the only one that works as intended. -* The way that crontabs are implemented is if both Dom and Dow are non-wildcards (as they are above in each of the -crontabs except for Schedule C’s second crontab), then the crontab is treated as a match if **either** the Dom **or** Dow -matches. diff --git a/docs/deprecated/images/design.jpeg b/docs/deprecated/images/design.jpeg deleted file mode 100644 index 65674c000e927865fea9b42c043e840c9de203c2..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 99905 zcmeFYcTii+lPC%f*kG_NqKP&jk%P(E*dT&LG9qV_O%NC`VDe`h10o|t5Yc2rG$Lo4 zoG~JcoK4Od?8mqF{cip4tKF^Ldv~keA9qxBX3k7ccdM&Ucb}dK-Zr}R5{e8WC)9w)6`McdGBDzaNe4m7b_&zZ)3F#wJk_QhT5EGLD z$R0j={21_;%B`_BEFjgKD?Ke(CwPqshb2>|zRHQt8YCS)PF z1t7c)ApFxtz<9&t4O@Tn`;SI+@9zCux9<=V-z?idCLkapyiIufF7bmq4;~N`-6J5p 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+ + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/index.xml b/docs/index.xml new file mode 100644 index 000000000000..44d35549a876 --- /dev/null +++ b/docs/index.xml @@ -0,0 +1,17 @@ + + + My New Hugo Site – My New Hugo Site + http://awslabs.github.com/karpenter/docs/ + Recent content on My New Hugo Site + Hugo -- gohugo.io + en-us + + + + + + + + + + diff --git a/docs/js/deflate.js b/docs/js/deflate.js new file mode 100644 index 000000000000..b452c84e9e86 --- /dev/null +++ b/docs/js/deflate.js @@ -0,0 +1,1652 @@ +/* Copyright (C) 1999 Masanao Izumo +* Version: 1.0.1 +* LastModified: Dec 25 1999 +*/ + +/* Interface: +* data = deflate(src); +*/ +const deflate = (function () { + /* constant parameters */ + var zip_WSIZE = 32768; // Sliding Window size + var zip_STORED_BLOCK = 0; + var zip_STATIC_TREES = 1; + var zip_DYN_TREES = 2; + + /* for deflate */ + var zip_DEFAULT_LEVEL = 6; + var zip_FULL_SEARCH = true; + var zip_INBUFSIZ = 32768; // Input buffer size + var zip_INBUF_EXTRA = 64; // Extra buffer + var zip_OUTBUFSIZ = 1024 * 8; + var zip_window_size = 2 * zip_WSIZE; + var zip_MIN_MATCH = 3; + var zip_MAX_MATCH = 258; + var zip_BITS = 16; + // for SMALL_MEM + var zip_LIT_BUFSIZE = 0x2000; + var zip_HASH_BITS = 13; + // for MEDIUM_MEM + // var zip_LIT_BUFSIZE = 0x4000; + // var zip_HASH_BITS = 14; + // for BIG_MEM + // var zip_LIT_BUFSIZE = 0x8000; + // var zip_HASH_BITS = 15; + //if(zip_LIT_BUFSIZE > zip_INBUFSIZ) + // alert("error: zip_INBUFSIZ is too small"); + //if((zip_WSIZE<<1) > (1< zip_BITS-1) + // alert("error: zip_HASH_BITS is too large"); + //if(zip_HASH_BITS < 8 || zip_MAX_MATCH != 258) + // alert("error: Code too clever"); + var zip_DIST_BUFSIZE = zip_LIT_BUFSIZE; + var zip_HASH_SIZE = 1 << zip_HASH_BITS; + var zip_HASH_MASK = zip_HASH_SIZE - 1; + var zip_WMASK = zip_WSIZE - 1; + var zip_NIL = 0; // Tail of hash chains + var zip_TOO_FAR = 4096; + var zip_MIN_LOOKAHEAD = zip_MAX_MATCH + zip_MIN_MATCH + 1; + var zip_MAX_DIST = zip_WSIZE - zip_MIN_LOOKAHEAD; + var zip_SMALLEST = 1; + var zip_MAX_BITS = 15; + var zip_MAX_BL_BITS = 7; + var zip_LENGTH_CODES = 29; + var zip_LITERALS = 256; + var zip_END_BLOCK = 256; + var zip_L_CODES = zip_LITERALS + 1 + zip_LENGTH_CODES; + var zip_D_CODES = 30; + var zip_BL_CODES = 19; + var zip_REP_3_6 = 16; + var zip_REPZ_3_10 = 17; + var zip_REPZ_11_138 = 18; + var zip_HEAP_SIZE = 2 * zip_L_CODES + 1; + var zip_H_SHIFT = parseInt((zip_HASH_BITS + zip_MIN_MATCH - 1) / + zip_MIN_MATCH); + + /* variables */ + var zip_free_queue; + var zip_qhead, zip_qtail; + var zip_initflag; + var zip_outbuf = null; + var zip_outcnt, zip_outoff; + var zip_complete; + var zip_window; + var zip_d_buf; + var zip_l_buf; + var zip_prev; + var zip_bi_buf; + var zip_bi_valid; + var zip_block_start; + var zip_ins_h; + var zip_hash_head; + var zip_prev_match; + var zip_match_available; + var zip_match_length; + var zip_prev_length; + var zip_strstart; + var zip_match_start; + var zip_eofile; + var zip_lookahead; + var zip_max_chain_length; + var zip_max_lazy_match; + var zip_compr_level; + var zip_good_match; + var zip_nice_match; + var zip_dyn_ltree; + var zip_dyn_dtree; + var zip_static_ltree; + var zip_static_dtree; + var zip_bl_tree; + var zip_l_desc; + var zip_d_desc; + var zip_bl_desc; + var zip_bl_count; + var zip_heap; + var zip_heap_len; + var zip_heap_max; + var zip_depth; + var zip_length_code; + var zip_dist_code; + var zip_base_length; + var zip_base_dist; + var zip_flag_buf; + var zip_last_lit; + var zip_last_dist; + var zip_last_flags; + var zip_flags; + var zip_flag_bit; + var zip_opt_len; + var zip_static_len; + var zip_deflate_data; + var zip_deflate_pos; + + /* objects (deflate) */ + + function zip_DeflateCT() { + this.fc = 0; // frequency count or bit string + this.dl = 0; // father node in Huffman tree or length of bit string + } + + function zip_DeflateTreeDesc() { + this.dyn_tree = null; // the dynamic tree + this.static_tree = null; // corresponding static tree or NULL + this.extra_bits = null; // extra bits for each code or NULL + this.extra_base = 0; // base index for extra_bits + this.elems = 0; // max number of elements in the tree + this.max_length = 0; // max bit length for the codes + this.max_code = 0; // largest code with non zero frequency + } + + /* Values for max_lazy_match, good_match and max_chain_length, depending on + * the desired pack level (0..9). The values given below have been tuned to + * exclude worst case performance for pathological files. Better values may be + * found for specific files. + */ + function zip_DeflateConfiguration(a, b, c, d) { + this.good_length = a; // reduce lazy search above this match length + this.max_lazy = b; // do not perform lazy search above this match length + this.nice_length = c; // quit search above this match length + this.max_chain = d; + } + + function zip_DeflateBuffer() { + this.next = null; + this.len = 0; + this.ptr = new Array(zip_OUTBUFSIZ); + this.off = 0; + } + + /* constant tables */ + var zip_extra_lbits = [ + 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 0]; + var zip_extra_dbits = [ + 0, 0, 0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 9, 10, 10, 11, 11, 12, 12, 13, 13]; + var zip_extra_blbits = [ + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 7]; + var zip_bl_order = [16, 17, 18, 0, 8, 7, 9, 6, 10, 5, 11, 4, 12, 3, 13, 2, 14, 1, 15]; + var zip_configuration_table = [ + new zip_DeflateConfiguration(0, 0, 0, 0), + new zip_DeflateConfiguration(4, 4, 8, 4), + new zip_DeflateConfiguration(4, 5, 16, 8), + new zip_DeflateConfiguration(4, 6, 32, 32), + new zip_DeflateConfiguration(4, 4, 16, 16), + new zip_DeflateConfiguration(8, 16, 32, 32), + new zip_DeflateConfiguration(8, 16, 128, 128), + new zip_DeflateConfiguration(8, 32, 128, 256), + new zip_DeflateConfiguration(32, 128, 258, 1024), + new zip_DeflateConfiguration(32, 258, 258, 4096)]; + + + /* routines (deflate) */ + + function zip_deflate_start(level) { + var i; + + if (!level) + level = zip_DEFAULT_LEVEL; + else if (level < 1) + level = 1; + else if (level > 9) + level = 9; + + zip_compr_level = level; + zip_initflag = false; + zip_eofile = false; + if (zip_outbuf != null) + return; + + zip_free_queue = zip_qhead = zip_qtail = null; + zip_outbuf = new Array(zip_OUTBUFSIZ); + zip_window = new Array(zip_window_size); + zip_d_buf = new Array(zip_DIST_BUFSIZE); + zip_l_buf = new Array(zip_INBUFSIZ + zip_INBUF_EXTRA); + zip_prev = new Array(1 << zip_BITS); + zip_dyn_ltree = new Array(zip_HEAP_SIZE); + for (i = 0; i < zip_HEAP_SIZE; i++) + zip_dyn_ltree[i] = new zip_DeflateCT(); + zip_dyn_dtree = new Array(2 * zip_D_CODES + 1); + for (i = 0; i < 2 * zip_D_CODES + 1; i++) + zip_dyn_dtree[i] = new zip_DeflateCT(); + zip_static_ltree = new Array(zip_L_CODES + 2); + for (i = 0; i < zip_L_CODES + 2; i++) + zip_static_ltree[i] = new zip_DeflateCT(); + zip_static_dtree = new Array(zip_D_CODES); + for (i = 0; i < zip_D_CODES; i++) + zip_static_dtree[i] = new zip_DeflateCT(); + zip_bl_tree = new Array(2 * zip_BL_CODES + 1); + for (i = 0; i < 2 * zip_BL_CODES + 1; i++) + zip_bl_tree[i] = new zip_DeflateCT(); + zip_l_desc = new zip_DeflateTreeDesc(); + zip_d_desc = new zip_DeflateTreeDesc(); + zip_bl_desc = new zip_DeflateTreeDesc(); + zip_bl_count = new Array(zip_MAX_BITS + 1); + zip_heap = new Array(2 * zip_L_CODES + 1); + zip_depth = new Array(2 * zip_L_CODES + 1); + zip_length_code = new Array(zip_MAX_MATCH - zip_MIN_MATCH + 1); + zip_dist_code = new Array(512); + zip_base_length = new Array(zip_LENGTH_CODES); + zip_base_dist = new Array(zip_D_CODES); + zip_flag_buf = new Array(parseInt(zip_LIT_BUFSIZE / 8)); + } + + function zip_deflate_end() { + zip_free_queue = zip_qhead = zip_qtail = null; + zip_outbuf = null; + zip_window = null; + zip_d_buf = null; + zip_l_buf = null; + zip_prev = null; + zip_dyn_ltree = null; + zip_dyn_dtree = null; + zip_static_ltree = null; + zip_static_dtree = null; + zip_bl_tree = null; + zip_l_desc = null; + zip_d_desc = null; + zip_bl_desc = null; + zip_bl_count = null; + zip_heap = null; + zip_depth = null; + zip_length_code = null; + zip_dist_code = null; + zip_base_length = null; + zip_base_dist = null; + zip_flag_buf = null; + } + + function zip_reuse_queue(p) { + p.next = zip_free_queue; + zip_free_queue = p; + } + + function zip_new_queue() { + var p; + + if (zip_free_queue != null) { + p = zip_free_queue; + zip_free_queue = zip_free_queue.next; + } + else + p = new zip_DeflateBuffer(); + p.next = null; + p.len = p.off = 0; + + return p; + } + + function zip_head1(i) { + return zip_prev[zip_WSIZE + i]; + } + + function zip_head2(i, val) { + return zip_prev[zip_WSIZE + i] = val; + } + + /* put_byte is used for the compressed output, put_ubyte for the + * uncompressed output. However unlzw() uses window for its + * suffix table instead of its output buffer, so it does not use put_ubyte + * (to be cleaned up). + */ + function zip_put_byte(c) { + zip_outbuf[zip_outoff + zip_outcnt++] = c; + if (zip_outoff + zip_outcnt == zip_OUTBUFSIZ) + zip_qoutbuf(); + } + + /* Output a 16 bit value, lsb first */ + function zip_put_short(w) { + w &= 0xffff; + if (zip_outoff + zip_outcnt < zip_OUTBUFSIZ - 2) { + zip_outbuf[zip_outoff + zip_outcnt++] = (w & 0xff); + zip_outbuf[zip_outoff + zip_outcnt++] = (w >>> 8); + } else { + zip_put_byte(w & 0xff); + zip_put_byte(w >>> 8); + } + } + + /* ========================================================================== + * Insert string s in the dictionary and set match_head to the previous head + * of the hash chain (the most recent string with same hash key). Return + * the previous length of the hash chain. + * IN assertion: all calls to to INSERT_STRING are made with consecutive + * input characters and the first MIN_MATCH bytes of s are valid + * (except for the last MIN_MATCH-1 bytes of the input file). + */ + function zip_INSERT_STRING() { + zip_ins_h = ((zip_ins_h << zip_H_SHIFT) + ^ (zip_window[zip_strstart + zip_MIN_MATCH - 1] & 0xff)) + & zip_HASH_MASK; + zip_hash_head = zip_head1(zip_ins_h); + zip_prev[zip_strstart & zip_WMASK] = zip_hash_head; + zip_head2(zip_ins_h, zip_strstart); + } + + /* Send a code of the given tree. c and tree must not have side effects */ + function zip_SEND_CODE(c, tree) { + zip_send_bits(tree[c].fc, tree[c].dl); + } + + /* Mapping from a distance to a distance code. dist is the distance - 1 and + * must not have side effects. dist_code[256] and dist_code[257] are never + * used. + */ + function zip_D_CODE(dist) { + return (dist < 256 ? zip_dist_code[dist] + : zip_dist_code[256 + (dist >> 7)]) & 0xff; + } + + /* ========================================================================== + * Compares to subtrees, using the tree depth as tie breaker when + * the subtrees have equal frequency. This minimizes the worst case length. + */ + function zip_SMALLER(tree, n, m) { + return tree[n].fc < tree[m].fc || + (tree[n].fc == tree[m].fc && zip_depth[n] <= zip_depth[m]); + } + + /* ========================================================================== + * read string data + */ + function zip_read_buff(buff, offset, n) { + var i; + for (i = 0; i < n && zip_deflate_pos < zip_deflate_data.length; i++) + buff[offset + i] = + zip_deflate_data.charCodeAt(zip_deflate_pos++) & 0xff; + return i; + } + + /* ========================================================================== + * Initialize the "longest match" routines for a new file + */ + function zip_lm_init() { + var j; + + /* Initialize the hash table. */ + for (j = 0; j < zip_HASH_SIZE; j++) + // zip_head2(j, zip_NIL); + zip_prev[zip_WSIZE + j] = 0; + /* prev will be initialized on the fly */ + + /* Set the default configuration parameters: + */ + zip_max_lazy_match = zip_configuration_table[zip_compr_level].max_lazy; + zip_good_match = zip_configuration_table[zip_compr_level].good_length; + if (!zip_FULL_SEARCH) + zip_nice_match = zip_configuration_table[zip_compr_level].nice_length; + zip_max_chain_length = zip_configuration_table[zip_compr_level].max_chain; + + zip_strstart = 0; + zip_block_start = 0; + + zip_lookahead = zip_read_buff(zip_window, 0, 2 * zip_WSIZE); + if (zip_lookahead <= 0) { + zip_eofile = true; + zip_lookahead = 0; + return; + } + zip_eofile = false; + /* Make sure that we always have enough lookahead. This is important + * if input comes from a device such as a tty. + */ + while (zip_lookahead < zip_MIN_LOOKAHEAD && !zip_eofile) + zip_fill_window(); + + /* If lookahead < MIN_MATCH, ins_h is garbage, but this is + * not important since only literal bytes will be emitted. + */ + zip_ins_h = 0; + for (j = 0; j < zip_MIN_MATCH - 1; j++) { + // UPDATE_HASH(ins_h, window[j]); + zip_ins_h = ((zip_ins_h << zip_H_SHIFT) ^ (zip_window[j] & 0xff)) & zip_HASH_MASK; + } + } + + /* ========================================================================== + * Set match_start to the longest match starting at the given string and + * return its length. Matches shorter or equal to prev_length are discarded, + * in which case the result is equal to prev_length and match_start is + * garbage. + * IN assertions: cur_match is the head of the hash chain for the current + * string (strstart) and its distance is <= MAX_DIST, and prev_length >= 1 + */ + function zip_longest_match(cur_match) { + var chain_length = zip_max_chain_length; // max hash chain length + var scanp = zip_strstart; // current string + var matchp; // matched string + var len; // length of current match + var best_len = zip_prev_length; // best match length so far + + /* Stop when cur_match becomes <= limit. To simplify the code, + * we prevent matches with the string of window index 0. + */ + var limit = (zip_strstart > zip_MAX_DIST ? zip_strstart - zip_MAX_DIST : zip_NIL); + + var strendp = zip_strstart + zip_MAX_MATCH; + var scan_end1 = zip_window[scanp + best_len - 1]; + var scan_end = zip_window[scanp + best_len]; + + /* Do not waste too much time if we already have a good match: */ + if (zip_prev_length >= zip_good_match) + chain_length >>= 2; + + // Assert(encoder->strstart <= window_size-MIN_LOOKAHEAD, "insufficient lookahead"); + + do { + // Assert(cur_match < encoder->strstart, "no future"); + matchp = cur_match; + + /* Skip to next match if the match length cannot increase + * or if the match length is less than 2: + */ + if (zip_window[matchp + best_len] != scan_end || + zip_window[matchp + best_len - 1] != scan_end1 || + zip_window[matchp] != zip_window[scanp] || + zip_window[++matchp] != zip_window[scanp + 1]) { + continue; + } + + /* The check at best_len-1 can be removed because it will be made + * again later. (This heuristic is not always a win.) + * It is not necessary to compare scan[2] and match[2] since they + * are always equal when the other bytes match, given that + * the hash keys are equal and that HASH_BITS >= 8. + */ + scanp += 2; + matchp++; + + /* We check for insufficient lookahead only every 8th comparison; + * the 256th check will be made at strstart+258. + */ + do { + } while (zip_window[++scanp] == zip_window[++matchp] && + zip_window[++scanp] == zip_window[++matchp] && + zip_window[++scanp] == zip_window[++matchp] && + zip_window[++scanp] == zip_window[++matchp] && + zip_window[++scanp] == zip_window[++matchp] && + zip_window[++scanp] == zip_window[++matchp] && + zip_window[++scanp] == zip_window[++matchp] && + zip_window[++scanp] == zip_window[++matchp] && + scanp < strendp); + + len = zip_MAX_MATCH - (strendp - scanp); + scanp = strendp - zip_MAX_MATCH; + + if (len > best_len) { + zip_match_start = cur_match; + best_len = len; + if (zip_FULL_SEARCH) { + if (len >= zip_MAX_MATCH) break; + } else { + if (len >= zip_nice_match) break; + } + + scan_end1 = zip_window[scanp + best_len - 1]; + scan_end = zip_window[scanp + best_len]; + } + } while ((cur_match = zip_prev[cur_match & zip_WMASK]) > limit + && --chain_length != 0); + + return best_len; + } + + /* ========================================================================== + * Fill the window when the lookahead becomes insufficient. + * Updates strstart and lookahead, and sets eofile if end of input file. + * IN assertion: lookahead < MIN_LOOKAHEAD && strstart + lookahead > 0 + * OUT assertions: at least one byte has been read, or eofile is set; + * file reads are performed for at least two bytes (required for the + * translate_eol option). + */ + function zip_fill_window() { + var n, m; + + // Amount of free space at the end of the window. + var more = zip_window_size - zip_lookahead - zip_strstart; + + /* If the window is almost full and there is insufficient lookahead, + * move the upper half to the lower one to make room in the upper half. + */ + if (more == -1) { + /* Very unlikely, but possible on 16 bit machine if strstart == 0 + * and lookahead == 1 (input done one byte at time) + */ + more--; + } else if (zip_strstart >= zip_WSIZE + zip_MAX_DIST) { + /* By the IN assertion, the window is not empty so we can't confuse + * more == 0 with more == 64K on a 16 bit machine. + */ + // Assert(window_size == (ulg)2*WSIZE, "no sliding with BIG_MEM"); + + // System.arraycopy(window, WSIZE, window, 0, WSIZE); + for (n = 0; n < zip_WSIZE; n++) + zip_window[n] = zip_window[n + zip_WSIZE]; + + zip_match_start -= zip_WSIZE; + zip_strstart -= zip_WSIZE; /* we now have strstart >= MAX_DIST: */ + zip_block_start -= zip_WSIZE; + + for (n = 0; n < zip_HASH_SIZE; n++) { + m = zip_head1(n); + zip_head2(n, m >= zip_WSIZE ? m - zip_WSIZE : zip_NIL); + } + for (n = 0; n < zip_WSIZE; n++) { + /* If n is not on any hash chain, prev[n] is garbage but + * its value will never be used. + */ + m = zip_prev[n]; + zip_prev[n] = (m >= zip_WSIZE ? m - zip_WSIZE : zip_NIL); + } + more += zip_WSIZE; + } + // At this point, more >= 2 + if (!zip_eofile) { + n = zip_read_buff(zip_window, zip_strstart + zip_lookahead, more); + if (n <= 0) + zip_eofile = true; + else + zip_lookahead += n; + } + } + + /* ========================================================================== + * Processes a new input file and return its compressed length. This + * function does not perform lazy evaluationof matches and inserts + * new strings in the dictionary only for unmatched strings or for short + * matches. It is used only for the fast compression options. + */ + function zip_deflate_fast() { + while (zip_lookahead != 0 && zip_qhead == null) { + var flush; // set if current block must be flushed + + /* Insert the string window[strstart .. strstart+2] in the + * dictionary, and set hash_head to the head of the hash chain: + */ + zip_INSERT_STRING(); + + /* Find the longest match, discarding those <= prev_length. + * At this point we have always match_length < MIN_MATCH + */ + if (zip_hash_head != zip_NIL && + zip_strstart - zip_hash_head <= zip_MAX_DIST) { + /* To simplify the code, we prevent matches with the string + * of window index 0 (in particular we have to avoid a match + * of the string with itself at the start of the input file). + */ + zip_match_length = zip_longest_match(zip_hash_head); + /* longest_match() sets match_start */ + if (zip_match_length > zip_lookahead) + zip_match_length = zip_lookahead; + } + if (zip_match_length >= zip_MIN_MATCH) { + // check_match(strstart, match_start, match_length); + + flush = zip_ct_tally(zip_strstart - zip_match_start, + zip_match_length - zip_MIN_MATCH); + zip_lookahead -= zip_match_length; + + /* Insert new strings in the hash table only if the match length + * is not too large. This saves time but degrades compression. + */ + if (zip_match_length <= zip_max_lazy_match) { + zip_match_length--; // string at strstart already in hash table + do { + zip_strstart++; + zip_INSERT_STRING(); + /* strstart never exceeds WSIZE-MAX_MATCH, so there are + * always MIN_MATCH bytes ahead. If lookahead < MIN_MATCH + * these bytes are garbage, but it does not matter since + * the next lookahead bytes will be emitted as literals. + */ + } while (--zip_match_length != 0); + zip_strstart++; + } else { + zip_strstart += zip_match_length; + zip_match_length = 0; + zip_ins_h = zip_window[zip_strstart] & 0xff; + // UPDATE_HASH(ins_h, window[strstart + 1]); + zip_ins_h = ((zip_ins_h << zip_H_SHIFT) ^ (zip_window[zip_strstart + 1] & 0xff)) & zip_HASH_MASK; + + //#if MIN_MATCH != 3 + // Call UPDATE_HASH() MIN_MATCH-3 more times + //#endif + + } + } else { + /* No match, output a literal byte */ + flush = zip_ct_tally(0, zip_window[zip_strstart] & 0xff); + zip_lookahead--; + zip_strstart++; + } + if (flush) { + zip_flush_block(0); + zip_block_start = zip_strstart; + } + + /* Make sure that we always have enough lookahead, except + * at the end of the input file. We need MAX_MATCH bytes + * for the next match, plus MIN_MATCH bytes to insert the + * string following the next match. + */ + while (zip_lookahead < zip_MIN_LOOKAHEAD && !zip_eofile) + zip_fill_window(); + } + } + + function zip_deflate_better() { + /* Process the input block. */ + while (zip_lookahead != 0 && zip_qhead == null) { + /* Insert the string window[strstart .. strstart+2] in the + * dictionary, and set hash_head to the head of the hash chain: + */ + zip_INSERT_STRING(); + + /* Find the longest match, discarding those <= prev_length. + */ + zip_prev_length = zip_match_length; + zip_prev_match = zip_match_start; + zip_match_length = zip_MIN_MATCH - 1; + + if (zip_hash_head != zip_NIL && + zip_prev_length < zip_max_lazy_match && + zip_strstart - zip_hash_head <= zip_MAX_DIST) { + /* To simplify the code, we prevent matches with the string + * of window index 0 (in particular we have to avoid a match + * of the string with itself at the start of the input file). + */ + zip_match_length = zip_longest_match(zip_hash_head); + /* longest_match() sets match_start */ + if (zip_match_length > zip_lookahead) + zip_match_length = zip_lookahead; + + /* Ignore a length 3 match if it is too distant: */ + if (zip_match_length == zip_MIN_MATCH && + zip_strstart - zip_match_start > zip_TOO_FAR) { + /* If prev_match is also MIN_MATCH, match_start is garbage + * but we will ignore the current match anyway. + */ + zip_match_length--; + } + } + /* If there was a match at the previous step and the current + * match is not better, output the previous match: + */ + if (zip_prev_length >= zip_MIN_MATCH && + zip_match_length <= zip_prev_length) { + var flush; // set if current block must be flushed + + // check_match(strstart - 1, prev_match, prev_length); + flush = zip_ct_tally(zip_strstart - 1 - zip_prev_match, + zip_prev_length - zip_MIN_MATCH); + + /* Insert in hash table all strings up to the end of the match. + * strstart-1 and strstart are already inserted. + */ + zip_lookahead -= zip_prev_length - 1; + zip_prev_length -= 2; + do { + zip_strstart++; + zip_INSERT_STRING(); + /* strstart never exceeds WSIZE-MAX_MATCH, so there are + * always MIN_MATCH bytes ahead. If lookahead < MIN_MATCH + * these bytes are garbage, but it does not matter since the + * next lookahead bytes will always be emitted as literals. + */ + } while (--zip_prev_length != 0); + zip_match_available = 0; + zip_match_length = zip_MIN_MATCH - 1; + zip_strstart++; + if (flush) { + zip_flush_block(0); + zip_block_start = zip_strstart; + } + } else if (zip_match_available != 0) { + /* If there was no match at the previous position, output a + * single literal. If there was a match but the current match + * is longer, truncate the previous match to a single literal. + */ + if (zip_ct_tally(0, zip_window[zip_strstart - 1] & 0xff)) { + zip_flush_block(0); + zip_block_start = zip_strstart; + } + zip_strstart++; + zip_lookahead--; + } else { + /* There is no previous match to compare with, wait for + * the next step to decide. + */ + zip_match_available = 1; + zip_strstart++; + zip_lookahead--; + } + + /* Make sure that we always have enough lookahead, except + * at the end of the input file. We need MAX_MATCH bytes + * for the next match, plus MIN_MATCH bytes to insert the + * string following the next match. + */ + while (zip_lookahead < zip_MIN_LOOKAHEAD && !zip_eofile) + zip_fill_window(); + } + } + + function zip_init_deflate() { + if (zip_eofile) + return; + zip_bi_buf = 0; + zip_bi_valid = 0; + zip_ct_init(); + zip_lm_init(); + + zip_qhead = null; + zip_outcnt = 0; + zip_outoff = 0; + + if (zip_compr_level <= 3) { + zip_prev_length = zip_MIN_MATCH - 1; + zip_match_length = 0; + } + else { + zip_match_length = zip_MIN_MATCH - 1; + zip_match_available = 0; + } + + zip_complete = false; + } + + /* ========================================================================== + * Same as above, but achieves better compression. We use a lazy + * evaluation for matches: a match is finally adopted only if there is + * no better match at the next window position. + */ + function zip_deflate_internal(buff, off, buff_size) { + var n; + + if (!zip_initflag) { + zip_init_deflate(); + zip_initflag = true; + if (zip_lookahead == 0) { // empty + zip_complete = true; + return 0; + } + } + + if ((n = zip_qcopy(buff, off, buff_size)) == buff_size) + return buff_size; + + if (zip_complete) + return n; + + if (zip_compr_level <= 3) // optimized for speed + zip_deflate_fast(); + else + zip_deflate_better(); + if (zip_lookahead == 0) { + if (zip_match_available != 0) + zip_ct_tally(0, zip_window[zip_strstart - 1] & 0xff); + zip_flush_block(1); + zip_complete = true; + } + return n + zip_qcopy(buff, n + off, buff_size - n); + } + + function zip_qcopy(buff, off, buff_size) { + var n, i, j; + + n = 0; + while (zip_qhead != null && n < buff_size) { + i = buff_size - n; + if (i > zip_qhead.len) + i = zip_qhead.len; + // System.arraycopy(qhead.ptr, qhead.off, buff, off + n, i); + for (j = 0; j < i; j++) + buff[off + n + j] = zip_qhead.ptr[zip_qhead.off + j]; + + zip_qhead.off += i; + zip_qhead.len -= i; + n += i; + if (zip_qhead.len == 0) { + var p; + p = zip_qhead; + zip_qhead = zip_qhead.next; + zip_reuse_queue(p); + } + } + + if (n == buff_size) + return n; + + if (zip_outoff < zip_outcnt) { + i = buff_size - n; + if (i > zip_outcnt - zip_outoff) + i = zip_outcnt - zip_outoff; + // System.arraycopy(outbuf, outoff, buff, off + n, i); + for (j = 0; j < i; j++) + buff[off + n + j] = zip_outbuf[zip_outoff + j]; + zip_outoff += i; + n += i; + if (zip_outcnt == zip_outoff) + zip_outcnt = zip_outoff = 0; + } + return n; + } + + /* ========================================================================== + * Allocate the match buffer, initialize the various tables and save the + * location of the internal file attribute (ascii/binary) and method + * (DEFLATE/STORE). + */ + function zip_ct_init() { + var n; // iterates over tree elements + var bits; // bit counter + var length; // length value + var code; // code value + var dist; // distance index + + if (zip_static_dtree[0].dl != 0) return; // ct_init already called + + zip_l_desc.dyn_tree = zip_dyn_ltree; + zip_l_desc.static_tree = zip_static_ltree; + zip_l_desc.extra_bits = zip_extra_lbits; + zip_l_desc.extra_base = zip_LITERALS + 1; + zip_l_desc.elems = zip_L_CODES; + zip_l_desc.max_length = zip_MAX_BITS; + zip_l_desc.max_code = 0; + + zip_d_desc.dyn_tree = zip_dyn_dtree; + zip_d_desc.static_tree = zip_static_dtree; + zip_d_desc.extra_bits = zip_extra_dbits; + zip_d_desc.extra_base = 0; + zip_d_desc.elems = zip_D_CODES; + zip_d_desc.max_length = zip_MAX_BITS; + zip_d_desc.max_code = 0; + + zip_bl_desc.dyn_tree = zip_bl_tree; + zip_bl_desc.static_tree = null; + zip_bl_desc.extra_bits = zip_extra_blbits; + zip_bl_desc.extra_base = 0; + zip_bl_desc.elems = zip_BL_CODES; + zip_bl_desc.max_length = zip_MAX_BL_BITS; + zip_bl_desc.max_code = 0; + + // Initialize the mapping length (0..255) -> length code (0..28) + length = 0; + for (code = 0; code < zip_LENGTH_CODES - 1; code++) { + zip_base_length[code] = length; + for (n = 0; n < (1 << zip_extra_lbits[code]); n++) + zip_length_code[length++] = code; + } + // Assert (length == 256, "ct_init: length != 256"); + + /* Note that the length 255 (match length 258) can be represented + * in two different ways: code 284 + 5 bits or code 285, so we + * overwrite length_code[255] to use the best encoding: + */ + zip_length_code[length - 1] = code; + + /* Initialize the mapping dist (0..32K) -> dist code (0..29) */ + dist = 0; + for (code = 0; code < 16; code++) { + zip_base_dist[code] = dist; + for (n = 0; n < (1 << zip_extra_dbits[code]); n++) { + zip_dist_code[dist++] = code; + } + } + // Assert (dist == 256, "ct_init: dist != 256"); + dist >>= 7; // from now on, all distances are divided by 128 + for (; code < zip_D_CODES; code++) { + zip_base_dist[code] = dist << 7; + for (n = 0; n < (1 << (zip_extra_dbits[code] - 7)); n++) + zip_dist_code[256 + dist++] = code; + } + // Assert (dist == 256, "ct_init: 256+dist != 512"); + + // Construct the codes of the static literal tree + for (bits = 0; bits <= zip_MAX_BITS; bits++) + zip_bl_count[bits] = 0; + n = 0; + while (n <= 143) { zip_static_ltree[n++].dl = 8; zip_bl_count[8]++; } + while (n <= 255) { zip_static_ltree[n++].dl = 9; zip_bl_count[9]++; } + while (n <= 279) { zip_static_ltree[n++].dl = 7; zip_bl_count[7]++; } + while (n <= 287) { zip_static_ltree[n++].dl = 8; zip_bl_count[8]++; } + /* Codes 286 and 287 do not exist, but we must include them in the + * tree construction to get a canonical Huffman tree (longest code + * all ones) + */ + zip_gen_codes(zip_static_ltree, zip_L_CODES + 1); + + /* The static distance tree is trivial: */ + for (n = 0; n < zip_D_CODES; n++) { + zip_static_dtree[n].dl = 5; + zip_static_dtree[n].fc = zip_bi_reverse(n, 5); + } + + // Initialize the first block of the first file: + zip_init_block(); + } + + /* ========================================================================== + * Initialize a new block. + */ + function zip_init_block() { + var n; // iterates over tree elements + + // Initialize the trees. + for (n = 0; n < zip_L_CODES; n++) zip_dyn_ltree[n].fc = 0; + for (n = 0; n < zip_D_CODES; n++) zip_dyn_dtree[n].fc = 0; + for (n = 0; n < zip_BL_CODES; n++) zip_bl_tree[n].fc = 0; + + zip_dyn_ltree[zip_END_BLOCK].fc = 1; + zip_opt_len = zip_static_len = 0; + zip_last_lit = zip_last_dist = zip_last_flags = 0; + zip_flags = 0; + zip_flag_bit = 1; + } + + /* ========================================================================== + * Restore the heap property by moving down the tree starting at node k, + * exchanging a node with the smallest of its two sons if necessary, stopping + * when the heap property is re-established (each father smaller than its + * two sons). + */ + function zip_pqdownheap( + tree, // the tree to restore + k) { // node to move down + var v = zip_heap[k]; + var j = k << 1; // left son of k + + while (j <= zip_heap_len) { + // Set j to the smallest of the two sons: + if (j < zip_heap_len && + zip_SMALLER(tree, zip_heap[j + 1], zip_heap[j])) + j++; + + // Exit if v is smaller than both sons + if (zip_SMALLER(tree, v, zip_heap[j])) + break; + + // Exchange v with the smallest son + zip_heap[k] = zip_heap[j]; + k = j; + + // And continue down the tree, setting j to the left son of k + j <<= 1; + } + zip_heap[k] = v; + } + + /* ========================================================================== + * Compute the optimal bit lengths for a tree and update the total bit length + * for the current block. + * IN assertion: the fields freq and dad are set, heap[heap_max] and + * above are the tree nodes sorted by increasing frequency. + * OUT assertions: the field len is set to the optimal bit length, the + * array bl_count contains the frequencies for each bit length. + * The length opt_len is updated; static_len is also updated if stree is + * not null. + */ + function zip_gen_bitlen(desc) { // the tree descriptor + var tree = desc.dyn_tree; + var extra = desc.extra_bits; + var base = desc.extra_base; + var max_code = desc.max_code; + var max_length = desc.max_length; + var stree = desc.static_tree; + var h; // heap index + var n, m; // iterate over the tree elements + var bits; // bit length + var xbits; // extra bits + var f; // frequency + var overflow = 0; // number of elements with bit length too large + + for (bits = 0; bits <= zip_MAX_BITS; bits++) + zip_bl_count[bits] = 0; + + /* In a first pass, compute the optimal bit lengths (which may + * overflow in the case of the bit length tree). + */ + tree[zip_heap[zip_heap_max]].dl = 0; // root of the heap + + for (h = zip_heap_max + 1; h < zip_HEAP_SIZE; h++) { + n = zip_heap[h]; + bits = tree[tree[n].dl].dl + 1; + if (bits > max_length) { + bits = max_length; + overflow++; + } + tree[n].dl = bits; + // We overwrite tree[n].dl which is no longer needed + + if (n > max_code) + continue; // not a leaf node + + zip_bl_count[bits]++; + xbits = 0; + if (n >= base) + xbits = extra[n - base]; + f = tree[n].fc; + zip_opt_len += f * (bits + xbits); + if (stree != null) + zip_static_len += f * (stree[n].dl + xbits); + } + if (overflow == 0) + return; + + // This happens for example on obj2 and pic of the Calgary corpus + + // Find the first bit length which could increase: + do { + bits = max_length - 1; + while (zip_bl_count[bits] == 0) + bits--; + zip_bl_count[bits]--; // move one leaf down the tree + zip_bl_count[bits + 1] += 2; // move one overflow item as its brother + zip_bl_count[max_length]--; + /* The brother of the overflow item also moves one step up, + * but this does not affect bl_count[max_length] + */ + overflow -= 2; + } while (overflow > 0); + + /* Now recompute all bit lengths, scanning in increasing frequency. + * h is still equal to HEAP_SIZE. (It is simpler to reconstruct all + * lengths instead of fixing only the wrong ones. This idea is taken + * from 'ar' written by Haruhiko Okumura.) + */ + for (bits = max_length; bits != 0; bits--) { + n = zip_bl_count[bits]; + while (n != 0) { + m = zip_heap[--h]; + if (m > max_code) + continue; + if (tree[m].dl != bits) { + zip_opt_len += (bits - tree[m].dl) * tree[m].fc; + tree[m].fc = bits; + } + n--; + } + } + } + + /* ========================================================================== + * Generate the codes for a given tree and bit counts (which need not be + * optimal). + * IN assertion: the array bl_count contains the bit length statistics for + * the given tree and the field len is set for all tree elements. + * OUT assertion: the field code is set for all tree elements of non + * zero code length. + */ + function zip_gen_codes(tree, // the tree to decorate + max_code) { // largest code with non zero frequency + var next_code = new Array(zip_MAX_BITS + 1); // next code value for each bit length + var code = 0; // running code value + var bits; // bit index + var n; // code index + + /* The distribution counts are first used to generate the code values + * without bit reversal. + */ + for (bits = 1; bits <= zip_MAX_BITS; bits++) { + code = ((code + zip_bl_count[bits - 1]) << 1); + next_code[bits] = code; + } + + /* Check that the bit counts in bl_count are consistent. The last code + * must be all ones. + */ + // Assert (code + encoder->bl_count[MAX_BITS]-1 == (1<> 1; n >= 1; n--) + zip_pqdownheap(tree, n); + + /* Construct the Huffman tree by repeatedly combining the least two + * frequent nodes. + */ + do { + n = zip_heap[zip_SMALLEST]; + zip_heap[zip_SMALLEST] = zip_heap[zip_heap_len--]; + zip_pqdownheap(tree, zip_SMALLEST); + + m = zip_heap[zip_SMALLEST]; // m = node of next least frequency + + // keep the nodes sorted by frequency + zip_heap[--zip_heap_max] = n; + zip_heap[--zip_heap_max] = m; + + // Create a new node father of n and m + tree[node].fc = tree[n].fc + tree[m].fc; + // depth[node] = (char)(MAX(depth[n], depth[m]) + 1); + if (zip_depth[n] > zip_depth[m] + 1) + zip_depth[node] = zip_depth[n]; + else + zip_depth[node] = zip_depth[m] + 1; + tree[n].dl = tree[m].dl = node; + + // and insert the new node in the heap + zip_heap[zip_SMALLEST] = node++; + zip_pqdownheap(tree, zip_SMALLEST); + + } while (zip_heap_len >= 2); + + zip_heap[--zip_heap_max] = zip_heap[zip_SMALLEST]; + + /* At this point, the fields freq and dad are set. We can now + * generate the bit lengths. + */ + zip_gen_bitlen(desc); + + // The field len is now set, we can generate the bit codes + zip_gen_codes(tree, max_code); + } + + /* ========================================================================== + * Scan a literal or distance tree to determine the frequencies of the codes + * in the bit length tree. Updates opt_len to take into account the repeat + * counts. (The contribution of the bit length codes will be added later + * during the construction of bl_tree.) + */ + function zip_scan_tree(tree,// the tree to be scanned + max_code) { // and its largest code of non zero frequency + var n; // iterates over all tree elements + var prevlen = -1; // last emitted length + var curlen; // length of current code + var nextlen = tree[0].dl; // length of next code + var count = 0; // repeat count of the current code + var max_count = 7; // max repeat count + var min_count = 4; // min repeat count + + if (nextlen == 0) { + max_count = 138; + min_count = 3; + } + tree[max_code + 1].dl = 0xffff; // guard + + for (n = 0; n <= max_code; n++) { + curlen = nextlen; + nextlen = tree[n + 1].dl; + if (++count < max_count && curlen == nextlen) + continue; + else if (count < min_count) + zip_bl_tree[curlen].fc += count; + else if (curlen != 0) { + if (curlen != prevlen) + zip_bl_tree[curlen].fc++; + zip_bl_tree[zip_REP_3_6].fc++; + } else if (count <= 10) + zip_bl_tree[zip_REPZ_3_10].fc++; + else + zip_bl_tree[zip_REPZ_11_138].fc++; + count = 0; prevlen = curlen; + if (nextlen == 0) { + max_count = 138; + min_count = 3; + } else if (curlen == nextlen) { + max_count = 6; + min_count = 3; + } else { + max_count = 7; + min_count = 4; + } + } + } + + /* ========================================================================== + * Send a literal or distance tree in compressed form, using the codes in + * bl_tree. + */ + function zip_send_tree(tree, // the tree to be scanned + max_code) { // and its largest code of non zero frequency + var n; // iterates over all tree elements + var prevlen = -1; // last emitted length + var curlen; // length of current code + var nextlen = tree[0].dl; // length of next code + var count = 0; // repeat count of the current code + var max_count = 7; // max repeat count + var min_count = 4; // min repeat count + + /* tree[max_code+1].dl = -1; */ /* guard already set */ + if (nextlen == 0) { + max_count = 138; + min_count = 3; + } + + for (n = 0; n <= max_code; n++) { + curlen = nextlen; + nextlen = tree[n + 1].dl; + if (++count < max_count && curlen == nextlen) { + continue; + } else if (count < min_count) { + do { zip_SEND_CODE(curlen, zip_bl_tree); } while (--count != 0); + } else if (curlen != 0) { + if (curlen != prevlen) { + zip_SEND_CODE(curlen, zip_bl_tree); + count--; + } + // Assert(count >= 3 && count <= 6, " 3_6?"); + zip_SEND_CODE(zip_REP_3_6, zip_bl_tree); + zip_send_bits(count - 3, 2); + } else if (count <= 10) { + zip_SEND_CODE(zip_REPZ_3_10, zip_bl_tree); + zip_send_bits(count - 3, 3); + } else { + zip_SEND_CODE(zip_REPZ_11_138, zip_bl_tree); + zip_send_bits(count - 11, 7); + } + count = 0; + prevlen = curlen; + if (nextlen == 0) { + max_count = 138; + min_count = 3; + } else if (curlen == nextlen) { + max_count = 6; + min_count = 3; + } else { + max_count = 7; + min_count = 4; + } + } + } + + /* ========================================================================== + * Construct the Huffman tree for the bit lengths and return the index in + * bl_order of the last bit length code to send. + */ + function zip_build_bl_tree() { + var max_blindex; // index of last bit length code of non zero freq + + // Determine the bit length frequencies for literal and distance trees + zip_scan_tree(zip_dyn_ltree, zip_l_desc.max_code); + zip_scan_tree(zip_dyn_dtree, zip_d_desc.max_code); + + // Build the bit length tree: + zip_build_tree(zip_bl_desc); + /* opt_len now includes the length of the tree representations, except + * the lengths of the bit lengths codes and the 5+5+4 bits for the counts. + */ + + /* Determine the number of bit length codes to send. The pkzip format + * requires that at least 4 bit length codes be sent. (appnote.txt says + * 3 but the actual value used is 4.) + */ + for (max_blindex = zip_BL_CODES - 1; max_blindex >= 3; max_blindex--) { + if (zip_bl_tree[zip_bl_order[max_blindex]].dl != 0) break; + } + /* Update opt_len to include the bit length tree and counts */ + zip_opt_len += 3 * (max_blindex + 1) + 5 + 5 + 4; + // Tracev((stderr, "\ndyn trees: dyn %ld, stat %ld", + // encoder->opt_len, encoder->static_len)); + + return max_blindex; + } + + /* ========================================================================== + * Send the header for a block using dynamic Huffman trees: the counts, the + * lengths of the bit length codes, the literal tree and the distance tree. + * IN assertion: lcodes >= 257, dcodes >= 1, blcodes >= 4. + */ + function zip_send_all_trees(lcodes, dcodes, blcodes) { // number of codes for each tree + var rank; // index in bl_order + + // Assert (lcodes >= 257 && dcodes >= 1 && blcodes >= 4, "not enough codes"); + // Assert (lcodes <= L_CODES && dcodes <= D_CODES && blcodes <= BL_CODES, + // "too many codes"); + // Tracev((stderr, "\nbl counts: ")); + zip_send_bits(lcodes - 257, 5); // not +255 as stated in appnote.txt + zip_send_bits(dcodes - 1, 5); + zip_send_bits(blcodes - 4, 4); // not -3 as stated in appnote.txt + for (rank = 0; rank < blcodes; rank++) { + // Tracev((stderr, "\nbl code %2d ", bl_order[rank])); + zip_send_bits(zip_bl_tree[zip_bl_order[rank]].dl, 3); + } + + // send the literal tree + zip_send_tree(zip_dyn_ltree, lcodes - 1); + + // send the distance tree + zip_send_tree(zip_dyn_dtree, dcodes - 1); + } + + /* ========================================================================== + * Determine the best encoding for the current block: dynamic trees, static + * trees or store, and output the encoded block to the zip file. + */ + function zip_flush_block(eof) { // true if this is the last block for a file + var opt_lenb, static_lenb; // opt_len and static_len in bytes + var max_blindex; // index of last bit length code of non zero freq + var stored_len; // length of input block + + stored_len = zip_strstart - zip_block_start; + zip_flag_buf[zip_last_flags] = zip_flags; // Save the flags for the last 8 items + + // Construct the literal and distance trees + zip_build_tree(zip_l_desc); + // Tracev((stderr, "\nlit data: dyn %ld, stat %ld", + // encoder->opt_len, encoder->static_len)); + + zip_build_tree(zip_d_desc); + // Tracev((stderr, "\ndist data: dyn %ld, stat %ld", + // encoder->opt_len, encoder->static_len)); + /* At this point, opt_len and static_len are the total bit lengths of + * the compressed block data, excluding the tree representations. + */ + + /* Build the bit length tree for the above two trees, and get the index + * in bl_order of the last bit length code to send. + */ + max_blindex = zip_build_bl_tree(); + + // Determine the best encoding. Compute first the block length in bytes + opt_lenb = (zip_opt_len + 3 + 7) >> 3; + static_lenb = (zip_static_len + 3 + 7) >> 3; + + // Trace((stderr, "\nopt %lu(%lu) stat %lu(%lu) stored %lu lit %u dist %u ", + // opt_lenb, encoder->opt_len, + // static_lenb, encoder->static_len, stored_len, + // encoder->last_lit, encoder->last_dist)); + + if (static_lenb <= opt_lenb) + opt_lenb = static_lenb; + if (stored_len + 4 <= opt_lenb // 4: two words for the lengths + && zip_block_start >= 0) { + var i; + + /* The test buf != NULL is only necessary if LIT_BUFSIZE > WSIZE. + * Otherwise we can't have processed more than WSIZE input bytes since + * the last block flush, because compression would have been + * successful. If LIT_BUFSIZE <= WSIZE, it is never too late to + * transform a block into a stored block. + */ + zip_send_bits((zip_STORED_BLOCK << 1) + eof, 3); /* send block type */ + zip_bi_windup(); /* align on byte boundary */ + zip_put_short(stored_len); + zip_put_short(~stored_len); + + // copy block + /* + p = &window[block_start]; + for(i = 0; i < stored_len; i++) + put_byte(p[i]); + */ + for (i = 0; i < stored_len; i++) + zip_put_byte(zip_window[zip_block_start + i]); + + } else if (static_lenb == opt_lenb) { + zip_send_bits((zip_STATIC_TREES << 1) + eof, 3); + zip_compress_block(zip_static_ltree, zip_static_dtree); + } else { + zip_send_bits((zip_DYN_TREES << 1) + eof, 3); + zip_send_all_trees(zip_l_desc.max_code + 1, + zip_d_desc.max_code + 1, + max_blindex + 1); + zip_compress_block(zip_dyn_ltree, zip_dyn_dtree); + } + + zip_init_block(); + + if (eof != 0) + zip_bi_windup(); + } + + /* ========================================================================== + * Save the match info and tally the frequency counts. Return true if + * the current block must be flushed. + */ + function zip_ct_tally( + dist, // distance of matched string + lc) { // match length-MIN_MATCH or unmatched char (if dist==0) + zip_l_buf[zip_last_lit++] = lc; + if (dist == 0) { + // lc is the unmatched char + zip_dyn_ltree[lc].fc++; + } else { + // Here, lc is the match length - MIN_MATCH + dist--; // dist = match distance - 1 + // Assert((ush)dist < (ush)MAX_DIST && + // (ush)lc <= (ush)(MAX_MATCH-MIN_MATCH) && + // (ush)D_CODE(dist) < (ush)D_CODES, "ct_tally: bad match"); + + zip_dyn_ltree[zip_length_code[lc] + zip_LITERALS + 1].fc++; + zip_dyn_dtree[zip_D_CODE(dist)].fc++; + + zip_d_buf[zip_last_dist++] = dist; + zip_flags |= zip_flag_bit; + } + zip_flag_bit <<= 1; + + // Output the flags if they fill a byte + if ((zip_last_lit & 7) == 0) { + zip_flag_buf[zip_last_flags++] = zip_flags; + zip_flags = 0; + zip_flag_bit = 1; + } + // Try to guess if it is profitable to stop the current block here + if (zip_compr_level > 2 && (zip_last_lit & 0xfff) == 0) { + // Compute an upper bound for the compressed length + var out_length = zip_last_lit * 8; + var in_length = zip_strstart - zip_block_start; + var dcode; + + for (dcode = 0; dcode < zip_D_CODES; dcode++) { + out_length += zip_dyn_dtree[dcode].fc * (5 + zip_extra_dbits[dcode]); + } + out_length >>= 3; + // Trace((stderr,"\nlast_lit %u, last_dist %u, in %ld, out ~%ld(%ld%%) ", + // encoder->last_lit, encoder->last_dist, in_length, out_length, + // 100L - out_length*100L/in_length)); + if (zip_last_dist < parseInt(zip_last_lit / 2) && + out_length < parseInt(in_length / 2)) + return true; + } + return (zip_last_lit == zip_LIT_BUFSIZE - 1 || + zip_last_dist == zip_DIST_BUFSIZE); + /* We avoid equality with LIT_BUFSIZE because of wraparound at 64K + * on 16 bit machines and because stored blocks are restricted to + * 64K-1 bytes. + */ + } + + /* ========================================================================== + * Send the block data compressed using the given Huffman trees + */ + function zip_compress_block( + ltree, // literal tree + dtree) { // distance tree + var dist; // distance of matched string + var lc; // match length or unmatched char (if dist == 0) + var lx = 0; // running index in l_buf + var dx = 0; // running index in d_buf + var fx = 0; // running index in flag_buf + var flag = 0; // current flags + var code; // the code to send + var extra; // number of extra bits to send + + if (zip_last_lit != 0) do { + if ((lx & 7) == 0) + flag = zip_flag_buf[fx++]; + lc = zip_l_buf[lx++] & 0xff; + if ((flag & 1) == 0) { + zip_SEND_CODE(lc, ltree); /* send a literal byte */ + // Tracecv(isgraph(lc), (stderr," '%c' ", lc)); + } else { + // Here, lc is the match length - MIN_MATCH + code = zip_length_code[lc]; + zip_SEND_CODE(code + zip_LITERALS + 1, ltree); // send the length code + extra = zip_extra_lbits[code]; + if (extra != 0) { + lc -= zip_base_length[code]; + zip_send_bits(lc, extra); // send the extra length bits + } + dist = zip_d_buf[dx++]; + // Here, dist is the match distance - 1 + code = zip_D_CODE(dist); + // Assert (code < D_CODES, "bad d_code"); + + zip_SEND_CODE(code, dtree); // send the distance code + extra = zip_extra_dbits[code]; + if (extra != 0) { + dist -= zip_base_dist[code]; + zip_send_bits(dist, extra); // send the extra distance bits + } + } // literal or match pair ? + flag >>= 1; + } while (lx < zip_last_lit); + + zip_SEND_CODE(zip_END_BLOCK, ltree); + } + + /* ========================================================================== + * Send a value on a given number of bits. + * IN assertion: length <= 16 and value fits in length bits. + */ + var zip_Buf_size = 16; // bit size of bi_buf + function zip_send_bits( + value, // value to send + length) { // number of bits + /* If not enough room in bi_buf, use (valid) bits from bi_buf and + * (16 - bi_valid) bits from value, leaving (width - (16-bi_valid)) + * unused bits in value. + */ + if (zip_bi_valid > zip_Buf_size - length) { + zip_bi_buf |= (value << zip_bi_valid); + zip_put_short(zip_bi_buf); + zip_bi_buf = (value >> (zip_Buf_size - zip_bi_valid)); + zip_bi_valid += length - zip_Buf_size; + } else { + zip_bi_buf |= value << zip_bi_valid; + zip_bi_valid += length; + } + } + + /* ========================================================================== + * Reverse the first len bits of a code, using straightforward code (a faster + * method would use a table) + * IN assertion: 1 <= len <= 15 + */ + function zip_bi_reverse( + code, // the value to invert + len) { // its bit length + var res = 0; + do { + res |= code & 1; + code >>= 1; + res <<= 1; + } while (--len > 0); + return res >> 1; + } + + /* ========================================================================== + * Write out any remaining bits in an incomplete byte. + */ + function zip_bi_windup() { + if (zip_bi_valid > 8) { + zip_put_short(zip_bi_buf); + } else if (zip_bi_valid > 0) { + zip_put_byte(zip_bi_buf); + } + zip_bi_buf = 0; + zip_bi_valid = 0; + } + + function zip_qoutbuf() { + if (zip_outcnt != 0) { + var q, i; + q = zip_new_queue(); + if (zip_qhead == null) + zip_qhead = zip_qtail = q; + else + zip_qtail = zip_qtail.next = q; + q.len = zip_outcnt - zip_outoff; + // System.arraycopy(zip_outbuf, zip_outoff, q.ptr, 0, q.len); + for (i = 0; i < q.len; i++) + q.ptr[i] = zip_outbuf[zip_outoff + i]; + zip_outcnt = zip_outoff = 0; + } + } + + return function deflate(str, level) { + var i, j; + + zip_deflate_data = str; + zip_deflate_pos = 0; + if (typeof level == "undefined") + level = zip_DEFAULT_LEVEL; + zip_deflate_start(level); + + var buff = new Array(1024); + var aout = []; + while ((i = zip_deflate_internal(buff, 0, buff.length)) > 0) { + var cbuf = new Array(i); + for (j = 0; j < i; j++) { + cbuf[j] = String.fromCharCode(buff[j]); + } + aout[aout.length] = cbuf.join(""); + } + zip_deflate_data = null; // G.C. + return aout.join(""); + }; +})(); \ No newline at end of file diff --git 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t=/\b(?:alignas|alignof|asm|auto|bool|break|case|catch|char|char8_t|char16_t|char32_t|class|compl|concept|const|consteval|constexpr|constinit|const_cast|continue|co_await|co_return|co_yield|decltype|default|delete|do|double|dynamic_cast|else|enum|explicit|export|extern|float|for|friend|goto|if|inline|int|int8_t|int16_t|int32_t|int64_t|uint8_t|uint16_t|uint32_t|uint64_t|long|mutable|namespace|new|noexcept|nullptr|operator|private|protected|public|register|reinterpret_cast|requires|return|short|signed|sizeof|static|static_assert|static_cast|struct|switch|template|this|thread_local|throw|try|typedef|typeid|typename|union|unsigned|using|virtual|void|volatile|wchar_t|while)\b/;e.languages.cpp=e.languages.extend("c",{"class-name":[{pattern:RegExp("(\\b(?:class|concept|enum|struct|typename)\\s+)(?!)\\w+".replace(//g,function(){return 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+Prism.languages.scss=Prism.languages.extend("css",{comment:{pattern:/(^|[^\\])(?:\/\*[\s\S]*?\*\/|\/\/.*)/,lookbehind:!0},atrule:{pattern:/@[\w-]+(?:\([^()]+\)|[^(])*?(?=\s+[{;])/,inside:{rule:/@[\w-]+/}},url:/(?:[-a-z]+-)?url(?=\()/i,selector:{pattern:/(?=\S)[^@;{}()]?(?:[^@;{}()]|#\{\$[-\w]+\})+(?=\s*\{(?:\}|\s|[^}]+[:{][^}]+))/m,inside:{parent:{pattern:/&/,alias:"important"},placeholder:/%[-\w]+/,variable:/\$[-\w]+|#\{\$[-\w]+\}/}},property:{pattern:/(?:[\w-]|\$[-\w]+|#\{\$[-\w]+\})+(?=\s*:)/,inside:{variable:/\$[-\w]+|#\{\$[-\w]+\}/}}}),Prism.languages.insertBefore("scss","atrule",{keyword:[/@(?:if|else(?: if)?|for|each|while|import|extend|debug|warn|mixin|include|function|return|content)/i,{pattern:/( +)(?:from|through)(?= 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+Prism.languages.sql={comment:{pattern:/(^|[^\\])(?:\/\*[\s\S]*?\*\/|(?:--|\/\/|#).*)/,lookbehind:!0},variable:[{pattern:/@(["'`])(?:\\[\s\S]|(?!\1)[^\\])+\1/,greedy:!0},/@[\w.$]+/],string:{pattern:/(^|[^@\\])("|')(?:\\[\s\S]|(?!\2)[^\\]|\2\2)*\2/,greedy:!0,lookbehind:!0},function:/\b(?:AVG|COUNT|FIRST|FORMAT|LAST|LCASE|LEN|MAX|MID|MIN|MOD|NOW|ROUND|SUM|UCASE)(?=\s*\()/i,keyword:/\b(?:ACTION|ADD|AFTER|ALGORITHM|ALL|ALTER|ANALYZE|ANY|APPLY|AS|ASC|AUTHORIZATION|AUTO_INCREMENT|BACKUP|BDB|BEGIN|BERKELEYDB|BIGINT|BINARY|BIT|BLOB|BOOL|BOOLEAN|BREAK|BROWSE|BTREE|BULK|BY|CALL|CASCADED?|CASE|CHAIN|CHAR(?:ACTER|SET)?|CHECK(?:POINT)?|CLOSE|CLUSTERED|COALESCE|COLLATE|COLUMNS?|COMMENT|COMMIT(?:TED)?|COMPUTE|CONNECT|CONSISTENT|CONSTRAINT|CONTAINS(?:TABLE)?|CONTINUE|CONVERT|CREATE|CROSS|CURRENT(?:_DATE|_TIME|_TIMESTAMP|_USER)?|CURSOR|CYCLE|DATA(?:BASES?)?|DATE(?:TIME)?|DAY|DBCC|DEALLOCATE|DEC|DECIMAL|DECLARE|DEFAULT|DEFINER|DELAYED|DELETE|DELIMITERS?|DENY|DESC|DESCRIBE|DETERMINISTIC|DISABLE|DISCARD|DISK|DISTINCT|DISTINCTROW|DISTRIBUTED|DO|DOUBLE|DROP|DUMMY|DUMP(?:FILE)?|DUPLICATE|ELSE(?:IF)?|ENABLE|ENCLOSED|END|ENGINE|ENUM|ERRLVL|ERRORS|ESCAPED?|EXCEPT|EXEC(?:UTE)?|EXISTS|EXIT|EXPLAIN|EXTENDED|FETCH|FIELDS|FILE|FILLFACTOR|FIRST|FIXED|FLOAT|FOLLOWING|FOR(?: 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