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Getting started with the Dapr client Python SDK
Client
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How to get up and running with the Dapr Python SDK

The Dapr client package allows you to interact with other Dapr applications from a Python application.

{{% alert title="Note" color="primary" %}} If you haven't already, [try out one of the quickstarts]({{< ref quickstarts >}}) for a quick walk-through on how to use the Dapr Python SDK with an API building block.

{{% /alert %}}

Prerequisites

[Install the Dapr Python package]({{< ref "python#installation" >}}) before getting started.

Import the client package

The dapr package contains the DaprClient, which is used to create and use a client.

from dapr.clients import DaprClient

Initialising the client

You can initialise a Dapr client in multiple ways:

Default values:

When you initialise the client without any parameters it will use the default values for a Dapr sidecar instance (127.0.0.1:50001).

from dapr.clients import DaprClient

with DaprClient() as d:
    # use the client

Specifying an endpoint on initialisation:

When passed as an argument in the constructor, the gRPC endpoint takes precedence over any configuration or environment variable.

from dapr.clients import DaprClient

with DaprClient("mydomain:50051?tls=true") as d:
    # use the client

Configuration options:

Dapr Sidecar Endpoints

You can use the standardised DAPR_GRPC_ENDPOINT environment variable to specify the gRPC endpoint. When this variable is set, the client can be initialised without any arguments:

export DAPR_GRPC_ENDPOINT="mydomain:50051?tls=true"
from dapr.clients import DaprClient

with DaprClient() as d:
    # the client will use the endpoint specified in the environment variables

The legacy environment variables DAPR_RUNTIME_HOST, DAPR_HTTP_PORT and DAPR_GRPC_PORT are also supported, but DAPR_GRPC_ENDPOINT takes precedence.

Dapr API Token

If your Dapr instance is configured to require the DAPR_API_TOKEN environment variable, you can set it in the environment and the client will use it automatically.
You can read more about Dapr API token authentication here.

Health timeout

On client initialisation, a health check is performed against the Dapr sidecar (/healthz/outbound). The client will wait for the sidecar to be up and running before proceeding.

The default healthcheck timeout is 60 seconds, but it can be overridden by setting the DAPR_HEALTH_TIMEOUT environment variable.

Retries and timeout

The Dapr client can retry a request if a specific error code is received from the sidecar. This is configurable through the DAPR_API_MAX_RETRIES environment variable and is picked up automatically, not requiring any code changes. The default value for DAPR_API_MAX_RETRIES is 0, which means no retries will be made.

You can fine-tune more retry parameters by creating a dapr.clients.retry.RetryPolicy object and passing it to the DaprClient constructor:

from dapr.clients.retry import RetryPolicy

retry = RetryPolicy(
    max_attempts=5, 
    initial_backoff=1, 
    max_backoff=20, 
    backoff_multiplier=1.5,
    retryable_http_status_codes=[408, 429, 500, 502, 503, 504],
    retryable_grpc_status_codes=[StatusCode.UNAVAILABLE, StatusCode.DEADLINE_EXCEEDED, ]
)

with DaprClient(retry_policy=retry) as d:
    ...

or for actors:

factory = ActorProxyFactory(retry_policy=RetryPolicy(max_attempts=3))
proxy = ActorProxy.create('DemoActor', ActorId('1'), DemoActorInterface, factory)

Timeout can be set for all calls through the environment variable DAPR_API_TIMEOUT_SECONDS. The default value is 60 seconds.

Note: You can control timeouts on service invocation separately, by passing a timeout parameter to the invoke_method method.

Error handling

Initially, errors in Dapr followed the Standard gRPC error model. However, to provide more detailed and informative error messages, in version 1.13 an enhanced error model has been introduced which aligns with the gRPC Richer error model. In response, the Python SDK implemented DaprGrpcError, a custom exception class designed to improve the developer experience.
It's important to note that the transition to using DaprGrpcError for all gRPC status exceptions is a work in progress. As of now, not every API call in the SDK has been updated to leverage this custom exception. We are actively working on this enhancement and welcome contributions from the community.

Example of handling DaprGrpcError exceptions when using the Dapr python-SDK:

try:
    d.save_state(store_name=storeName, key=key, value=value)
except DaprGrpcError as err:
    print(f'Status code: {err.code()}')
    print(f"Message: {err.message()}")
    print(f"Error code: {err.error_code()}")
    print(f"Error info(reason): {err.error_info.reason}")
    print(f"Resource info (resource type): {err.resource_info.resource_type}")
    print(f"Resource info (resource name): {err.resource_info.resource_name}")
    print(f"Bad request (field): {err.bad_request.field_violations[0].field}")
    print(f"Bad request (description): {err.bad_request.field_violations[0].description}")

Building blocks

The Python SDK allows you to interface with all of the [Dapr building blocks]({{< ref building-blocks >}}).

Invoke a service

The Dapr Python SDK provides a simple API for invoking services via either HTTP or gRPC (deprecated). The protocol can be selected by setting the DAPR_API_METHOD_INVOCATION_PROTOCOL environment variable, defaulting to HTTP when unset. GRPC service invocation in Dapr is deprecated and GRPC proxying is recommended as an alternative.

from dapr.clients import DaprClient

with DaprClient() as d:
    # invoke a method (gRPC or HTTP GET)    
    resp = d.invoke_method('service-to-invoke', 'method-to-invoke', data='{"message":"Hello World"}')

    # for other HTTP verbs the verb must be specified
    # invoke a 'POST' method (HTTP only)    
    resp = d.invoke_method('service-to-invoke', 'method-to-invoke', data='{"id":"100", "FirstName":"Value", "LastName":"Value"}', http_verb='post')

The base endpoint for HTTP api calls is specified in the DAPR_HTTP_ENDPOINT environment variable. If this variable is not set, the endpoint value is derived from the DAPR_RUNTIME_HOST and DAPR_HTTP_PORT variables, whose default values are 127.0.0.1 and 3500 accordingly.

The base endpoint for gRPC calls is the one used for the client initialisation (explained above).

  • For a full guide on service invocation visit [How-To: Invoke a service]({{< ref howto-invoke-discover-services.md >}}).
  • Visit Python SDK examples for code samples and instructions to try out service invocation.

Save & get application state

from dapr.clients import DaprClient

with DaprClient() as d:
    # Save state
    d.save_state(store_name="statestore", key="key1", value="value1")

    # Get state
    data = d.get_state(store_name="statestore", key="key1").data

    # Delete state
    d.delete_state(store_name="statestore", key="key1")
  • For a full list of state operations visit [How-To: Get & save state]({{< ref howto-get-save-state.md >}}).
  • Visit Python SDK examples for code samples and instructions to try out state management.

Query application state (Alpha)

    from dapr import DaprClient

    query = '''
    {
        "filter": {
            "EQ": { "state": "CA" }
        },
        "sort": [
            {
                "key": "person.id",
                "order": "DESC"
            }
        ]
    }
    '''

    with DaprClient() as d:
        resp = d.query_state(
            store_name='state_store',
            query=query,
            states_metadata={"metakey": "metavalue"},  # optional
        )
  • For a full list of state store query options visit [How-To: Query state]({{< ref howto-state-query-api.md >}}).
  • Visit Python SDK examples for code samples and instructions to try out state store querying.

Publish & subscribe

Publish messages

from dapr.clients import DaprClient

with DaprClient() as d:
    resp = d.publish_event(pubsub_name='pubsub', topic_name='TOPIC_A', data='{"message":"Hello World"}')

Subscribe to messages

from cloudevents.sdk.event import v1
from dapr.ext.grpc import App
import json

app = App()

# Default subscription for a topic
@app.subscribe(pubsub_name='pubsub', topic='TOPIC_A')
def mytopic(event: v1.Event) -> None:
    data = json.loads(event.Data())
    print(f'Received: id={data["id"]}, message="{data ["message"]}"' 
          ' content_type="{event.content_type}"',flush=True)

# Specific handler using Pub/Sub routing
@app.subscribe(pubsub_name='pubsub', topic='TOPIC_A',
               rule=Rule("event.type == \"important\"", 1))
def mytopic_important(event: v1.Event) -> None:
    data = json.loads(event.Data())
    print(f'Received: id={data["id"]}, message="{data ["message"]}"' 
          ' content_type="{event.content_type}"',flush=True)
  • For more information about pub/sub, visit [How-To: Publish & subscribe]({{< ref howto-publish-subscribe.md >}}).
  • Visit Python SDK examples for code samples and instructions to try out pub/sub.

Streaming message subscription

You can create a streaming subscription to a PubSub topic using either the subscribe or subscribe_handler methods.

The subscribe method returns an iterable Subscription object, which allows you to pull messages from the stream by using a for loop (ex. for message in subscription) or by calling the next_message method. This will block on the main thread while waiting for messages. When done, you should call the close method to terminate the subscription and stop receiving messages.

The subscribe_with_handler method accepts a callback function that is executed for each message received from the stream. It runs in a separate thread, so it doesn't block the main thread. The callback should return a TopicEventResponse (ex. TopicEventResponse('success')), indicating whether the message was processed successfully, should be retried, or should be discarded. The method will automatically manage message acknowledgements based on the returned status. The call to subscribe_with_handler method returns a close function, which should be called to terminate the subscription when you're done.

Here's an example of using the subscribe method:

import time

from dapr.clients import DaprClient
from dapr.clients.grpc.subscription import StreamInactiveError, StreamCancelledError

counter = 0


def process_message(message):
    global counter
    counter += 1
    # Process the message here
    print(f'Processing message: {message.data()} from {message.topic()}...')
    return 'success'


def main():
    with DaprClient() as client:
        global counter

        subscription = client.subscribe(
            pubsub_name='pubsub', topic='TOPIC_A', dead_letter_topic='TOPIC_A_DEAD'
        )

        try:
            for message in subscription:
                if message is None:
                    print('No message received. The stream might have been cancelled.')
                    continue

                try:
                    response_status = process_message(message)

                    if response_status == 'success':
                        subscription.respond_success(message)
                    elif response_status == 'retry':
                        subscription.respond_retry(message)
                    elif response_status == 'drop':
                        subscription.respond_drop(message)

                    if counter >= 5:
                        break
                except StreamInactiveError:
                    print('Stream is inactive. Retrying...')
                    time.sleep(1)
                    continue
                except StreamCancelledError:
                    print('Stream was cancelled')
                    break
                except Exception as e:
                    print(f'Error occurred during message processing: {e}')

        finally:
            print('Closing subscription...')
            subscription.close()


if __name__ == '__main__':
    main()

And here's an example of using the subscribe_with_handler method:

import time

from dapr.clients import DaprClient
from dapr.clients.grpc._response import TopicEventResponse

counter = 0


def process_message(message):
    # Process the message here
    global counter
    counter += 1
    print(f'Processing message: {message.data()} from {message.topic()}...')
    return TopicEventResponse('success')


def main():
    with (DaprClient() as client):
        # This will start a new thread that will listen for messages
        # and process them in the `process_message` function
        close_fn = client.subscribe_with_handler(
            pubsub_name='pubsub', topic='TOPIC_A', handler_fn=process_message,
            dead_letter_topic='TOPIC_A_DEAD'
        )

        while counter < 5:
            time.sleep(1)

        print("Closing subscription...")
        close_fn()


if __name__ == '__main__':
    main()
  • For more information about pub/sub, visit [How-To: Publish & subscribe]({{< ref howto-publish-subscribe.md >}}).
  • Visit Python SDK examples for code samples and instructions to try out streaming pub/sub.

Interact with output bindings

from dapr.clients import DaprClient

with DaprClient() as d:
    resp = d.invoke_binding(binding_name='kafkaBinding', operation='create', data='{"message":"Hello World"}')
  • For a full guide on output bindings visit [How-To: Use bindings]({{< ref howto-bindings.md >}}).
  • Visit Python SDK examples for code samples and instructions to try out output bindings.

Retrieve secrets

from dapr.clients import DaprClient

with DaprClient() as d:
    resp = d.get_secret(store_name='localsecretstore', key='secretKey')
  • For a full guide on secrets visit [How-To: Retrieve secrets]({{< ref howto-secrets.md >}}).
  • Visit Python SDK examples for code samples and instructions to try out retrieving secrets

Configuration

Get configuration

from dapr.clients import DaprClient

with DaprClient() as d:
    # Get Configuration
    configuration = d.get_configuration(store_name='configurationstore', keys=['orderId'], config_metadata={})

Subscribe to configuration

import asyncio
from time import sleep
from dapr.clients import DaprClient

async def executeConfiguration():
    with DaprClient() as d:
        storeName = 'configurationstore'

        key = 'orderId'

        # Wait for sidecar to be up within 20 seconds.
        d.wait(20)

        # Subscribe to configuration by key.
        configuration = await d.subscribe_configuration(store_name=storeName, keys=[key], config_metadata={})
        while True:
            if configuration != None:
                items = configuration.get_items()
                for key, item in items:
                    print(f"Subscribe key={key} value={item.value} version={item.version}", flush=True)
            else:
                print("Nothing yet")
        sleep(5)

asyncio.run(executeConfiguration())
  • Learn more about managing configurations via the [How-To: Manage configuration]({{< ref howto-manage-configuration.md >}}) guide.
  • Visit Python SDK examples for code samples and instructions to try out configuration.

Distributed Lock

from dapr.clients import DaprClient

def main():
    # Lock parameters
    store_name = 'lockstore'  # as defined in components/lockstore.yaml
    resource_id = 'example-lock-resource'
    client_id = 'example-client-id'
    expiry_in_seconds = 60

    with DaprClient() as dapr:
        print('Will try to acquire a lock from lock store named [%s]' % store_name)
        print('The lock is for a resource named [%s]' % resource_id)
        print('The client identifier is [%s]' % client_id)
        print('The lock will will expire in %s seconds.' % expiry_in_seconds)

        with dapr.try_lock(store_name, resource_id, client_id, expiry_in_seconds) as lock_result:
            assert lock_result.success, 'Failed to acquire the lock. Aborting.'
            print('Lock acquired successfully!!!')

        # At this point the lock was released - by magic of the `with` clause ;)
        unlock_result = dapr.unlock(store_name, resource_id, client_id)
        print('We already released the lock so unlocking will not work.')
        print('We tried to unlock it anyway and got back [%s]' % unlock_result.status)
  • Learn more about using a distributed lock: [How-To: Use a lock]({{< ref howto-use-distributed-lock.md >}}).
  • Visit Python SDK examples for code samples and instructions to try out distributed lock.

Cryptography

from dapr.clients import DaprClient

message = 'The secret is "passw0rd"'

def main():
    with DaprClient() as d:
        resp = d.encrypt(
            data=message.encode(),
            options=EncryptOptions(
                component_name='crypto-localstorage',
                key_name='rsa-private-key.pem',
                key_wrap_algorithm='RSA',
            ),
        )
        encrypt_bytes = resp.read()

        resp = d.decrypt(
            data=encrypt_bytes,
            options=DecryptOptions(
                component_name='crypto-localstorage',
                key_name='rsa-private-key.pem',
            ),
        )
        decrypt_bytes = resp.read()

        print(decrypt_bytes.decode())  # The secret is "passw0rd"
  • For a full list of state operations visit [How-To: Use the cryptography APIs]({{< ref howto-cryptography.md >}}).
  • Visit Python SDK examples for code samples and instructions to try out cryptography

Workflow

from dapr.ext.workflow import WorkflowRuntime, DaprWorkflowContext, WorkflowActivityContext
from dapr.clients import DaprClient

instanceId = "exampleInstanceID"
workflowComponent = "dapr"
workflowName = "hello_world_wf"
eventName = "event1"
eventData = "eventData"

def main():
    with DaprClient() as d:
        host = settings.DAPR_RUNTIME_HOST
        port = settings.DAPR_GRPC_PORT
        workflowRuntime = WorkflowRuntime(host, port)
        workflowRuntime = WorkflowRuntime()
        workflowRuntime.register_workflow(hello_world_wf)
        workflowRuntime.register_activity(hello_act)
        workflowRuntime.start()

        # Start the workflow
        start_resp = d.start_workflow(instance_id=instanceId, workflow_component=workflowComponent,
                        workflow_name=workflowName, input=inputData, workflow_options=workflowOptions)
        print(f"start_resp {start_resp.instance_id}")

        # ...

        # Pause Test
        d.pause_workflow(instance_id=instanceId, workflow_component=workflowComponent)
        getResponse = d.get_workflow(instance_id=instanceId, workflow_component=workflowComponent)
        print(f"Get response from {workflowName} after pause call: {getResponse.runtime_status}")

        # Resume Test
        d.resume_workflow(instance_id=instanceId, workflow_component=workflowComponent)
        getResponse = d.get_workflow(instance_id=instanceId, workflow_component=workflowComponent)
        print(f"Get response from {workflowName} after resume call: {getResponse.runtime_status}")
        
        sleep(1)
        # Raise event
        d.raise_workflow_event(instance_id=instanceId, workflow_component=workflowComponent,
                    event_name=eventName, event_data=eventData)

        sleep(5)
        # Purge Test
        d.purge_workflow(instance_id=instanceId, workflow_component=workflowComponent)
        try:
            getResponse = d.get_workflow(instance_id=instanceId, workflow_component=workflowComponent)
        except DaprInternalError as err:
            if nonExistentIDError in err._message:
                print("Instance Successfully Purged")

        
        # Kick off another workflow for termination purposes 
        # This will also test using the same instance ID on a new workflow after
        # the old instance was purged
        start_resp = d.start_workflow(instance_id=instanceId, workflow_component=workflowComponent,
                        workflow_name=workflowName, input=inputData, workflow_options=workflowOptions)
        print(f"start_resp {start_resp.instance_id}")

        # Terminate Test
        d.terminate_workflow(instance_id=instanceId, workflow_component=workflowComponent)
        sleep(1)
        getResponse = d.get_workflow(instance_id=instanceId, workflow_component=workflowComponent)
        print(f"Get response from {workflowName} after terminate call: {getResponse.runtime_status}")

        # Purge Test
        d.purge_workflow(instance_id=instanceId, workflow_component=workflowComponent)
        try:
            getResponse = d.get_workflow(instance_id=instanceId, workflow_component=workflowComponent)
        except DaprInternalError as err:
            if nonExistentIDError in err._message:
                print("Instance Successfully Purged")

        workflowRuntime.shutdown()
  • Learn more about authoring and managing workflows:
    • [How-To: Author a workflow]({{< ref howto-author-workflow.md >}}).
    • [How-To: Manage a workflow]({{< ref howto-manage-workflow.md >}}).
  • Visit Python SDK examples for code samples and instructions to try out Dapr Workflow.

Related links

Python SDK examples