Getting started with the Dapr client Python SDK

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.

Note

If you haven’t already, try out one of the quickstarts for a quick walk-through on how to use the Dapr Python SDK with an API building block.

Prerequisites

Install the Dapr Python package 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

Environment variables:

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/outboud). The client will wait for the sidecar to be up and running before proceeding.

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

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.

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.
• 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.
• 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.
• Visit Python SDK examples for code samples and instructions to try out state store querying.

Publish & subscribe to messages

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.
• Visit Python SDK examples for code samples and instructions to try out 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.
• 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.
• 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 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.
• Visit Python SDK examples for code samples and instructions to try out distributed lock.

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.
  • How-To: Manage a workflow.
• Visit Python SDK examples for code samples and instructions to try out Dapr Workflow.

Related links

Python SDK examples