The documentation you are viewing is for Dapr v1.12 which is an older version of Dapr. For up-to-date documentation, see the latest version.
Getting started with the Dapr client 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.
Building blocks
The Python SDK allows you to interface with all of the Dapr building blocks.
Invoke a service
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')
- 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:
- Visit Python SDK examples for code samples and instructions to try out Dapr Workflow.
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