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Python Client for Google Cloud Pub/Sub

Project description

This is a shared codebase for gcloud-rest-pubsub and gcloud-rest-pubsub

Latest PyPI Version Python Version Support (gcloud-rest-pubsub) Python Version Support (gcloud-rest-pubsub)


$ pip install --upgrade gcloud-{aio,rest}-pubsub



The official Google publisher returns a future which is mostly useable as-is. gcloud-rest returns the future without change while gcloud-rest wraps it in asyncio terms.

An HTTP-oriented version, in keeping with the other gcloud-{aio,rest}-* libraries, will likely be coming soon – though our current approach works reasonably well for allowing the official grpc client to be used under asyncio, we continue to see threading oddities now and again which we’ve not been able to solve. As such, we do not wholeheartedly recommend using the SubscriberClient of this library in production, though a resilient enough environment for your use-case may be possible.

Here’s the rough usage pattern for subscribing:

from import SubscriberClient
from import SubscriberMessage

client = SubscriberClient()
# create subscription if it doesn't already exist

async def message_callback(message: SubscriberMessage) -> None:
        # just an example: process the message however you need to here...
        result = handle(message)
        await upload_result(result)
    except Exception:

# subscribe to the subscription, receiving a Future that acts as a keepalive
keep_alive = client.subscribe(

# have the client run forever, pulling messages from this subscription,
# passing them to the specified callback function, and wrapping it in an
# asyncio task.


Our create_subscription method is a thin wrapper and thus supports almost all keyword configuration arguments from the official pubsub client which you can find in the official Google documentation.

Since the underlying Google implementation of Scheduler only allows for the concrete ThreadScheduler which is also the default, we have opted not to expose this configuration option. Additionally, we would like to fully deprecate said Google implementation in favour of a fully asyncio implementation which uses the event loop as the scheduler.

When subscribing to a subscription you can optionally pass in a FlowControl instance.

example_flow_control = FlowControl(

keep_alive = client.subscribe(

Understanding how modifying FlowControl affects how your pubsub runtime will operate can be confusing so here’s a handy dandy guide!

Welcome to @TheKevJames and @jonathan-johnston’s guide to configuring Google Pubsub Subscription policies! Settle in, grab a drink, and stay a while.

The Subscriber is controlled by a FlowControl configuration tuple defined in gcloud-rest and subsequently in google-cloud-pubsub.

That configuration object f gets used by the Subscriber in the following ways:

Max Concurrency

The subscriber stops leasing new tasks whenever too many messages or too many message bytes have been leased for currently leased tasks x:

    len(x) / f.max_messages,
    sum(x.bytes) / f.max_bytes
) >= 1.0

And leasing is resumed when there is some breathing room in terms of message counts or byte counts:

    len(x) / f.max_messages,
    sum(x.bytes) / f.max_bytes
) < 0.8

In practice, this means we should set these values with the following restrictions:

  • the maximum number of concurrently leased messages at peak is: = f.max_messages + f.max_messages mod batch_size

  • the maximum memory usage of our leased messages at peak is: = f.max_bytes + f.max_bytes mod (batch_size * bytes_per_messages)

  • these values are constrain each other, ie. we limit ourselves to the lesser of these values, with batch_size calculated dynamically in PubSub itself

Aside: it seems like OCNs on Pubsub are ~1538 bytes each.

Leasing Requests

When leasing new tasks, the Subscriber simply continues to request messages from the PubSub subscription until the aforementioned message concurrency or total message bytes limits are hit. At that point, the message consumer is paused while the messages are processed and resumed when the resume condition is met.

Message processing and message leasing are carried out in parallel. When a message batch is received from the PubSub subscription the messages are scheduled for processing immediately on a concurrent.futures.ThreadPoolExecutor. This Scheduler should be filling up as fast as grpc can make requests to Google Pubsub, which should be Fast Enough(tm) to keep it filled, given those requests are batched.

Task Expiry

Any task which has not been acked or nacked counts against the current leased task count. Our worker thread should ensure all tasks are acked or nacked, but the FlowControl config allows us to handle any other cases. Note that leasing works as follows:

  • When a subscriber leases a task, Google Pubsub will not re-lease that task until subscription.ack_deadline_seconds = 10 (configurable per-subscription) seconds have passed.

  • If a client calls ack() on a task, it is immediately removed from Google Pubsub.

  • If a client calls nack() on a task, it immediately allows Google Pubsub to re-lease that task to a new client. The client drops the task from its memory.

  • If f.max_lease_duration passes between a message being leased and acked, the client will send a nack (see above workflow). It will NOT drop the task from its memory – eg. the worker(task) process may still be run.


  • all steps are best-effort, eg. read “a task will be deleted” as “a task will probably get deleted, if the distributed-system luck is with you”

  • in the above workflow “Google Pubsub” refers to the server-side system, eg. managed by Google where the tasks are actually stored.

In practice, we should thus set f.max_lease_duration to no lower than our 95% percentile task latency at high load. The lower this value is, the better our throughput will be in extreme cases.


The PublisherClient is a dead-simple alternative to the official Google Cloud Pub/Sub publisher client. The main design goal was to eliminate all the additional gRPC overhead implemented by the upstream client.

If migrating between this library and the official one, the main difference is this: the gcloud-{aio,rest}-pubsub publisher’s .publish() method immediately publishes the messages you’ve provided, rather than maintaining our own publishing queue, implementing batching and flow control, etc. If you’re looking for a full-featured publishing library with all the bells and whistles built in, you may be interested in the upstream provider. If you’re looking to manage your own batching / timeouts / retry / threads / etc, this library should be a bit easier to work with.

Sample usage:

from import PubsubMessage
from import PublisherClient

async with aiohttp.ClientSession() as session:
    client = PublisherClient(session=session)

    topic = client.topic_path('my-gcp-project', 'my-topic-name')

    messages = [
        PubsubMessage(b'payload', attribute='value'),
        PubsubMessage(b'other payload', other_attribute='whatever',
                      more_attributes='something else'),
    response = await client.publish(topic, messages)
    # response == {'messageIds': ['1', '2']}


For testing purposes, you may want to use gcloud-rest-pubsub along with a local GCS emulator. Setting the $PUBSUB_EMULATOR_HOST environment variable to the local address of your emulator should be enough to do the trick.

For example, using the official Google Pubsub emulator:

gcloud beta emulators pubsub start --host-port=

Any gcloud-rest-pubsub Publisher requests made with that environment variable set will query the emulator instead of the official GCS APIs.

For easier ergonomics, you may be interested in messagebird/gcloud-pubsub-emulator.


Please see our contributing guide.

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