Agents - Self-organizing Stream Processors

What is an Agent?

An agent is a distributed system processing the events in a stream.

Every event is a message in the stream and is structured as a key/value pair that can be described using models for type safety and straightforward serialization support.

Streams can be divided equally in a round-robin manner, or partitioned by the message key; this decides how the stream divides between available agent instances in the cluster.

Create an agent

To create an agent, you need to use the @app.agent decorator on an async function taking a stream as the argument. Further, it must iterate over the stream using the async for keyword to process the stream:

# faustexample.py

import faust

app = faust.App('example', broker='kafka://localhost:9092')


@app.agent()
async def myagent(stream):
    async for event in stream:
        ...  # process event
Start a worker for the agent

The faust worker program can be used to start a worker from the same directory as the faustexample.py file:

$ faust -A faustexample worker -l info

Whenever a worker is started or stopped, this will force the cluster to rebalance and divide available partitions between all the workers.

Fault tolerance

If the worker for a partition fails, or is blocked from the network for any reason, there’s no need to worry because Kafka will move that partition to a worker that’s online.

Faust also takes advantage of “standby tables” and a custom partition manager that prefers to promote any node with a full copy of the data, saving startup time and ensuring availability.

This is an agent that adds numbers (full example):

# examples/agent.py
import faust

# The model describes the data sent to our agent,
# We will use a JSON serialized dictionary
# with two integer fields: a, and b.
class Add(faust.Record):
    a: int
    b: int

# Next, we create the Faust application object that
# configures our environment.
app = faust.App('agent-example')

# The Kafka topic used by our agent is named 'adding',
# and we specify that the values in this topic are of the Add model.
# (you can also specify the key_type if your topic uses keys).
topic = app.topic('adding', value_type=Add)

@app.agent(topic)
async def adding(stream):
    async for value in stream:
        # here we receive Add objects, add a + b.
        yield value.a + value.b

Starting a worker will start a single instance of this agent:

$ faust -A examples.agent worker -l info

To send values to it, open a second console to run this program:

# examples/send_to_agent.py
import asyncio
from .agent import Add, adding

async def send_value() -> None:
    print(await adding.ask(Add(a=4, b=4)))

if __name__ == '__main__':
    loop = asyncio.get_event_loop()
    loop.run_until_complete(send_value())

$ python examples/send_to_agent.py

The Agent.ask() method adds additional metadata to the message: the return address (reply-to) and a correlating id (correlation_id).

When the agent sees a message with a return address, it will reply with the result generated from that request.

Static types

Faust is typed using the type annotations available in Python 3.6, and can be checked using the mypy type checker.

Add type hints to your agent function like this:

from typing import AsyncIterable
from faust import StreamT

@app.agent(topic)
async def adding(stream: StreamT[Add]) -> AsyncIterable[int]:
    async for value in stream:
        yield value.a + value.b

The StreamT type used for the agent’s stream argument is a subclass of AsyncIterable extended with the stream API. You could type this call using AsyncIterable, but then https://pypi.org/project/mypy/ would stop you with a typing error should you use stream-specific methods such as .group_by(), through(), etc.

Defining Agents

The Channel

The channel argument to the agent decorator defines the source of events that the agent reads from.

This can be:

  • A channel

    Channels are in-memory, and work like a asyncio.Queue.

    They also form a basic abstraction useful for integrating with many messaging systems (RabbitMQ, Redis, ZeroMQ, etc.)

  • A topic description (as returned by app.topic())

    Describes one or more topics to subscribe to, including a recipe of how to deserialize it:

    topic = app.topic('topic_name1', 'topic_name2',
                  key_type=Model,
                  value_type=Model,
                  ...)

    Should the topic description provide multiple topic names, the main topic of the agent will be the first topic in that list ("topic_name1").

    The key_type and value_type describe how to serialize and deserialize messages in the topic, and you provide it as a model (such as faust.Record), a faust.Codec, or the name of a serializer.

    If not specified it will use the default serializer defined by the app.

Tip

If you don’t specify a topic, the agent will use the agent name as the topic: the name will be the fully qualified name of the agent function (e.g., examples.agent.adder).

See also

The Stream

The agent decorator expects a function taking a single argument (unary).

The stream passed in as the argument to the agent is an async iterable Stream instance, created from the topic/channel provided to the decorator:

@app.agent(topic_or_channel)
async def myagent(stream):
    async for item in stream:
        ...

Iterating over this stream, using the async for keyword will iterate over messages in the topic/channel.

If you need to repartition the stream, you may use the group_by() method of the Stream API, like in this example where we repartition by account ID:

# examples/groupby.py
import faust

class BankTransfer(faust.Record):
    account_id: str
    amount: float

app = faust.App('groupby')
topic = app.topic('groupby', value_type=BankTransfer)

@app.agent(topic)
async def stream(s):
    async for transfer in s.group_by(BankTransfer.account_id):
        # transfers will now be distributed such that transfers
        # with the same account_id always arrives to the same agent
        # instance
        ...

See also

Concurrency

Use the concurrency argument to start multiple instances of an agent on every worker instance. Each agent instance (actor) will process items in the stream concurrently (and in no particular order).

Warning

Concurrent instances of an agent will process the stream out-of-order, so you cannot mutate tables from within the agent function:

An agent having concurrency > 1, can only read from a table, never write.

Here’s an agent example that can safely process the stream out of order.

Our hypothetical backend system publishes a message to the Kafka “news” topic every time a news article is published by an author.

We define an agent that consumes from this topic and for every new article will retrieve the full article over HTTP, then store that in a database:

class Article(faust.Record, isodates=True):
    url: str
    date_published: datetime

news_topic = app.topic('news', value_type=Article)

@app.agent(news_topic, concurrency=10)
async def imports_news(articles):
    async for article in articles:
        async with app.http_client.get(article.url) as response:
            await store_article_in_db(response)

Sinks

Sinks can be used to perform additional actions after an agent has processed an event in the stream, such as forwarding alerts to a monitoring system, logging to Slack, etc. A sink can be callable, async callable, a topic/channel or another agent.

Function Callback

Regular functions take a single argument (the result after processing):

def mysink(value):
    print(f'AGENT YIELD: {value!r}')

@app.agent(sink=[mysink])
async def myagent(stream):
    async for value in stream:
        yield process_value(value)
Async Function Callback

Asynchronous functions also work:

async def mysink(value):
    print(f'AGENT YIELD: {value!r}')
    # OBS This will force the agent instance that yielded this value
    # to sleep for 1.0 second before continuing on the next event
    # in the stream.
    await asyncio.sleep(1)

@app.agent(sink=[mysink])
async def myagent(stream):
    ...
Topic

Specifying a topic as the sink means the agent will forward all processed values to that topic:

agent_log_topic = app.topic('agent_log')

@app.agent(sink=[agent_log_topic])
async def myagent(stream):
    ...
Another Agent

Specifying another agent as the sink means the agent will forward all processed values to that other agent:

@app.agent()
async def agent_b(stream):
    async for event in stream:
        print(f'AGENT B RECEIVED: {event!r}')

@app.agent(sink=[agent_b])
async def agent_a(stream):
    async for event in stream:
        print(f'AGENT A RECEIVED: {event!r}')

When agents raise an error

If an agent raises an exception during processing of an event will we mark that event as completed? (acked)

Currently the source message will be acked and not processed again, simply because it violates “”exactly-once” semantics”.

It is common to think that we can just retry that event, but it is not as easy as it seems. Let’s analyze our options apart from marking the event as complete.

  • Retrying

    The retry would have to stop processing of the topic so that order is maintained: the next offset in the topic can only be processed after the event is retried.

    We can move the event to the “back of the queue”, but that means the topic is now out of order.

  • Crashing

    Crashing the instance to require human intervention is a choice, but far from ideal considering how common mistakes in code and unexpected exceptions are. It may be better to log the error and have ops replay and reprocess the stream on notification.

Using Agents

Cast or Ask?

When communicating with an agent, you can ask for the result of the request to be forwarded to another topic: this is the reply_to topic.

The reply_to topic may be the topic of another agent, a source topic populated by a different system, or it may be a local ephemeral topic collecting replies to the current process.

If you perform a cast, you’re passively sending something to the agent, and it will not reply back.

Systems perform better when no synchronization is required, so you should try to solve your problems in a streaming manner. If B needs to happen after A, try to have A call B instead (which could be accomplished using reply_to=B).

cast(value, *, key=None, partition=None)

A cast is non-blocking as it will not wait for a reply:

await adder.cast(Add(a=2, b=2))

The agent will receive the request, but it will not send a reply.

ask(value, *, key=None, partition=None, reply_to=None, correlation_id=None)

Asking an agent will send a reply back to process that sent the request:

value = await adder.ask(Add(a=2, b=2))
assert value == 4
send(key, value, partition, reply_to=None, correlation_id=None)

The Agent.send method is the underlying mechanism used by cast and ask.

Use it to send the reply to another agent:

await adder.send(value=Add(a=2, b=2), reply_to=another_agent)

Streaming Map/Reduce

These map/reduce operations are shortcuts used to stream lots of values into agents while at the same time gathering the results.

map streams results as they come in (out-of-order), and join waits until all the steps are complete (back-to-order) and return the results in a list with order preserved:

map(values: Union[AsyncIterable[V], Iterable[V]])

Map takes an async iterable, or a regular iterable, and returns an async iterator yielding results as they come in:

async for reply in agent.map([1, 2, 3, 4, 5, 6, 7, 8]):
    print(f'RECEIVED REPLY: {reply!r}')

The iterator will start before all the messages have been sent, and should be efficient even for infinite lists.

As the map executes concurrently, the replies will not appear in any particular order.

kvmap(items: Union[AsyncIterable[Tuple[K, V], Iterable[Tuple[K, V]]]])

Same as map, but takes an async iterable/iterable of (key, value) tuples, where the key in each pair is used as the Kafka message key.

join(values: Union[AsyncIterable[V], Iterable[V]])

Join works like map but will wait until all of the values have been processed and returns them as a list in the original order.

The await will continue only after the map sequence is over, and all results are accounted for, so do not attempt to use join together with infinite data structures ;-)

results = await pow2.join([1, 2, 3, 4, 5, 6, 7, 8])
assert results == [1, 4, 9, 16, 25, 36, 49, 64]
kvjoin(items: Union[AsyncIterable[Tuple[K, V]], Iterable[Tuple[K, V]]])

Same as join, but takes an async iterable/iterable of (key, value) tuples, where the key in each pair is used as the message key.