fastkafka 0.1.0rc0

FastKafka is a powerful and easy-to-use Python library for building asynchronous web services that interact with Kafka topics. Built on top of FastAPI, Starlette, Pydantic, AIOKafka and AsyncAPI, FastKafka simplifies the process of writing producers and consumers for Kafka topics.

FastKafka

Effortless Kafka integration for your web services

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FastKafka is a powerful and easy-to-use Python library for building asynchronous web services that interact with Kafka topics. Built on top of FastAPI, Starlette, Pydantic, AIOKafka and AsyncAPI, FastKafka simplifies the process of writing producers and consumers for Kafka topics, handling all the parsing, networking, task scheduling and data generation automatically. With FastKafka, you can quickly prototype and develop high-performance Kafka-based services with minimal code, making it an ideal choice for developers looking to streamline their workflow and accelerate their projects.

Install

FastKafka works on macOS, Linux, and most Unix-style operating systems. You can install it with pip as usual:

pip install fastkafka

Writing server code

Here is an example python script using FastKafka that takes data from a Kafka topic, makes a prediction using a predictive model, and outputs the prediction to another Kafka topic.

Messages

FastKafka uses Pydantic to parse input JSON-encoded data into Python objects, making it easy to work with structured data in your Kafka-based applications. Pydantic’s BaseModel class allows you to define messages using a declarative syntax, making it easy to specify the fields and types of your messages.

This example defines two message classes for use in a FastKafka application:

• The InputData class is used to represent input data for a predictive model. It has three fields: user_id, feature_1, and feature_2. The user_id field is of type NonNegativeInt, which is a subclass of int that only allows non-negative integers. The feature_1 and feature_2 fields are both lists of floating-point numbers and integers, respectively.

• The Prediction class is used to represent the output of the predictive model. It has two fields: user_id and score. The score field is a floating-point number and it represents the prediction made by the model, such as the probability of churn in the next 28 days.

These message classes will be used to parse and validate incoming data in Kafka consumers and producers.

from typing import List
from pydantic import BaseModel, Field, NonNegativeInt

class InputData(BaseModel):
    user_id: NonNegativeInt = Field(..., example=202020, description="ID of a user")
    feature_1: List[float] = Field(
        ...,
        example=[1.2, 2.3, 4.5, 6.7, 0.1],
        description="input feature 1",
    )
    feature_2: List[int] = Field(
        ...,
        example=[2, 4, 3, 1, 0],
        description="input feature 2",
    )

class Prediction(BaseModel):
    user_id: NonNegativeInt = Field(..., example=202020, description="ID of a user")
    score: float = Field(
        ...,
        example=0.4321,
        description="Prediction score (e.g. the probability of churn in the next 28 days)",
        ge=0.0,
        le=1.0,
    )

These message classes will be used to parse and validate incoming data in a Kafka consumer and to produce a JSON-encoded message in a producer. Using Pydantic’s BaseModel in combination with FastKafka makes it easy to work with structured data in your Kafka-based applications.

Application

This example shows how to initialize a FastKafka application.

It starts by defining a dictionary called kafka_brokers, which contains two entries: "localhost" and "production", specifying local development and production Kafka brokers. Each entry specifies the URL, port, and other details of a Kafka broker. This dictionary is used for generating the documentation only and it is not being checked by the actual server.

Next, an object of the FastAPI class is created. It role is to serve the documentation and to start and shutdown FastKafka.

Finally, an object of the FastKafka class is initialized with the minimum set of arguments:

• app: an FastAPI application used for serving the documentation and starting/shutting down the service

• kafka_brokers: a dictionary used for generation of documentation

• bootstrap_servers: a host[:port] string or list of host[:port] strings that a consumer or a producer should contact to bootstrap initial cluster metadata

from os import environ

from fastapi import FastAPI
from fastkafka.application import FastKafka

kafka_brokers = {
    "localhost": {
        "url": "localhost",
        "description": "local development kafka broker",
        "port": 9092,
    },
    "production": {
        "url": "kafka.airt.ai",
        "description": "production kafka broker",
        "port": 9092,
        "protocol": "kafka-secure",
        "security": {"type": "plain"},
    },
}

app = FastAPI(
    title="FastKafka Example",
    contact={"name": "airt.ai", "url": "https://airt.ai", "email": "[e-mail]"},
    version="0.0.1",
    description="A simple example on how to use FastKafka",
)

bootstrap_servers = f"{environ['KAFKA_HOSTNAME']}:{environ['KAFKA_PORT']}"

kafka_app = FastKafka(
    app,
    kafka_brokers=kafka_brokers,
    bootstrap_servers=bootstrap_servers,
)

Function decorators

FastKafka provides convenient function decorators @kafka_app.consumes and @kafka_app.produces to allow you to delegate the actual process of

• consuming and producing data to Kafka, and

• decoding and encoding JSON encode messages

from user defined functions to the framework. The FastKafka framework delegates these jobs to AIOKafka and Pydantic libraries.

These decorators make it easy to specify the processing logic for your Kafka consumers and producers, allowing you to focus on the core business logic of your application without worrying about the underlying Kafka integration.

This following example shows how to use the @kafka_app.consumes and @kafka_app.produces decorators in a FastKafka application:

• The @kafka_app.consumes decorator is applied to the on_input_data function, which specifies that this function should be called whenever a message is received on the “input_data” Kafka topic. The on_input_data function takes a single argument which is expected to be an instance of the InputData message class. Specifying the type of the single argument is instructing the Pydantic to use InputData.parse_raw() on the consumed message before passing it to the user defined function on_input_data.

• The @produces decorator is applied to the to_predictions function, which specifies that this function should produce a message to the “predictions” Kafka topic whenever it is called. The to_predictions function takes two arguments: user_id and score. It creates a new Prediction message with these values and then returns it. The framework will call the Prediction.json().encode("utf-8") function on the returned value and produce it to the specified topic.

@kafka_app.consumes(topic="input_data", auto_offset_reset="latest", group_id="my_group")
async def on_input_data(msg: InputData):
    global model
    score = await model.predict(feature_1=msg.feature_1, feature_2=msg.feature_2)
    await to_predictions(user_id=msg.user_id, score=score)


@kafka_app.produces(topic="predictions")
async def to_predictions(user_id: int, score: float) -> Prediction:
    prediction = Prediction(user_id=user_id, score=score)
    return prediction


# this is a mock up for testing, should be replaced with the real model
class Model:
    async def predict(self, feature_1: List[int], feature_2: List[float]) -> float:
        return 0.87


model = Model()

Running the service

The service can be started using any ASGI web server such as Uvicorn in the same way as starting the FastAPI application.

Starting the service using Uvicorn

This example shows how to start the FastKafka service using Uvicorn. We will concatenate the code snippets from above and save them in a file "server.py"

??? Example

This example contains the content of the file "server.py":

```python

from typing import List
from pydantic import BaseModel, Field, NonNegativeInt

class InputData(BaseModel):
    user_id: NonNegativeInt = Field(..., example=202020, description="ID of a user")
    feature_1: List[float] = Field(
        ...,
        example=[1.2, 2.3, 4.5, 6.7, 0.1],
        description="input feature 1",
    )
    feature_2: List[int] = Field(
        ...,
        example=[2, 4, 3, 1, 0],
        description="input feature 2",
    )

class Prediction(BaseModel):
    user_id: NonNegativeInt = Field(..., example=202020, description="ID of a user")
    score: float = Field(
        ...,
        example=0.4321,
        description="Prediction score (e.g. the probability of churn in the next 28 days)",
        ge=0.0,
        le=1.0,
    )



from os import environ

from fastapi import FastAPI
from fastkafka.application import FastKafka

kafka_brokers = {
    "localhost": {
        "url": "localhost",
        "description": "local development kafka broker",
        "port": 9092,
    },
    "production": {
        "url": "kafka.airt.ai",
        "description": "production kafka broker",
        "port": 9092,
        "protocol": "kafka-secure",
        "security": {"type": "plain"},
    },
}

app = FastAPI(
    title="FastKafka Example",
    contact={"name": "airt.ai", "url": "https://airt.ai", "email": "[e-mail]"},
    version="0.0.1",
    description="A simple example on how to use FastKafka",
)

bootstrap_servers = f"{environ['KAFKA_HOSTNAME']}:{environ['KAFKA_PORT']}"

kafka_app = FastKafka(
    app,
    kafka_brokers=kafka_brokers,
    bootstrap_servers=bootstrap_servers,
)



@kafka_app.consumes(topic="input_data", auto_offset_reset="latest", group_id="my_group")
async def on_input_data(msg: InputData):
    global model
    score = await model.predict(feature_1=msg.feature_1, feature_2=msg.feature_2)
    await to_predictions(user_id=msg.user_id, score=score)


@kafka_app.produces(topic="predictions")
async def to_predictions(user_id: int, score: float) -> Prediction:
    prediction = Prediction(user_id=user_id, score=score)
    return prediction


# this is a mock up for testing, should be replaced with the real model
class Model:
    async def predict(self, feature_1: List[int], feature_2: List[float]) -> float:
        return 0.87


model = Model()


```

We start by generating the documentation for the FastKafka service by running the following command:

fastkafka generate-docs server:kafka_app

Next, we start the Uvicorn server by running:

uvicorn server:app

The meaning of the server:app notation is to import the symbol app from the module "server", typically from a file named "server.py".

[INFO] fastkafka.testing: Generating docs for: server:kafka_app
[INFO] fastkafka._components.asyncapi: Old async specifications at '/tmp/tmp0ns3f279/asyncapi/spec/asyncapi.yml' does not exist.
[INFO] fastkafka._components.asyncapi: New async specifications generated at: '/tmp/tmp0ns3f279/asyncapi/spec/asyncapi.yml'
[INFO] fastkafka._components.asyncapi: Async docs generated at 'asyncapi/docs'
[INFO] fastkafka._components.asyncapi: Output of '$ npx -y -p @asyncapi/generator ag asyncapi/spec/asyncapi.yml @asyncapi/html-template -o asyncapi/docs --force-write'npm WARN deprecated har-validator@5.1.5: this library is no longer supported
npm WARN deprecated uuid@3.4.0: Please upgrade  to version 7 or higher.  Older versions may use Math.random() in certain circumstances, which is known to be problematic.  See https://v8.dev/blog/math-random for details.
npm WARN deprecated request@2.88.2: request has been deprecated, see https://github.com/request/request/issues/3142
npm WARN deprecated readdir-scoped-modules@1.1.0: This functionality has been moved to @npmcli/fs
npm WARN deprecated @npmcli/move-file@1.1.2: This functionality has been moved to @npmcli/fs
npm WARN deprecated mkdirp@0.3.5: Legacy versions of mkdirp are no longer supported. Please update to mkdirp 1.x. (Note that the API surface has changed to use Promises in 1.x.)
npm WARN deprecated mkdirp@0.3.5: Legacy versions of mkdirp are no longer supported. Please update to mkdirp 1.x. (Note that the API surface has changed to use Promises in 1.x.)


Done! ✨
Check out your shiny new generated files at /tmp/tmp0ns3f279/asyncapi/docs.

npm notice 
npm notice New minor version of npm available! 9.2.0 -> 9.3.1
npm notice Changelog: <https://github.com/npm/cli/releases/tag/v9.3.1>
npm notice Run `npm install -g npm@9.3.1` to update!
npm notice 

INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
INFO:     Started parent process [2367]
INFO:     Started server process [2369]
INFO:     Waiting for application startup.
INFO:     Started server process [2370]
INFO:     Waiting for application startup.
[INFO] fastkafka._components.asyncapi: Keeping the old async specifications at: '/tmp/tmp0ns3f279/asyncapi/spec/asyncapi.yml'
[INFO] fastkafka._components.asyncapi: Skipping generating async documentation in '/tmp/tmp0ns3f279/asyncapi/docs'
[INFO] fastkafka.application: _create_producer() : created producer using the config: '{'bootstrap_servers': 'davor-fastkafka-kafka-1:9092'}'
[INFO] fastkafka._components.asyncapi: Keeping the old async specifications at: '/tmp/tmp0ns3f279/asyncapi/spec/asyncapi.yml'
[INFO] fastkafka._components.asyncapi: Skipping generating async documentation in '/tmp/tmp0ns3f279/asyncapi/docs'
[INFO] fastkafka.application: _create_producer() : created producer using the config: '{'bootstrap_servers': 'davor-fastkafka-kafka-1:9092'}'
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop() starting...
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer created using the following parameters: {'bootstrap_servers': 'davor-fastkafka-kafka-1:9092', 'auto_offset_reset': 'latest', 'max_poll_records': 100, 'group_id': 'my_group'}
INFO:     Application startup complete.
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer started.
[INFO] aiokafka.consumer.subscription_state: Updating subscribed topics to: frozenset({'input_data'})
[INFO] aiokafka.consumer.consumer: Subscribed to topic(s): {'input_data'}
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer subscribed.
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop() starting...
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer created using the following parameters: {'bootstrap_servers': 'davor-fastkafka-kafka-1:9092', 'auto_offset_reset': 'latest', 'max_poll_records': 100, 'group_id': 'my_group'}
INFO:     Application startup complete.
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer started.
[INFO] aiokafka.consumer.subscription_state: Updating subscribed topics to: frozenset({'input_data'})
[INFO] aiokafka.consumer.consumer: Subscribed to topic(s): {'input_data'}
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer subscribed.
[ERROR] aiokafka.consumer.group_coordinator: Group Coordinator Request failed: [Error 15] CoordinatorNotAvailableError
[ERROR] aiokafka.cluster: Topic input_data not found in cluster metadata
[ERROR] aiokafka.consumer.group_coordinator: Group Coordinator Request failed: [Error 15] CoordinatorNotAvailableError
[WARNING] aiokafka.cluster: Topic input_data is not available during auto-create initialization
[ERROR] aiokafka.consumer.group_coordinator: Group Coordinator Request failed: [Error 15] CoordinatorNotAvailableError
[WARNING] aiokafka.cluster: Topic input_data is not available during auto-create initialization
[ERROR] aiokafka.consumer.group_coordinator: Group Coordinator Request failed: [Error 15] CoordinatorNotAvailableError
[WARNING] aiokafka.cluster: Topic input_data is not available during auto-create initialization
[INFO] aiokafka.consumer.group_coordinator: Discovered coordinator 1003 for group my_group
[INFO] aiokafka.consumer.group_coordinator: Revoking previously assigned partitions set() for group my_group
[INFO] aiokafka.consumer.group_coordinator: (Re-)joining group my_group
[INFO] aiokafka.consumer.group_coordinator: Discovered coordinator 1003 for group my_group
[INFO] aiokafka.consumer.group_coordinator: Revoking previously assigned partitions set() for group my_group
[INFO] aiokafka.consumer.group_coordinator: (Re-)joining group my_group
[INFO] aiokafka.consumer.group_coordinator: Joined group 'my_group' (generation 1) with member_id aiokafka-0.8.0-b6b4b108-bd0d-4759-bac6-3115811deff9
[INFO] aiokafka.consumer.group_coordinator: Elected group leader -- performing partition assignments using roundrobin
[WARNING] kafka.coordinator.assignors.roundrobin: No partition metadata for topic input_data
[INFO] aiokafka.consumer.group_coordinator: (Re-)joining group my_group
[INFO] aiokafka.consumer.group_coordinator: Joined group 'my_group' (generation 2) with member_id aiokafka-0.8.0-b6b4b108-bd0d-4759-bac6-3115811deff9
[INFO] aiokafka.consumer.group_coordinator: Elected group leader -- performing partition assignments using roundrobin
[WARNING] kafka.coordinator.assignors.roundrobin: No partition metadata for topic input_data
[INFO] aiokafka.consumer.group_coordinator: Joined group 'my_group' (generation 2) with member_id aiokafka-0.8.0-4172ff84-e85a-4f92-ac86-763696c34e7c
[INFO] aiokafka.consumer.group_coordinator: Successfully synced group my_group with generation 2
[INFO] aiokafka.consumer.group_coordinator: Setting newly assigned partitions set() for group my_group
[INFO] aiokafka.consumer.group_coordinator: Successfully synced group my_group with generation 2
[INFO] aiokafka.consumer.group_coordinator: Setting newly assigned partitions set() for group my_group
INFO:     Shutting down
INFO:     Waiting for application shutdown.
[INFO] aiokafka.consumer.group_coordinator: LeaveGroup request succeeded
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer stopped.
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop() finished.
INFO:     Application shutdown complete.
INFO:     Finished server process [2369]
INFO:     Shutting down
INFO:     Waiting for application shutdown.
[INFO] aiokafka.consumer.group_coordinator: LeaveGroup request succeeded
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer stopped.
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop() finished.
INFO:     Application shutdown complete.
INFO:     Finished server process [2370]
INFO:     Stopping parent process [2367]

When the service is started, several log messages are printed to the console, including information about the application startup, AsyncAPI specification generation, and consumer loop status.

During the lifetime of the service, incoming requests will be processed by the FastKafka application and appropriate actions will be taken based on the defined Kafka consumers and producers. For example, if a message is received on the “input_data” Kafka topic, the on_input_data function will be called to process the message, and if the to_predictions function is called, it will produce a message to the “predictions” Kafka topic. The service will continue to run until it is shut down, at which point the application shutdown process will be initiated and the service will stop.

Checking out the documentation

FastKafka automatically generates documentation from functions decorated with @kafka_app.consumes and @kafka_app.produces decorators using AsyncAPI and then uses FastAPI for serving it. When started with Uvicorn in the example above, you will see the following lines logging the generation of the docs:

Done! ✨
Check out your shiny new generated files at //tmp/tmp********/asyncapi/docs.

and for starting the web service for serving them:

INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)

Clicking on the link above redirects to the following url:

http://127.0.0.1:8000/asyncapi/index.html

and opens the web page with the documentation.

The first section of the documentation relates to servers and it is generated from the kafka_brokers parameter passed to the constructor of FastKafka object:

{'localhost': {'description': 'local development kafka broker',
               'port': 9092,
               'url': 'localhost'},
 'production': {'description': 'production kafka broker',
                'port': 9092,
                'protocol': 'kafka-secure',
                'security': {'type': 'plain'},
                'url': 'kafka.airt.ai'}}

The generated documentation is as follows:

Next, you can see the documentation generated from the @consumes decorator when used on the function on_input_data with a single parameter of type InputData:

class InputData(BaseModel):
    user_id: NonNegativeInt = Field(..., example=202020, description="ID of a user")
    feature_1: List[float] = Field(
        ...,
        example=[1.2, 2.3, 4.5, 6.7, 0.1],
        description="input feature 1",
    )
    feature_2: List[int] = Field(
        ...,
        example=[2, 4, 3, 1, 0],
        description="input feature 2",
    )

@kafka_app.consumes(topic="input_data", auto_offset_reset="latest", group_id="my_group")
async def on_input_data(msg: InputData):
    global model
    score = await model.predict(feature_1=msg.feature_1, feature_2=msg.feature_2)
    await to_predictions(user_id=msg.user_id, score=score)

The resulting documentation is generated as follows:

Testing the service

from os import environ

import anyio
import asyncer
from tqdm.notebook import tqdm, trange

from fastkafka.helpers import (
    consumes_messages,
    create_missing_topics,
    produce_messages,
    wait_for_get_url,
)

bootstrap_servers = f"{environ['KAFKA_HOSTNAME']}:{environ['KAFKA_PORT']}"

create_missing_topics(
    ["input_data", "predictions"],
    bootstrap_servers=bootstrap_servers,
)

[INFO] fastkafka.helpers: create_missing_topics(['input_data', 'predictions']): new_topics = [NewTopic(topic=predictions,num_partitions=3)]

msgs = [
    dict(user_id=i, feature_1=[(i / 1_000) ** 2], feature_2=[i % 177])
    for i in trange(100_000, desc="generating messages")
]

async with asyncer.create_task_group() as tg:

    tg.soonify(run_on_uvicorn)(script, cancel_after=45, workers=4)

    await wait_for_get_url(
        "http://127.0.0.1:8000", timeout=30, desc="waiting for uvicorn"
    )

    tg.soonify(consumes_messages)(
        msgs_count=len(msgs), topic="predictions", bootstrap_servers=bootstrap_servers
    )

    await anyio.sleep(2)

    tg.soonify(produce_messages)(
        msgs=msgs, topic="input_data", bootstrap_servers=bootstrap_servers
    )

generating messages:   0%|          | 0/100000 [00:00<?, ?it/s]

waiting for uvicorn:   0%|          | 0/30 [00:00<?, ?it/s]

[INFO] fastkafka.testing: Generating docs for: server:kafka_app
[INFO] fastkafka._components.asyncapi: Old async specifications at '/tmp/tmpt3t46s4d/asyncapi/spec/asyncapi.yml' does not exist.
[INFO] fastkafka._components.asyncapi: New async specifications generated at: '/tmp/tmpt3t46s4d/asyncapi/spec/asyncapi.yml'
[INFO] fastkafka._components.asyncapi: Async docs generated at 'asyncapi/docs'
[INFO] fastkafka._components.asyncapi: Output of '$ npx -y -p @asyncapi/generator ag asyncapi/spec/asyncapi.yml @asyncapi/html-template -o asyncapi/docs --force-write'

Done! ✨
Check out your shiny new generated files at /tmp/tmpt3t46s4d/asyncapi/docs.


[INFO] aiokafka.consumer.subscription_state: Updating subscribed topics to: frozenset({'predictions'})
[INFO] aiokafka.consumer.group_coordinator: Metadata for topic has changed from {} to {'predictions': 3}. 

consuming from 'predictions':   0%|          | 0/100000 [00:00<?, ?it/s]

producing to 'input_data':   0%|          | 0/100000 [00:00<?, ?it/s]

INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
INFO:     Started parent process [2407]
INFO:     Started server process [2410]
INFO:     Waiting for application startup.
INFO:     Started server process [2412]
INFO:     Waiting for application startup.
INFO:     Started server process [2409]
INFO:     Waiting for application startup.
INFO:     Started server process [2411]
INFO:     Waiting for application startup.
[INFO] fastkafka._components.asyncapi: Keeping the old async specifications at: '/tmp/tmpt3t46s4d/asyncapi/spec/asyncapi.yml'
[INFO] fastkafka._components.asyncapi: Skipping generating async documentation in '/tmp/tmpt3t46s4d/asyncapi/docs'
[INFO] fastkafka.application: _create_producer() : created producer using the config: '{'bootstrap_servers': 'davor-fastkafka-kafka-1:9092'}'
[INFO] fastkafka._components.asyncapi: Keeping the old async specifications at: '/tmp/tmpt3t46s4d/asyncapi/spec/asyncapi.yml'
[INFO] fastkafka._components.asyncapi: Skipping generating async documentation in '/tmp/tmpt3t46s4d/asyncapi/docs'
[INFO] fastkafka.application: _create_producer() : created producer using the config: '{'bootstrap_servers': 'davor-fastkafka-kafka-1:9092'}'
[INFO] fastkafka._components.asyncapi: Keeping the old async specifications at: '/tmp/tmpt3t46s4d/asyncapi/spec/asyncapi.yml'
[INFO] fastkafka._components.asyncapi: Skipping generating async documentation in '/tmp/tmpt3t46s4d/asyncapi/docs'
[INFO] fastkafka.application: _create_producer() : created producer using the config: '{'bootstrap_servers': 'davor-fastkafka-kafka-1:9092'}'
[INFO] fastkafka._components.asyncapi: Keeping the old async specifications at: '/tmp/tmpt3t46s4d/asyncapi/spec/asyncapi.yml'
[INFO] fastkafka._components.asyncapi: Skipping generating async documentation in '/tmp/tmpt3t46s4d/asyncapi/docs'
[INFO] fastkafka.application: _create_producer() : created producer using the config: '{'bootstrap_servers': 'davor-fastkafka-kafka-1:9092'}'
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop() starting...
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer created using the following parameters: {'bootstrap_servers': 'davor-fastkafka-kafka-1:9092', 'auto_offset_reset': 'latest', 'max_poll_records': 100, 'group_id': 'my_group'}
INFO:     Application startup complete.
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop() starting...
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer created using the following parameters: {'bootstrap_servers': 'davor-fastkafka-kafka-1:9092', 'auto_offset_reset': 'latest', 'max_poll_records': 100, 'group_id': 'my_group'}
INFO:     Application startup complete.
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer started.
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer started.
[INFO] aiokafka.consumer.subscription_state: Updating subscribed topics to: frozenset({'input_data'})
[INFO] aiokafka.consumer.subscription_state: Updating subscribed topics to: frozenset({'input_data'})
[INFO] aiokafka.consumer.consumer: Subscribed to topic(s): {'input_data'}
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer subscribed.
[INFO] aiokafka.consumer.consumer: Subscribed to topic(s): {'input_data'}
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer subscribed.
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop() starting...
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer created using the following parameters: {'bootstrap_servers': 'davor-fastkafka-kafka-1:9092', 'auto_offset_reset': 'latest', 'max_poll_records': 100, 'group_id': 'my_group'}
INFO:     Application startup complete.
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop() starting...
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer created using the following parameters: {'bootstrap_servers': 'davor-fastkafka-kafka-1:9092', 'auto_offset_reset': 'latest', 'max_poll_records': 100, 'group_id': 'my_group'}
INFO:     Application startup complete.
[INFO] aiokafka.consumer.group_coordinator: Discovered coordinator 1003 for group my_group
[INFO] aiokafka.consumer.group_coordinator: Revoking previously assigned partitions set() for group my_group
[INFO] aiokafka.consumer.group_coordinator: (Re-)joining group my_group
[INFO] aiokafka.consumer.group_coordinator: Joined group 'my_group' (generation 4) with member_id aiokafka-0.8.0-f6481c0e-b301-4ed6-9532-ee2e34977057
[INFO] aiokafka.consumer.group_coordinator: Elected group leader -- performing partition assignments using roundrobin
[INFO] aiokafka.consumer.group_coordinator: Discovered coordinator 1003 for group my_group
[INFO] aiokafka.consumer.group_coordinator: Revoking previously assigned partitions set() for group my_group
[INFO] aiokafka.consumer.group_coordinator: (Re-)joining group my_group
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer started.
[INFO] aiokafka.consumer.group_coordinator: Successfully synced group my_group with generation 4
[INFO] aiokafka.consumer.group_coordinator: Setting newly assigned partitions {TopicPartition(topic='input_data', partition=0)} for group my_group
[INFO] aiokafka.consumer.subscription_state: Updating subscribed topics to: frozenset({'input_data'})
[INFO] aiokafka.consumer.consumer: Subscribed to topic(s): {'input_data'}
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer subscribed.
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer started.
[INFO] aiokafka.consumer.subscription_state: Updating subscribed topics to: frozenset({'input_data'})
[INFO] aiokafka.consumer.consumer: Subscribed to topic(s): {'input_data'}
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer subscribed.
[INFO] aiokafka.consumer.group_coordinator: Discovered coordinator 1003 for group my_group
[INFO] aiokafka.consumer.group_coordinator: Revoking previously assigned partitions set() for group my_group
[INFO] aiokafka.consumer.group_coordinator: (Re-)joining group my_group
[INFO] aiokafka.consumer.group_coordinator: Discovered coordinator 1003 for group my_group
[INFO] aiokafka.consumer.group_coordinator: Revoking previously assigned partitions set() for group my_group
[INFO] aiokafka.consumer.group_coordinator: (Re-)joining group my_group
INFO:     127.0.0.1:47648 - "GET / HTTP/1.1" 307 Temporary Redirect
INFO:     127.0.0.1:47648 - "GET /asyncapi HTTP/1.1" 307 Temporary Redirect
INFO:     127.0.0.1:47648 - "GET /index.html HTTP/1.1" 200 OK
[WARNING] aiokafka.consumer.group_coordinator: Heartbeat failed for group my_group because it is rebalancing
[INFO] aiokafka.consumer.group_coordinator: Revoking previously assigned partitions frozenset({TopicPartition(topic='input_data', partition=0)}) for group my_group
[INFO] aiokafka.consumer.group_coordinator: (Re-)joining group my_group
[INFO] aiokafka.consumer.group_coordinator: Joined group 'my_group' (generation 5) with member_id aiokafka-0.8.0-f6481c0e-b301-4ed6-9532-ee2e34977057
[INFO] aiokafka.consumer.group_coordinator: Elected group leader -- performing partition assignments using roundrobin
[INFO] aiokafka.consumer.group_coordinator: Joined group 'my_group' (generation 5) with member_id aiokafka-0.8.0-97685270-e1f6-4247-91e0-6b00c42919f8
[INFO] aiokafka.consumer.group_coordinator: Joined group 'my_group' (generation 5) with member_id aiokafka-0.8.0-53ab1c22-d188-4aca-bfeb-dad1574be34f
[INFO] aiokafka.consumer.group_coordinator: Joined group 'my_group' (generation 5) with member_id aiokafka-0.8.0-176f0cae-7bad-4620-9955-b182b1f0804f
[INFO] aiokafka.consumer.group_coordinator: Successfully synced group my_group with generation 5
[INFO] aiokafka.consumer.group_coordinator: Setting newly assigned partitions set() for group my_group
[INFO] aiokafka.consumer.group_coordinator: Successfully synced group my_group with generation 5
[INFO] aiokafka.consumer.group_coordinator: Setting newly assigned partitions set() for group my_group
[INFO] aiokafka.consumer.group_coordinator: Successfully synced group my_group with generation 5
[INFO] aiokafka.consumer.group_coordinator: Successfully synced group my_group with generation 5
[INFO] aiokafka.consumer.group_coordinator: Setting newly assigned partitions set() for group my_group
[INFO] aiokafka.consumer.group_coordinator: Setting newly assigned partitions {TopicPartition(topic='input_data', partition=0)} for group my_group
INFO:     Shutting down
INFO:     Waiting for application shutdown.
[INFO] aiokafka.consumer.group_coordinator: LeaveGroup request succeeded
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer stopped.
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop() finished.
INFO:     Application shutdown complete.
INFO:     Finished server process [2409]
INFO:     Shutting down
[WARNING] aiokafka.consumer.group_coordinator: Heartbeat failed for group my_group because it is rebalancing
[INFO] aiokafka.consumer.group_coordinator: Revoking previously assigned partitions frozenset({TopicPartition(topic='input_data', partition=0)}) for group my_group
[INFO] aiokafka.consumer.group_coordinator: (Re-)joining group my_group
[WARNING] aiokafka.consumer.group_coordinator: Heartbeat failed for group my_group because it is rebalancing
[INFO] aiokafka.consumer.group_coordinator: Revoking previously assigned partitions frozenset() for group my_group
[INFO] aiokafka.consumer.group_coordinator: (Re-)joining group my_group
[WARNING] aiokafka.consumer.group_coordinator: Heartbeat failed for group my_group because it is rebalancing
[INFO] aiokafka.consumer.group_coordinator: Revoking previously assigned partitions frozenset() for group my_group
[INFO] aiokafka.consumer.group_coordinator: (Re-)joining group my_group
[INFO] aiokafka.consumer.group_coordinator: Joined group 'my_group' (generation 6) with member_id aiokafka-0.8.0-f6481c0e-b301-4ed6-9532-ee2e34977057
[INFO] aiokafka.consumer.group_coordinator: Joined group 'my_group' (generation 6) with member_id aiokafka-0.8.0-97685270-e1f6-4247-91e0-6b00c42919f8
[INFO] aiokafka.consumer.group_coordinator: Elected group leader -- performing partition assignments using roundrobin
[INFO] aiokafka.consumer.group_coordinator: Joined group 'my_group' (generation 6) with member_id aiokafka-0.8.0-176f0cae-7bad-4620-9955-b182b1f0804f
[INFO] aiokafka.consumer.group_coordinator: Successfully synced group my_group with generation 6
[INFO] aiokafka.consumer.group_coordinator: Successfully synced group my_group with generation 6
[INFO] aiokafka.consumer.group_coordinator: Successfully synced group my_group with generation 6
[INFO] aiokafka.consumer.group_coordinator: Setting newly assigned partitions set() for group my_group
[INFO] aiokafka.consumer.group_coordinator: Setting newly assigned partitions {TopicPartition(topic='input_data', partition=0)} for group my_group
[INFO] aiokafka.consumer.group_coordinator: Setting newly assigned partitions set() for group my_group
INFO:     Waiting for application shutdown.
[INFO] aiokafka.consumer.group_coordinator: LeaveGroup request succeeded
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer stopped.
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop() finished.
INFO:     Application shutdown complete.
INFO:     Finished server process [2410]
INFO:     Shutting down
INFO:     Waiting for application shutdown.
[INFO] aiokafka.consumer.group_coordinator: LeaveGroup request succeeded
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer stopped.
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop() finished.
INFO:     Application shutdown complete.
INFO:     Finished server process [2411]
INFO:     Shutting down
INFO:     Waiting for application shutdown.
[INFO] aiokafka.consumer.group_coordinator: LeaveGroup request succeeded
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer stopped.
[INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop() finished.
INFO:     Application shutdown complete.
INFO:     Finished server process [2412]
INFO:     Stopping parent process [2407]