![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
\n\n\n\n## Writing server code\n\nTo demonstrate FastKafka simplicity of using `@produces` and `@consumes`\ndecorators, we will focus on a simple app.\n\nThe app will consume JSON messages containing positive floats from one topic, log\nthem, and then produce incremented values to another topic.\n\n### Messages\n\nFastKafka uses [Pydantic](https://docs.pydantic.dev/) to parse input\nJSON-encoded data into Python objects, making it easy to work with\nstructured data in your Kafka-based applications. Pydantic’s\n[`BaseModel`](https://docs.pydantic.dev/usage/models/) class allows you\nto define messages using a declarative syntax, making it easy to specify\nthe fields and types of your messages.\n\nThis example defines one `Data` mesage class. This Class will model the\nconsumed and produced data in our app demo, it contains one\n`NonNegativeFloat` field `data` that will be logged and “processed”\nbefore being produced to another topic.\n\nThese message class will be used to parse and validate incoming data in\nKafka consumers and producers.\n\n``` python\nfrom pydantic import BaseModel, Field, NonNegativeFloat\n\n\nclass Data(BaseModel):\n data: NonNegativeFloat = Field(\n ..., example=0.5, description=\"Float data example\"\n )\n```\n\n### Application\n\nThis example shows how to initialize a FastKafka application.\n\nIt starts by defining a dictionary called `kafka_brokers`, which\ncontains two entries: `\"localhost\"` and `\"production\"`, specifying local\ndevelopment and production Kafka brokers. Each entry specifies the URL,\nport, and other details of a Kafka broker. This dictionary is used for\nboth generating the documentation and later to run the actual server\nagainst one of the given kafka broker.\n\nNext, an object of the\n[`FastKafka`](https://airtai.github.io/fastkafka/docs/api/fastkafka#fastkafka.FastKafka)\nclass is initialized with the minimum set of arguments:\n\n- `kafka_brokers`: a dictionary used for generation of documentation\n\nWe will also import and create a logger so that we can log the incoming\ndata in our consuming function.\n\n``` python\nfrom logging import getLogger\nfrom fastkafka import FastKafka\n\nlogger = getLogger(\"Demo Kafka app\")\n\nkafka_brokers = {\n \"localhost\": {\n \"url\": \"localhost\",\n \"description\": \"local development kafka broker\",\n \"port\": 9092,\n },\n \"production\": {\n \"url\": \"kafka.airt.ai\",\n \"description\": \"production kafka broker\",\n \"port\": 9092,\n \"protocol\": \"kafka-secure\",\n \"security\": {\"type\": \"plain\"},\n },\n}\n\nkafka_app = FastKafka(\n title=\"Demo Kafka app\",\n kafka_brokers=kafka_brokers,\n)\n```\n\n### Function decorators\n\nFastKafka provides convenient function decorators `@kafka_app.consumes`\nand `@kafka_app.produces` to allow you to delegate the actual process of\n\n- consuming and producing data to Kafka, and\n\n- decoding and encoding JSON messages\n\nfrom user defined functions to the framework. The FastKafka framework\ndelegates these jobs to AIOKafka and Pydantic libraries.\n\nThese decorators make it easy to specify the processing logic for your\nKafka consumers and producers, allowing you to focus on the core\nbusiness logic of your application without worrying about the underlying\nKafka integration.\n\nThis following example shows how to use the `@kafka_app.consumes` and\n`@kafka_app.produces` decorators in a FastKafka application:\n\n- The `@kafka_app.consumes` decorator is applied to the `on_input_data`\n function, which specifies that this function should be called whenever\n a message is received on the “input_data” Kafka topic. The\n `on_input_data` function takes a single argument which is expected to\n be an instance of the `Data` message class. Specifying the type of the\n single argument is instructing the Pydantic to use `Data.parse_raw()`\n on the consumed message before passing it to the user defined function\n `on_input_data`.\n\n- The `@produces` decorator is applied to the `to_output_data` function,\n which specifies that this function should produce a message to the\n “output_data” Kafka topic whenever it is called. The `to_output_data`\n function takes a single float argument `data`. It it increments the\n data returns it wrapped in a `Data` object. The framework will call\n the `Data.json().encode(\"utf-8\")` function on the returned value and\n produce it to the specified topic.\n\n``` python\n@kafka_app.consumes(topic=\"input_data\", auto_offset_reset=\"latest\")\nasync def on_input_data(msg: Data):\n logger.info(f\"Got data: {msg.data}\")\n await to_output_data(msg.data)\n\n\n@kafka_app.produces(topic=\"output_data\")\nasync def to_output_data(data: float) -> Data:\n processed_data = Data(data=data+1.0)\n return processed_data\n```\n\n## Testing the service\n\nThe service can be tested using the\n[`Tester`](https://airtai.github.io/fastkafka/docs/api/fastkafka/testing/Tester#fastkafka.testing.Tester)\ninstances which internally starts InMemory implementation of Kafka\nbroker.\n\nThe Tester will redirect your consumes and produces decorated functions\nto the InMemory Kafka broker so that you can quickly test your app\nwithout the need for a running Kafka broker and all its dependencies.\n\n``` python\nfrom fastkafka.testing import Tester\n\nmsg = Data(\n data=0.1,\n)\n\n# Start Tester app and create InMemory Kafka broker for testing\nasync with Tester(kafka_app) as tester:\n # Send Data message to input_data topic\n await tester.to_input_data(msg)\n\n # Assert that the kafka_app responded with incremented data in output_data topic\n await tester.awaited_mocks.on_output_data.assert_awaited_with(\n Data(data=1.1), timeout=2\n )\n```\n\n [INFO] fastkafka._testing.in_memory_broker: InMemoryBroker._start() called\n [INFO] fastkafka._testing.in_memory_broker: InMemoryBroker._patch_consumers_and_producers(): Patching consumers and producers!\n [INFO] fastkafka._testing.in_memory_broker: InMemoryBroker starting\n [INFO] fastkafka._application.app: _create_producer() : created producer using the config: '{'bootstrap_servers': 'localhost:9092'}'\n [INFO] fastkafka._testing.in_memory_broker: AIOKafkaProducer patched start() called()\n [INFO] fastkafka._application.app: _create_producer() : created producer using the config: '{'bootstrap_servers': 'localhost:9092'}'\n [INFO] fastkafka._testing.in_memory_broker: AIOKafkaProducer patched start() called()\n [INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop() starting...\n [INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer created using the following parameters: {'bootstrap_servers': 'localhost:9092', 'auto_offset_reset': 'latest', 'max_poll_records': 100}\n [INFO] fastkafka._testing.in_memory_broker: AIOKafkaConsumer patched start() called()\n [INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer started.\n [INFO] fastkafka._testing.in_memory_broker: AIOKafkaConsumer patched subscribe() called\n [INFO] fastkafka._testing.in_memory_broker: AIOKafkaConsumer.subscribe(), subscribing to: ['input_data']\n [INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer subscribed.\n [INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop() starting...\n [INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer created using the following parameters: {'bootstrap_servers': 'localhost:9092', 'auto_offset_reset': 'earliest', 'max_poll_records': 100}\n [INFO] fastkafka._testing.in_memory_broker: AIOKafkaConsumer patched start() called()\n [INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer started.\n [INFO] fastkafka._testing.in_memory_broker: AIOKafkaConsumer patched subscribe() called\n [INFO] fastkafka._testing.in_memory_broker: AIOKafkaConsumer.subscribe(), subscribing to: ['output_data']\n [INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer subscribed.\n [INFO] Demo Kafka app: Got data: 0.1\n [INFO] fastkafka._testing.in_memory_broker: AIOKafkaConsumer patched stop() called\n [INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer stopped.\n [INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop() finished.\n [INFO] fastkafka._testing.in_memory_broker: AIOKafkaProducer patched stop() called\n [INFO] fastkafka._testing.in_memory_broker: AIOKafkaConsumer patched stop() called\n [INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer stopped.\n [INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop() finished.\n [INFO] fastkafka._testing.in_memory_broker: AIOKafkaProducer patched stop() called\n [INFO] fastkafka._testing.in_memory_broker: InMemoryBroker._stop() called\n [INFO] fastkafka._testing.in_memory_broker: InMemoryBroker stopping\n\n### Recap\n\nWe have created a simple FastKafka application. The app will consume the\n`Data` from the `input_data` topic, log it and produce the incremented\ndata to `output_data` topic.\n\nTo test the app we have:\n\n1. Created the app\n\n2. Started our Tester class which mirrors the developed app topics for\n testing purposes\n\n3. Sent Data message to `input_data` topic\n\n4. Asserted and checked that the developed service has reacted to Data\n message\n\n## Running the service\n\nThe service can be started using builtin faskafka run CLI command.\nBefore we can do that, we will concatenate the code snippets from above\nand save them in a file `\"application.py\"`\n\n``` python\n# content of the \"application.py\" file\n\nfrom pydantic import BaseModel, Field, NonNegativeFloat\n\nfrom fastkafka import FastKafka\nfrom fastkafka._components.logger import get_logger\n\nlogger = get_logger(__name__)\n\nclass Data(BaseModel):\n data: NonNegativeFloat = Field(\n ..., example=0.5, description=\"Float data example\"\n )\n\nkafka_brokers = {\n \"localhost\": {\n \"url\": \"localhost\",\n \"description\": \"local development kafka broker\",\n \"port\": 9092,\n },\n \"production\": {\n \"url\": \"kafka.airt.ai\",\n \"description\": \"production kafka broker\",\n \"port\": 9092,\n \"protocol\": \"kafka-secure\",\n \"security\": {\"type\": \"plain\"},\n },\n}\n\nkafka_app = FastKafka(\n title=\"Demo Kafka app\",\n kafka_brokers=kafka_brokers,\n)\n\n@kafka_app.consumes(topic=\"input_data\", auto_offset_reset=\"latest\")\nasync def on_input_data(msg: Data):\n logger.info(f\"Got data: {msg.data}\")\n await to_output_data(msg.data)\n\n\n@kafka_app.produces(topic=\"output_data\")\nasync def to_output_data(data: float) -> Data:\n processed_data = Data(data=data+1.0)\n return processed_data\n```\n\nTo run the service, use the FastKafka CLI command and pass the module\n(in this case, the file where the app implementation is located) and the\napp simbol to the command.\n\n``` sh\nfastkafka run --num-workers=1 --kafka-broker localhost application:kafka_app\n```\n\nAfter running the command, you should see the following output in your\ncommand line:\n\n [1504]: 23-05-31 11:36:45.874 [INFO] fastkafka._application.app: set_kafka_broker() : Setting bootstrap_servers value to 'localhost:9092'\n [1504]: 23-05-31 11:36:45.875 [INFO] fastkafka._application.app: _create_producer() : created producer using the config: '{'bootstrap_servers': 'localhost:9092'}'\n [1504]: 23-05-31 11:36:45.937 [INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop() starting...\n [1504]: 23-05-31 11:36:45.937 [INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer created using the following parameters: {'bootstrap_servers': 'localhost:9092', 'auto_offset_reset': 'latest', 'max_poll_records': 100}\n [1504]: 23-05-31 11:36:45.956 [INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer started.\n [1504]: 23-05-31 11:36:45.956 [INFO] aiokafka.consumer.subscription_state: Updating subscribed topics to: frozenset({'input_data'})\n [1504]: 23-05-31 11:36:45.956 [INFO] aiokafka.consumer.consumer: Subscribed to topic(s): {'input_data'}\n [1504]: 23-05-31 11:36:45.956 [INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer subscribed.\n [1506]: 23-05-31 11:36:45.993 [INFO] fastkafka._application.app: set_kafka_broker() : Setting bootstrap_servers value to 'localhost:9092'\n [1506]: 23-05-31 11:36:45.994 [INFO] fastkafka._application.app: _create_producer() : created producer using the config: '{'bootstrap_servers': 'localhost:9092'}'\n [1506]: 23-05-31 11:36:46.014 [INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop() starting...\n [1506]: 23-05-31 11:36:46.015 [INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer created using the following parameters: {'bootstrap_servers': 'localhost:9092', 'auto_offset_reset': 'latest', 'max_poll_records': 100}\n [1506]: 23-05-31 11:36:46.040 [INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer started.\n [1506]: 23-05-31 11:36:46.042 [INFO] aiokafka.consumer.subscription_state: Updating subscribed topics to: frozenset({'input_data'})\n [1506]: 23-05-31 11:36:46.043 [INFO] aiokafka.consumer.consumer: Subscribed to topic(s): {'input_data'}\n [1506]: 23-05-31 11:36:46.043 [INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer subscribed.\n [1506]: 23-05-31 11:36:46.068 [ERROR] aiokafka.cluster: Topic input_data not found in cluster metadata\n [1506]: 23-05-31 11:36:46.070 [INFO] aiokafka.consumer.group_coordinator: Metadata for topic has changed from {} to {'input_data': 0}. \n [1504]: 23-05-31 11:36:46.131 [WARNING] aiokafka.cluster: Topic input_data is not available during auto-create initialization\n [1504]: 23-05-31 11:36:46.132 [INFO] aiokafka.consumer.group_coordinator: Metadata for topic has changed from {} to {'input_data': 0}. \n [1506]: 23-05-31 11:37:00.237 [ERROR] aiokafka: Unable connect to node with id 0: [Errno 111] Connect call failed ('172.28.0.12', 9092)\n [1506]: 23-05-31 11:37:00.237 [ERROR] aiokafka: Unable to update metadata from [0]\n [1504]: 23-05-31 11:37:00.238 [ERROR] aiokafka: Unable connect to node with id 0: [Errno 111] Connect call failed ('172.28.0.12', 9092)\n [1504]: 23-05-31 11:37:00.238 [ERROR] aiokafka: Unable to update metadata from [0]\n [1506]: 23-05-31 11:37:00.294 [INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer stopped.\n [1506]: 23-05-31 11:37:00.294 [INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop() finished.\n Starting process cleanup, this may take a few seconds...\n 23-05-31 11:37:00.345 [INFO] fastkafka._server: terminate_asyncio_process(): Terminating the process 1504...\n 23-05-31 11:37:00.345 [INFO] fastkafka._server: terminate_asyncio_process(): Terminating the process 1506...\n [1504]: 23-05-31 11:37:00.347 [INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop(): Consumer stopped.\n [1504]: 23-05-31 11:37:00.347 [INFO] fastkafka._components.aiokafka_consumer_loop: aiokafka_consumer_loop() finished.\n 23-05-31 11:37:00.607 [INFO] fastkafka._server: terminate_asyncio_process(): Process 1506 was already terminated.\n 23-05-31 11:37:00.822 [INFO] fastkafka._server: terminate_asyncio_process(): Process 1504 was already terminated.\n\n## Documentation\n\nThe kafka app comes with builtin documentation generation using\n[AsyncApi HTML generator](https://www.asyncapi.com/tools/generator).\n\nAsyncApi requires Node.js to be installed and we provide the following\nconvenience command line for it:\n\n``` sh\nfastkafka docs install_deps\n```\n\n 23-05-31 11:38:24.128 [INFO] fastkafka._components.docs_dependencies: AsyncAPI generator installed\n\nTo generate the documentation programatically you just need to call the\nfollowing command:\n\n``` sh\nfastkafka docs generate application:kafka_app\n```\n\n 23-05-31 11:38:25.113 [INFO] fastkafka._components.asyncapi: Old async specifications at '/content/asyncapi/spec/asyncapi.yml' does not exist.\n 23-05-31 11:38:25.118 [INFO] fastkafka._components.asyncapi: New async specifications generated at: '/content/asyncapi/spec/asyncapi.yml'\n 23-05-31 11:38:43.455 [INFO] fastkafka._components.asyncapi: Async docs generated at 'asyncapi/docs'\n 23-05-31 11:38:43.455 [INFO] fastkafka._components.asyncapi: Output of '$ npx -y -p @asyncapi/generator ag asyncapi/spec/asyncapi.yml @asyncapi/html-template -o asyncapi/docs --force-write'\n\n Done! ✨\n Check out your shiny new generated files at /content/asyncapi/docs.\n\nThis will generate the *asyncapi* folder in relative path where all your\ndocumentation will be saved. You can check out the content of it with:\n\n``` sh\nls -l asyncapi\n```\n\n total 8\n drwxr-xr-x 4 root root 4096 May 31 11:38 docs\n drwxr-xr-x 2 root root 4096 May 31 11:38 spec\n\nIn docs folder you will find the servable static html file of your\ndocumentation. This can also be served using our `fastkafka docs serve`\nCLI command (more on that in our guides).\n\nIn spec folder you will find a asyncapi.yml file containing the async\nAPI specification of your application.\n\nWe can locally preview the generated documentation by running the\nfollowing command:\n\n``` sh\nfastkafka docs serve application:kafka_app\n```\n\n 23-05-31 11:38:45.250 [INFO] fastkafka._components.asyncapi: New async specifications generated at: '/content/asyncapi/spec/asyncapi.yml'\n 23-05-31 11:39:04.410 [INFO] fastkafka._components.asyncapi: Async docs generated at 'asyncapi/docs'\n 23-05-31 11:39:04.411 [INFO] fastkafka._components.asyncapi: Output of '$ npx -y -p @asyncapi/generator ag asyncapi/spec/asyncapi.yml @asyncapi/html-template -o asyncapi/docs --force-write'\n\n Done! ✨\n Check out your shiny new generated files at /content/asyncapi/docs.\n\n\n Serving documentation on http://127.0.0.1:8000\n 127.0.0.1 - - [31/May/2023 11:39:14] \"GET / HTTP/1.1\" 200 -\n 127.0.0.1 - - [31/May/2023 11:39:14] \"GET /css/global.min.css HTTP/1.1\" 200 -\n 127.0.0.1 - - [31/May/2023 11:39:14] \"GET /js/asyncapi-ui.min.js HTTP/1.1\" 200 -\n 127.0.0.1 - - [31/May/2023 11:39:14] \"GET /css/asyncapi.min.css HTTP/1.1\" 200 -\n Interupting serving of documentation and cleaning up...\n\nFrom the parameters passed to the application constructor, we get the\ndocumentation bellow:\n\n``` python\nfrom fastkafka import FastKafka\n\nkafka_brokers = {\n \"localhost\": {\n \"url\": \"localhost\",\n \"description\": \"local development kafka broker\",\n \"port\": 9092,\n },\n \"production\": {\n \"url\": \"kafka.airt.ai\",\n \"description\": \"production kafka broker\",\n \"port\": 9092,\n \"protocol\": \"kafka-secure\",\n \"security\": {\"type\": \"plain\"},\n },\n}\n\nkafka_app = FastKafka(\n title=\"Demo Kafka app\",\n kafka_brokers=kafka_brokers,\n)\n```\n\n\n\nThe following documentation snippet are for the consumer as specified in\nthe code above:\n\n\n\nThe following documentation snippet are for the producer as specified in\nthe code above:\n\n\n\nFinally, all messages as defined as subclasses of *BaseModel* are\ndocumented as well:\n\n](https://raw.githubusercontent.com/airtai/fastkafka/main/nbs/images/screenshot-kafka-messages.png)\n\n## License\n\nFastKafka is licensed under the Apache License 2.0\n\nA permissive license whose main conditions require preservation of\ncopyright and license notices. Contributors provide an express grant of\npatent rights. Licensed works, modifications, and larger works may be\ndistributed under different terms and without source code.\n\nThe full text of the license can be found\n[here](https://raw.githubusercontent.com/airtai/fastkafka/main/LICENSE).\n"
},
{
"path": "docker/.semgrepignore",
"content": "dev.yml\n\n"
},
{
"path": "docker/dev.yml",
"content": "version: \"3\"\nservices:\n fastkafka-devel: #nosemgrep\n image: ghcr.io/airtai/nbdev-mkdocs\n hostname: $DOCKER_COMPOSE_PROJECT-devel\n container_name: $DOCKER_COMPOSE_PROJECT-devel\n ports:\n - \"${PORT_PREFIX}8888:8888\"\n - \"${PORT_PREFIX}4000:4000\"\n - \"${PORT_PREFIX}6006:6006\"\n volumes:\n - $AIRT_PROJECT:/work/fastkafka\n - /etc/passwd:/etc/passwd\n - /etc/group:/etc/group\n - /etc/shadow:/etc/shadow\n - $HOME/.ssh:$HOME/.ssh\n - $HOME/.gitconfig:/root/.gitconfig\n environment:\n USER: $USER\n USERNAME: $USERNAME\n PRESERVE_ENVS: $PRESERVE_ENVS\n OPENAI_API_KEY: $OPENAI_API_KEY\n"
},
{
"path": "docusaurus/babel.config.js",
"content": "module.exports = {\n presets: [require.resolve('@docusaurus/core/lib/babel/preset')],\n};\n"
},
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"path": "docusaurus/docusaurus.config.js",
"content": "// @ts-check\n// Note: type annotations allow type checking and IDEs autocompletion\n\nconst lightCodeTheme = require('prism-react-renderer/themes/github');\nconst darkCodeTheme = require('prism-react-renderer/themes/dracula');\n\nmodule.exports = async function configCreatorAsync() {\n /** @type {import('@docusaurus/types').Config} */\n const config = {\n title: 'FastKafka',\n tagline: 'Effortless Kafka integration for web services',\n customFields: {\n description:\n 'Powerful and easy-to-use open-source framework for building asynchronous web services that interact with Kafka.',\n },\n favicon: 'img/AIRT_icon_blue.svg',\n\n // Set the production url of your site here\n url: 'https://fastkafka.airt.ai/',\n // Set the /![](\"/img/home-icon.svg\"){kind=icon}
FastKafka
{value.user.userName}
\n{value.description}
\nFor anything not covered here, join our Discord
\n{description}
\n{description}
\nFunction decorators with type hints specifying Pydantic classes for JSON encoding/decoding, automatic message routing and documentation generation.
\n![](\"img/docusaurus-plushie-banner.jpeg\")
\n ![](\"img/robot-footer.svg\")
\n ![](\"img/robot-hero.svg\")
\n Open-source framework for building asynchronous web
\nservices that interact with Kafka
\nOpen-source framework for building asynchronous web services that interact with Kafka
\n