Introducción a los conectores Kafka

Programación

1. Información general

Apache Kafka® es una plataforma de transmisión distribuida. En un tutorial anterior, discutimos cómo implementar consumidores y productores de Kafka usando Spring.

En este tutorial, aprenderemos a usar Kafka Connectors.

Echaremos un vistazo a:

  • Diferentes tipos de conectores Kafka
  • Funciones y modos de Kafka Connect
  • Configuración de conectores mediante archivos de propiedades y la API REST

2. Conceptos básicos de Kafka Connect y Kafka Connectors

Kafka Connect es un marco para conectar Kafka con sistemas externos como bases de datos, almacenes de valores clave, índices de búsqueda y sistemas de archivos, utilizando los denominados conectores .

Los conectores Kafka son componentes listos para usar que pueden ayudarnos a importar datos de sistemas externos a temas de Kafka y exportar datos de temas de Kafka a sistemas externos . Podemos utilizar implementaciones de conectores existentes para fuentes y receptores de datos comunes o implementar nuestros propios conectores.

Un conector de origen recopila datos de un sistema. Los sistemas de origen pueden ser bases de datos completas, tablas de flujos o agentes de mensajes. Un conector de origen también podría recopilar métricas de servidores de aplicaciones en temas de Kafka, haciendo que los datos estén disponibles para el procesamiento de transmisiones con baja latencia.

Un conector receptor entrega datos de los temas de Kafka a otros sistemas, que pueden ser índices como Elasticsearch, sistemas por lotes como Hadoop o cualquier tipo de base de datos.

Algunos conectores son mantenidos por la comunidad, mientras que otros son respaldados por Confluent o sus socios. Realmente, podemos encontrar conectores para los sistemas más populares, como S3, JDBC y Cassandra, solo por nombrar algunos.

3. Características

Las características de Kafka Connect incluyen:

  • Un marco para conectar sistemas externos con Kafka: simplifica el desarrollo, la implementación y la administración de conectores
  • Modos distribuidos e independientes: nos ayuda a implementar grandes clústeres aprovechando la naturaleza distribuida de Kafka, así como las configuraciones para el desarrollo, las pruebas y las implementaciones de pequeña producción.
  • Interfaz REST: podemos administrar conectores usando una API REST
  • Gestión de compensación automática: Kafka Connect nos ayuda a manejar el proceso de confirmación de compensación, lo que nos ahorra la molestia de implementar esta parte del desarrollo de conectores propensa a errores manualmente
  • Distribuido y escalable de forma predeterminada: Kafka Connect utiliza el protocolo de administración de grupo existente; podemos agregar más trabajadores para escalar un clúster de Kafka Connect
  • Integración de streaming y por lotes: Kafka Connect es una solución ideal para unir sistemas de datos por lotes y de streaming en conexión con las capacidades existentes de Kafka
  • Transformaciones: nos permiten realizar modificaciones simples y ligeras en mensajes individuales.

4. Configuración

En lugar de usar la distribución simple de Kafka, descargaremos Confluent Platform, una distribución de Kafka proporcionada por Confluent, Inc., la compañía detrás de Kafka. Confluent Platform viene con algunas herramientas y clientes adicionales, en comparación con Kafka simple, así como algunos conectores adicionales preconstruidos.

Para nuestro caso, la edición Open Source es suficiente, que se puede encontrar en el sitio de Confluent.

5. Inicio rápido de Kafka Connect

Para empezar, analizaremos el principio de Kafka Connect, utilizando sus conectores más básicos, que son el conector de origen de archivo y el conector de receptor de archivo .

Convenientemente, Confluent Platform viene con ambos conectores, así como configuraciones de referencia.

5.1. Configuración del conector de origen

Para el conector de origen, la configuración de referencia está disponible en $ CONFLUENT_HOME / etc / kafka / connect-file-source.properties :

name=local-file-source connector.class=FileStreamSource tasks.max=1 topic=connect-test file=test.txt

Esta configuración tiene algunas propiedades que son comunes para todos los conectores de origen:

  • nombre es un nombre especificado por el usuario para la instancia del conector
  • connector.class especifica la clase de implementación, básicamente el tipo de conector
  • tasks.max especifica cuántas instancias de nuestro conector de origen deben ejecutarse en paralelo, y
  • tema define el tema al que el conector debe enviar la salida

En este caso, también tenemos un atributo específico del conector:

  • archivo define el archivo desde el cual el conector debe leer la entrada

Entonces, para que esto funcione, creemos un archivo básico con algo de contenido:

echo -e "foo\nbar\n" > $CONFLUENT_HOME/test.txt

Tenga en cuenta que el directorio de trabajo es $ CONFLUENT_HOME.

5.2. Configuración del conector del fregadero

Para nuestro conector de fregadero, usaremos la configuración de referencia en $ CONFLUENT_HOME / etc / kafka / connect-file-sink.properties :

name=local-file-sink connector.class=FileStreamSink tasks.max=1 file=test.sink.txt topics=connect-test

Lógicamente, contiene exactamente los mismos parámetros, aunque esta vez connector.class especifica la implementación del conector del receptor, y el archivo es la ubicación donde el conector debe escribir el contenido.

5.3. Configuración de trabajador

Finalmente, tenemos que configurar el trabajador de Connect, que integrará nuestros dos conectores y hará el trabajo de leer desde el conector fuente y escribir en el conector del disipador.

Para eso, podemos usar $ CONFLUENT_HOME / etc / kafka / connect-standalone.properties :

bootstrap.servers=localhost:9092 key.converter=org.apache.kafka.connect.json.JsonConverter value.converter=org.apache.kafka.connect.json.JsonConverter key.converter.schemas.enable=false value.converter.schemas.enable=false offset.storage.file.filename=/tmp/connect.offsets offset.flush.interval.ms=10000 plugin.path=/share/java

Note that plugin.path can hold a list of paths, where connector implementations are available

As we'll use connectors bundled with Kafka, we can set plugin.path to $CONFLUENT_HOME/share/java. Working with Windows, it might be necessary to provide an absolute path here.

For the other parameters, we can leave the default values:

  • bootstrap.servers contains the addresses of the Kafka brokers
  • key.converter and value.converter define converter classes, which serialize and deserialize the data as it flows from the source into Kafka and then from Kafka to the sink
  • key.converter.schemas.enable and value.converter.schemas.enable are converter-specific settings
  • offset.storage.file.filename is the most important setting when running Connect in standalone mode: it defines where Connect should store its offset data
  • offset.flush.interval.ms defines the interval at which the worker tries to commit offsets for tasks

And the list of parameters is quite mature, so check out the official documentation for a complete list.

5.4. Kafka Connect in Standalone Mode

And with that, we can start our first connector setup:

$CONFLUENT_HOME/bin/connect-standalone \ $CONFLUENT_HOME/etc/kafka/connect-standalone.properties \ $CONFLUENT_HOME/etc/kafka/connect-file-source.properties \ $CONFLUENT_HOME/etc/kafka/connect-file-sink.properties

First off, we can inspect the content of the topic using the command line:

$CONFLUENT_HOME/bin/kafka-console-consumer --bootstrap-server localhost:9092 --topic connect-test --from-beginning

As we can see, the source connector took the data from the test.txt file, transformed it into JSON, and sent it to Kafka:

{"schema":{"type":"string","optional":false},"payload":"foo"} {"schema":{"type":"string","optional":false},"payload":"bar"}

And, if we have a look at the folder $CONFLUENT_HOME, we can see that a file test.sink.txt was created here:

cat $CONFLUENT_HOME/test.sink.txt foo bar

As the sink connector extracts the value from the payload attribute and writes it to the destination file, the data in test.sink.txt has the content of the original test.txt file.

Now let's add more lines to test.txt.

When we do, we see that the source connector detects these changes automatically.

We only have to make sure to insert a newline at the end, otherwise, the source connector won't consider the last line.

At this point, let's stop the Connect process, as we'll start Connect in distributed mode in a few lines.

6. Connect's REST API

Until now, we made all configurations by passing property files via the command line. However, as Connect is designed to run as a service, there is also a REST API available.

By default, it is available at //localhost:8083. A few endpoints are:

  • GET /connectors – returns a list with all connectors in use
  • GET /connectors/{name} – returns details about a specific connector
  • POST /connectors – creates a new connector; the request body should be a JSON object containing a string name field and an object config field with the connector configuration parameters
  • GET /connectors/{name}/status – returns the current status of the connector – including if it is running, failed or paused – which worker it is assigned to, error information if it has failed, and the state of all its tasks
  • DELETE /connectors/{name} – deletes a connector, gracefully stopping all tasks and deleting its configuration
  • GET /connector-plugins – returns a list of connector plugins installed in the Kafka Connect cluster

The official documentation provides a list with all endpoints.

We'll use the REST API for creating new connectors in the following section.

7. Kafka Connect in Distributed Mode

The standalone mode works perfectly for development and testing, as well as smaller setups. However, if we want to make full use of the distributed nature of Kafka, we have to launch Connect in distributed mode.

By doing so, connector settings and metadata are stored in Kafka topics instead of the file system. As a result, the worker nodes are really stateless.

7.1. Starting Connect

A reference configuration for distributed mode can be found at $CONFLUENT_HOME/etc/kafka/connect-distributed.properties.

Parameters are mostly the same as for standalone mode. There are only a few differences:

  • group.id defines the name of the Connect cluster group. The value must be different from any consumer group ID
  • offset.storage.topic, config.storage.topic and status.storage.topic define topics for these settings. For each topic, we can also define a replication factor

Again, the official documentation provides a list with all parameters.

We can start Connect in distributed mode as follows:

$CONFLUENT_HOME/bin/connect-distributed $CONFLUENT_HOME/etc/kafka/connect-distributed.properties

7.2. Adding Connectors Using the REST API

Now, compared to the standalone startup command, we didn't pass any connector configurations as arguments. Instead, we have to create the connectors using the REST API.

To set up our example from before, we have to send two POST requests to //localhost:8083/connectors containing the following JSON structs.

First, we need to create the body for the source connector POST as a JSON file. Here, we'll call it connect-file-source.json:

{ "name": "local-file-source", "config": { "connector.class": "FileStreamSource", "tasks.max": 1, "file": "test-distributed.txt", "topic": "connect-distributed" } }

Note how this looks pretty similar to the reference configuration file we used the first time.

And then we POST it:

curl -d @"$CONFLUENT_HOME/connect-file-source.json" \ -H "Content-Type: application/json" \ -X POST //localhost:8083/connectors

Then, we'll do the same for the sink connector, calling the file connect-file-sink.json:

{ "name": "local-file-sink", "config": { "connector.class": "FileStreamSink", "tasks.max": 1, "file": "test-distributed.sink.txt", "topics": "connect-distributed" } }

And perform the POST like before:

curl -d @$CONFLUENT_HOME/connect-file-sink.json \ -H "Content-Type: application/json" \ -X POST //localhost:8083/connectors

If needed, we can verify, that this setup is working correctly:

$CONFLUENT_HOME/bin/kafka-console-consumer --bootstrap-server localhost:9092 --topic connect-distributed --from-beginning {"schema":{"type":"string","optional":false},"payload":"foo"} {"schema":{"type":"string","optional":false},"payload":"bar"}

And, if we have a look at the folder $CONFLUENT_HOME, we can see that a file test-distributed.sink.txt was created here:

cat $CONFLUENT_HOME/test-distributed.sink.txt foo bar

After we tested the distributed setup, let's clean up, by removing the two connectors:

curl -X DELETE //localhost:8083/connectors/local-file-source curl -X DELETE //localhost:8083/connectors/local-file-sink

8. Transforming Data

8.1. Supported Transformations

Transformations enable us to make simple and lightweight modifications to individual messages.

Kafka Connect supports the following built-in transformations:

  • InsertField – Add a field using either static data or record metadata
  • ReplaceField – Filter or rename fields
  • MaskField – Replace a field with the valid null value for the type (zero or an empty string, for example)
  • HoistField – Wrap the entire event as a single field inside a struct or a map
  • ExtractField – Extract a specific field from struct and map and include only this field in the results
  • SetSchemaMetadata – Modify the schema name or version
  • TimestampRouter – Modify the topic of a record based on original topic and timestamp
  • RegexRouter – Modify the topic of a record based on original topic, a replacement string, and a regular expression

A transformation is configured using the following parameters:

  • transforms – A comma-separated list of aliases for the transformations
  • transforms.$alias.type – Class name for the transformation
  • transforms.$alias.$transformationSpecificConfig – Configuration for the respective transformation

8.2. Applying a Transformer

To test some transformation features, let's set up the following two transformations:

  • First, let's wrap the entire message as a JSON struct
  • After that, let's add a field to that struct

Before applying our transformations, we have to configure Connect to use schemaless JSON, by modifying the connect-distributed.properties:

key.converter.schemas.enable=false value.converter.schemas.enable=false

After that, we have to restart Connect, again in distributed mode:

$CONFLUENT_HOME/bin/connect-distributed $CONFLUENT_HOME/etc/kafka/connect-distributed.properties

Again, we need to create the body for the source connector POST as a JSON file. Here, we'll call it connect-file-source-transform.json.

Besides the already known parameters, we add a few lines for the two required transformations:

{ "name": "local-file-source", "config": { "connector.class": "FileStreamSource", "tasks.max": 1, "file": "test-transformation.txt", "topic": "connect-transformation", "transforms": "MakeMap,InsertSource", "transforms.MakeMap.type": "org.apache.kafka.connect.transforms.HoistField$Value", "transforms.MakeMap.field": "line", "transforms.InsertSource.type": "org.apache.kafka.connect.transforms.InsertField$Value", "transforms.InsertSource.static.field": "data_source", "transforms.InsertSource.static.value": "test-file-source" } }

After that, let's perform the POST:

curl -d @$CONFLUENT_HOME/connect-file-source-transform.json \ -H "Content-Type: application/json" \ -X POST //localhost:8083/connectors

Let's write some lines to our test-transformation.txt:

Foo Bar

If we now inspect the connect-transformation topic, we should get the following lines:

{"line":"Foo","data_source":"test-file-source"} {"line":"Bar","data_source":"test-file-source"}

9. Using Ready Connectors

After using these simple connectors, let's have a look at more advanced ready-to-use connectors, and how to install them.

9.1. Where to Find Connectors

Pre-built connectors are available from different sources:

  • A few connectors are bundled with plain Apache Kafka (source and sink for files and console)
  • Some more connectors are bundled with Confluent Platform (ElasticSearch, HDFS, JDBC, and AWS S3)
  • Also check out Confluent Hub, which is kind of an app store for Kafka connectors. The number of offered connectors is growing continuously:
    • Confluent connectors (developed, tested, documented and are fully supported by Confluent)
    • Certified connectors (implemented by a 3rd party and certified by Confluent)
    • Community-developed and -supported connectors
  • Beyond that, Confluent also provides a Connectors Page, with some connectors which are also available at the Confluent Hub, but also with some more community connectors
  • And finally, there are also vendors, who provide connectors as part of their product. For example, Landoop provides a streaming library called Lenses, which also contains a set of ~25 open source connectors (many of them also cross-listed in other places)

9.2. Installing Connectors from Confluent Hub

The enterprise version of Confluent provides a script for installing Connectors and other components from Confluent Hub (the script is not included in the Open Source version). If we're using the enterprise version, we can install a connector using the following command:

$CONFLUENT_HOME/bin/confluent-hub install confluentinc/kafka-connect-mqtt:1.0.0-preview

9.3. Installing Connectors Manually

If we need a connector, which is not available on Confluent Hub or if we have the Open Source version of Confluent, we can install the required connectors manually. For that, we have to download and unzip the connector, as well as move the included libs to the folder specified as plugin.path.

For each connector, the archive should contain two folders that are interesting for us:

  • The lib folder contains the connector jar, for example, kafka-connect-mqtt-1.0.0-preview.jar, as well as some more jars required by the connector
  • The etc folder holds one or more reference config files

We have to move the lib folder to $CONFLUENT_HOME/share/java, or whichever path we specified as plugin.path in connect-standalone.properties and connect-distributed.properties. In doing so, it might also make sense to rename the folder to something meaningful.

We can use the config files from etc either by referencing them while starting in standalone mode, or we can just grab the properties and create a JSON file from them.

10. Conclusion

In this tutorial, we had a look at how to install and use Kafka Connect.

Analizamos los tipos de conectores, tanto de fuente como de sumidero. También analizamos algunas características y modos en los que se puede ejecutar Connect. Luego, revisamos los transformadores. Y finalmente, aprendimos dónde conseguirlo y cómo instalar conectores personalizados.

Como siempre, los archivos de configuración se pueden encontrar en GitHub.