What is Debezium Kafka Connector? What are its usecases? How to create Debezium Kafka Connector? What are its Advantages, Disadvantages and limitations?

Nixon Data What is Debezium Kafka Connector? What are its usecases? How to create Debezium Kafka Connector? What are its Advantages, Disadvantages and limitations?

What is Debezium Kafka Connector? What are its use-cases? How to create Debezium Kafka Connector? What are its Advantages, Disadvantages, and limitations?

What is Debezium Kafka Connector?

Debezium Nixondata

Debezium is an open-source distributed platform for change data capture. It captures row-level changes in databases, and streams them in real-time to Apache Kafka, from where they can be further processed by other systems. The Debezium Kafka Connector is a plugin for the Kafka Connect framework that allows Debezium to stream database changes directly to Kafka. It can be used to integrate a wide variety of databases, including MySQL, PostgreSQL, and MongoDB, with Kafka-based data pipelines.

How does Debezium Kafka Connector works?

The Debezium Kafka Connector works by connecting to the database, and reading the transaction logs or binary logs to capture any changes that are made to the database. The connector then streams these changes in real-time to a Kafka topic, where they can be consumed by other systems or applications.

The connector uses a database-specific connector to connect to the database and read the change data. For example, it uses the MySQL connector to connect to MySQL, and the PostgreSQL connector to connect to PostgreSQL.

Once the changes are captured, they are serialized into a format that can be sent over the network, such as Avro or JSON. The connector also adds metadata to the messages, such as the timestamp of the change and the primary key of the affected row.

The Kafka Connect framework provides the mechanism for running the connector as a standalone process, or as a distributed process, and also provides features like fault-tolerance, and the ability to scale the connector horizontally.

The Debezium Kafka Connector can be used in a variety of use cases, such as replicating data to other databases, building real-time data pipelines, and

auditing database changes.

What are the Uses cases of Debezium Kafka connector ?

Debezium Kafka connector has several use cases, some of which include:

  1. Data replication: The connector can be used to replicate data from a primary database to one or more secondary databases in real-time, providing a highly available and up-to-date copy of the data.
  2. Real-time data pipelines: The connector can be used to stream changes from a database to Kafka, where they can be consumed by other systems for real-time processing, such as data warehousing, analytics, and reporting.
  3. Auditing and compliance: The connector can be used to capture all changes made to a database, and log them to a separate system for auditing and compliance purposes.
  4. Event-driven architectures: The connector can be used to trigger events in response to changes in a database, such as sending notifications, updating caches, and triggering other processes.
  5. Microservices: The connector can be used in a microservices architecture, where each service has its own database, and changes to one service’s database need to be propagated to other services.
  6. Data governance: The connector can be used to capture and stream metadata, such as timestamps, user details and other information that can be used to track data lineage, understand data provenance, and track data quality.
  7. Streaming Analytics: You can use the connector to stream the data into kafka and then perform real-time analytics over it using stream-processing frameworks like Apache Kafka Streams, Apache Flink, Apache Storm, etc.

Advantages, Disadvantages and Limitations

Advantages of using Debezium Kafka connector:

  1. Real-time data streaming: The connector streams changes from a database to Kafka in real-time, allowing other systems to consume the data as soon as it becomes available.
  2. Support for multiple databases: The connector supports a wide variety of databases, including MySQL, PostgreSQL, MongoDB, and Oracle, making it a versatile tool for different use cases.
  3. Scalability: The connector can be run as a standalone process or as a distributed process, and it can be scaled horizontally to handle large amounts of data.
  4. Fault-tolerance: The connector uses the Kafka Connect framework, which provides built-in fault-tolerance and automatic recovery, ensuring that data is not lost even in case of failures.
  5. Easy integration: The connector is easy to integrate with other systems, as it streams data to Kafka, a widely used and well-documented platform.

Disadvantages of using Debezium Kafka connector:

  1. Complexity: Setting up and configuring the connector can be complex, especially when working with multiple databases or in a distributed environment.
  2. Resource consumption: The connector consumes resources on both the database and Kafka side, which can lead to performance issues if not properly configured.
  3. Latency: Depending on the database and the size of the changes being captured, there may be a small amount of latency between when a change is made to the database and when it is streamed to Kafka.

Limitations of using Debezium Kafka connector:

  1. Limited support for certain databases: While the connector supports a wide variety of databases, there may be certain databases for which support is limited or not available.
  2. Complexity in handling DDL changes: The connector does not handle DDL changes made to the database, which can lead to issues when a table structure is changed.
  3. Limited support for certain data types: The connector may have limited support for certain data types, such as binary data or large objects.
  4. Data loss and consistency issues: If not properly set up, the connector may miss some changes or duplicate data, which can lead to data loss and consistency issues.

Ways to run Debezium Kafka connector

Debezium Kafka connector can be run in several ways, some of which include:

  1. Standalone mode: The connector can be run as a standalone process, where it connects to the database, captures changes, and streams them to Kafka. This mode is suitable for small-scale and simple use cases.
  2. Distributed mode: The connector can be run in a distributed mode, where multiple instances of the connector are used to handle large amounts of data or to provide fault-tolerance. In this mode, the connector can be run using a tool like Apache Kafka Connect, which provides built-in fault-tolerance, scalability, and management capabilities.
  3. Kubernetes: The connector can be deployed in a Kubernetes cluster, where it can take advantage of Kubernetes features such as automatic scaling and self-healing.
  4. Cloud-based: The connector can be deployed to cloud-based platforms such as AWS, GCP, and Azure, where it can be managed and scaled using cloud-specific tools.
  5. As a service: The connector can be run as a service, where it’s managed and scaled by a third-party provider, such as Confluent.

The choice of running mode largely depends on the requirements of your use case, including scalability, performance, and ease of management. It’s always recommended to evaluate which mode best fits your needs and make sure to have a good understanding of the underlying infrastructure and services.

How to configure

Creating a Debezium Kafka connector to read from a MySQL database involves the following steps:

  1. Install and configure Kafka and Zookeeper: This includes setting up a Kafka cluster and installing and configuring Zookeeper.
  2. Install and configure the Debezium MySQL connector: This includes installing the Debezium MySQL connector and configuring it to connect to the MySQL database.
  3. Configure the Kafka Connect cluster: This includes setting up a Kafka Connect cluster and configuring it to use the Debezium MySQL connector.
  4. Start the connector: This includes starting the connector and providing it with the necessary configuration, such as the connection details for the MySQL database and the Kafka cluster.

Here is an example of how to configure and start a Debezium MySQL connector using the Kafka Connect REST API:

  1. Create a connector configuration file, called mysql.json, with the following content:
{
    "name": "mysql-connector",
    "config": {
        "connector.class": "io.debezium.connector.mysql.MySqlConnector",
        "tasks.max": "1",
        "database.hostname": "your-mysql-hostname",
        "database.port": "3306",
        "database.user": "your-mysql-username",
        "database.password": "your-mysql-password",
        "database.server.id": "your-unique-server-id",
        "database.server.name": "your-server-name",
        "database.whitelist": "your-database-name",
        "database.history.kafka.bootstrap.servers": "your-kafka-bootstrap-servers",
        "database.history.kafka.topic": "your-kafka-topic"
    }
}

2. Start the connector by sending a POST request to the Kafka Connect REST API endpoint, for example:

curl -X POST -H “Content-Type: application/json” –data @mysql.json http://your-connect-host:8083/connectors

This will start the Debezium MySQL connector and it will begin streaming changes from the specified MySQL database to the specified Kafka topic.

It’s important to note that this is a very basic example, and you may need to adjust the configuration to suit your specific use case and environment. Additionally, it’s important to have a good understanding of the underlying infrastructure and services when setting up and running a Debezium Kafka connector.

Event Generated from Debezium Kafka connector

When the Debezium Kafka connector captures a change from a database, it converts the change into a message, and streams the message to a Kafka topic. The structure of the message, also known as an event, depends on the format that the connector is configured to use.

The most commonly used format is Avro, which is a compact, binary, and self-describing format. Avro events are serialized and contain the following fields:

  1. "schema": This field contains the Avro schema of the event, which describes the structure of the event’s data.
  2. "payload": This field contains the actual data of the event, which includes the following fields:
    • "before": This field contains a copy of the row before the change. It’s only present for events of type "delete" and "update"
    • "after": This field contains a copy of the row after the change. It’s present for events of type "create", "update", and "delete".
    • "source": This field contains metadata about the event source, such as the database name, table name, and server name.
    • "op": This field contains the type of change, such as "c" for create, "u" for update, and "d" for delete.
    • "ts_ms": This field contains the timestamp of the change in milliseconds.

An example of a Avro event from Debezium Kafka connector could look like this:

{
    "schema": {
        "type": "struct",
        "fields": [
            {
                "type": "struct",
                "name": "before",
                "fields": [
                    {
                        "type": "string",
                        "name": "name"
                    },
                    {
                        "type": "int",
                        "name": "id"
                    },
                ]
            },
            {
                "type": "struct",
                "name": "after",
                "fields": [
                    {
                        "type": "string",
                        "name": "name"
                    },
                    {
                        "type": "int",
                        "name": "id"
                    },
                ]
            },
            {
                "type": "struct",
                "name": "source",
                "fields": [
                    {
                        "type": "string",
                        "name": "db"
                    },
                    {
                        "type": "string",
                        "name": "table"
                    },
                ]
            },
            {
                "type": "string",
                "name": "op"
            },
            {
                "type": "int",
                "name": "ts_ms"
            },
        ]
    },
    "payload": {
        "before": {
            "name": "john",
            "id": 1
        },
        "after": {
            "name": "paul",
            "id": 1
        },
        "source": {
            "db": "mydb",
            "table": "users"
        },
        "op": "u",
        "ts_ms": 16234567890
    }
}

How to Customize event schema generated from Debezium Kafka connector?

The structure of the event generated from the Debezium Kafka connector can be customized by modifying the Avro schema that is used to serialize the event. The Avro schema defines the fields and data types of the event and is used by the connector to convert the change data into a message.

To customize the event structure, you can do the following:

  1. Define a custom Avro schema: This can be done by creating a new Avro schema file that defines the fields and data types of the event in the desired format. You can use the Avro schema editor to create the schema.
  2. Configure the connector to use the custom schema: This can be done by modifying the connector configuration to specify the path to the custom schema file. The connector will then use this schema to serialize the events.
  3. Restart the connector: After modifying the configuration, you need to restart the connector for the changes to take effect.

It’s worth noting that customizing the event structure can have an impact on the existing systems that consume the events and may require updates to those systems to handle the new structure. Additionally, when customizing the event structure, it’s important to ensure that it still contains the necessary metadata, such as the primary key of the affected row, the timestamp of the change, and the operation type, in order to maintain its functionalities.

It’s also worth noting that if you’re using a stream-processing framework like Apache Kafka Streams, Apache Flink, or Apache Storm to consume the events, you can also leverage the built-in operators provided by these frameworks to transform the events into a desired format and structure.

It’s important to have a good understanding of the underlying infrastructure and services when customizing the event structure and to evaluate the impact of the changes on the existing systems before making any modifications.

Example

Here is an example of how to customize the structure of the event generated from a Debezium Kafka connector that is reading from a MySQL database:

  1. Define a custom Avro schema: Create a new Avro schema file called custom_event.avsc, which defines the desired fields and data types of the event. For example:
{
    "type": "record",
    "name": "CustomEvent",
    "fields": [
        {
            "name": "id",
            "type": "int"
        },
        {
            "name": "name",
            "type": "string"
        },
        {
            "name": "operation",
            "type": "string"
        }
    ]
}

This schema defines a custom event with three fields: id, name, and operation.

  1. Configure the connector to use the custom schema: Modify the connector configuration to specify the path to the custom schema file. For example:
  • Create a new Avro schema file called custom_event.avsc, which defines the desired fields and data types of the event.
  • Modify the connector configuration to specify the path to the custom schema file. This can be done by adding the following properties to the connector configuration:

"value.converter.schema.registry.url": "http://your-schema-registry-url",

"value.converter.schema.registry.topic": "your-custom-topic-name",

  • Register the custom schema in the schema registry by sending a POST request to the schema registry’s endpoint, for example:

curl -X POST -H “Content-Type: application/vnd.schemaregistry.v1+json” –data @custom_event.avsc http://your-schema-registry-url/schemas

  • Start the connector with the modified configuration.

This will configure the connector to use the custom schema defined in the custom_event.avsc file to serialize the events. The schema registry will handle the storage of the schema and it will be used by the connector to validate and serialize the events.

It’s important to note that the above is a simplified example and the actual process of registering the