Kafka Connect🔗

Kafka Connect is a popular framework for moving data in and out of Kafka via connectors. There are many different connectors available, such as the S3 sink for writing data from Kafka to S3 and Debezium source connectors for writing change data capture records from relational databases to Kafka.

It has a straightforward, decentralized, distributed architecture. A cluster consists of a number of worker processes, and a connector runs tasks on these processes to perform the work. Connector deployment is configuration driven, so generally no code needs to be written to run a connector.

Apache Iceberg Sink Connector🔗

The Apache Iceberg Sink Connector for Kafka Connect is a sink connector for writing data from Kafka into Iceberg tables.

Features🔗

Commit coordination for centralized Iceberg commits
Exactly-once delivery semantics
Multi-table fan-out
Automatic table creation and schema evolution
Field name mapping via Iceberg’s column mapping functionality

Installation🔗

The connector zip archive is created as part of the Iceberg build. You can run the build via:

./gradlew -x test -x integrationTest clean build

The zip archive will be found under ./kafka-connect/kafka-connect-runtime/build/distributions. There is one distribution that bundles the Hive Metastore client and related dependencies, and one that does not. Copy the distribution archive into the Kafka Connect plugins directory on all nodes.

Requirements🔗

The sink relies on KIP-447 for exactly-once semantics. This requires Kafka 2.5 or later.

Configuration🔗

Property	Description
iceberg.tables	Comma-separated list of destination tables
iceberg.tables.dynamic-enabled	Set to `true` to route to a table specified in `routeField` instead of using `routeRegex`, default is `false`
iceberg.tables.route-field	For multi-table fan-out, the name of the field used to route records to tables
iceberg.tables.default-commit-branch	Default branch for commits, main is used if not specified
iceberg.tables.default-id-columns	Default comma-separated list of columns that identify a row in tables (primary key)
iceberg.tables.default-partition-by	Default comma-separated list of partition field names to use when creating tables
iceberg.tables.auto-create-enabled	Set to `true` to automatically create destination tables, default is `false`
iceberg.tables.evolve-schema-enabled	Set to `true` to add any missing record fields to the table schema, default is `false`
iceberg.tables.schema-force-optional	Set to `true` to set columns as optional during table create and evolution, default is `false` to respect schema
iceberg.tables.schema-case-insensitive	Set to `true` to look up table columns by case-insensitive name, default is `false` for case-sensitive
iceberg.tables.auto-create-props.*	Properties set on new tables during auto-create
iceberg.tables.write-props.*	Properties passed through to Iceberg writer initialization, these take precedence
iceberg.table.\<table name>.commit-branch	Table-specific branch for commits, use `iceberg.tables.default-commit-branch` if not specified
iceberg.table.\<table name>.id-columns	Comma-separated list of columns that identify a row in the table (primary key)
iceberg.table.\<table name>.partition-by	Comma-separated list of partition fields to use when creating the table
iceberg.table.\<table name>.route-regex	The regex used to match a record's `routeField` to a table
iceberg.control.topic	Name of the control topic, default is `control-iceberg`
iceberg.control.commit.interval-ms	Commit interval in msec, default is 300,000 (5 min)
iceberg.control.commit.timeout-ms	Commit timeout interval in msec, default is 30,000 (30 sec)
iceberg.control.commit.threads	Number of threads to use for commits, default is (cores * 2)
iceberg.catalog	Name of the catalog, default is `iceberg`
iceberg.catalog.*	Properties passed through to Iceberg catalog initialization
iceberg.hadoop-conf-dir	If specified, Hadoop config files in this directory will be loaded
iceberg.hadoop.*	Properties passed through to the Hadoop configuration
iceberg.kafka.*	Properties passed through to control topic Kafka client initialization

If iceberg.tables.dynamic-enabled is false (the default) then you must specify iceberg.tables. If iceberg.tables.dynamic-enabled is true then you must specify iceberg.tables.route-field which will contain the name of the table.

Kafka configuration🔗

By default the connector will attempt to use Kafka client config from the worker properties for connecting to the control topic. If that config cannot be read for some reason, Kafka client settings can be set explicitly using iceberg.kafka.* properties.

Message format🔗

Messages should be converted to a struct or map using the appropriate Kafka Connect converter.

Catalog configuration🔗

The iceberg.catalog.* properties are required for connecting to the Iceberg catalog. The core catalog types are included in the default distribution, including REST, Glue, DynamoDB, Hadoop, Nessie, JDBC, and Hive. JDBC drivers are not included in the default distribution, so you will need to include those if needed. When using a Hive catalog, you can use the distribution that includes the Hive metastore client, otherwise you will need to include that yourself.

To set the catalog type, you can set iceberg.catalog.type to rest, hive, or hadoop. For other catalog types, you need to instead set iceberg.catalog.catalog-impl to the name of the catalog class.

REST example🔗

"iceberg.catalog.type": "rest",
"iceberg.catalog.uri": "https://catalog-service",
"iceberg.catalog.credential": "<credential>",
"iceberg.catalog.warehouse": "<warehouse>",

Hive example🔗

NOTE: Use the distribution that includes the HMS client (or include the HMS client yourself). Use S3FileIO when using S3 for storage (the default is HadoopFileIO with HiveCatalog).

"iceberg.catalog.type": "hive",
"iceberg.catalog.uri": "thrift://hive:9083",
"iceberg.catalog.io-impl": "org.apache.iceberg.aws.s3.S3FileIO",
"iceberg.catalog.warehouse": "s3a://bucket/warehouse",
"iceberg.catalog.client.region": "us-east-1",
"iceberg.catalog.s3.access-key-id": "<AWS access>",
"iceberg.catalog.s3.secret-access-key": "<AWS secret>",

Glue example🔗

"iceberg.catalog.catalog-impl": "org.apache.iceberg.aws.glue.GlueCatalog",
"iceberg.catalog.warehouse": "s3a://bucket/warehouse",
"iceberg.catalog.io-impl": "org.apache.iceberg.aws.s3.S3FileIO",

Nessie example🔗

"iceberg.catalog.catalog-impl": "org.apache.iceberg.nessie.NessieCatalog",
"iceberg.catalog.uri": "http://localhost:19120/api/v2",
"iceberg.catalog.ref": "main",
"iceberg.catalog.warehouse": "s3a://bucket/warehouse",
"iceberg.catalog.io-impl": "org.apache.iceberg.aws.s3.S3FileIO",

Notes🔗

Depending on your setup, you may need to also set iceberg.catalog.s3.endpoint, iceberg.catalog.s3.staging-dir, or iceberg.catalog.s3.path-style-access. See the Iceberg docs for full details on configuring catalogs.

Azure ADLS configuration example🔗

When using ADLS, Azure requires the passing of AZURE_CLIENT_ID, AZURE_TENANT_ID, and AZURE_CLIENT_SECRET for its Java SDK. If you're running Kafka Connect in a container, be sure to inject those values as environment variables. See the Azure Identity Client library for Java for more information.

An example of these would be:

AZURE_CLIENT_ID=e564f687-7b89-4b48-80b8-111111111111
AZURE_TENANT_ID=95f2f365-f5b7-44b1-88a1-111111111111
AZURE_CLIENT_SECRET="XXX"

Where the CLIENT_ID is the Application ID of a registered application under App Registrations, the TENANT_ID is from your Azure Tenant Properties, and the CLIENT_SECRET is created within the "Certificates & Secrets" section, under "Manage" after choosing your specific App Registration. You might have to choose "Client secrets" in the middle panel and the "+" in front of "New client secret" to generate one. Be sure to set this variable to the Value and not the Id.

It's also important that the App Registration is granted the Role Assignment "Storage Blob Data Contributor" in your Storage Account's Access Control (IAM), or it won't be able to write new files there.

Then, within the Connector's configuration, you'll want to include the following:

"iceberg.catalog.type": "rest",
"iceberg.catalog.uri": "https://catalog:8181",
"iceberg.catalog.warehouse": "abfss://storage-container-name@storageaccount.dfs.core.windows.net/warehouse",
"iceberg.catalog.io-impl": "org.apache.iceberg.azure.adlsv2.ADLSFileIO",
"iceberg.catalog.include-credentials": "true"

Where storage-container-name is the container name within your Azure Storage Account, /warehouse is the location within that container where your Apache Iceberg files will be written by default (or if iceberg.tables.auto-create-enabled=true), and the include-credentials parameter passes along the Azure Java client credentials along. This will configure the Iceberg Sink connector to connect to the REST catalog implementation at iceberg.catalog.uri to obtain the required Connection String for the ADLSv2 client

Google GCS configuration example🔗

By default, Application Default Credentials (ADC) will be used to connect to GCS. Details on how ADC works can be found in the Google Cloud documentation.

"iceberg.catalog.type": "rest",
"iceberg.catalog.uri": "https://catalog:8181",
"iceberg.catalog.warehouse": "gs://bucket-name/warehouse",
"iceberg.catalog.io-impl": "org.apache.iceberg.google.gcs.GCSFileIO"

Hadoop configuration🔗

When using HDFS or Hive, the sink will initialize the Hadoop configuration. First, config files from the classpath are loaded. Next, if iceberg.hadoop-conf-dir is specified, config files are loaded from that location. Finally, any iceberg.hadoop.* properties from the sink config are applied. When merging these, the order of precedence is sink config > config dir > classpath.

Examples🔗

Initial setup🔗

Source topic🔗

This assumes the source topic already exists and is named events.

Control topic🔗

If your Kafka cluster has auto.create.topics.enable set to true (the default), then the control topic will be automatically created. If not, then you will need to create the topic first. The default topic name is control-iceberg:

bin/kafka-topics  \
  --command-config command-config.props \
  --bootstrap-server ${CONNECT_BOOTSTRAP_SERVERS} \
  --create \
  --topic control-iceberg \
  --partitions 1

NOTE: Clusters running on Confluent Cloud have auto.create.topics.enable set to false by default.

Iceberg catalog configuration🔗

Configuration properties with the prefix iceberg.catalog. will be passed to Iceberg catalog initialization. See the Iceberg docs for details on how to configure a particular catalog.

Single destination table🔗

This example writes all incoming records to a single table.

Create the destination table🔗

CREATE TABLE default.events (
    id STRING,
    type STRING,
    ts TIMESTAMP,
    payload STRING)
PARTITIONED BY (hours(ts))

Connector config🔗

This example config connects to a Iceberg REST catalog.

{
"name": "events-sink",
"config": {
    "connector.class": "org.apache.iceberg.connect.IcebergSinkConnector",
    "tasks.max": "2",
    "topics": "events",
    "iceberg.tables": "default.events",
    "iceberg.catalog.type": "rest",
    "iceberg.catalog.uri": "https://localhost",
    "iceberg.catalog.credential": "<credential>",
    "iceberg.catalog.warehouse": "<warehouse name>"
    }
}

Multi-table fan-out, static routing🔗

This example writes records with type set to list to the table default.events_list, and writes records with type set to create to the table default.events_create. Other records will be skipped.

Create two destination tables🔗

CREATE TABLE default.events_list (
    id STRING,
    type STRING,
    ts TIMESTAMP,
    payload STRING)
PARTITIONED BY (hours(ts));

CREATE TABLE default.events_create (
    id STRING,
    type STRING,
    ts TIMESTAMP,
    payload STRING)
PARTITIONED BY (hours(ts));

Connector config🔗

{
"name": "events-sink",
"config": {
    "connector.class": "org.apache.iceberg.connect.IcebergSinkConnector",
    "tasks.max": "2",
    "topics": "events",
    "iceberg.tables": "default.events_list,default.events_create",
    "iceberg.tables.route-field": "type",
    "iceberg.table.default.events_list.route-regex": "list",
    "iceberg.table.default.events_create.route-regex": "create",
    "iceberg.catalog.type": "rest",
    "iceberg.catalog.uri": "https://localhost",
    "iceberg.catalog.credential": "<credential>",
    "iceberg.catalog.warehouse": "<warehouse name>"
    }
}

Multi-table fan-out, dynamic routing🔗

This example writes to tables with names from the value in the db_table field. If a table with the name does not exist, then the record will be skipped. For example, if the record's db_table field is set to default.events_list, then the record is written to the default.events_list table.

Create two destination tables🔗

See above for creating two tables.

Connector config🔗

{
"name": "events-sink",
"config": {
    "connector.class": "org.apache.iceberg.connect.IcebergSinkConnector",
    "tasks.max": "2",
    "topics": "events",
    "iceberg.tables.dynamic-enabled": "true",
    "iceberg.tables.route-field": "db_table",
    "iceberg.catalog.type": "rest",
    "iceberg.catalog.uri": "https://localhost",
    "iceberg.catalog.credential": "<credential>",
    "iceberg.catalog.warehouse": "<warehouse name>"
    }
}