Important

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JDBC Sink Connector¶

The JDBC sink connector allows you to export data from Kafka topics to any relational database with a JDBC driver. By using JDBC, this connector can support a wide variety of databases without requiring a dedicated connector for each one. The connector polls data from Kafka to write to the database based on the topics subscription. It is possible to achieve idempotent writes with upserts. Auto-creation of tables, and limited auto-evolution is also supported.

Quick Start¶

Prerequisites:

Confluent Platform is installed and services are running by using the Confluent CLI. This quick start assumes that you are using the Confluent CLI, but standalone installations are also supported. By default ZooKeeper, Kafka, Schema Registry, Kafka Connect REST API, and Kafka Connect are started with the confluent start command. For more information, see installation_archive.
SQLite is installed. You can also use another database. If you are using another database, be sure to adjust the connection.url setting. Confluent Platform includes JDBC drivers for SQLite and PostgreSQL, but if you’re using a different database you must also verify that the JDBC driver is available on the Kafka Connect process’s CLASSPATH.
Kafka and Schema Registry are running locally on the default ports.

To see the basic functionality of the connector, we’ll be copying Avro data from a single topic to a local SQLite database.

Create SQLite Database and Load Data¶

Create a SQLite database with this command:

$ sqlite3 test.db

Your output should resemble:

SQLite version 3.19.3 2017-06-27 16:48:08
Enter ".help" for usage hints.
sqlite>

In the SQLite command prompt, create a table and seed it with some data:

sqlite> CREATE TABLE accounts(id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL, name VARCHAR(255));

sqlite> INSERT INTO accounts(name) VALUES('alice');

sqlite> INSERT INTO accounts(name) VALUES('bob');

Tip

You can run SELECT * from accounts; to verify your table has been created.

Load the JDBC Sink Connector¶

Load the predefined JDBC sink connector.

Optional: View the available predefined connectors with this command:

confluent list connectors

Your output should resemble:

Bundled Predefined Connectors (edit configuration under etc/):
  elasticsearch-sink
  file-source
  file-sink
  jdbc-source
  jdbc-sink
  hdfs-sink
  s3-sink

Load the the jdbc-sink connector:

confluent load jdbc-sink

Your output should resemble:

{
  "name": "jdbc-sink",
  "config": {
    "connector.class": "io.confluent.connect.jdbc.JdbcSinkConnector",
    "tasks.max": "1",
    "topics": "orders",
    "connection.url": "jdbc:sqlite:test.db",
    "auto.create": "true",
    "name": "jdbc-sink"
  },
  "tasks": [],
  "type": null
}

Tip

For non-CLI users, you can load the JDBC sink connector with this command:

<path-to-confluent>/bin/connect-standalone \
<path-to-confluent>/etc/schema-registry/connect-avro-standalone.properties \
<path-to-confluent>/etc/kafka-connect-jdbc/sink-quickstart-sqlite.properties

Produce a Record in SQLite¶

Produce a record into the orders topic.

$ ./bin/kafka-avro-console-producer \
 --broker-list localhost:9092 --topic orders \
 --property value.schema='{"type":"record","name":"myrecord","fields":[{"name":"id","type":"int"},{"name":"product", "type": "string"}, {"name":"quantity", "type": "int"}, {"name":"price",
 "type": "float"}]}'

The console producer waits for input.

Copy and paste the following record into the terminal and press Enter:
```
{"id": 999, "product": "foo", "quantity": 100, "price": 50}
```
Query the SQLite database and you should see that the orders table was automatically created and contains the record.
```
$ sqlite3 test.db
sqlite> SELECT * from orders;
foo|50.0|100|999
```

Features¶

The sink connector requires knowledge of schemas, so you should use a suitable converter e.g. the Avro converter that comes with the schema registry, or the JSON converter with schemas enabled. Kafka record keys if present can be primitive types or a Connect struct, and the record value must be a Connect struct. Fields being selected from Connect structs must be of primitive types. If the data in the topic is not of a compatible format, implementing a custom Converter may be necessary.

The default is for primary keys to not be extracted with pk.mode set to none, which is not suitable for advanced usage such as upsert semantics and when the connector is responsible for auto-creating the destination table. There are different modes that enable to use fields from the Kafka record key, the Kafka record value, or the Kafka coordinates for the record.

Refer to primary key configuration options for further detail.

The default insert.mode is insert. If it is configured as upsert, the connector will use upsert semantics rather than plain INSERT statements. Upsert semantics refer to atomically adding a new row or updating the existing row if there is a primary key constraint violation, which provides idempotence.

If there are failures, the Kafka offset used for recovery may not be up-to-date with what was committed as of the time of the failure, which can lead to re-processing during recovery. The upsert mode is highly recommended as it helps avoid constraint violations or duplicate data if records need to be re-processed.

Aside from failure recovery, the source topic may also naturally contain multiple records over time with the same primary key, making upserts desirable.

As there is no standard syntax for upsert, the following table describes the database-specific DML that is used.

Database	Upsert style
MySQL	INSERT .. ON DUPLICATE KEY REPLACE ..
Oracle	MERGE ..
PostgreSQL	INSERT .. ON CONFLICT .. DO UPDATE SET ..
SQLite	INSERT OR REPLACE ..
SQL Server	MERGE ..
Other	not supported

Tip

Make sure the JDBC user has the appropriate permissions for DDL.

If auto.create is enabled, the connector can CREATE the destination table if it is found to be missing. The creation takes place online with records being consumed from the topic, since the connector uses the record schema as a basis for the table definition. Primary keys are specified based on the key configuration settings.

If auto.evolve is enabled, the connector can perform limited auto-evolution by issuing ALTER on the destination table when it encounters a record for which a column is found to be missing. Since data-type changes and removal of columns can be dangerous, the connector does not attempt to perform such evolutions on the table. Addition of primary key constraints is also not attempted.

For both auto-creation and auto-evolution, the nullability of a column is based on the optionality of the corresponding field in the schema, and default values are also specified based on the default value of the corresponding field if applicable. We use the following mapping from Connect schema types to database-specific types:

Schema Type	MySQL	Oracle	PostgreSQL	SQLite	SQL Server	Vertica
INT8	TINYINT	NUMBER(3,0)	SMALLINT	NUMERIC	TINYINT	INT
INT16	SMALLINT	NUMBER(5,0)	SMALLINT	NUMERIC	SMALLINT	INT
INT32	INT	NUMBER(10,0)	INT	NUMERIC	INT	INT
INT64	BIGINT	NUMBER(19,0)	BIGINT	NUMERIC	BIGINT	INT
FLOAT32	FLOAT	BINARY_FLOAT	REAL	REAL	REAL	FLOAT
FLOAT64	DOUBLE	BINARY_DOUBLE	DOUBLE PRECISION	REAL	FLOAT	FLOAT
BOOLEAN	TINYINT	NUMBER(1,0)	BOOLEAN	NUMERIC	BIT	BOOLEAN
STRING	VARCHAR(256)	NCLOB	TEXT	TEXT	VARCHAR(MAX)	VARCHAR(1024)
BYTES	VARBINARY(1024)	BLOB	BYTEA	BLOB	VARBINARY(MAX)	VARBINARY(1024)
‘Decimal’	DECIMAL(65,s)	NUMBER(*,s)	DECIMAL	NUMERIC	DECIMAL(38,s)	DECIMAL(18,s)
‘Date’	DATE	DATE	DATE	NUMERIC	DATE	DATE
‘Time’	TIME(3)	DATE	TIME	NUMERIC	TIME	TIME
‘Timestamp’	TIMESTAMP(3)	TIMESTAMP	TIMESTAMP	NUMERIC	DATETIME2	TIMESTAMP

Auto-creation or auto-evolution is not supported for databases not mentioned here.

Important

For backwards-compatible table schema evolution, new fields in record schemas must be optional or have a default value. If you need to delete a field, the table schema should be manually altered to either drop the corresponding column, assign it a default value, or make it nullable.