Name	Name	Last commit message	Last commit date
parent directory ..
data/mysql	data/mysql
README.adoc	README.adoc
docker-compose.yml	docker-compose.yml
ksqldb-jdbc-sink.adoc	ksqldb-jdbc-sink.adoc

Streaming data from Kafka to a Database using Kafka Connect JDBC Sink

Tested with Confluent Platform 6.1, ksqlDB 0.15, Kafka Connect JDBC connector 5.5.3

This uses Docker Compose to run the Kafka Connect worker.

Bring the Docker Compose up
```
docker-compose up -d
```

Make sure everything is up and running

➜ docker-compose ps
     Name                    Command                  State                     Ports
---------------------------------------------------------------------------------------------------
broker            /etc/confluent/docker/run        Up             0.0.0.0:9092->9092/tcp
kafka-connect     bash -c cd /usr/share/conf ...   Up (healthy)   0.0.0.0:8083->8083/tcp, 9092/tcp
kafkacat          /bin/sh -c apk add jq;           Up
                  wh ...
ksqldb            /usr/bin/docker/run              Up             0.0.0.0:8088->8088/tcp
mysql             docker-entrypoint.sh mysqld      Up             0.0.0.0:3306->3306/tcp, 33060/tcp
schema-registry   /etc/confluent/docker/run        Up             0.0.0.0:8081->8081/tcp
zookeeper         /etc/confluent/docker/run        Up             2181/tcp, 2888/tcp, 3888/tcp

Create test topic + data using ksqlDB (but it’s still just a Kafka topic under the covers)

docker exec -it ksqldb ksql http://ksqldb:8088

CREATE STREAM TEST01 (KEY_COL VARCHAR KEY, COL1 INT, COL2 VARCHAR)
  WITH (KAFKA_TOPIC='test01', PARTITIONS=1, VALUE_FORMAT='AVRO');

INSERT INTO TEST01 (KEY_COL, COL1, COL2) VALUES ('X',1,'FOO');
INSERT INTO TEST01 (KEY_COL, COL1, COL2) VALUES ('Y',2,'BAR');

SHOW TOPICS;
PRINT test01 FROM BEGINNING LIMIT 2;

Create the Sink connector

curl -X PUT http://localhost:8083/connectors/sink-jdbc-mysql-01/config \
     -H "Content-Type: application/json" -d '{
    "connector.class"                    : "io.confluent.connect.jdbc.JdbcSinkConnector",
    "connection.url"                     : "jdbc:mysql://mysql:3306/demo",
    "topics"                             : "test01",
    "key.converter"                      : "org.apache.kafka.connect.storage.StringConverter",
    "value.converter"                    : "io.confluent.connect.avro.AvroConverter",
    "value.converter.schema.registry.url": "http://schema-registry:8081",
    "connection.user"                    : "connect_user",
    "connection.password"                : "asgard",
    "auto.create"                        : true,
    "auto.evolve"                        : true,
    "insert.mode"                        : "insert",
    "pk.mode"                            : "record_key",
    "pk.fields"                          : "MESSAGE_KEY"
}'

Things to customise for your environment:

topics : the source topic(s) you want to send to S3
key.converter : match the serialisation of your source data (see here)

value.converter : match the serialisation of your source data (see here)

Check data in target db

docker exec -it mysql bash -c 'mysql -u root -p$MYSQL_ROOT_PASSWORD demo'

mysql> SHOW TABLES;
+----------------+
| Tables_in_demo |
+----------------+
| test01         |
+----------------+
1 row in set (0.00 sec)

mysql> SELECT * FROM test01;
+------+------+-------------+
| COL1 | COL2 | MESSAGE_KEY |
+------+------+-------------+
|    1 | FOO  | X           |
|    2 | BAR  | Y           |
+------+------+-------------+
2 rows in set (0.00 sec)

Insert some more data. Notice what happens if you re-use a key.

INSERT INTO TEST01 (KEY_COL, COL1, COL2) VALUES ('Z',1,'WOO');
INSERT INTO TEST01 (KEY_COL, COL1, COL2) VALUES ('Y',4,'PFF');

The Kafka Connect tasks status shows that there’s a duplicate key error

$ curl -s http://localhost:8083/connectors/sink-jdbc-mysql-01/tasks/0/status | jq '.'
{
  "id": 0,
  "state": "FAILED",
  "worker_id": "kafka-connect:8083",
  "trace": "org.apache.kafka.connect.errors.ConnectException: Exiting WorkerSinkTask due to unrecoverable exception.\n\tat org.apache.kafka.connect.runtime.WorkerSinkTask.deliverMessages(WorkerSinkTask.java:614)\n\tat org.apache.kafka.connect.runtime.WorkerSinkTask.poll(WorkerSinkTask.java:329)\n\tat org.apache.kafka.connect.runtime.WorkerSinkTask.iteration(WorkerSinkTask.java:232)\n\tat org.apache.kafka.connect.runtime.WorkerSinkTask.execute(WorkerSinkTask.java:201)\n\tat org.apache.kafka.connect.runtime.WorkerTask.doRun(WorkerTask.java:185)\n\tat org.apache.kafka.connect.runtime.WorkerTask.run(WorkerTask.java:234)\n\tat java.base/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:515)\n\tat java.base/java.util.concurrent.FutureTask.run(FutureTask.java:264)\n\tat java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)\n\tat java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)\n\tat java.base/java.lang.Thread.run(Thread.java:834)\nCaused by: org.apache.kafka.connect.errors.ConnectException: java.sql.SQLException: Exception chain:\njava.sql.BatchUpdateException: Duplicate entry 'Y' for key 'test01.PRIMARY'\njava.sql.SQLIntegrityConstraintViolationException: Duplicate entry 'Y' for key 'test01.PRIMARY'\n\n\tat io.confluent.connect.jdbc.sink.JdbcSinkTask.put(JdbcSinkTask.java:89)\n\tat org.apache.kafka.connect.runtime.WorkerSinkTask.deliverMessages(WorkerSinkTask.java:586)\n\t... 10 more\nCaused by: java.sql.SQLException: Exception chain:\njava.sql.BatchUpdateException: Duplicate entry 'Y' for key 'test01.PRIMARY'\njava.sql.SQLIntegrityConstraintViolationException: Duplicate entry 'Y' for key 'test01.PRIMARY'\n\n\tat io.confluent.connect.jdbc.sink.JdbcSinkTask.put(JdbcSinkTask.java:86)\n\t... 11 more\n"
}

Update the Sink connector to use UPSERT mode

curl -X PUT http://localhost:8083/connectors/sink-jdbc-mysql-01/config \
     -H "Content-Type: application/json" -d '{
    "connector.class": "io.confluent.connect.jdbc.JdbcSinkConnector",
    "connection.url": "jdbc:mysql://mysql:3306/demo",
    "topics": "test01",
    "key.converter": "org.apache.kafka.connect.storage.StringConverter",
    "connection.user": "connect_user",
    "connection.password": "asgard",
    "auto.create": true,
    "auto.evolve": true,
    "insert.mode": "upsert",
    "pk.mode": "record_key",
    "pk.fields": "MESSAGE_KEY"
}'

Now the row for key Y updates, whilst Z is added:

mysql> SELECT * FROM test01;
+------+------+-------------+
| COL1 | COL2 | MESSAGE_KEY |
+------+------+-------------+
|    1 | FOO  | X           |
|    4 | PFF  | Y           |
|    1 | WOO  | Z           |
+------+------+-------------+
3 rows in set (0.00 sec)

Drop fields, add fields - note how the target schema evolves in-place

# Drop the existing connector
curl -X DELETE http://localhost:8083/connectors/sink-jdbc-mysql-01

# Create a new one, reading from the same topic with new config
# Because it's got a new name, the connector will re-read all the messages
# from the topic.
curl -X PUT http://localhost:8083/connectors/sink-jdbc-mysql-02/config \
     -H "Content-Type: application/json" -d '{
    "connector.class": "io.confluent.connect.jdbc.JdbcSinkConnector",
    "connection.url": "jdbc:mysql://mysql:3306/demo",
    "topics": "test01",
    "key.converter": "org.apache.kafka.connect.storage.StringConverter",
    "connection.user": "connect_user",
    "connection.password": "asgard",
    "auto.create": true,
    "auto.evolve": true,
    "insert.mode": "upsert",
    "pk.mode": "record_key",
    "pk.fields": "MESSAGE_KEY",
    "transforms": "dropSome,addSome",
    "transforms.dropSome.type": "org.apache.kafka.connect.transforms.ReplaceField$Value",
    "transforms.dropSome.blacklist": "COL2",
    "transforms.addSome.type":"org.apache.kafka.connect.transforms.InsertField$Value",
    "transforms.addSome.partition.field": "_partition",
    "transforms.addSome.timestamp.field" : "RECORD_TS"
}'

mysql> describe test01;
+-------------+--------------+------+-----+---------+-------+
| Field       | Type         | Null | Key | Default | Extra |
+-------------+--------------+------+-----+---------+-------+
| COL1        | int          | YES  |     | NULL    |       |
| COL2        | varchar(256) | YES  |     | NULL    |       |
| MESSAGE_KEY | varchar(256) | NO   | PRI | NULL    |       |
| _partition  | int          | YES  |     | NULL    |       |
| RECORD_TS   | datetime(3)  | YES  |     | NULL    |       |
+-------------+--------------+------+-----+---------+-------+
5 rows in set (0.00 sec)

mysql> select * from test01;
+------+------+-------------+------------+-------------------------+
| COL1 | COL2 | MESSAGE_KEY | _partition | RECORD_TS               |
+------+------+-------------+------------+-------------------------+
|    1 | FOO  | X           |          0 | 2021-03-11 11:50:00.759 |
|    4 | PFF  | Y           |          0 | 2021-03-11 11:50:47.761 |
|    1 | WOO  | Z           |          0 | 2021-03-11 11:50:47.682 |
+------+------+-------------+------------+-------------------------+
3 rows in set (0.00 sec)

Write some CSV and JSON to new topics

docker exec -i kafkacat kafkacat \
        -b broker:29092 -P \
        -t some_json_data <<EOF
{ "ID": 1, "Artist": "Rick Astley", "Song": "Never Gonna Give You Up" }
{ "ID": 2, "Artist": "asdfasd", "Song": "dsfjfghg" }
EOF

docker exec -i kafkacat kafkacat \
        -b broker:29092 -P \
        -t some_json_data_with_a_schema <<EOF
{ "schema": { "type": "struct", "optional": false, "version": 1, "fields": [ { "field": "ID", "type": "string", "optional": true }, { "field": "Artist", "type": "string", "optional": true }, { "field": "Song", "type": "string", "optional": true } ] }, "payload": { "ID": "1", "Artist": "Rick Astley", "Song": "Never Gonna Give You Up" } }
EOF

docker exec -i kafkacat kafkacat \
        -b broker:29092 -P \
        -t some_csv_data <<EOF
1,Rick Astley,Never Gonna Give You Up
EOF

Stream the JSON data that has a schema to DB:

curl -X PUT http://localhost:8083/connectors/sink-jdbc-mysql-02-json/config \
     -H "Content-Type: application/json" -d '{
    "connector.class"               : "io.confluent.connect.jdbc.JdbcSinkConnector",
    "connection.url"                : "jdbc:mysql://mysql:3306/demo",
    "topics"                        : "some_json_data_with_a_schema",
    "key.converter"                 : "org.apache.kafka.connect.storage.StringConverter",
    "value.converter"               : "org.apache.kafka.connect.json.JsonConverter",
    "value.converter.schemas.enable": "true",
    "connection.user"               : "connect_user",
    "connection.password"           : "asgard",
    "auto.create"                   : true,
    "auto.evolve"                   : true,
    "insert.mode"                   : "insert"
}'

Use ksqlDB to apply a schema to the CSV and schemaless-JSON, and show off INSERT INTO for merging two topics into one with a common schema

CREATE STREAM SOME_JSON (ID INT, ARTIST VARCHAR, SONG VARCHAR)
  WITH (KAFKA_TOPIC='some_json_data', VALUE_FORMAT='JSON');

SET 'auto.offset.reset' = 'earliest';

CREATE STREAM SOME_JSON_AS_AVRO
  WITH (VALUE_FORMAT='AVRO') AS
    SELECT * FROM SOME_JSON;

CREATE STREAM SOME_CSV (ID INT, ARTIST VARCHAR, SONG VARCHAR)
  WITH (KAFKA_TOPIC='some_csv_data', VALUE_FORMAT='DELIMITED');

INSERT INTO SOME_JSON_AS_AVRO SELECT * FROM SOME_CSV;

Create a sink for the reserialized data

curl -X PUT http://localhost:8083/connectors/sink-jdbc-mysql-02-avro/config \
     -H "Content-Type: application/json" -d '{
    "connector.class"                    : "io.confluent.connect.jdbc.JdbcSinkConnector",
    "connection.url"                     : "jdbc:mysql://mysql:3306/demo",
    "topics"                             : "SOME_JSON_AS_AVRO",
    "key.converter"                      : "org.apache.kafka.connect.storage.StringConverter",
    "value.converter"                    : "io.confluent.connect.avro.AvroConverter",
    "value.converter.schema.registry.url": "http://schema-registry:8081",
    "connection.user"                    : "connect_user",
    "connection.password"                : "asgard",
    "auto.create"                        : true,
    "auto.evolve"                        : true,
    "insert.mode"                        : "insert"
}'

References

From Zero to Hero with Kafka Connect
Confluent Hub
JDBC Sink connector docs
Skipping bad records with the Kafka Connect JDBC sink connector
Single Message Transform blog
InsertField Single Message Transform

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