Oracle Database Sink (JDBC) Connector for Confluent Cloud¶

The fully-managed Oracle Database Sink connector for Confluent Cloud allows you to export data from Apache Kafka® topics to an Oracle database (JDBC). The connector polls data from Kafka to write to the database based on the topic subscription. It is possible to achieve idempotent writes with upserts. Auto-creation of tables and limited auto-evolution is also supported.

Note

This is a Quick Start for the fully-managed cloud connector. If you are installing the connector locally for Confluent Platform, see JDBC Connector (Source and Sink) for Confluent Platform.

Features¶

The Oracle Database Sink connector supports the following features:

Idempotent writes: 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 automatically adding a new row or updating the existing row if there is a primary key constraint violation, which provides idempotence.

Important

When a target table includes columns with CLOB, INSERT or UPSERT performance may be degraded. Try to use VARCHAR or VARCHAR2 instead.
SSL support: Supports one-way SSL.
Schemas: The connector supports Avro, JSON Schema, and Protobuf input value formats. The connector supports Avro, JSON Schema, Protobuf, and String input key formats. Schema Registry must be enabled to use a Schema Registry-based format.
Primary key support: Supported PK modes are kafka, none, record_key, and record_value. Used in conjunction with the PK Fields property.
Table and column auto-creation: auto.create and auto-evolve are supported. If tables or columns are missing, they can be created automatically. Table names are created based on Kafka topic names.
At least once delivery: This connector guarantees that records from the Kafka topic are delivered at least once.
Supports multiple tasks: The connector supports running one or more tasks. More tasks may improve performance.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Managed and Custom Connectors section.

Limitations¶

Be sure to review the following information.

For connector limitations, see Oracle Database Sink Connector limitations.
If you plan to use one or more Single Message Transforms (SMTs), see SMT Limitations.
If you plan to use Confluent Cloud Schema Registry, see Schema Registry Enabled Environments.

Quick Start¶

Use this quick start to get up and running with the Confluent Cloud Oracle Database Sink connector. The quick start provides the basics of selecting the connector and configuring it to stream events

Prerequisites

Authorized access to a Confluent Cloud cluster on Amazon Web Services (AWS), Microsoft Azure (Azure), or Google Cloud.
Authorized access to an Oracle database.
The Oracle Database version must be 11.2.0.4 or later.
The database and Kafka cluster should be in the same region. If you use a different region, be aware that you may incur additional data transfer charges.
For networking considerations, see Networking and DNS. To use a set of public egress IP addresses, see Public Egress IP Addresses for Confluent Cloud Connectors.
The Confluent CLI installed and configured for the cluster. See Install the Confluent CLI.
Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.
At least one source Kafka topic must exist in your Confluent Cloud cluster before creating the sink connector.
See Database considerations for additional information.

Using the Confluent Cloud Console¶

Step 1: Launch your Confluent Cloud cluster¶

See the Quick Start for Confluent Cloud for installation instructions.

Step 2: Add a connector¶

In the left navigation menu, click Connectors. If you already have connectors in your cluster, click + Add connector.

Step 3: Select your connector¶

Click the Oracle Database Sink connector card.

Step 4: Enter the connector details¶

Note

Ensure you have all your prerequisites completed.
An asterisk ( * ) designates a required entry.

At the Add Oracle Database Sink Connector screen, complete the following:

If you’ve already populated your Kafka topics, select the topics you want to connect from the Topics list.

To create a new topic, click +Add new topic.

Note

Configuration properties that are not shown in the Cloud Console use the default values. See Configuration Properties for all property values and definitions.

Select the Input Kafka record value format (data coming from the Kafka topic): AVRO, JSON_SR, or PROTOBUF. A valid schema must be available in Schema Registry to use a schema-based message format.
Select an insert mode:
- INSERT: Use the standard INSERT row function. An error occurs if the row already exists in the table.
- UPSERT: This mode is similar to INSERT. However, if the row already exists, the UPSERT function overwrites column values with the new values provided.
Show advanced configurations
- Schema context: Select a schema context to use for this connector, if using a schema-based data format. This property defaults to the Default context, which configures the connector to use the default schema set up for Schema Registry in your Confluent Cloud environment. A schema context allows you to use separate schemas (like schema sub-registries) tied to topics in different Kafka clusters that share the same Schema Registry environment. For example, if you select a non-default context, a Source connector uses only that schema context to register a schema and a Sink connector uses only that schema context to read from. For more information about setting up a schema context, see What are schema contexts and when should you use them?.
- Auto create table: Whether to automatically create the destination table if it is missing.
- Auto add columns: Whether to automatically add columns in the table if they are missing.
- Database timezone: Name of the JDBC timezone that should be used in the connector when inserting time-based values.
- Table name format: A format string for the destination table name, which may contain ${topic} as a placeholder for the originating topic name. For example, to create a table named kafka-orders based on a Kafka topic named orders, you would enter kafka-${topic} in this field.
- Table types: The comma-separated types of database tables to which the sink connector can write.
- Fields included: List of comma-separated record value field names. If empty, all fields from the record value are used.
- PK mode: The primary key mode.
- PK Fields: List of comma-separated primary key field names.
- When to quote SQL identifiers: When to quote table names, column names, and other identifiers in SQL statements.
- Max rows per batch: Maximum number of rows to include in a single batch when polling for new data. This setting can be used to limit the amount of data buffered internally in the connector.
- Input Kafka record key format: Sets the input Kafka record key format. Valid entries are AVRO, JSON_SR, PROTOBUF, STRING. A valid schema must be available in Schema Registry to use a schema-based message format.
- Delete on null: Whether to treat null record values as deletes. Requires pk.mode to be record_key.
- For Transforms and Predicates, see the Single Message Transforms (SMT) documentation for details.
See Configuration Properties for all property values and definitions.
Click Continue.

Step 5: Check for records¶

Verify that rows are populating the database.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Managed and Custom Connectors section.

Tip

When you launch a connector, a Dead Letter Queue topic is automatically created. See Confluent Cloud Dead Letter Queue for details.

Using the Confluent CLI¶

Complete the following steps to set up and run the connector using the Confluent CLI.

Note

Make sure you have all your prerequisites completed.

Step 1: List the available connectors¶

Enter the following command to list available connectors:

confluent connect plugin list

Step 2: List the connector configuration properties¶

Enter the following command to show the connector configuration properties:

confluent connect plugin describe <connector-plugin-name>

The command output shows the required and optional configuration properties.

Step 3: Create the connector configuration file¶

Create a JSON file that contains the connector configuration properties. The following example shows the required connector properties. See the Configuration Properties for configuration property values and descriptions.

{
  "connector.class": "OracleDatabaseSink",
  "input.data.format": "AVRO",
  "name": "OracleDatabaseSink_0",
  "kafka.auth.mode": "KAFKA_API_KEY",
  "kafka.api.key": "<my-kafka-api-key>",
  "kafka.api.secret": "<my-kafka-api-secret>",
  "connection.host ": "<connection-host",
  "connection.port": "1521",
  "connection.user": "<user-name>",
  "connection.password": "<user-password>",
  "db.name": "<database-name>",
  "ssl.server.cert.dn": "<distinguished-database-server-name>",
  "ssl.rootcertfile": "<certificate-text>",
  "tasks.max": "1",
  "topics": "<topic-name>",
}

Note the following property definitions:

"connector.class": Identifies the connector plugin name.
"input.data.format": Sets the input Kafka record value format (data coming from the Kafka topic). Valid entries are AVRO, JSON_SR, and PROTOBUF. You must have Confluent Cloud Schema Registry configured if using a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See input.key.format in the Configuration Properties for additional options.
"name": Sets a name for your new connector.

"kafka.auth.mode": Identifies the connector authentication mode you want to use. There are two options: SERVICE_ACCOUNT or KAFKA_API_KEY (the default). To use an API key and secret, specify the configuration properties kafka.api.key and kafka.api.secret, as shown in the example configuration (above). To use a service account, specify the Resource ID in the property kafka.service.account.id=<service-account-resource-ID>. To list the available service account resource IDs, use the following command:
```
confluent iam service-account list
```
For example:
```
confluent iam service-account list

   Id     | Resource ID |       Name        |    Description
+---------+-------------+-------------------+-------------------
   123456 | sa-l1r23m   | sa-1              | Service account 1
   789101 | sa-l4d56p   | sa-2              | Service account 2
```
"connection.<...>": The database connection properties. The connection.host entry will look similar to database-1.<id>.us-west-2.rds.amazonaws.com. For details, see Database Connection Details.
"ssl.rootcertfile": The default ssl.mode is verify-full. Use the property ssl.rootcertfile and add the contents of the text certificate file for the property value. For example, "ssl.rootcertfile": "<certificate-text>". See the Configuration Properties for additional ssl.mode options.
"ssl.server.cert.dn": The default ssl.mode is verify-full. With this mode, you must provide the distinguished server name. See the Configuration Properties for additional ssl.mode options.
"tasks.max": Enter the maximum number of tasks for the connector to use. More tasks may improve performance (that is, consumer lag is reduced with multiple tasks running).
"topics": Enter the topic name or a comma-separated list of topic names.

Single Message Transforms: See the Single Message Transforms (SMT) documentation for details about adding SMTs using the CLI.

See Configuration Properties for all property values and descriptions.

Step 4: Load the properties file and create the connector¶

Enter the following command to load the configuration and start the connector:

confluent connect cluster create --config-file <file-name>.json

For example:

confluent connect cluster create --config-file oracle-db-sink-config.json

Example output:

Created connector OracleDatabaseSink_0 lcc-do6vzd

Step 5: Check the connector status¶

Enter the following command to check the connector status:

confluent connect cluster list

Example output:

ID           |             Name         | Status  | Type | Trace
+------------+--------------------------+---------+------+-------+
lcc-do6vzd   | OracleDatabaseSink_0     | RUNNING | sink |       |

Step 6: Check for records.¶

Verify that rows are populating the database.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Managed and Custom Connectors section.

Tip

When you launch a connector, a Dead Letter Queue topic is automatically created. See Confluent Cloud Dead Letter Queue for details.

Configuration Properties¶

Use the following configuration properties with the fully-managed connector. For self-managed connector property definitions and other details, see the connector docs in Self-managed connectors for Confluent Platform.

Which topics do you want to get data from?¶

topics

Identifies the topic name or a comma-separated list of topic names.

Type: list
Importance: high

Schema Config¶

schema.context.name

Add a schema context name. A schema context represents an independent scope in Schema Registry. It is a separate sub-schema tied to topics in different Kafka clusters that share the same Schema Registry instance. If not used, the connector uses the default schema configured for Schema Registry in your Confluent Cloud environment.

Type: string
Default: default
Importance: medium

Input messages¶

input.data.format

Sets the input Kafka record value format. Valid entries are AVRO, JSON_SR, or PROTOBUF. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, and PROTOBUF.

Type: string
Importance: high

input.key.format

Sets the input Kafka record key format. This need to be set to a proper format if using pk.mode=record_key. Valid entries are AVRO, JSON_SR, PROTOBUF, STRING. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, and PROTOBUF.

Type: string
Importance: high

delete.enabled

Whether to treat null record values as deletes. Requires pk.mode to be record_key.

Type: boolean
Default: false
Importance: low

How should we connect to your data?¶

name

Sets a name for your connector.

Type: string
Valid Values: A string at most 64 characters long
Importance: high

Kafka Cluster credentials¶

kafka.auth.mode

Kafka Authentication mode. It can be one of KAFKA_API_KEY or SERVICE_ACCOUNT. It defaults to KAFKA_API_KEY mode.

Type: string
Default: KAFKA_API_KEY
Valid Values: KAFKA_API_KEY, SERVICE_ACCOUNT
Importance: high

kafka.api.key

Kafka API Key. Required when kafka.auth.mode==KAFKA_API_KEY.

Type: password
Importance: high

kafka.service.account.id

The Service Account that will be used to generate the API keys to communicate with Kafka Cluster.

Type: string
Importance: high

kafka.api.secret

Secret associated with Kafka API key. Required when kafka.auth.mode==KAFKA_API_KEY.

Type: password
Importance: high

How should we connect to your database?¶

connection.host

Depending on the service environment, certain network access limitations may exist. Make sure the connector can reach your service. Do not include jdbc:xxxx:// in the connection hostname property (e.g. database-1.abc234ec2.us-west.rds.amazonaws.com).

Type: string
Importance: high

connection.port

JDBC connection port.

Type: int
Valid Values: [0,…,65535]
Importance: high

connection.user

JDBC connection user.

Type: string
Importance: high

connection.password

JDBC connection password.

Type: password
Importance: high

db.name

JDBC database name.

Type: string
Valid Values: Must match the regex ^[a-zA-Z][a-zA-Z0-9$#_.]*$
Importance: high

ssl.mode

What SSL mode should we use to connect to your database. disabled disables SSL entirely. verify-ca uses SSL for encryption and performs authentication of the server CA. verify-ca option requires a Java truststore containing the server CA and the truststore password to be provided.

Type: string
Default: verify-full
Importance: high

ssl.truststorefile

The trust store containing server CA certificate. Only required if using verify-ca or verify-full ssl mode.

Type: password
Default: [hidden]
Importance: low

ssl.truststorepassword

The trust store password containing server CA certificate. Only required if using verify-ca or verify-full ssl mode.

Type: password
Default: [hidden]
Importance: low

ssl.server.cert.dn

Use this paramter to specify the distinguished name (DN) of the database server. Only required if using verify-full ssl mode.

Type: string
Importance: low

Database details¶

insert.mode

The insertion mode to use. INSERT uses the standard INSERT row function. An error occurs if the row already exists in the table; UPSERT mode is similar to INSERT. However, if the row already exists, the UPSERT function overwrites column values with the new values provided.

Type: string
Default: INSERT
Importance: high

table.name.format

A format string for the destination table name, which may contain ${topic} as a placeholder for the originating topic name.

For example, kafka_${topic} for the topic ‘orders’ will map to the table name ‘kafka_orders’.

Type: string
Default: ${topic}
Importance: medium

table.types

The comma-separated types of database tables to which the sink connector can write. By default this is TABLE, but any combination of TABLE and VIEW is allowed. Not all databases support writing to views, and when they do the sink connector will fail if the view definition does not match the records’ schemas (regardless of auto.evolve).

Type: list
Default: TABLE
Importance: low

fields.whitelist

List of comma-separated record value field names. If empty, all fields from the record value are utilized, otherwise used to filter to the desired fields.

Type: list
Importance: medium

db.timezone

Name of the JDBC timezone used in the connector when querying with time-based criteria. Defaults to UTC.

Type: string
Default: UTC
Importance: medium

date.timezone

Name of the JDBC timezone that should be used in the connector when inserting DATE type values. Defaults to DB_TIMEZONE that uses the timezone set for db.timzeone configuration (to maintain backward compatibility). It is recommended to set this to UTC to avoid conversion for DATE type values.

Type: string
Default: DB_TIMEZONE
Valid Values: DB_TIMEZONE, UTC
Importance: medium

Primary Key¶

pk.mode

The primary key mode, also refer to pk.fields documentation for interplay. Supported modes are:

none: No keys utilized.

kafka: Apache Kafka® coordinates are used as the PK.

record_value: Field(s) from the record value are used, which must be a struct.

record_key: Field(s) from the record key are used, which must be a struct.

Type: string
Valid Values: kafka, none, record_key, record_value
Importance: high

pk.fields

List of comma-separated primary key field names. The runtime interpretation of this config depends on the pk.mode:

none: Ignored as no fields are used as primary key in this mode.

kafka: Must be a trio representing the Kafka coordinates, defaults to __connect_topic,__connect_partition,__connect_offset if empty.

record_value: If empty, all fields from the value struct will be used, otherwise used to extract the desired fields.

Type: list
Importance: high

SQL/DDL Support¶

auto.create

Whether to automatically create the destination table if it is missing.

Type: boolean
Default: false
Importance: medium

auto.evolve

Whether to automatically add columns in the table if they are missing.

Type: boolean
Default: false
Importance: medium

quote.sql.identifiers

When to quote table names, column names, and other identifiers in SQL statements. For backward compatibility, the default is ‘always’.

Type: string
Default: ALWAYS
Valid Values: ALWAYS, NEVER
Importance: medium

Connection details¶

batch.sizes

Maximum number of rows to include in a single batch when polling for new data. This setting can be used to limit the amount of data buffered internally in the connector.

Type: int
Default: 3000
Valid Values: [1,…,5000]
Importance: low

Consumer configuration¶

max.poll.interval.ms

The maximum delay between subsequent consume requests to Kafka. This configuration property may be used to improve the performance of the connector, if the connector cannot send records to the sink system. Defaults to 300000 milliseconds (5 minutes).

Type: long
Default: 300000 (5 minutes)
Valid Values: [60000,…,1800000] for non-dedicated clusters and [60000,…] for dedicated clusters
Importance: low

max.poll.records

The maximum number of records to consume from Kafka in a single request. This configuration property may be used to improve the performance of the connector, if the connector cannot send records to the sink system. Defaults to 500 records.

Type: long
Default: 500
Valid Values: [1,…,500] for non-dedicated clusters and [1,…] for dedicated clusters
Importance: low

Number of tasks for this connector¶

tasks.max

Maximum number of tasks for the connector.

Type: int
Valid Values: [1,…]
Importance: high

Database considerations¶

Note the following issues to keep in mind.

String type is mapped to CLOB when auto.create=true. For example, you have the following Avro schema:

{
  "connect.name": "ksql.ratings",
  "fields": [
    {
      "name": "rating_id",
      "type": "long"
    },
    {
      "name": "user_id",
      "type": "int"
    },
    ...
    {
      "name": "channel",
      "type": "string"
    },
    {
      "name": "message",
      "type": "string"
    }
  ],
  "name": "ratings",
  "namespace": "ksql",
  "type": "record"
}

These values are mapped to CLOB in the table schema:

Name        Null?    Type
----------- -------- ----------
rating_id   NOT NULL NUMBER(19)
user_id     NOT NULL NUMBER(10)
stars       NOT NULL NUMBER(10)
route_id    NOT NULL NUMBER(10)
rating_time NOT NULL NUMBER(19)
channel     NOT NULL CLOB
message     NOT NULL CLOB

Since String is mapped to CLOB when auto.create=true, a field using the String type cannot be used as a primary key. If you want to use a String type field as a primary key, you should create a table in the database first and then use auto.create=false. If not, an exception occurs containing the following line:

...
"stringValue": "Exception chain:\njava.sql.SQLException: ORA-02329:
column of datatype LOB cannot be unique or a primary key
...

The table name and column names are case sensitive. For example, you have the following Avro schema:

{
  "connect.name": "ksql.pageviews",
  "fields": [
    {
      "name": "viewtime",
      "type": "long"
    },
    {
      "name": "userid",
      "type": "string"
    },
    {
      "name": "pageid",
      "type": "string"
    }
  ],
  "name": "pageviews",
  "namespace": "ksql",
  "type": "record"
}

A table named PAGEVIEWS is created, which causes the exception where pageviews is not found.

create table pageviews (
  userid VARCHAR(10) NOT NULL PRIMARY KEY,
  pageid VARCHAR(50),
  viewtime VARCHAR(50)
  );

Table PAGEVIEWS created.

DESC pageviews;
Name     Null?    Type
-------- -------- ------------
USERID   NOT NULL VARCHAR2(10)
PAGEID            VARCHAR2(50)
VIEWTIME          VARCHAR2(50)

An exception message similar to the following one will be in the DLQ:

{
  "key": "__connect.errors.exception.message",
  "stringValue": "Table \"pageviews\" is missing and auto-creation
  is disabled"
}

To resolve this issue, create a table in Oracle Database first and use auto.create=false.

create table "pageviews" (
  "userid" VARCHAR(10) NOT NULL PRIMARY KEY,
  "pageid" VARCHAR(50),
  "viewtime" VARCHAR(50)
  );

Table "pageviews" created.


DESC "pageviews";

Name     Null?    Type
-------- -------- ------------
userid   NOT NULL VARCHAR2(10)
pageid            VARCHAR2(50)
viewtime          VARCHAR2(50)

Note

Note that SQL standards define databases to be case insensitive for identifiers and keywords unless they are quoted. What this means is that CREATE TABLE test_case creates a table named TEST_CASE and CREATE TABLE "test_case" creates a table named test_case. This is also true of table column identifiers. For additional information about identifier quoting, see Database Identifiers, Quoting, and Case Sensitivity.

Next Steps¶

For an example that shows fully-managed Confluent Cloud connectors in action with Confluent Cloud ksqlDB, see the Cloud ETL Demo. This example also shows how to use Confluent CLI to manage your resources in Confluent Cloud.