<a id="cc-oracle-db-sink"></a>

# Oracle Database Sink (JDBC) Connector for Confluent Cloud

The fully managed Oracle Database Sink connector for Confluent Cloud exports 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 Quick Start is for the fully managed Confluent Cloud connector. If you are
  installing the connector locally for Confluent Platform, see [JDBC Connector (Source and
  Sink) for Confluent Platform](https://docs.confluent.io/kafka-connectors/jdbc/current/).
- If you require private networking for fully managed connectors, make sure to set up the proper
  networking beforehand. For more information, see [Manage Networking for Confluent Cloud Connectors](networking/internet-resource.md#clusters-connect-cloud).

## 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](../get-started/schema-registry.md#cloud-sr-config) 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.
* **Secret manager integration**: The connector supports secret manager integration. For `Password` based authentication, the connector can retrieve the following configurations from an integrated secret manager at runtime as needed.

  | **Secret manager managed configuration**   | **Type**   |
  |--------------------------------------------|------------|
  | `connection.user`                          | `STRING`   |
  | `connection.password`                      | `PASSWORD` |

  For more information, see [Create a secret manager integration in Confluent Cloud](secret-manager-integration/overview.md#cloud-secret-manager-quickstart).

For more information and examples to use with the Confluent Cloud API for Connect,
see the [Confluent Cloud API for Connect Usage Examples](connect-api-section.md#ccloud-connect-api) section.

## Limitations

Be sure to review the following information.

* For connector limitations, see [Oracle Database Sink Connector](limits.md#cc-oracle-db-sink-limits) limitations.
* If you plan to use one or more Single Message Transformations (SMTs), see [SMT Limitations](single-message-transforms.md#cc-single-message-transforms-limitations).

## 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

<a id="cc-oracle-db-sink-prereqs"></a>

Prerequisites
: - Authorized access to a [Confluent Cloud](https://www.confluent.io/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](overview.md#connect-internet-access-resources). To use a set of public egress IP addresses, see [Public Egress IP Addresses for Confluent Cloud Connectors](static-egress-ip.md#cc-static-egress-ips).
  - The Confluent CLI installed and configured for the cluster. See [Install the Confluent CLI](https://docs.confluent.io/confluent-cli/current/install.html).
  - [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).
  - At least one source Kafka topic must exist in your Confluent Cloud cluster before creating the sink connector.
  - See [Database considerations](#cc-oracle-db-sink-db-troubleshooting) for additional information.

### Using the Confluent Cloud Console

#### Step 1: Launch your Confluent Cloud cluster

To create and launch a Kafka cluster in Confluent Cloud, see [Create a kafka cluster in Confluent Cloud](../get-started/index.md#cloud-create-kafka-cluster).

#### 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.

![Oracle Database Sink Connector Card](images/ccloud-oracle-database-sink-icon.png)

<a id="cc-oracle-db-sink-connection"></a>

#### Step 4: Enter the connector details

#### NOTE
* Ensure you have all your [prerequisites](#cc-oracle-db-sink-prereqs) completed.
* An asterisk ( \* ) designates a required entry.

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

### Topic selection

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**.

### Kafka access

1. Select the way you want to provide **Kafka Cluster credentials**. You can
   choose one of the following options:
   - **My account**: This setting allows your connector to globally access everything
     that you have access to. With a user account, the connector uses an API key and
     secret to access the Kafka cluster. This option is not recommended for production.
   - **Service account**: This setting limits the access for your connector by using a
     [service account](service-account.md#s3-cloud-service-account). This option is recommended for
     production.
   - **Use an existing API key**: This setting allows you to specify an API key and a
     secret pair. You can use an existing pair or create a new one. This method is not
     recommended for production environments.

   #### NOTE
   Freight clusters support only service accounts for Kafka authentication.
2. Click **Continue**.

### Authentication

1. Configure the authentication properties:

   **Authentication method**
   - **Authentication method**: Select how you want to authenticate with your database.
   - **Use secret manager**: Fetch sensitive configuration values from a secret manager.

   **Secret manager configuration**
   - **Secret manager**: Select the secret manager to use for retrieving sensitive data.
   - **Configurations from Secret manager**: Select the configurations
     whose values Confluent Cloud should
     fetch from the secret manager.
   - **Provider Integration**: Select an existing provider integration that has access to your secret manager.

   **How should we connect to your database?**
   - **Connection host**: The JDBC connection host.
   - **Connection port**: The JDBC connection port.
   - **Connection user**: The JDBC connection user.
   - **Connection password**: The JDBC connection password.
   - **Database name**: The JDBC database name.
   - **SSL mode**: The SSL mode to use to connect to your database.
   - **Trust store**: The trust store file that contains the server CA
     certificate.
   - **Trust store password**: The password for the trust store file that
     contains the server CA certificate.
   - **Distinguished name (DN) of the database server**: Used to specify
     the distinguished name (DN) of the database server. Only required if
     using `verify-full` as the SSL mode.
2. Click **Continue**.

### Configuration

#### NOTE
Configuration properties that are not shown in the
Cloud Console use the default values.  See
[Configuration Properties](#cc-oracle-db-sink-config-properties) for all property values and
definitions.

- **Input Kafka record value format**: 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](../get-started/schema-registry.md#cloud-sr-config) to use a
  schema-based message format.
- **Insert mode**: 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?](../sr/faqs-cc.md#faq-schema-contexts).
- **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.
- **Timezone used for Date**: The name of the JDBC timezone the connector should use when inserting DATE
  type values. It defaults to `DB_TIMEZONE` that uses the timezone set for `db.timezone`
  configuration (to maintain backward compatibility). It is recommended to set this
  to `UTC` to avoid conversion for DATE type values.
- **Timestamp Precision Mode**: Controls the precision used when converting timestamps. If set to `microseconds`, the
  timestamp converts to microsecond precision. If set to `nanoseconds`, the timestamp
  converts to nanoseconds precision.
- **Timestamp Fields**: A comma-separated list of record value timestamp field names that the connector should
  convert to timestamps. These fields will be converted based on the precision mode
  specified in Timestamp Precision Mode. The timestamp fields included here must be
  Long or String type, and nested fields are not supported.
- **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](../get-started/schema-registry.md#cloud-sr-config) 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`.
- **String Value Column Name**: Name of the destination table column
  when the Kafka record value written using
  `StringConverter` is sinked to the DB table

  The raw string value is written into this column.
  If the destination table is auto-created,
  the column is created with the name specified
  in this field. If the table already exists and
  the column is missing, the column is added using
  `ALTER` when `auto.evolve` is set to `true`.

  #### NOTE
  Databases that treat quoted identifiers as case-sensitive,
  like PostgreSQL, ensure that the column name in the
  existing table exactly matches this value.

  Defaults to `record_value`.
- **Date Calendar System**: Controls the calendar used to interpret the time-since-epoch value in the Kafka t
  opic record during conversion to DATE or TIMESTAMP.
  - If you use `LEGACY` (the default), the connector uses the hybrid Gregorian/Julian calendar.
    This matches the behavior of older Java date and time APIs.
  - If you use `PROLEPTIC_GREGORIAN`, the connector uses the proleptic Gregorian calendar,
    which extends the Gregorian rules backward indefinitely and does not apply the 1582 cutover.
    This matches the behavior of modern Java date and time APIs (java.time).

  The ideal setting depends on whether the values in the source topic were populated using
  old or new Java date and time APIs. Changing this configuration on an existing connector
  might lead to a drift in the DATE/TIMESTAMP column’s values populated in the sink database.

**Additional Configs**

- **Value Converter Replace Null With Default**: Specifies whether to replace fields that have a default value and that are null to the default value. When set to `true`, the connector uses the default value; otherwise, it uses `null`. Applies to the `JSON` converter.
- **Value Converter Schema ID Deserializer**: Sets the class name of the schema ID deserializer for values. The deserializer reads schema IDs from message headers.
- **Value Converter Reference Subject Name Strategy**: Sets the subject reference name strategy for values. Valid entries are `DefaultReferenceSubjectNameStrategy` or `QualifiedReferenceSubjectNameStrategy`. You can use this strategy only with `PROTOBUF` format; the default strategy is `DefaultReferenceSubjectNameStrategy`.
- **Schema ID For Value Converter**: Sets the schema ID to use for deserialization when using `ConfigSchemaIdDeserializer`. This lets you specify a fixed schema ID for deserializing message values. This property is applicable only when `value.converter.value.schema.id.deserializer` is set to `ConfigSchemaIdDeserializer`.
- **Value Converter Schemas Enable**: Includes schema within each of the serialized values. Input messages must contain `schema` and `payload` fields and must not contain additional fields. For plain `JSON` data, set this to `false`. Applies to the `JSON` converter.
- **Errors Tolerance**: Use this property to configure the connector’s error handling behavior.

  #### WARNING
  Use this property with caution for sink connectors, as it can lead to data loss. If you set this property to `all`, the connector does not fail on errant records, but logs them (and sends to DLQ for sink connectors) and continues processing. If you set this property to `none`, the connector task fails on errant records.
- **Value Converter Ignore Default For Nullables**: When set to `true`, this property ensures that the corresponding record in Kafka is `null`, instead of showing the default column value. Applies to the `AVRO`, `PROTOBUF`, and `JSON_SR` converters.
- **Key Converter Schema ID Deserializer**: Sets the class name of the schema ID deserializer for keys. The deserializer reads schema IDs from message headers.
- **Value Converter Decimal Format**: Specifies the `JSON` or `JSON_SR` serialization format for Connect `DECIMAL` logical type values with two allowed literals:
  `BASE64` to serialize `DECIMAL` logical types as base64 encoded binary data, and
  `NUMERIC` to serialize `DECIMAL` logical type values in `JSON` or `JSON_SR` as a number representing the decimal value.
- **Schema GUID For Key Converter**: Sets the schema GUID to use for deserialization when using `ConfigSchemaIdDeserializer`. This lets you specify a fixed schema GUID for deserializing message keys. This property is applicable only when `key.converter.key.schema.id.deserializer` is set to `ConfigSchemaIdDeserializer`.
- **Schema GUID For Value Converter**: Sets the schema GUID to use for deserialization when using `ConfigSchemaIdDeserializer`. This lets you specify a fixed schema GUID for deserializing message values. This property is applicable only when `value.converter.value.schema.id.deserializer` is set to `ConfigSchemaIdDeserializer`.
- **Value Converter Connect Meta Data**: Enables the Connect converter to add its metadata to the output schema. Applies to Avro converters.
- **Value Converter Value Subject Name Strategy**: Determines how to construct the subject name under which the value schema is registered with Schema Registry.
- **Key Converter Key Subject Name Strategy**: Determines how to construct the subject name for key schema registration.
- **Schema ID For Key Converter**: Sets the schema ID to use for deserialization when using `ConfigSchemaIdDeserializer`. This lets you specify a fixed schema ID for deserializing message keys. This property is applicable only when `key.converter.key.schema.id.deserializer` is set to `ConfigSchemaIdDeserializer`.

**Auto-restart policy**

- **Enable Connector Auto-restart**: Enables the auto-restart behavior of the connector and its
  task in the event of user-actionable errors. Defaults to `true`, enabling the connector to
  automatically restart in case of user-actionable errors. Set this property to `false` to
  disable auto-restart for failed connectors. If disabled, you must manually restart the connector.

**Consumer configuration**

- **Max poll interval(ms)**: Sets the maximum delay between subsequent consume requests to Kafka. Use this property to
  improve connector performance in cases when the connector cannot send records to the sink system.
  The default is 300,000 milliseconds (5 minutes).
- **Max poll records**: Sets the maximum number of records to consume from Kafka in a single request. Use this property to
  improve connector performance in cases when the connector cannot send records to the sink system.
  The default is 500 records.

**Transforms**

- **Single Message Transformations**: To add a new SMT, see [Add transforms](single-message-transforms.md#cc-single-message-transforms-ui).
  For more information about unsupported SMTs, see
  [Unsupported transformations](single-message-transforms.md#cc-single-message-transforms-unsupported-transforms).

**Processing position**

- **Set offsets**: Click **Set offsets** to define a specific offset for
  this connector to begin procession data from. For more information
  on managing offsets, see [Manage offsets](offsets.md#connect-custom-offsets).

See [Configuration Properties](#cc-oracle-db-sink-config-properties) for all property
values and definitions.

- Click **Continue**.

### Sizing

Based on the number of topic partitions you select, you will be provided
with a recommended number of tasks.

1. To change the number of recommended tasks, enter the number of
   [tasks](/platform/current/connect/concepts.html#tasks) for the connector to use in
   the **Tasks** field.
2. Click **Continue**.

### Review and Launch

1. Verify the connection details.
2. Click **Launch**.

   The status for the connector should go from **Provisioning** to
   **Running**.

#### 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 Connect Usage Examples](connect-api-section.md#ccloud-connect-api) section.

### 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](#cc-oracle-db-sink-prereqs) completed.

#### Step 1: List the available connectors

Enter the following command to list available connectors:

```none
confluent connect plugin list
```

#### Step 2: List the connector configuration properties

Enter the following command to show the connector configuration properties:

```none
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](#cc-oracle-db-sink-config-properties) for configuration property values and
descriptions.

```json
{
  "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**, **PROTOBUF**, **JSON**, or **STRING**. 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](#cc-oracle-db-sink-config-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](service-account.md#s3-cloud-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:
  ```bash
  confluent iam service-account list
  ```

  For example:
  ```bash
  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](https://docs.oracle.com/cd/E53672_01/doc.111191/e53673/GUID-915BAD52-BDBA-4ECB-BC72-BE42DE1FE4C7.htm#DB_CONNECTION_PAGE).
* `"ssl.rootcertfile"`: The default `ssl.mode` is `verify-full`.
  When using this mode, you must provide the PEM-formatted root certificate for the database.
  For example, `"ssl.rootcertfile": "-----BEGIN CERTIFICATE-----\nABCDfJP...Bbc\n4\n-----END CERTIFICATE-----\n"`.
  For additional `ssl.mode` options, see the [Configuration Properties](#cc-oracle-db-sink-config-properties).
* `"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](#cc-oracle-db-sink-config-properties) for additional `ssl.mode` options.
* `"tasks.max"`: Enter the maximum number of [tasks](/platform/current/connect/concepts.html#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.

**SMTs**: For details about adding SMTs using the Confluent CLI, see the [Single Message Transformations](single-message-transforms.md#cc-single-message-transforms) documentation.

See [Configuration Properties](#cc-oracle-db-sink-config-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:

```none
confluent connect cluster create --config-file <file-name>.json
```

For example:

```none
confluent connect cluster create --config-file oracle-db-sink-config.json
```

Example output:

```none
Created connector OracleDatabaseSink_0 lcc-do6vzd
```

#### Step 5: Check the connector status

Enter the following command to check the connector status:

```none
confluent connect cluster list
```

Example output:

```none
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 Connect Usage Examples](connect-api-section.md#ccloud-connect-api) section.

<a id="cc-oracle-db-sink-config-properties"></a>

## 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](/platform/current/connect/kafka_connectors.html).

### Which topics do you want to get data from?

`topics.regex`
: A regular expression that matches the names of the topics to consume from. This is useful when you want to consume from multiple topics that match a certain pattern without having to list them all individually.
  <br/>
  * Type: string
  * Importance: low

`topics`
: Identifies the topic name or a comma-separated list of topic names.
  <br/>
  * Type: list
  * Importance: high

`errors.deadletterqueue.topic.name`
: The name of the topic to be used as the dead letter queue (DLQ) for messages that result in an error when processed by this sink connector, or its transformations or converters. Defaults to ‘dlq-${connector}’ if not set. The DLQ topic will be created automatically if it does not exist. You can provide `${connector}` in the value to use it as a placeholder for the logical cluster ID.
  <br/>
  * Type: string
  * Default: dlq-${connector}
  * Importance: low

### 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.
  <br/>
  * Type: string
  * Default: default
  * Importance: medium

### Input messages

`input.data.format`
: Sets the input Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, JSON, or 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. JSON and STRING formats do not require Schema Registry. When JSON is selected, value.converter.schemas.enable must be set to true.
  <br/>
  * 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, JSON, or 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. JSON and STRING formats do not require Schema Registry. When pk.mode is set to record_key and JSON is selected, the record key must include an inline schema.
  <br/>
  * Type: string
  * Importance: high

`delete.enabled`
: Whether to treat null record values as deletes. Requires pk.mode to be record_key.
  <br/>
  * Type: boolean
  * Default: false
  * Importance: low

### How should we connect to your data?

`name`
: Sets a name for your connector.
  <br/>
  * 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, whenever possible.
  <br/>
  * Type: string
  * Valid Values: SERVICE_ACCOUNT, KAFKA_API_KEY
  * Importance: high

`kafka.api.key`
: Kafka API Key. Required when kafka.auth.mode==KAFKA_API_KEY.
  <br/>
  * 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.
  <br/>
  * Type: string
  * Importance: high

`kafka.api.secret`
: Secret associated with Kafka API key. Required when kafka.auth.mode==KAFKA_API_KEY.
  <br/>
  * Type: password
  * Importance: high

### Authentication method

`authentication.method`
: Select how you want to authenticate with your database.
  <br/>
  * Type: string
  * Default: Password
  * Importance: high

`secret.manager.enabled`
: Fetch sensitive configuration values from a secret manager.
  <br/>
  * Type: boolean
  * Default: false
  * Importance: high

### Secret manager configuration

`secret.manager`
: Select the secret manager to use for retrieving sensitive data.
  <br/>
  * Type: string
  * Importance: high

`secret.manager.managed.configs`
: Select the configurations to fetch their values from the secret manager.
  <br/>
  * Type: list
  * Importance: high

`secret.manager.provider.integration.id`
: Select an existing provider integration that has access to your secret manager.
  <br/>
  * Type: string
  * 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://](jdbc:xxxx://) in the connection hostname property (e.g. database-1.abc234ec2.us-west.rds.amazonaws.com).
  <br/>
  * Type: string
  * Importance: high

`connection.port`
: JDBC connection port.
  <br/>
  * Type: int
  * Valid Values: [0,…,65535]
  * Importance: high

`connection.user`
: JDBC connection user.
  <br/>
  * Type: string
  * Importance: high

`connection.password`
: JDBC connection password.
  <br/>
  * Type: password
  * Importance: high

`db.name`
: JDBC database name.
  <br/>
  * 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.
  <br/>
  * Type: string
  * Default: verify-full
  * Importance: high

`ssl.truststorefile`
: The binary trust store file that contains the server’s CA certificate. Only required if you use verify-ca or verify-full ssl mode. The connector supports files in JKS format. For REST API usage, you must base64-encode the binary trust store file and prefix it with `data:text/plain;base64,`. For example, first, encode the file `base64_truststore=$(cat /path/to/truststore.jks | base64)` and then use `data:text/plain;base64,$base64_truststore` as the value.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * Type: string
  * Default: INSERT
  * Importance: high

`table.name.format`
: A format string for the destination table name, which may contain ${topic} as a placeholder for the originating topic name.
  <br/>
  For example, kafka_${topic} for the topic ‘orders’ will map to the table name ‘kafka_orders’.
  <br/>
  * 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`).
  <br/>
  * 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. Not applicable when input.data.format is set to STRING.
  <br/>
  * Type: list
  * Importance: medium

`timestamp.fields.list`
: List of comma-separated record value timestamp field names that should be converted to timestamps. These fields will be converted based on precision mode specified in Timestamp Precision Mode. The timestamp fields included here should be Long or String type and nested fields are not supported. Not applicable when input.data.format is set to STRING.
  <br/>
  * Type: list
  * Importance: medium

`string.output.value.column.name`
: Name of the destination table column to use when the Kafka record value written using StringConverter is sinked to the DB table (Input Kafka record value format = STRING). The raw string value is written into this single column. If the destination table is being auto-created, the column is created with this name; if the table already exists and the column is missing, the column is added via ALTER when ‘auto.evolve’ is true. Note: in databases that treat quoted identifiers as case-sensitive (e.g. PostgreSQL), make sure the column name in the existing table exactly matches this value (including case). Defaults to ‘record_value’.
  <br/>
  * Type: string
  * Default: record_value
  * Importance: low

`db.timezone`
: Name of the JDBC timezone used in the connector when querying with time-based criteria. Defaults to UTC.
  <br/>
  * 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.
  <br/>
  * Type: string
  * Default: DB_TIMEZONE
  * Valid Values: DB_TIMEZONE, UTC
  * Importance: medium

`timestamp.precision.mode`
: Convert the Timestamp with precision. If set to microseconds the timestamp will be converted to microsecond precision. If set to nanoseconds the timestamp will be converted to nanoseconds precision. Not applicable when input.data.format is set to STRING.
  <br/>
  * Type: string
  * Default: microseconds
  * Importance: medium

`date.calendar.system`
: Conversion of time since epoch value in kafka topic record to DATE or TIMESTAMP depends on the calendar used to interpret it. If LEGACY is used, it will use the hybrid Gregorian/Julian calendar which was the default in the older java date time APIs. However, if ‘PROLEPTIC_GREGORIAN’ is used, then it will use the proleptic gregorian calendar which extends the Gregorian rules backward indefinitely and does not apply the 1582 cutover. This matches the behavior of modern Java date/time APIs (java.time). This is defaulted to LEGACY for backward compatibility. The ideal setting for this depends on whether the values in source topic were populated using old or new java date time APIs. Changing this configuration on an existing connector might lead to a drift in the DATE/TIMESTAMP column’s values populated in the sink database.
  <br/>
  * Type: string
  * Default: LEGACY
  * Importance: medium

### Primary Key

`pk.mode`
: The primary key mode, also refer to pk.fields documentation for interplay. Supported modes are:
  <br/>
  none: No keys utilized.
  <br/>
  kafka: Apache Kafka® coordinates are used as the PK.
  <br/>
  record_value: Field(s) from the record value are used, which must be a struct. This mode is not supported when input.data.format is set to STRING.
  <br/>
  record_key: Field(s) from the record key are used, which must be a struct.
  <br/>
  * 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:
  <br/>
  none: Ignored as no fields are used as primary key in this mode.
  <br/>
  kafka: Must be a trio representing the Kafka coordinates, defaults to \_\_connect_topic,_\_connect_partition,_\_connect_offset if empty.
  <br/>
  record_value: If empty, all fields from the value struct will be used, otherwise used to extract the desired fields.
  <br/>
  * Type: list
  * Importance: high

### SQL/DDL Support

`auto.create`
: Whether to automatically create the destination table if it is missing.
  <br/>
  * Type: boolean
  * Default: false
  * Importance: medium

`auto.evolve`
: Whether to automatically add columns in the table if they are missing.
  <br/>
  * 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’.
  <br/>
  * 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.
  <br/>
  * 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).
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * Type: int
  * Valid Values: [1,…]
  * Importance: high

### Additional Configs

`consumer.override.auto.offset.reset`
: Defines the behavior of the consumer when there is no committed position (which occurs when the group is first initialized) or when an offset is out of range. You can choose either to reset the position to the “earliest” offset (the default) or the “latest” offset. You can also select “none” if you would rather set the initial offset yourself and you are willing to handle out of range errors manually. More details: [https://docs.confluent.io/platform/current/installation/configuration/consumer-configs.html#auto-offset-reset](https://docs.confluent.io/platform/current/installation/configuration/consumer-configs.html#auto-offset-reset)
  <br/>
  * Type: string
  * Importance: low

`consumer.override.isolation.level`
: Controls how to read messages written transactionally. If set to read_committed, consumer.poll() will only return transactional messages which have been committed. If set to read_uncommitted (the default), consumer.poll() will return all messages, even transactional messages which have been aborted. Non-transactional messages will be returned unconditionally in either mode.  More details: [https://docs.confluent.io/platform/current/installation/configuration/consumer-configs.html#isolation-level](https://docs.confluent.io/platform/current/installation/configuration/consumer-configs.html#isolation-level)
  <br/>
  * Type: string
  * Importance: low

`header.converter`
: The converter class for the headers. This is used to serialize and deserialize the headers of the messages.
  <br/>
  * Type: string
  * Importance: low

`key.converter.use.schema.guid`
: The schema GUID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema GUID to be used for deserializing message keys. Only applicable when key.converter.key.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
  <br/>
  * Type: string
  * Importance: low

`key.converter.use.schema.id`
: The schema ID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema ID to be used for deserializing message keys. Only applicable when key.converter.key.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
  <br/>
  * Type: int
  * Importance: low

`value.converter.allow.optional.map.keys`
: Allow optional string map key when converting from Connect Schema to Avro Schema. Applicable for Avro Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.auto.register.schemas`
: Specify if the Serializer should attempt to register the Schema.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.connect.meta.data`
: Allow the Connect converter to add its metadata to the output schema. Applicable for Avro Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.enhanced.avro.schema.support`
: Enable enhanced schema support to preserve package information and Enums. Applicable for Avro Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.enhanced.protobuf.schema.support`
: Enable enhanced schema support to preserve package information. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.flatten.unions`
: Whether to flatten unions (oneofs). Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.generate.index.for.unions`
: Whether to generate an index suffix for unions. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.generate.struct.for.nulls`
: Whether to generate a struct variable for null values. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.int.for.enums`
: Whether to represent enums as integers. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.latest.compatibility.strict`
: Verify latest subject version is backward compatible when use.latest.version is true.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.object.additional.properties`
: Whether to allow additional properties for object schemas. Applicable for JSON_SR Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.optional.for.nullables`
: Whether nullable fields should be specified with an optional label. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.optional.for.proto2`
: Whether proto2 optionals are supported. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.scrub.invalid.names`
: Whether to scrub invalid names by replacing invalid characters with valid characters. Applicable for Avro and Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.use.latest.version`
: Use latest version of schema in subject for serialization when auto.register.schemas is false.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.use.optional.for.nonrequired`
: Whether to set non-required properties to be optional. Applicable for JSON_SR Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.use.schema.guid`
: The schema GUID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema GUID to be used for deserializing message values. Only applicable when value.converter.value.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
  <br/>
  * Type: string
  * Importance: low

`value.converter.use.schema.id`
: The schema ID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema ID to be used for deserializing message values. Only applicable when value.converter.value.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
  <br/>
  * Type: int
  * Importance: low

`value.converter.wrapper.for.nullables`
: Whether nullable fields should use primitive wrapper messages. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.wrapper.for.raw.primitives`
: Whether a wrapper message should be interpreted as a raw primitive at root level. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`errors.tolerance`
: Use this property if you would like to configure the connector’s error handling behavior. WARNING: This property should be used with CAUTION for SOURCE CONNECTORS as it may lead to dataloss. If you set this property to ‘all’, the connector will not fail on errant records, but will instead log them (and send to DLQ for Sink Connectors) and continue processing. If you set this property to ‘none’, the connector task will fail on errant records.
  <br/>
  * Type: string
  * Default: all
  * Importance: low

`key.converter.key.schema.id.deserializer`
: The class name of the schema ID deserializer for keys. This is used to deserialize schema IDs from the message headers.
  <br/>
  * Type: string
  * Default: io.confluent.kafka.serializers.schema.id.DualSchemaIdDeserializer
  * Importance: low

`key.converter.key.subject.name.strategy`
: How to construct the subject name for key schema registration.
  <br/>
  * Type: string
  * Default: TopicNameStrategy
  * Importance: low

`value.converter.decimal.format`
: Specify the JSON/JSON_SR serialization format for Connect DECIMAL logical type values with two allowed literals:
  <br/>
  BASE64 to serialize DECIMAL logical types as base64 encoded binary data and
  <br/>
  NUMERIC to serialize Connect DECIMAL logical type values in JSON/JSON_SR as a number representing the decimal value.
  <br/>
  * Type: string
  * Default: BASE64
  * Importance: low

`value.converter.flatten.singleton.unions`
: Whether to flatten singleton unions. Applicable for Avro and JSON_SR Converters.
  <br/>
  * Type: boolean
  * Default: false
  * Importance: low

`value.converter.ignore.default.for.nullables`
: When set to true, this property ensures that the corresponding record in Kafka is NULL, instead of showing the default column value. Applicable for AVRO,PROTOBUF and JSON_SR Converters.
  <br/>
  * Type: boolean
  * Default: false
  * Importance: low

`value.converter.reference.subject.name.strategy`
: Set the subject reference name strategy for value. Valid entries are DefaultReferenceSubjectNameStrategy or QualifiedReferenceSubjectNameStrategy. Note that the subject reference name strategy can be selected only for PROTOBUF format with the default strategy being DefaultReferenceSubjectNameStrategy.
  <br/>
  * Type: string
  * Default: DefaultReferenceSubjectNameStrategy
  * Importance: low

`value.converter.replace.null.with.default`
: Whether to replace fields that have a default value and that are null to the default value. When set to true, the default value is used, otherwise null is used. Applicable for JSON Converter.
  <br/>
  * Type: boolean
  * Default: true
  * Importance: low

`value.converter.schemas.enable`
: Must be set to true when input.data.format is JSON. Plain JSON without an inline schema is not supported. Applicable for JSON Converter.
  <br/>
  * Type: boolean
  * Default: false
  * Importance: low

`value.converter.value.schema.id.deserializer`
: The class name of the schema ID deserializer for values. This is used to deserialize schema IDs from the message headers.
  <br/>
  * Type: string
  * Default: io.confluent.kafka.serializers.schema.id.DualSchemaIdDeserializer
  * Importance: low

`value.converter.value.subject.name.strategy`
: Determines how to construct the subject name under which the value schema is registered with Schema Registry.
  <br/>
  * Type: string
  * Default: TopicNameStrategy
  * Importance: low

### Auto-restart policy

`auto.restart.on.user.error`
: Enable connector to automatically restart on user-actionable errors.
  <br/>
  * Type: boolean
  * Default: true
  * Importance: medium

<a id="cc-oracle-db-sink-db-troubleshooting"></a>

## Database considerations


Note the following issues to keep in mind.

1. String type is mapped to CLOB when `auto.create=true`. For example, you have the following Avro schema:
   ```json
   {
     "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:
   ```text
   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:
   ```text
   ...
   "stringValue": "Exception chain:\njava.sql.SQLException: ORA-02329:
   column of datatype LOB cannot be unique or a primary key
   ...
   ```
2. The table name and column names are case sensitive.  For example, you have the following Avro schema:
   ```json
   {
     "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.
   ```text
   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:
   ```text
   {
     "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`.
   ```text
   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](https://alberton.info/dbms_identifiers_and_case_sensitivity.html).

<a id="cc-oracle-db-sink-faq"></a>

## Frequently asked questions

Find answers to frequently asked questions about the Oracle Database Sink connector for Confluent Cloud.

### Deployment model and product fit

#### Can I run the Oracle Database Sink connector on my own Kafka Connect cluster (self-managed)?

The JDBC Sink connector (which supports Oracle Database) is available in both fully managed and self-managed versions.
This Quick Start is for the fully managed Confluent Cloud connector. If you are installing the connector locally for Confluent Platform, see
[JDBC Connector (Source and Sink) for Confluent Platform](https://docs.confluent.io/kafka-connectors/jdbc/current/).

### Database connectivity and authentication

#### Why do I see `java.sql.SQLRecoverableException: IO Error: The Network Adapter could not establish the connection`?

This error indicates the connector cannot reach the Oracle database from the Confluent Cloud workers. Common causes include:

* **Networking mismatch:** If the cluster is `PRIVATE_LINK` or `PCC`, outbound traffic is not enabled by default.
* **Firewall restrictions:** Confluent egress IP ranges or PrivateLink/VPC routes are not allowlisted in your network firewall.
* **Incorrect connection details:** The `connection.host` or `connection.port` value might be incorrect.

**Checklist:**

1. **Verify network type:** Is your Kafka cluster `PUBLIC` or `PRIVATE_LINK`?
2. **Check connection parameters:** Ensure `connection.host` and `connection.port` are correctly specified. The port is typically `1521` for Oracle.
3. **Test connectivity:** Use network diagnostic tools to verify connectivity from Confluent Cloud to your database endpoint.
4. **Allowlist IPs:** Confirm the Confluent egress IPs (or VPC CIDRs) are allowlisted at your database firewall.
5. **Verify PrivateLink configuration:** If using PrivateLink, ensure the VPC path from Confluent to your endpoint is correctly configured.

For networking considerations, see [Networking and DNS](overview.md#connect-internet-access-resources). To use a set of public egress IP addresses, see [Public Egress IP Addresses for Confluent Cloud Connectors](static-egress-ip.md#cc-static-egress-ips).

#### How do I configure SSL/TLS connections to my Oracle database?

The Oracle Database Sink connector supports SSL/TLS connections to Oracle databases. SSL/TLS behavior is controlled by the `ssl.mode` connector property. The default `ssl.mode` is `verify-full`, which requires a valid server certificate.

**Configuration:**

Set `ssl.mode` to one of the following values depending on your requirements:

* `disabled`: SSL/TLS is not used.
* `required`: The connection must use SSL/TLS, but the server certificate is not validated.
* `verify-ca`: The connection must use SSL/TLS and the server certificate must be valid and signed by a trusted CA.
* `verify-full`: The connection must use SSL/TLS, the certificate must be valid, and the server hostname must match the certificate.

When using `verify-ca` or `verify-full`, provide the PEM-formatted root certificate using the `ssl.rootcertfile` property. If your Oracle server uses a distinguished name for certificate validation, set `ssl.server.cert.dn` accordingly.

For all available SSL properties, see [Configuration Properties](#cc-oracle-db-sink-config-properties).

#### Why do I get `ORA-01017: invalid username/password; logon denied`?

This error indicates authentication failure. Verify the following:

* **Credentials are correct:** Double-check the `connection.user` and `connection.password` values.
* **User has proper permissions:** Ensure the database user has the necessary privileges to create tables, insert data, and perform upserts if `insert.mode` is set to `UPSERT`.
* **Special characters in password:** If your password contains special characters, ensure they are properly escaped in the JSON configuration.

### Data writing and insert mode

#### Why is data not appearing in my Oracle database tables?

If the connector status is `RUNNING` but data is not visible in the database:

* **Check topic subscription:** Verify the connector is subscribed to the correct Kafka topics using the `topics` property.
* **Review connector logs:** Check for errors or warnings in the connector logs in the Confluent Cloud Console.
* **Verify table mapping:** Ensure topic-to-table mappings are correct. By default, table names are created based on Kafka topic names.
* **Check auto-creation settings:** If `auto.create` is set to `false`, ensure the target tables already exist in the database.
* **Review data format:** Ensure the `input.data.format` matches the actual format of data in your Kafka topics.

#### When should I use `INSERT` or `UPSERT` mode?

* **INSERT mode (default):** Use when you want to append new records to the table. If a record with the same primary key already exists, the connector fails with a constraint violation error.
* **UPSERT mode:** Use when you want idempotent writes. The connector automatically adds a new row or updates the existing row if there is a primary key constraint violation. This is useful for avoiding duplicate rows when records are delivered more than once.

  #### IMPORTANT
  When a target table includes columns with `CLOB`, `INSERT` or `UPSERT` performance can be degraded. Try to use `VARCHAR` or `VARCHAR2` instead.

To enable `UPSERT` mode, set:

```json
{
  "insert.mode": "UPSERT"
}
```

Alternatively, use `UPDATE` mode when you only want to update existing rows. The connector issues SQL `UPDATE` statements. Records with keys that do not exist in the table fail.

#### What primary key modes are supported?

The connector supports the following **PK modes**:

* **kafka:** Uses the Kafka coordinates (topic, partition, offset) as the primary key.
* **none:** No primary key is used.
* **record_key:** Uses the Kafka record key as the primary key.
* **record_value:** Uses field(s) from the Kafka record value as the primary key.

Use the **PK modes** property in conjunction with the **PK Fields** property to specify which fields should be used as the primary key.

### Schema and table management

#### Can the connector automatically create tables if they don’t exist?

Yes. If `auto.create` is set to `true` (default), the connector automatically creates tables based on the Kafka topic schema.
Table names are created based on Kafka topic names by default.

The connector also supports `auto.evolve`, which allows automatic addition of new columns when the schema evolves.

#### NOTE
Automatic table creation uses data types inferred from the Kafka record schema. Review the created tables to ensure they meet your requirements.

#### What data formats are supported?

The connector supports the following input formats:

* **Value formats:** Avro, JSON Schema, and Protobuf
* **Key formats:** Avro, JSON Schema, Protobuf, and String

Schema Registry must be enabled to use a Schema Registry-based format.

### Performance and throughput

#### How can I improve connector performance and reduce consumer lag?

To improve performance:

* **Increase tasks:** The connector supports running one or more tasks. Set `tasks.max` to a higher value (typically equal to the number of topic partitions). More tasks can improve performance by parallelizing the writes.
* **Optimize batch size:** Adjust the batch size to write more records per database transaction, reducing the number of round trips to the database.
* **Avoid CLOB columns:** When a target table includes columns with `CLOB`, `INSERT` or `UPSERT` performance can be degraded. Use `VARCHAR` or `VARCHAR2` instead when possible.
* **Review network latency:** Ensure the database and Kafka cluster are in the same region. Cross-region writes can incur additional latency and data transfer charges.

#### The connector is healthy but lag is always `1 per partition`. Is this normal?

Yes. This is typical Kafka Connect behavior. The task pre-fetches the next record before committing the current offset.
Unless the lag is growing monotonically, `lag=1` is considered a healthy steady-state.

### Error handling and troubleshooting

#### Why do I see errors immediately after creating the connector?

Common causes include:

* **Invalid configuration:** Review all required configuration properties, especially database connection details.
* **Network connectivity issues:** Ensure the connector can reach the Oracle database (see networking section above).
* **Database permissions:** Verify the database user has the necessary privileges.
* **Schema Registry issues:** If using schema-based formats (Avro, Protobuf, JSON Schema), ensure [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) is enabled and accessible.

Review the connector logs in the Confluent Cloud Console for detailed error messages.

#### How do I handle failed records and use the Dead Letter Queue (DLQ)?

The connector supports a Dead Letter Queue for handling records that fail to be written to the database. Failed records are sent to a DLQ topic for later analysis.

For more details on DLQ configuration, see [View Connector Dead Letter Queue Errors in Confluent Cloud](dead-letter-queue.md#ccloud-dlq-topics).

#### Can I migrate from a self-managed Oracle Sink connector to the fully managed version?

Yes. When migrating:

1. Review the [Configuration Properties](#cc-oracle-db-sink-config-properties) to ensure all required configurations are supported in the fully managed version.
2. Not all configurations available in self-managed connectors are supported in the fully managed version. If a required property is missing, contact [Confluent Support](https://support.confluent.io/) to check if it can be set or if a feature request is needed.
3. Use connector offsets to ensure no data loss during migration. See [Manage Offsets for Fully Managed Connectors in Confluent Cloud](offsets.md#connect-custom-offsets) for more information.

#### What Oracle Database versions are supported?

The Oracle Database version must be 11.2.0.4 or later. See [Prerequisites](#cc-oracle-db-sink-prereqs) for all prerequisites.

## Next Steps

For an example that shows fully managed Confluent Cloud connectors in action with
Confluent Cloud for Apache Flink, see the [Cloud ETL Demo](/platform/current/tutorials/examples/cloud-etl/docs/index.html).
This example also shows how to use Confluent CLI to manage your resources in
Confluent Cloud.

[![image](images/topology.png)](https://docs.confluent.io/platform/current/tutorials/examples/cloud-etl/docs/index.html)