# PostgreSQL Sink (JDBC) Connector for Confluent Cloud
The fully managed PostgreSQL Sink (JDBC) connector for Confluent Cloud writes records
from Apache Kafka® topics into PostgreSQL tables. Table auto-creation and limited
auto-evolution are 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 PostgreSQL Sink connector provides 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 atomically adding a new row or updating the existing row if there is a primary key constraint violation, which provides idempotence.
* **SSL support**: Supports both one-way SSL (Server CA cert validation) and
mTLS, where the connector presents a client certificate so the database can
authenticate the connector. See
[How do I configure SSL/TLS connections to PostgreSQL?](#cc-postgresql-sink-ssl-tls-faq)
for details.
* **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. For more information, see [Table names and Kafka topic names](#cc-postgresql-sink-truncation-behavior).
* **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.
* **Provider integration support**: The connector supports Google Cloud’s native identity authorization,
Microsoft Entra ID-based authentication, and AWS IAM role-based authentication using Confluent
Provider Integration. For more information about provider integration setup, see the
[connector authentication](#cc-postgresql-sink-setup-connection) and
[AWS IAM Authentication Setup](#cc-postgresql-sink-aws-iam-auth).
* **PostgreSQL JSON and JSONB**: The connector supports sinking to PostgreSQL tables containing data stored as JSON or JSONB (JSON binary format). JSON or JSONB should be stored as STRING type in Kafka and matching columns should be defined as JSON or JSONB in PostgreSQL.
* **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 [PostgreSQL Sink Connector](limits.md#cc-postgresql-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).
## Table names and Kafka topic names
You can configure the connector to combine the value for `table.name.format`
and the Kafka topic name. If the resulting combined value (table name) exceeds
the maximum-permitted identifier length for the database version in use, the
connector truncates the value to the permitted identifier length.
For example, PostgreSQL 14 uses 63 bytes as its default identifier length
setting. If the value used for `table.name.format` and the Kafka topic name
exceeds 63 characters, only the first 63 characters from the combined name are
used.
For this reason, you should not run the connector with very long Kafka topic
names and table names. If the table name is truncated, and the connector
receives records from different upstream topics, the records map to the same
table name after truncation takes place. This results in a duplicate table name
collision.
#### NOTE
You can expect this connector behavior for any interactions with the
database, both DDL (table creation and evolution) and DML (insert, upsert,
and delete).
## Quick Start
Use this quick start to get up and running with the Confluent Cloud PostgreSQL sink
connector. The quick start provides the basics of selecting the connector and
configuring it to stream events to a PostgreSQL database.
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 a PostgreSQL database. The connector requires the
following privileges:
* DDL: `CREATE`, `ALTER`, and `DROP`
* DML: `SELECT`, `INSERT`, `UPDATE`, and `DELETE`
For more information, see [PostgreSQL Privileges](https://www.postgresql.org/docs/current/ddl-priv.html).
- 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).
- Kafka cluster credentials. The following lists the different ways you can provide credentials.
- Enter an existing [service account](service-account.md#s3-cloud-service-account) resource ID.
- Create a Confluent Cloud [service account](service-account.md#s3-cloud-service-account) for the connector. Make sure to review the ACL entries required in the [service account documentation](service-account.md#s3-cloud-service-account). Some connectors have specific ACL requirements.
- Create a Confluent Cloud API key and secret. To create a key and secret, you can use [confluent api-key create](https://docs.confluent.io/confluent-cli/current/command-reference/api-key/confluent_api-key_create.html) *or* you can autogenerate the API key and secret directly in the Cloud Console when setting up the connector.
### 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 **PostgreSQL Sink** connector card.
#### Step 4: Enter the connector details
#### NOTE
* Ensure you have all your [prerequisites](#cc-postgresql-sink-prereqs) completed.
* An asterisk ( \* ) designates a required entry.
At the **Add PostgreSQL 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**: How Confluent Cloud authenticates with Azure or Google Cloud.
Allowed values are:
* `Password`
* `Microsoft Entra ID application`
* `Google service account impersonation`
- **Use secret manager**: Fetch sensitive configuration values from a secret manager.
- **Provider Integration**: The provider integration Confluent Cloud uses to access your resource.
- **Database AWS region**: The AWS region of your RDS or Aurora database
instance, for example `us-east-1`. Required
when the authentication method is `IAM Roles`.
**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**: The provider integration Confluent Cloud uses to access your resource.
**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. Possible options
are `prefer`, `require`, `verify-ca`, and `verify-full`.
- `prefer` (default): Attempts to use a secure (encrypted) connection first and, failing
that, an unencrypted connection.
- `require`: Uses a secure (encrypted) connection, and fails if one cannot be established,
but does not perform certificate validation on the server.
- `verify-ca`: Uses SSL/TLS for encryption and performs certificate verification,
but does not perform hostname verification.
- `verify-full`: Uses SSL/TLS for encryption, certificate verification, and hostname verification.
- **SSL root cert**: The server root cert file used for certificate
validation. Only required if using `verify-ca` or `verify-full` for ssl
mode.
- **SSL client cert**: The client certificate file used for mutual TLS authentication.
Required when the database server requires client certificate authentication.
Must be a PEM encoded X509v3 certificate.
- **SSL client key**: The client private key file used for mutual TLS authentication.
Required when the database server requires client certificate authentication.
Must be in PKCS-8 DER format.
2. Click **Continue**.
### Configuration
#### NOTE
Configuration properties that are not shown in the
Cloud Console use the default values. See
[Configuration Properties](#cc-postgresql-sink-config-properties) for all property
values and definitions.
- **Input Kafka record value format**: Select an **Input Kafka record value format** (data coming from the
Kafka topic): AVRO, JSON_SR (JSON Schema), 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**–the insertion mode to use:
- `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 used in the
connector when querying with time-based criteria. Defaults to `UTC`.
- **Table name format**: A format string for the destination table
name, which may contain `${topic}` as a placeholder for the
originating topic name.
- **Timezone used for Date**: Name of the JDBC timezone used in the connector
when inserting DATE type values. Defaults to `DB_TIMEZONE` that uses the
timezone configured via `db.timezeone` for backward compatibility. To avoid conversion
for DATE type values, set as `UTC`.
- **Table types**: The comma-separated types of database tables to
which the sink connector can write.
- **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.
- **Timestamp Fields**: 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.
- **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**: The maximum number of rows to include in a single batch when polling for new data.
Each batch includes all insert statements as individual updates. Use this setting
to limit the amount of data buffered internally in the connector.
- **Input Kafka record key format**: Sets the input Kafka record key
format. This needs to be set to a proper format if using
`pk.mode=record_key`. Valid entries are AVRO, JSON_SR, PROTOBUF,
STRING. Note that you must have Confluent Cloud Schema Registry configured if
using a schema-based message format like AVRO, JSON_SR, and
PROTOBUF.
- **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 topic record during conversion to DATE or TIMESTAMP.
The ideal setting you choose depends on whether the values in the source topic
were populated using old or new Java date and time APIs.
- If you use `LEGACY` (the default), the connector uses the hybrid
Gregorian/Julian calendar. This matches the default behavior of older
Java date and time APIs.
- If you use `PROLEPTIC_GREGORIAN`, the connector uses the proleptic Gregorian
calendar (which extends Gregorian rules backward indefinitely). This matches
the behavior of modern Java date/time APIs (java.time).
#### WARNING
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-postgresql-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 the results in PostgreSQL
Verify that new records are being added to the PostgreSQL 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-postgresql-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
```
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 examples show required and optional connector properties for both
password and IAM role-based authentication.
Using password authentication:
```none
{
"connector.class": "PostgresSink",
"name": "PostgresSinkConnector_0",
"input.data.format": "AVRO",
"kafka.auth.mode": "KAFKA_API_KEY",
"kafka.api.key": "****************",
"kafka.api.secret": "****************************************************************",
"connection.host": "database-4..us-east-2.rds.amazonaws.com",
"connection.port": "5432",
"connection.user": "postgres",
"connection.password": "**************",
"db.name": "postgres",
"topics": "postgresql_ratings",
"insert.mode": "UPSERT",
"db.timezone": "UTC",
"auto.create": "true",
"auto.evolve": "true",
"pk.mode": "record_value",
"pk.fields": "user_id",
"tasks.max": "1"
}
```
Using IAM role-based authentication:
```none
{
"connector.class": "PostgresSink",
"name": "PostgresSinkConnector_0",
"input.data.format": "AVRO",
"kafka.auth.mode": "KAFKA_API_KEY",
"kafka.api.key": "****************",
"kafka.api.secret": "****************************************************************",
"connection.host": "database-4..us-east-2.rds.amazonaws.com",
"connection.port": "5432",
"connection.user": "db_user_with_iam_login",
"db.name": "postgres",
"topics": "postgresql_ratings",
"insert.mode": "UPSERT",
"db.timezone": "UTC",
"auto.create": "true",
"auto.evolve": "true",
"pk.mode": "record_value",
"pk.fields": "user_id",
"tasks.max": "1",
"authentication.method": "IAM Roles",
"provider.integration.id": "dlz-f3a90de",
"database.aws.region": "us-east-2"
}
```
Note the following property definitions. See the [PostgreSQL Sink
configuration properties](#cc-postgresql-sink-config-properties) for additional
property values and definitions.
* `"connector.class"`: Identifies the connector plugin name.
* `"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=`. 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
```
* The following provides more information about how to use the `ssl.mode` property:
- `prefer` (default): Attempts to use an encrypted connection. Falls back to
an unencrypted connection if SSL is unavailable. Used when `ssl.mode` is not
added to the connector configuration. Does not perform Certification Authority
(CA) validation.
- `require`: Uses a secure (encrypted) connection. The connector fails if a
secure connection cannot be established. Does not perform Certification
Authority (CA) validation.
- `verify-ca`: Similar to `require`, but also verifies the
server TLS certificate against the configured Certificate Authority
(CA) certificates. Fails if no valid matching CA certificates are found.
- `verify-full`: similar to `verify-ca`, but also verifies that
the server certificate matches the host to which the connection is
attempted.
If you choose `verify-ca` or `verify-full`, use the property
`ssl.rootcertfile` and provide the server root certificate as a
base64-encoded `data:` URL. For example,
`"ssl.rootcertfile": "data:application/x-x509-ca-cert;base64,LS0tLS1CRUdJTi..."`.
To authenticate the connector to the database with mTLS, provide
`ssl.clientcertfile` and `ssl.clientkeyfile`. Client authentication
works with any `ssl.mode`. Provide the client certificate
(PEM-encoded X.509v3) in `ssl.clientcertfile` and the corresponding
private key (PKCS-8 DER format) in `ssl.clientkeyfile`, each as a
base64-encoded `data:` URL.
For details and examples, see
[How do I configure SSL/TLS connections to PostgreSQL?](#cc-postgresql-sink-ssl-tls-faq).
* `"authentication.method"`: Set to `"IAM Roles"` to use AWS IAM role-based authentication. Other supported values are `"Password"`, `"Microsoft Entra ID application"`, and `"Google service account impersonation"`.
* `"provider.integration.id"`: The ID of the provider integration resource that contains the IAM role, service account, or application configuration. Required for every authentication method except `"Password"`.
* `"database.aws.region"`: The AWS region of the PostgreSQL database server for RDS/Aurora. Only applicable when using IAM role-based authentication.
* `"connection.password"`: Password of the PostgreSQL database user that has the required authorization. Only applicable when using password-based authentication.
* `"input.data.format"`: Sets the input Kafka record value format (data coming from the Kafka topic). Valid entries are **AVRO**, **JSON_SR** (JSON Schema), **PROTOBUF**, **JSON**, or **STRING**. You must have Confluent Cloud Schema Registry configured if using a schema-based message format.
* `"input.key.format"`: Sets the input record key format (data coming from the Kafka topic). Valid entries are **AVRO**, **JSON_SR** (JSON Schema), **PROTOBUF**, or **STRING**. You must have Confluent Cloud Schema Registry configured if using a schema-based message format.
* `"delete.on.null"`: Whether to treat null record values as deletes. Defaults to `false`. Requires `pk.mode` to be `record_key`. Defaults to `false`.
* `"topics"`: Identifies the topic name or a comma-separated list of topic names.
* `"insert.mode"`: Enter one of the following modes:
- `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.
* `db.timezone`: Name of the time zone the connector uses when inserting time-based values. Defaults to UTC.
* `"auto.create"` (tables) and `"auto-evolve"` (columns): (Optional) Sets whether to automatically create tables or columns if they are missing relative to the input record schema. If not entered in the configuration, both default to `false`. When\`\`auto.create\`\` is set to `true`, the connector creates a table name using `${topic}` (that is, the Kafka topic name). For more information, see [Table names and Kafka topic names](#cc-postgresql-sink-truncation-behavior) and the [PostgreSQL Sink configuration properties](#cc-postgresql-sink-config-properties).
* `"pk.mode"`: Supported modes are listed below:
- `kafka`: Kafka coordinates are used as the primary key. Must be used with the **PK Fields**.
- `none`: No primary keys used.
- `record_key`: Fields from the record key are used. May be a primitive or a struct.
- `record_value`: Fields from the Kafka record value are used. Must be a struct type.
* `"pk.fields"`: A list of comma-separated primary key field names. The runtime interpretation of this property depends on the `pk.mode` selected. Options are listed below:
- `kafka`: Must be three values representing the Kafka coordinates. If left empty, the coordinates default to `__connect_topic,__connect_partition,__connect_offset`.
- `none`: PK Fields not used.
- `record_key`: If left empty, all fields from the key struct are used. Otherwise, this is used to extract the fields in the property. A single field name must be configured for a primitive key.
- `record_value`: Used to extract fields from the record value. If left empty, all fields from the value struct are used.
* `"tasks.max"`: Maximum number of tasks the connector can run. See Confluent Cloud [connector limitations](limits.md#cc-postgresql-sink-limits) for additional task information.
**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-postgresql-sink-config-properties) for all property values and
definitions.
#### Step 4: Load the configuration file and create the connector
Enter the following command to load the configuration and start the connector:
```none
confluent connect cluster create --config-file .json
```
For example:
```none
confluent connect cluster create --config-file postgresql-sink-config.json
```
Example output:
```none
Created connector PostgresSinkConnector_0 lcc-ix4dl
```
#### 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
+-----------+--------------------------+---------+------+
lcc-ix4dl | PostgresSinkConnector_0 | RUNNING | sink
```
#### Step 6: Check the results in PostgreSQL.
Verify that new records are being added to the PostgreSQL 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.
## AWS IAM Authentication Setup
To use AWS IAM role-based authentication with the PostgreSQL Sink connector,
configure your PostgreSQL database as described in the following sections. The
same setup works for both Amazon RDS PostgreSQL and Aurora PostgreSQL.
### Database setup
1. Enable IAM authentication on your RDS or Aurora instance/cluster (the
**Database authentication** option in the AWS Console, or set
`--enable-iam-database-authentication` with the AWS CLI). For an
existing instance/cluster, this change applies immediately.
2. Connect to the database as the master/superuser and grant the IAM login role
to the database user the connector uses:
```sql
GRANT rds_iam TO db_user_with_iam_login;
```
Ensure `db_user_with_iam_login` already has the `INSERT`/`UPDATE`/`DELETE` privileges the connector needs on the target tables (and `CREATE` on the schema if you rely on `auto.create`/`auto.evolve`).
### IAM permission policy
Attach the following IAM permission policy to the IAM role you have integrated
with provider integration. The same policy shape applies to both RDS instances
and Aurora clusters, only the resource identifier differs.
For RDS PostgreSQL (use the per-instance `DbiResourceId`, an immutable string
of the form `db-XXXXXXXXXXXXXXXXXXXXXXXXXX`, not the human-friendly DB
instance identifier):
```json
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": "rds-db:connect",
"Resource": [
"arn:aws:rds-db:::dbuser:/"
]
}
]
}
```
For Aurora PostgreSQL (use the cluster `DbClusterResourceId`, an immutable
string of the form `cluster-XXXXXXXXXXXXXXXXXXXXXXXXXX`, not the cluster
identifier or the writer-instance resource ID):
```json
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": "rds-db:connect",
"Resource": [
"arn:aws:rds-db:::dbuser:/"
]
}
]
}
```
Replace the `region`, `account-id`, `db-instance-resource-id` (for RDS)
or `db-cluster-resource-id` (for Aurora), and `db-username` placeholders
with the values for your environment. You can find the resource ID on the
**Configuration** tab of your RDS instance or Aurora cluster in the AWS
Console, or by using `aws rds describe-db-instances --query
'DBInstances[].DbiResourceId'` / `aws rds describe-db-clusters --query
'DBClusters[].DbClusterResourceId'`. The IAM token the connector generates is
signed against the database hostname, so ensure the connector’s
`connection.host` is the canonical AWS-managed RDS or Aurora endpoint.
## 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.
* Type: string
* Importance: low
`topics`
: Identifies the topic name or a comma-separated list of topic names.
* 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.
* 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.
* 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.
* 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.
* 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, whenever possible.
* 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.
* 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
### Authentication method
`authentication.method`
: How Confluent Cloud authenticates with the database. Allowed values - `Password`, `Microsoft Entra ID application`, `Google service account impersonation` and `IAM Roles`.
* Type: string
* Default: Password
* Valid Values: Google service account impersonation, IAM Roles, Microsoft Entra ID application, Password
* Importance: high
`secret.manager.enabled`
: Fetch sensitive configuration values from a secret manager.
* Type: boolean
* Default: false
* Importance: high
`provider.integration.id`
: Select an existing integration that has access to your resource.
* Type: string
* Importance: high
`database.aws.region`
: The AWS region of your RDS or Aurora database instance, for example `us-east-1`. Required when the authentication method is `IAM Roles`.
* Type: string
* Default: “”
* Importance: high
### Secret manager configuration
`secret.manager`
: Select the secret manager to use for retrieving sensitive data.
* Type: string
* Importance: high
`secret.manager.managed.configs`
: Select the configurations to fetch their values from the secret manager.
* Type: list
* Importance: high
`secret.manager.provider.integration.id`
: Select an existing provider integration that has access to your secret manager.
* 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).
* 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
* Importance: high
`ssl.mode`
: What SSL mode should we use to connect to your database. `prefer` allows for the connection to not be encrypted and `require` allows for the connection to be encrypted but does not do certificate validation on the server. `verify-ca` and `verify-full` require a file containing SSL CA certificate to be provided. The server’s certificate will be verified to be signed by one of these authorities.\`\`verify-ca\`\` will verify that the server certificate is issued by a trusted CA. `verify-full` will verify that the server certificate is issued by a trusted CA and that the server hostname matches that in the certificate. Client authentication is not performed.
* Type: string
* Default: prefer
* Importance: high
`ssl.rootcertfile`
: The server root cert file used for certificate validation. Only required if using verify-ca or verify-full ssl mode. Must be a PEM encoded X509v3 certificate
* Type: password
* Default: [hidden]
* Importance: low
`ssl.clientcertfile`
: The client certificate file used for mutual TLS authentication. Required when the database server requires client certificate authentication. Must be a PEM encoded X509v3 certificate
* Type: password
* Default: [hidden]
* Importance: low
`ssl.clientkeyfile`
: The client private key file used for mutual TLS authentication. Required when the database server requires client certificate authentication. Must be in PKCS-8 DER format.
* Type: password
* Default: [hidden]
* 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`, `PARTITIONED 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. Not applicable when input.data.format is set to STRING.
* 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.
* 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’.
* 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.
* 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
`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.
* 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.
* 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:
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. This mode is not supported when input.data.format is set to STRING.
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
### 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)
* 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)
* Type: string
* Importance: low
`header.converter`
: The converter class for the headers. This is used to serialize and deserialize the headers of the messages.
* 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.
* 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.
* 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.
* Type: boolean
* Importance: low
`value.converter.auto.register.schemas`
: Specify if the Serializer should attempt to register the Schema.
* 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.
* Type: boolean
* Importance: low
`value.converter.enhanced.avro.schema.support`
: Enable enhanced schema support to preserve package information and Enums. Applicable for Avro Converters.
* Type: boolean
* Importance: low
`value.converter.enhanced.protobuf.schema.support`
: Enable enhanced schema support to preserve package information. Applicable for Protobuf Converters.
* Type: boolean
* Importance: low
`value.converter.flatten.unions`
: Whether to flatten unions (oneofs). Applicable for Protobuf Converters.
* Type: boolean
* Importance: low
`value.converter.generate.index.for.unions`
: Whether to generate an index suffix for unions. Applicable for Protobuf Converters.
* Type: boolean
* Importance: low
`value.converter.generate.struct.for.nulls`
: Whether to generate a struct variable for null values. Applicable for Protobuf Converters.
* Type: boolean
* Importance: low
`value.converter.int.for.enums`
: Whether to represent enums as integers. Applicable for Protobuf Converters.
* Type: boolean
* Importance: low
`value.converter.latest.compatibility.strict`
: Verify latest subject version is backward compatible when use.latest.version is true.
* Type: boolean
* Importance: low
`value.converter.object.additional.properties`
: Whether to allow additional properties for object schemas. Applicable for JSON_SR Converters.
* Type: boolean
* Importance: low
`value.converter.optional.for.nullables`
: Whether nullable fields should be specified with an optional label. Applicable for Protobuf Converters.
* Type: boolean
* Importance: low
`value.converter.optional.for.proto2`
: Whether proto2 optionals are supported. Applicable for Protobuf Converters.
* 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.
* Type: boolean
* Importance: low
`value.converter.use.latest.version`
: Use latest version of schema in subject for serialization when auto.register.schemas is false.
* Type: boolean
* Importance: low
`value.converter.use.optional.for.nonrequired`
: Whether to set non-required properties to be optional. Applicable for JSON_SR Converters.
* 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.
* 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.
* Type: int
* Importance: low
`value.converter.wrapper.for.nullables`
: Whether nullable fields should use primitive wrapper messages. Applicable for Protobuf Converters.
* 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.
* 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.
* 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.
* 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.
* 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:
BASE64 to serialize DECIMAL logical types as base64 encoded binary data and
NUMERIC to serialize Connect DECIMAL logical type values in JSON/JSON_SR as a number representing the decimal value.
* Type: string
* Default: BASE64
* Importance: low
`value.converter.flatten.singleton.unions`
: Whether to flatten singleton unions. Applicable for Avro and JSON_SR Converters.
* 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.
* 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.
* 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.
* 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.
* 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.
* 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.
* Type: string
* Default: TopicNameStrategy
* Importance: low
### Auto-restart policy
`auto.restart.on.user.error`
: Enable connector to automatically restart on user-actionable errors.
* Type: boolean
* Default: true
* Importance: medium
## Frequently asked questions
Find answers to frequently asked questions about the PostgreSQL Sink connector for Confluent Cloud.
### How do I resolve duplicate key errors?
The connector fails with duplicate key constraint violations when attempting to insert records that already exist in the PostgreSQL table.
You might encounter errors similar to:
```none
java.sql.BatchUpdateException: Batch entry 0 INSERT INTO "" ("") VALUES () was aborted:
ERROR: duplicate key value violates unique constraint ""
Detail: Key ()=() already exists.
```
**Root cause:**
When using the default `insert.mode` of `INSERT`, the connector attempts to insert new rows into the PostgreSQL table. If a record with the same primary key already exists, PostgreSQL rejects the operation with a duplicate key error. This commonly occurs when:
* The connector is restarted and reprocesses records.
* The same data is sent multiple times due to retries or rebalancing.
* Multiple connectors or processes are writing to the same table.
**Solution:**
Change the `insert.mode` configuration from `INSERT` to `UPSERT`:
```json
{
"insert.mode": "UPSERT"
}
```
The `UPSERT` mode uses PostgreSQL’s `ON CONFLICT` clause to update existing rows instead of inserting duplicates. This allows the connector to handle records with existing primary keys by updating the values rather than failing.
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.
For more information, see the `insert.mode` property in [Configuration Properties](#cc-postgresql-sink-config-properties).
### How do I configure SSL/TLS connections to PostgreSQL?
The PostgreSQL connector supports several SSL modes for secure
connections. SSL/TLS configuration is handled through the JDBC driver using the
`ssl.mode` property.
**Available SSL modes**:
* `prefer` (default): Attempts to use an encrypted connection. Falls back to
unencrypted if SSL is unavailable. This mode is enabled by default if
`ssl.mode` is not added to the connector configuration.
* `require`: Uses a secure connection. The connector fails if a secure
connection cannot be established. Does not perform Certificate Authority (CA)
validation.
* `verify-ca`: Similar to `require`, but also verifies the server TLS
certificate against configured CA certificates. Fails
if no valid matching CA certificates are found.
* `verify-full`: Similar to `verify-ca`, but also verifies that the server
certificate matches the host to which the connection is attempted.
**For \`\`verify-ca\`\` or \`\`verify-full\`\` modes**:
Use the `ssl.rootcertfile` property and provide the server root certificate
as a base64-encoded `data:` URL. Generate the value with:
```bash
echo "data:application/x-x509-ca-cert;base64,$(base64 -i server-ca.pem | tr -d '\n')"
```
Then set it in the connector configuration:
```none
"ssl.mode": "verify-ca",
"ssl.rootcertfile": "data:application/x-x509-ca-cert;base64,LS0tLS1CRUdJTiBDRVJUSUZJ..."
```
**Mutual TLS:**
With one-way SSL, only the database proves its identity to the connector. Use
mTLS when you also want the database to authenticate the connector. The
connector presents a client certificate that the database validates before
allowing the connection.
A TLS connection has two independent parts:
* **Encryption and server authentication**: whether the connection encrypts
traffic and whether the connector verifies the database server’s identity.
Controlled by `ssl.mode` and `ssl.rootcertfile`.
* **Client authentication**: whether the connector proves its own identity to
the database. Controlled by `ssl.clientcertfile` and `ssl.clientkeyfile`.
These parts are independent. `ssl.mode` does not control whether the connector
presents a client certificate. The connector sends its client certificate in
any SSL mode (including `prefer` and `require`) whenever an SSL handshake
occurs and the database requests one. The database server (not `ssl.mode`)
decides whether to require a client certificate, so you can use mTLS with any
`ssl.mode`.
To use mTLS, provide these additional properties:
* `ssl.clientcertfile`: The client certificate the connector presents to the
database. Must be a PEM-encoded X.509v3 certificate.
* `ssl.clientkeyfile`: The matching client private key. Must be in PKCS-8 DER
format. If your key is PEM-encoded, convert it first:
```bash
openssl pkcs8 -topk8 -inform PEM -outform DER -nocrypt -in client-key.pem -out client-key.der
```
In the Confluent Cloud Console, upload the client certificate and key files directly.
When you configure the connector with the Confluent CLI, REST API, or
Terraform, pass each certificate or key as a base64-encoded `data:` URL.
Generate the values with:
```bash
# Server CA certificate (ssl.rootcertfile)
echo "data:application/x-x509-ca-cert;base64,$(base64 -i server-ca.pem | tr -d '\n')"
# Client certificate (ssl.clientcertfile)
echo "data:application/x-x509-ca-cert;base64,$(base64 -i client-cert.pem | tr -d '\n')"
# Client private key (ssl.clientkeyfile)
echo "data:application/pkcs8;base64,$(base64 -i client-key.der | tr -d '\n')"
```
Then set the resulting strings in the connector configuration. The following
example uses `ssl.mode` `verify-ca`, which also validates the server with
`ssl.rootcertfile`:
```json
{
"ssl.mode": "verify-ca",
"ssl.rootcertfile": "data:application/x-x509-ca-cert;base64,LS0tLS1CRUdJTiBDRVJUSUZJ...",
"ssl.clientcertfile": "data:application/x-x509-ca-cert;base64,LS0tLS1CRUdJTiBDRVJUSUZ...",
"ssl.clientkeyfile": "data:application/pkcs8;base64,MIIEvQIBADANBgkqhkiG9w0B..."
}
```
Configure your PostgreSQL server to request and validate client
certificates for mTLS to take effect.
#### NOTE
The connector uses the PostgreSQL JDBC driver for SSL/TLS.
Ensure that your certificate format is compatible with the driver requirements.
**Troubleshooting SSL/TLS issues:**
* **Missing certificate error**: If you see certificate validation errors, ensure you have provided the complete certificate chain in `ssl.rootcertfile` as a base64-encoded `data:` URL.
* **Certificate format**: Provide each certificate or key as a base64-encoded `data:` URL (for example, `data:application/x-x509-ca-cert;base64,...`), not raw PEM text. Include the full certificate chain if your database uses intermediate certificates.
* **Hostname mismatch**: When using `verify-full`, ensure the `connection.host` matches the certificate’s Common Name (CN) or Subject Alternative Name (SAN).
* **Firewall rules**: Verify that the PostgreSQL port (default 5432) allows encrypted connections from Confluent Cloud.
* **Connection requires a valid client certificate**: A `FATAL: connection requires a valid client certificate` error means the server requires a client certificate but the connector did not present a valid one. Confirm that `ssl.clientcertfile` (PEM-encoded X.509v3) and `ssl.clientkeyfile` (PKCS-8 DER) are both set, that the key matches the certificate, and that the certificate is signed by a CA the PostgreSQL server trusts for client authentication.
For more information about SSL configuration, see [Configuration Properties](#cc-postgresql-sink-config-properties).
### How do I configure AWS IAM authentication for RDS PostgreSQL?
The PostgreSQL Sink connector supports Amazon IAM role-based
authentication for RDS and Aurora PostgreSQL using Confluent Provider
Integration. To use it, set the following connector properties:
* Set **Authentication method** to `IAM Roles`.
* Select the **Provider Integration** that has access to your database.
* Set **Database AWS region** to the region of your RDS or Aurora instance.
Do not set the database password when using IAM authentication.
For the database user, IAM policy, and Provider Integration setup steps, see
[AWS IAM Authentication Setup](#cc-postgresql-sink-aws-iam-auth).
### Why is `auto.create` or `auto.evolve` not creating tables or columns?
The connector fails to automatically create tables or add new columns even though `auto.create` or `auto.evolve` is set to `true`.
**Root cause:**
Table creation or schema evolution failures typically occur due to:
* **Insufficient database permissions**: The PostgreSQL user account lacks `CREATE` or `ALTER` privileges.
* **Schema compatibility issues**: The Kafka record schema cannot be mapped to a valid PostgreSQL table structure.
* **Column count limits**: PostgreSQL has limits on the number of columns per table. If your schema exceeds this limit (typically 1,600 columns), table creation fails.
* **Data type incompatibilities**: Some Kafka schema types might not have a direct PostgreSQL equivalent.
* **Reserved keywords**: Table or column names that are PostgreSQL reserved keywords can cause creation to fail.
**Solution:**
* **Grant required permissions**: Ensure the PostgreSQL user has the necessary DDL privileges. See [Prerequisites](#cc-postgresql-sink-prereqs) for the complete list of required privileges (`CREATE`, `ALTER`, and `DROP`).
* **Pre-create tables**: For complex schemas or production environments, consider creating tables manually with the exact schema you need, then set both `auto.create` and `auto.evolve` to `false`.
* **Monitor column counts**: If your schema has many fields, check that you’re within PostgreSQL’s column limit. Consider restructuring your data model if you exceed this limit.
* **Verify schema format**: Ensure you’re using a schema-based format (Avro, JSON Schema, or Protobuf) with [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) enabled. The connector cannot auto-create tables from schemaless data.
* **Check naming conventions**: Avoid PostgreSQL reserved keywords for table and column names. If necessary, the connector quotes identifiers, but pre-creating tables with proper naming is recommended.
### How do I configure primary keys correctly?
You need to understand how to configure `pk.mode` and `pk.fields` for your PostgreSQL Sink connector.
**Configuration options:**
The `pk.mode` property determines how primary keys are assigned. Supported modes are:
* `kafka`: Uses Kafka coordinates (topic, partition, offset) as the primary key. This guarantees uniqueness but doesn’t reflect your business logic.
```json
{
"pk.mode": "kafka",
"pk.fields": "__connect_topic,__connect_partition,__connect_offset"
}
```
If `pk.fields` is not specified, these default field names are used automatically.
* `none`: No primary key is used. Rows are inserted without unique constraints.
```json
{
"pk.mode": "none"
}
```
Use this mode with caution as it can lead to duplicate records.
* `record_key`: Uses fields from the Kafka record key as the primary key.
```json
{
"pk.mode": "record_key",
"pk.fields": "user_id"
}
```
For primitive keys, specify a single field name. For struct keys, specify the field names to use, or leave `pk.fields` empty to use all fields from the key.
* `record_value`: Uses fields from the Kafka record value as the primary key.
```json
{
"pk.mode": "record_value",
"pk.fields": "order_id,line_item_id"
}
```
The record value must be a struct type. Specify comma-separated field names, or leave `pk.fields` empty to use all fields.
**Common errors:**
* **Mismatched field names**: If `pk.fields` references fields that don’t exist in the record key or value (depending on `pk.mode`), the connector fails.
* **Missing pk.fields with primitive keys**: When using `pk.mode=record_key` with a primitive key type (like String or Integer), you must specify exactly one field name in `pk.fields`.
* **Incompatible with delete.on.null**: The `delete.on.null` property (which treats null values as deletes) requires `pk.mode=record_key`. Other modes are not compatible.
For more information, see the `pk.mode` and `pk.fields` properties in [Configuration Properties](#cc-postgresql-sink-config-properties).
### How do I handle database connection failures?
The connector fails to connect to the PostgreSQL database or experiences intermittent connection issues.
**Common connection errors:**
* **Network connectivity**: The connector cannot reach the PostgreSQL host due to firewall rules, security groups, or network configuration.
* **Authentication failures**: Incorrect username or password, or the PostgreSQL user is not permitted to connect from the connector’s IP address.
* **Database does not exist**: The database specified in `db.name` does not exist on the PostgreSQL server.
* **Connection timeout**: The PostgreSQL server is not responding within the expected timeout period.
* **SSL/TLS errors**: Certificate validation failures when using `ssl.mode` of `verify-full` or `verify-ca`.
**Troubleshooting steps:**
* **Review connector logs**: Check the connector logs in the Confluent Cloud Console for specific error messages that can help identify the root cause.
* **Verify network access**: Ensure the PostgreSQL database is accessible from Confluent Cloud. For networking considerations, see [Networking and DNS](overview.md#connect-internet-access-resources). If using private networking, ensure it is properly configured. The database and Kafka cluster should be in the same region to avoid additional data transfer charges.
* **Test credentials manually**: Use a PostgreSQL client to verify the connection string, username, and password:
```bash
psql -h -p -U -d
```
* **Check PostgreSQL user permissions**: Ensure the user has the necessary permissions. See [Prerequisites](#cc-postgresql-sink-prereqs) for required privileges.
* **Verify database exists**: Confirm that the database specified in `db.name` exists:
```sql
\l
```
* **Check SSL configuration**: If using SSL, verify that your `ssl.mode` and `ssl.rootcertfile` (if required) are correctly configured. See the “How do I configure SSL/TLS connections to PostgreSQL?” question in this section.
### How do I handle timezone-related issues?
The connector writes incorrect timestamps to PostgreSQL, or you need to control how time-based values are handled.
**Configuration:**
Use the `db.timezone` property to specify the timezone for time-based values:
```json
{
"db.timezone": "UTC"
}
```
The default value is `UTC`. You can specify any valid timezone name recognized by PostgreSQL.
**Common scenarios:**
* **UTC timestamps**: Keep the default `db.timezone=UTC` if your application works with UTC timestamps. This is the recommended approach for most use cases.
* **Local timezone**: If you need to store timestamps in a specific local timezone, set `db.timezone` to the appropriate timezone:
```json
{
"db.timezone": "America/New_York"
}
```
* **PostgreSQL timezone setting**: Ensure your PostgreSQL database timezone settings are compatible with the connector’s `db.timezone` configuration. You can check the database timezone with:
```sql
SHOW timezone;
```
**Troubleshooting:**
* **Incorrect timestamps**: If timestamps appear offset by several hours, verify that `db.timezone` matches your expected timezone.
* **Daylight saving time**: When using local timezones, be aware of daylight saving time transitions which can affect timestamp values.
* **Timezone names**: Use standard timezone names (like `America/New_York`) rather than abbreviations (like `EST`) for consistent behavior across daylight saving time changes.
For more information, see the `db.timezone` property in [Configuration Properties](#cc-postgresql-sink-config-properties).
### Why are my table names being truncated?
The connector creates tables with truncated names, or multiple topics are writing to the same table.
**Root cause:**
PostgreSQL has a default identifier length limit of 63 bytes. When the combined value of `table.name.format` and the Kafka topic name exceeds this limit, the connector truncates the name to 63 characters. This can cause issues when:
* Multiple topics with similar long names truncate to the same table name, causing a collision.
* The truncated table name doesn’t match your expected naming convention.
**Solution:**
* **Use shorter topic names**: Keep Kafka topic names short to avoid truncation, especially when using `table.name.format` that adds prefixes or suffixes.
* **Customize table.name.format**: Modify the `table.name.format` configuration to use shorter prefixes or different naming patterns:
```json
{
"table.name.format": "${topic}"
}
```
The default format is `${topic}`, which uses the topic name directly. Adding prefixes or suffixes increases the likelihood of truncation.
* **Pre-create tables**: For better control over table names, create tables manually before running the connector and set `auto.create` to `false`.
* **Plan for unique names**: Ensure that even after truncation to 63 characters, your table names remain unique across all topics.
For more information about table name handling and truncation behavior, see [Table names and Kafka topic names](#cc-postgresql-sink-truncation-behavior).
### Why is my data not appearing in PostgreSQL tables?
The connector is running, but data from Kafka topics is not being written to PostgreSQL tables.
**Root cause:**
This issue typically occurs due to:
* **Schema mismatches**: The schema of the Kafka records doesn’t match the PostgreSQL table schema, causing the connector to skip or fail processing records.
* **Serialization errors**: The connector cannot deserialize the Kafka records due to missing or incompatible schemas in Confluent Cloud Schema Registry.
* **Connector is paused or failed**: The connector status shows as paused or failed, preventing data flow.
* **Consumer lag**: The connector is not consuming messages from the topic due to consumer group issues or offset problems.
* **Filtering or transformations**: SMTs or filtering logic is dropping records before they reach PostgreSQL.
* **Permission issues**: The PostgreSQL user lacks necessary DML permissions (`SELECT`, `INSERT`, `UPDATE`, `DELETE`).
**Solution:**
* **Verify connector status**: Check that the connector status is `Running` in the Confluent Cloud Console. If it shows `Failed` or `Paused`, review the error messages and restart the connector.
* **Check Schema Registry**: Ensure that the Kafka topic has a valid schema registered in [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) that matches your `input.data.format` (Avro, JSON_SR, or Protobuf).
* **Validate table schema**: Confirm that the PostgreSQL table schema is compatible with the Kafka record schema. Check column names, data types, and nullable constraints.
* **Verify permissions**: Ensure the PostgreSQL user has the required DML privileges. See [Prerequisites](#cc-postgresql-sink-prereqs) for the complete list of required permissions.
* **Review connector logs**: Look for serialization errors, schema compatibility issues, or other error messages in the connector logs.
* **Monitor consumer lag**: Check if the connector is consuming messages from the topic. High consumer lag can indicate processing issues.
* **Verify SMT configuration**: If you’re using [Single Message Transformations](single-message-transforms.md#cc-single-message-transforms), ensure they’re not inadvertently dropping records.
### How do I handle schema evolution?
You need to add new fields to your Kafka topic schema and want them to be reflected in the PostgreSQL table.
**Using auto.evolve:**
When `auto.evolve` is set to `true`, the connector automatically adds new columns to the PostgreSQL table when it encounters new fields in the Kafka record schema:
```json
{
"auto.evolve": "true"
}
```
**Important considerations:**
* **Backward compatibility**: The connector can add new columns but cannot remove existing columns or change column types. Schema changes must be backward compatible.
* **Default values**: New columns are added with NULL as the default value unless your schema specifies a default.
* **Database permissions**: The PostgreSQL user must have `ALTER` privileges to evolve the schema. See [Prerequisites](#cc-postgresql-sink-prereqs) for required permissions.
* **Schema Registry**: Use [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) with schema compatibility rules to ensure controlled schema evolution. The recommended compatibility modes are `BACKWARD` or `FULL`.
* **Column limit**: Be aware of PostgreSQL’s column count limit (typically 1,600 columns). Adding many fields over time can eventually exceed this limit.
**Manual schema management:**
For production environments, consider managing schema changes manually:
1. Set `auto.evolve` to `false`.
2. Coordinate schema changes between your Kafka topics and PostgreSQL tables.
3. Use Confluent Cloud Schema Registry with strict compatibility rules to prevent incompatible schema changes.
4. Apply schema changes to PostgreSQL tables first, then update the Kafka topic schema.
This approach gives you more control and prevents unexpected schema changes in your database.
### How do I handle JSON and JSONB data types?
You want to sink data to PostgreSQL tables containing JSON or JSONB columns.
**Configuration:**
The connector supports writing to PostgreSQL tables with JSON or JSONB columns. To use this feature:
1. Define your PostgreSQL table with JSON or JSONB column types:
```sql
CREATE TABLE events (
id INTEGER PRIMARY KEY,
event_data JSONB
);
```
2. In your Kafka topic, store the JSON data as a STRING type.
3. The connector automatically writes the string value to the JSON or JSONB column in PostgreSQL.
**Important notes:**
* **Data format**: The JSON data must be stored as STRING type in Kafka, not as a complex schema type.
* **JSON vs JSONB**: PostgreSQL stores JSON as text and JSONB in a binary format. JSONB is generally recommended for better performance and indexing capabilities.
* **Schema validation**: Ensure the string value contains valid JSON. Invalid JSON causes insertion errors.
* **Quoting**: The connector handles the necessary quoting and escaping when writing to JSON/JSONB columns.
**Example configuration:**
```json
{
"connector.class": "PostgresSink",
"topics": "events_topic",
"input.data.format": "AVRO",
"auto.create": "false",
"db.name": "mydb"
}
```
In this example, if your Avro schema has a field of type STRING containing JSON data, it is written to the corresponding JSONB column in PostgreSQL.
## 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.
[](https://docs.confluent.io/platform/current/tutorials/examples/cloud-etl/docs/index.html)