Important

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Teradata Source Connector Configuration Properties

To use this connector, specify the name of the connector class in the connector.class configuration property.

connector.class=io.confluent.connect.teradata.TeradataSourceConnector

Connector-specific configuration properties are described below.

Connection

teradata.database
Name of a database * Type: string * Importance: high
teradata.url

URL for remote database

  • Type: string
  • Valid Values: URI with one of these schemes: ‘jdbc’
  • Importance: high
teradata.username

Username for database authentication

  • Type: string
  • Importance: high
teradata.password

Password for database authentication

  • Type: password
  • Importance: high

Database Connection Security

If you wish to enable encryption for data being read or written to Teradata, you will need to pass an additional configuration parameter in the teradata.url, ENCRYPTDATA=ON,. Whenever you specify any options in the teradata URL, you _must_ leave a trailing comma. For example, a configuration with encryption enabled would look like:

connection.url="jdbc:teradata://127.0.0.1/ENCRYPTDATA=ON,"

For full documentation of these URL options, see the JDBC Driver documentation

Database

connection.attempts

Maximum number of attempts to retrieve a valid JDBC connection.

  • Type: int
  • Default: 3
  • Importance: low
connection.backoff.ms

Backoff time in milliseconds between connection attempts.

  • Type: long
  • Default: 10000
  • Importance: low
catalog.pattern

Catalog pattern to fetch table metadata from the database.

  • Type: string
  • Default: null
    • “” retrieves those without a catalog
    • null (default) indicates that the schema name is not used to narrow the search and that all table metadata is fetched, regardless of the catalog.
  • Importance: medium
table.whitelist

List of tables to include in copying. If specified, table.blacklist may not be set. Use a comma-separated list to specify multiple tables (for example, table.whitelist: "User, Address, Email").

  • Type: list
  • Default: “”
  • Importance: medium
table.blacklist

List of tables to exclude from copying. If specified, table.whitelist may not be set. Use a comma-separated list to specify multiple tables (for example, table.blacklist: "User, Address, Email").

  • Type: list
  • Default: “”
  • Importance: medium
schema.pattern

Schema pattern to fetch table metadata from the database.

  • Type: string
  • Default: null
    • "" retrieves those without a schema.
    • null (default) indicates that the schema name is not used to narrow the search and that all table metadata is fetched, regardless of the schema.
  • Importance: medium
numeric.precision.mapping

Whether or not to attempt mapping NUMERIC values by precision to integral types. This option is now deprecated. A future version may remove it completely. Please use numeric.mapping instead.

  • Type: boolean
  • Default: false
  • Importance: low
numeric.mapping

Map NUMERIC values by precision and optionally scale to integral or decimal types.

  • Type: string
  • Default: null
  • Valid Values: [none, precision_only, best_fit]
    • Use none if all NUMERIC columns are to be represented by Connect’s DECIMAL logical type.
    • Use best_fit if NUMERIC columns should be cast to Connect’s INT8, INT16, INT32, INT64, or FLOAT64 based upon the column’s precision and scale.
    • Use precision_only to map NUMERIC columns based only on the column’s precision assuming that column’s scale is 0.
    • The none option is the default, but may lead to serialization issues with Avro since Connect’s DECIMAL type is mapped to its binary representation, and best_fit will often be preferred since it maps to the most appropriate primitive type.
  • Importance: low

Mode

mode

The mode for updating a table each time it is polled. Options include:

  • Type: string
  • Default: “”
  • Valid Values: [, bulk, timestamp, incrementing, timestamp+incrementing]
    • bulk: perform a bulk load of the entire table each time it is polled
    • incrementing: use a strictly incrementing column on each table to detect only new rows. Note that this will not detect modifications or deletions of existing rows.
    • timestamp: use a timestamp (or timestamp-like) column to detect new and modified rows. This assumes the column is updated with each write, and that values are monotonically incrementing, but not necessarily unique.
    • timestamp+incrementing: use two columns, a timestamp column that detects new and modified rows and a strictly incrementing column which provides a globally unique ID for updates so each row can be assigned a unique stream offset.
  • Importance: high
  • Dependents: incrementing.column.name, timestamp.column.name, validate.non.null
incrementing.column.name

The name of the strictly incrementing column to use to detect new rows. Any empty value indicates the column should be autodetected by looking for an auto-incrementing column. This column may not be nullable.

  • Type: string
  • Default: “”
  • Importance: medium
timestamp.column.name

Comma separated list of one or more timestamp columns to detect new or modified rows using the COALESCE SQL function. Rows whose first non-null timestamp value is greater than the largest previous timestamp value seen will be discovered with each poll. At least one column should not be nullable.

  • Type: list
  • Default: “”
  • Importance: medium
validate.non.null

By default, the Teradata connector will validate that all incrementing and timestamp tables have NOT NULL set for the columns being used as their ID/timestamp. If the tables don’t, Teradata connector will fail to start. Setting this to false will disable these checks.

  • Type: boolean
  • Default: true
  • Importance: low
query

If specified, the query to perform to select new or updated rows. Use this setting if you want to join tables, select subsets of columns in a table, or filter data. If used, this connector will only copy data using this query – whole-table copying will be disabled. Different query modes may still be used for incremental updates, but in order to properly construct the incremental query, it must be possible to append a WHERE clause to this query (i.e. no WHERE clauses may be used). If you use a WHERE clause, it must handle incremental queries itself.

  • Type: string
  • Default: “”
  • 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
  • Importance: medium

Connector

table.types

By default, the Teradata connector will only detect tables with type TABLE from the source Database. This config allows a command separated list of table types to extract.

  • Type: list.

    • TABLE
    • VIEW
    • SYSTEM TABLE
    • GLOBAL TEMPORARY
    • LOCAL TEMPORARY
    • ALIAS
    • SYNONYM

    In most cases it only makes sense to have either TABLE or VIEW.

  • Default: TABLE

  • Importance: low

poll.interval.ms

Frequency in ms to poll for new data in each table.

  • Type: int
  • Default: 5000
  • Importance: high
batch.max.rows

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: 100
  • Importance: low
table.poll.interval.ms

Frequency in ms to poll for new or removed tables, which may result in updated task configurations to start polling for data in added tables or stop polling for data in removed tables.

  • Type: long
  • Default: 60000
  • Importance: low
topic.prefix

Prefix to prepend to table names to generate the name of the Apache Kafka® topic to publish data to, or in the case of a custom query, the full name of the topic to publish to.

  • Type: string
  • Importance: high
timestamp.delay.interval.ms

How long to wait after a row with certain timestamp appears before we include it in the result. You may choose to add some delay to allow transactions with earlier timestamp to complete. The first execution will fetch all available records (i.e. starting at timestamp 0) until current time minus the delay. Every following execution will get data from the last time we fetched until current time minus the delay.

  • Type: long
  • Default: 0
  • Importance: high
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

Confluent Platform license

confluent.topic.bootstrap.servers

A list of host/port pairs to use for establishing the initial connection to the Kafka cluster used for licensing. All servers in the cluster will be discovered from the initial connection. This list should be in the form <code>host1:port1,host2:port2,…</code>. Since these servers are just used for the initial connection to discover the full cluster membership (which may change dynamically), this list need not contain the full set of servers (you may want more than one, though, in case a server is down).

  • Type: list
  • Importance: high
confluent.topic

Name of the Kafka topic used for Confluent Platform configuration, including licensing information.

  • Type: string
  • Default: _confluent-command
  • Importance: low
confluent.topic.replication.factor

The replication factor for the Kafka topic used for Confluent Platform configuration, including licensing information. This is used only if the topic does not already exist, and the default of 3 is appropriate for production use. If you are using a development environment with less than 3 brokers, you must set this to the number of brokers (often 1).

  • Type: int
  • Default: 3
  • Importance: low

Confluent license properties

Note

This connector is proprietary and requires a license. The license information is stored in the _confluent-command topic. If the broker requires SSL for connections, you must include the security-related confluent.topic.* properties as described below.

confluent.license

Confluent issues enterprise license keys to each subscriber. The license key is text that you can copy and paste as the value for confluent.license. A trial license allows using the connector for a 30-day trial period. A developer license allows using the connector indefinitely for single-broker development environments.

If you are a subscriber, please contact Confluent Support for more information.

  • Type: string
  • Default: “”
  • Valid Values: Confluent Platform license
  • Importance: high
confluent.topic.ssl.truststore.location

The location of the trust store file.

  • Type: string
  • Default: null
  • Importance: high
confluent.topic.ssl.truststore.password

The password for the trust store file. If a password is not set access to the truststore is still available, but integrity checking is disabled.

  • Type: password
  • Default: null
  • Importance: high
confluent.topic.ssl.keystore.location

The location of the key store file. This is optional for client and can be used for two-way authentication for client.

  • Type: string
  • Default: null
  • Importance: high
confluent.topic.ssl.keystore.password

The store password for the key store file. This is optional for client and only needed if ssl.keystore.location is configured.

  • Type: password
  • Default: null
  • Importance: high
confluent.topic.ssl.key.password

The password of the private key in the key store file. This is optional for client.

  • Type: password
  • Default: null
  • Importance: high
confluent.topic.security.protocol

Protocol used to communicate with brokers. Valid values are: PLAINTEXT, SSL, SASL_PLAINTEXT, SASL_SSL.

  • Type: string
  • Default: “PLAINTEXT”
  • Importance: medium

License topic configuration

A Confluent enterprise license is stored in the _confluent-command topic. This topic is created by default and contains the license that corresponds to the license key supplied through the confluent.license property.

Note

No public keys are stored in Kafka topics.

The following describes how the default _confluent-command topic is generated under different scenarios:

  • A 30-day trial license is automatically generated for the _confluent command topic if you do not add the confluent.license property or leave this property empty (for example, confluent.license=).
  • Adding a valid license key (for example, confluent.license=<valid-license-key>) adds a valid license in the _confluent-command topic.

Here is an example of the minimal properties for development and testing.

You can change the name of the _confluent-command topic using the confluent.topic property (for instance, if your environment has strict naming conventions). The example below shows this change and the configured Kafka bootstrap server.

confluent.topic=foo_confluent-command
confluent.topic.bootstrap.servers=localhost:9092

The example above shows the minimally required bootstrap server property that you can use for development and testing. For a production environment, you add the normal producer, consumer, and topic configuration properties to the connector properties, prefixed with confluent.topic..

License topic ACLs

The _confluent-command topic contains the license that corresponds to the license key supplied through the confluent.license property. It is created by default. Connectors that access this topic require the following ACLs configured:

  • CREATE and DESCRIBE on the resource cluster, if the connector needs to create the topic.
  • DESCRIBE, READ, and WRITE on the _confluent-command topic.

You can provide access either individually for each principal that will use the license or use a wildcard entry to allow all clients. The following examples show commands that you can use to configure ACLs for the resource cluster and _confluent-command topic.

  1. Set a CREATE and DESCRIBE ACL on the resource cluster:

    kafka-acls --bootstrap-server localhost:9092 --command-config adminclient-configs.conf \
--add --allow-principal User:<principal> \
--operation CREATE --operation DESCRIBE --cluster

  2. Set a DESCRIBE, READ, and WRITE ACL on the _confluent-command topic:

    kafka-acls --bootstrap-server localhost:9092 --command-config adminclient-configs.conf \
--add --allow-principal User:<principal> \
--operation DESCRIBE --operation READ --operation WRITE --topic _confluent-command

Overriding Default Configuration Properties

You can override the replication factor using confluent.topic.replication.factor. For example, when using a Kafka cluster as a destination with less than three brokers (for development and testing) you should set the confluent.topic.replication.factor property to 1.

You can override producer-specific properties by using the confluent.topic.producer. prefix and consumer-specific properties by using the confluent.topic.consumer. prefix.

You can use the defaults or customize the other properties as well. For example, the confluent.topic.client.id property defaults to the name of the connector with -licensing suffix. You can specify the configuration settings for brokers that require SSL or SASL for client connections using this prefix.

You cannot override the cleanup policy of a topic because the topic always has a single partition and is compacted. Also, do not specify serializers and deserializers using this prefix; they are ignored if added.