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Stream CDC Data from Oracle

  • how-to
To continuously load change data capture (CDC) events from an Oracle database into Enterprise Analytics, you configure the Debezium Oracle connector to publish changes to a Kafka topic, then create a Kafka link and collection in Enterprise Analytics.

The Debezium Oracle connector captures row-level changes in Oracle tables and publishes them as JSON events to a Kafka topic. Enterprise Analytics reads from that topic using a Kafka link.

Prerequisites

  • A running Kafka cluster with a Debezium connector configured for your Oracle database, producing messages in JSON, Avro, or Protobuf format.

  • If using JSON format, schema embedding must be disabled for both key and value converters in your Debezium configuration:

    key.converter.schemas.enable=false
value.converter.schemas.enable=false

  • A Kafka link in Enterprise Analytics. See Create a Kafka Pipeline Link.

Create an Enterprise Analytics Collection

The Kafka topic for an Oracle table follows the naming convention <topic.prefix>.<schema>.<table>. For example, if topic.prefix is oracle, the schema is ADMIN, and the table is ORDERS, the topic name is oracle.ADMIN.ORDERS.

The following example creates a collection on a Kafka link to ingest Debezium Oracle CDC records:

CREATE COLLECTION `Default`.`Default`.oracleOrders
    PRIMARY KEY (ID: bigint)
    ON `oracle.ADMIN.ORDERS` AT kafkaLink
    WITH {
        "storage-format": {"format": "column"},
        "kafka-sink": "true",
        "keySerializationType": "JSON",
        "valueSerializationType": "JSON",
        "cdcEnabled": "true",
        "cdcDetails": {
            "cdcSource": "ORACLE",
            "cdcSourceConnector": "DEBEZIUM"
        }
    };
Parameter Description

PRIMARY KEY

The primary key field and its data type. Must match a column in the Oracle table.

ON

The Kafka topic name. Must match the topic the Debezium connector publishes to.

AT

The name of the Kafka link that connects to your Kafka cluster.

cdcEnabled

Set to "true" to enable CDC processing for this collection.

cdcSource

Set to "ORACLE" to indicate the upstream CDC source is Oracle.

cdcSourceConnector

Set to "DEBEZIUM" to indicate the Debezium connector is in use.

To start ingesting data, connect the Kafka link:

CONNECT LINK kafkaLink;

See Connect or Disconnect a Remote Link.

Data Type Handling and Limitations

During ingestion, Debezium may convert Oracle types in ways that differ from the native Oracle representation. Ingestion is performed based on the contents of the after field in each event. No schema metadata is used.

The following table shows how Oracle types map to Enterprise Analytics types with the default Debezium configuration.

Oracle Type Enterprise Analytics Type

CHAR[(M)], NCHAR[(M)], NVARCHAR2[(M)], VARCHAR[(M)], VARCHAR2[(M)]

STRING

BLOB, CLOB, NCLOB

STRING

BINARY_FLOAT, BINARY_DOUBLE

DOUBLE

DECIMAL[(P, S)]

STRING or BIGINT

DOUBLE PRECISION, FLOAT[(P)], NUMBER[(P[, *])], REAL

OBJECT

INTEGER, INT

STRING

NUMBER(P, S <= 0)

BIGINT

NUMBER(P, S > 0)

STRING

NUMERIC[(P, S)]

STRING or BIGINT

SMALLINT

STRING

DATE

BIGINT

INTERVAL DAY[(M)] TO SECOND

DOUBLE

INTERVAL YEAR[(M)] TO MONTH

DOUBLE

TIMESTAMP(0-3), TIMESTAMP, TIMESTAMP(4-6), TIMESTAMP(7-9)

BIGINT

TIMESTAMP WITH TIME ZONE, TIMESTAMP WITH LOCAL TIME ZONE

STRING

Unsupported Oracle Types

The following Oracle types are not supported by Debezium:

  • BFILE (binary file)

  • BOOLEAN

  • Direct joins in the FROM clause of UPDATE and DELETE statements

  • Data Use Case Domains

  • JavaScript-based stored procedures

  • LONG

  • LONG RAW

  • ROWID (not supported on XStream)

  • Table value constructors (TVCs)

  • UROWID (Universal ROWID)

  • VECTOR

Debezium Configuration Options

Debezium provides configuration options to reduce data type mismatches. For example, setting decimal.handling.mode = double causes Oracle decimal values to be ingested as DOUBLE values in Kafka. For a full list of options, see the Debezium Oracle connector documentation.

Test your complete data model with all expected data types before deploying to production to verify data is ingested as expected under your chosen configuration.

See Also