Ogg

Function

Oracle GoldenGate (a.k.a ogg) is a comprehensive software package for real-time data capture and replication in heterogeneous IT environments. The product set enables high availability solutions, real-time data integration, transactional change data capture, data replication, transformations, and verification between operational and analytical enterprise systems. Ogg provides a format schema for changelog and supports to serialize messages using JSON.

Flink supports to interpret Ogg JSON as INSERT/UPDATE/DELETE messages into Flink SQL system. This is useful in many cases to leverage this feature, such as:

• Synchronizing incremental data from databases to other systems
• Auditing logs
• Real-time materialized views on databases
• Temporal join changing history of a database table and so on.

Flink also supports to encode the INSERT/UPDATE/DELETE messages in Flink SQL as Ogg JSON, and emit to external systems like Kafka. However, currently Flink cannot combine UPDATE_BEFORE and UPDATE_AFTER into a single UPDATE message. Therefore, Flink encodes UPDATE_BEFORE and UPDATE_AFTER as DELETE and INSERT Ogg messages.

Supported Connectors

• Kafka
• FileSystem

Parameter Description

Metadata

The following example shows how to access Canal metadata fields in Kafka:

CREATE TABLE KafkaTable (
  origin_ts TIMESTAMP(3) METADATA FROM 'value.ingestion-timestamp' VIRTUAL,
  event_time TIMESTAMP(3) METADATA FROM 'value.event-timestamp' VIRTUAL,
  origin_table STRING METADATA FROM 'value.table' VIRTUAL,
  primary_keys ARRAY<STRING> METADATA FROM 'value.primary_keys' VIRTUAL,
  user_id BIGINT,
  item_id BIGINT,
  behavior STRING
) WITH (
  'connector' = 'kafka',
  'topic' = 'kafkaTopic',
  'properties.bootstrap.servers' = 'KafkaAddress1:KafkaPort,KafkaAddress2:KafkaPort',
  'properties.group.id' = 'GroupId',
  'scan.startup.mode' = 'earliest-offset',
  'value.format' = 'ogg-json'
);

Example

Use ogg-json to read Ogg records in Kafka and output them to Print.

1. Create a datasource connection for the communication with the VPC and subnet where Kafka locates and bind the connection to the queue. Set a security group and inbound rule to allow access of the queue and test the connectivity of the queue using the Kafka IP address. For example, locate a general-purpose queue where the job runs and choose More > Test Address Connectivity in the Operation column. If the connection is successful, the datasource is bound to the queue. Otherwise, the binding fails.
2. Create a Flink OpenSource SQL job and select Flink 1.15. Copy the following statement and submit the job:

   CREATE TABLE kafkaSource (
     id bigint,
     name string,
     description string,  
     weight DECIMAL(10, 2)
   ) WITH (
     'connector' = 'kafka',
     'topic' = 'kafkaTopic',
     'properties.bootstrap.servers' = 'KafkaAddress1:KafkaPort,KafkaAddress2:KafkaPort',
     'properties.group.id' = 'GroupId',
     'scan.startup.mode' = 'latest-offset',
     'format' = 'ogg-json'
   );
   
   CREATE TABLE printSink (
     id bigint,
     name string,
     description string,  
     weight DECIMAL(10, 2)
   ) WITH (
     'connector' = 'print'
   );
   insert into printSink select * from kafkaSource;    
   

3. Insert the data below into the appropriate Kafka topics. The data shows that the Oracle PRODUCTS table has four columns: id, name, description, and weight. This JSON message represents an update event on the PRODUCTS table, where the weight value of the row with id = 111 has been changed from 5.18 to 5.15.

   {
     "before": {
       "id": 111,
       "name": "scooter",
       "description": "Big 2-wheel scooter",
       "weight": 5.18
     },
     "after": {
       "id": 111,
       "name": "scooter",
       "description": "Big 2-wheel scooter",
       "weight": 5.15
     },
     "op_type": "U",
     "op_ts": "2020-05-13 15:40:06.000000",
     "current_ts": "2020-05-13 15:40:07.000000",
     "primary_keys": [
       "id"
     ],
     "pos": "00000000000000000000143",
     "table": "PRODUCTS"
   }

4. Perform the following operations to view the data result in the taskmanager.out file:
   1. Log in to the DLI console. In the navigation pane, choose Job Management > Flink Jobs.
   2. Click the name of the corresponding Flink job, choose Run Log, click OBS Bucket, and locate the folder of the log you want to view according to the date.
   3. Go to the folder of the date, find the folder whose name contains taskmanager, download the .out file, and view result logs.

   -U[111, scooter, Big 2-wheel scooter, 5.18]
   +U[111, scooter, Big 2-wheel scooter, 5.15]