Kafka Connect is a tool for streaming data between Apache Kafka and other
data systems in a scalable and reliable way. Kafka Connect can ingest entire databases or collect metrics from all your
application servers into Kafka topics, making the data available for stream processing with low
latency. An export connector can deliver data from Kafka topics into secondary indexes like
Elasticsearch or into batch systems such as Hadoop for offline analysis. (Source: Confluent Website)
This Kafka Connect handler lets you to integrate using the Kafka Connect API which can be managed using Schema Registry on Confluent Platform. The Kafka Connect Handler takes change data capture operations from a source trail file and generates data structs (org.apache.kafka.connect.data.Struct) as well as the associated schemas (org.apache.kafka.connect.data.Schema). The data structs are serialized via configured converters then enqueued onto Kafka topics. The topic name used corresponds to the fully qualified source table name as obtained from the GoldenGate trail file. Individual operations consist of inserts, updates, and delete operations executed on the source RDBMS. Insert and update operation data include the after change data. Delete operations include the before change data. A primary key update is a special case for an update where one or more of the primary key(s) is/are changed. The primary key update represents a special case in that without the before image data it is not possible to determine what row is actually changing when only in possession of the after change data. The default behavior of a primary key update is to ABEND in the Kafka Connect formatter. However, the formatter can be configured to simply treat these operations as regular updates or to treat them as deletes and then an insert which is the closest big data modeling to the substance of the transaction.
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Difference between official GoldenGate Kafka Handler and Kafka Connect Handler
The Kafka Handler officially released in Oracle GoldenGate for Big Data 12.2.0.1.x is slightly different in functionality than the Kafka Connect Handler/Formatter included in this opensource component.
Note: This Kafka Connect adapter source code is open-source and free, however, you would need to purchase Oracle GoldenGate for Big Data license for the Oracle GoldenGate infrastructure to run this open-source code.
Thomas Vengal is Director of Product Management at Oracle. Previously, he worked at Informatica as a product manager and development manager at HP Software. He has been working in the application and data management domain for over 15 years. He holds M.Tech in Data Science from BITS Pilani and an MBA from Perth Graduate School of Business.
Thomas Vengal is Director of Product Management at Oracle. Previously, he worked at Informatica as a product manager and development manager at HP Software. He has been working in the application and data management domain for over 15 years. He holds M.Tech in Data Science from BITS Pilani and an MBA from Perth Graduate School of Business.
Kafka Connect is a tool for streaming data between Apache Kafka and other
\ndata systems in a scalable and reliable way. Kafka Connect can ingest entire databases or collect metrics from all your
\napplication servers into Kafka topics, making the data available for stream processing with low
\nlatency. An export connector can deliver data from Kafka topics into secondary indexes like
\nElasticsearch or into batch systems such as Hadoop for offline analysis. (Source: Confluent Website)
This Kafka Connect handler lets you to integrate using the Kafka Connect API which can be managed using Schema Registry on Confluent Platform. The Kafka Connect Handler takes change data capture operations from a source trail file and generates data structs (org.apache.kafka.connect.data.Struct) as well as the associated schemas (org.apache.kafka.connect.data.Schema). The data structs are serialized via configured converters then enqueued onto Kafka topics. The topic name used corresponds to the fully qualified source table name as obtained from the GoldenGate trail file. Individual operations consist of inserts, updates, and delete operations executed on the source RDBMS. Insert and update operation data include the after change data. Delete operations include the before change data. A primary key update is a special case for an update where one or more of the primary key(s) is/are changed. The primary key update represents a special case in that without the before image data it is not possible to determine what row is actually changing when only in possession of the after change data. The default behavior of a primary key update is to ABEND in the Kafka Connect formatter. However, the formatter can be configured to simply treat these operations as regular updates or to treat them as deletes and then an insert which is the closest big data modeling to the substance of the transaction.
Difference between official GoldenGate Kafka Handler and Kafka Connect Handler
\n
\nThe Kafka Handler officially released in Oracle GoldenGate for Big Data 12.2.0.1.x is slightly different in functionality than the Kafka Connect Handler/Formatter included in this opensource component.
In the context of Kafka, Oracle GoldenGate provides a way of streaming all changes made to a table, or set of tables, and making them available to other processes in our data pipeline. These processes could include microservices relying on an up-to-date feed of data from a particular table, as well as persisting a replica copy of the data from the source system into a common datastore for analysis alongside data from other systems.
Note that Oracle Golden Gate for Big Data also has its own native Kafka Handler, which can produce data in various formats directly to Kafka (rather than integrating with the Kafka Connect framework).
Oracle GoldenGate (OGG) works on the concept of an Extract process which reads the source-specific transaction log and writes an OGG trail file in a generic OGG format. From this a Replicat process reads the trail file and delivers the transactions to the target.
#The handler propertiesgg.handler.confluent.type=oracle.goldengate.kafkaconnect.KafkaConnectHandlergg.handler.confluent.kafkaProducerConfigFile=confluent.propertiesgg.handler.confluent.mode=txgg.handler.confluent.sourceRecordGeneratorClass=oracle.goldengate.kafkaconnect.DefaultSourceRecordGenerator
#The formatter propertiesgg.handler.confluent.format=oracle.goldengate.kafkaconnect.formatter.KafkaConnectFormattergg.handler.confluent.format.insertOpKey=Igg.handler.confluent.format.updateOpKey=Ugg.handler.confluent.format.deleteOpKey=Dgg.handler.confluent.format.treatAllColumnsAsStrings=falsegg.handler.confluent.format.iso8601Format=falsegg.handler.confluent.format.pkUpdateHandling=abend
value.converter=org.apache.kafka.connect.json.JsonConverterkey.converter=org.apache.kafka.connect.json.JsonConverterinternal.value.converter=org.apache.kafka.connect.json.JsonConverterinternal.key.converter=org.apache.kafka.connect.json.JsonConverter
As a side note, the schema coming through from OGG Kafka Connect connector is listing these timestamp fields as strings, as we can see with a bit of fancy jq processing to show the schema entry for one of the fields (op_ts):
Whilst in the configuration file you need to also add another entry, telling the connector to ignore the schema that is passed from Kafka and instead dynamically infer the types (as well as honour the dynamic mappings that we specified)
Looking at the row count, we can see that all the records from the topic have been successfully replayed from Kafka and loaded into Elasticsearch. This ability to replay data on demand whilst developing and testing the ingest into a subsequent pipeline is a massive benefit of using Kafka!
To explore the data further you can click on the add button that you get when hovering over each of the fields on the left of the page, which will add them as columns to the main table, replacing the default _source (which shows all fields):
The OGG Kafka Connect handler will automatically create a topic for every table that it receives from OGG. So all we need to do now is add each table to the Elasticsearch Sink configuration. For this, I created a second version of the configuration file, at /opt/elasticsearch-2.4.0/config/elasticsearch-kafka-connect-full.properties
Each of the columns with abbreviated headings are different transactions run, and as soon as you see numbers above zero in them it indicates that you should be getting data in the Oracle tables, and thus through into Kafka and Elasticsearch.
But, this includes the records that were pre-seeded by Swingbench before we set up the OGG extract. How do we know how many have been read by GoldenGate since, and should therefore be downstream on Kafka, and Elasticsearch? Enter logdump. This is a GoldenGate tool that gives a commandline interface to analysing the OGG trail file itself. You can read more about it here, here, and here.
So from OGG, the data flows via the Kafka Connect connect into a Kafka topic, one per table. We can count how many messages there are on the corresponding Kafka topic by running a console consumer, redirecting the messages to file (and using & to return control to the console):
Since the console consumer process is still running in the background (type fg to bring it back to the foreground if you want to cancel it), you can re-issue the wc as required to see the current count of messages on the topic.
On the MBeans tab there are a list of MBeans under which the bespoke application metrics (as opposed to JVM ones like heap memory usage) are found. For example, the rate at which data is being received and sent from the cluster:
Robin is a Principal DevEx Engineer at Decodable as well as an Oracle Groundbreaker Ambassador. His career has always involved data, from the old worlds of COBOL and DB2, through the worlds of Oracle and Hadoop, and into the current world with Kafka. His particular interests are analytics, systems architecture, performance testing and optimization. He blogs at and and can be found tweeting grumpy geek thoughts as @rmoff. Outside of work he enjoys drinking good beer and eating fried breakfasts, although generally not at the same time. 152ee80cbc
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