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CDC Hadoop Dataflow

A low latency, multi-tenant Change Data Capture(CDC) pipeline to continuously replicate data from OLTP(MySQL) to OLAP(NoSQL) systems with no impact to the source.

This project demonstrate how to build dataflow pipeline to move data from O]operational databases(MySQL, Oracle) to analytics databases(Hadoop, MongoDB, MarkLogic) in real-time using Change Data Capture(CDC), Kafka and tools like Apache NiFi, Kafka Streams or Spark to process and ingest data into Hadoop.

cdc architecture

Features

  1. Capture changes from many Data Sources and types.
  2. Feed data to many client types (real-time, slow/catch-up, full bootstrap).
  3. Multi-tenant: can contain data from many different databases, support multiple consumers.
  4. Non-intrusive architecture for change capture.
  5. Both batch and near real time delivery.
  6. Isolate fast consumers from slow consumers.
  7. Isolate sources from consumers
    1. Schema changes
    2. Physical layout changes
    3. Speed mismatch
  8. Change filtering
    1. Filtering of database changes at the database level, schema level, table level, and row/column level.
  9. Buffer change records in Kafka for flexible consumption from an arbitrary time point in the change stream including full bootstrap capability of the entire data.
  10. Guaranteed in-commit-order and at-least-once delivery with high availability (at least once vs. exactly once)
  11. Resilience and Recoverability
  12. Schema-awareness

Setup

Install and Run MySQL

Install source MySQL database and configure it with row based replication as per instructions.

Install and Run Kafka

Follow the instructions

Install and Run Maxwell

cd cdc/maxwell
# curl -L -0 https://github.com/zendesk/maxwell/releases/download/v1.0.0/maxwell-1.1.2.tar.gz | tar --strip-components=1 -zx -C .
curl -L -0 https://github.com/xmlking/maxwell/releases/download/1.1.2.1/maxwell-1.1.2.1-kafka-connect.tar.gz | tar --strip-components=1 -zx -C .

Run

cd cdc/maxwell

  1. Run with stdout producer (for testing only)

    bin/maxwell --user='maxwell' --password='XXXXXX' --host='127.0.0.1' --producer=stdout

  2. Run with kafka producer

    bin/maxwell

Test

Manual Testing

If all goes well you'll see maxwell replaying your inserts:

mysql -u root -p

mysql> CREATE TABLE test.shop
       (
         id BIGINT(20) NOT NULL AUTO_INCREMENT,
         version BIGINT(20) NOT NULL,
         name VARCHAR(255) NOT NULL,
         owner VARCHAR(255) NOT NULL,
         phone_number VARCHAR(255) NOT NULL,
         primary key (id, name)
       );
mysql> INSERT INTO test.shop (version, name, owner, phone_number) values (0, 'aaa', 'bbb', '3331114444');
Query OK, 1 row affected (0.02 sec)

(maxwell)
{"database":"test","table":"shop","pk.id":4,"pk.name":"aaa"}
{"database":"test","table":"shop","type":"insert","ts":1458510224,"xid":33531,"commit":true,"data":{"owner":"bbb","name":"aaa","phone_number":"3331114444","id":4,"version":0}}

Testing via Grails App

You can also use testApp to generate load.

Reference

  1. Maxwell's Daemon
  2. LinkedIn: Creating A Low Latency Change Data Capture System With Databus
  3. Introducing Maxwell, a mysql-to-kafka binlog processor
  4. Martin Kleppman's blog: Using logs to build a solid data infrastructure
  5. Bottled Water: Real-time integration of PostgreSQL and Kafka
  6. debezium-examples
  7. Tutorial on using NiFi's built-in CDC - 3 parts

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