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{"id":2740,"date":"2019-10-01T19:12:51","date_gmt":"2019-10-01T19:12:51","guid":{"rendered":"http:\/\/minervadb.com\/?p=2740"},"modified":"2019-10-01T19:12:51","modified_gmt":"2019-10-01T19:12:51","slug":"database-replication-from-mysql-to-clickhouse-for-high-performance-webscale-analytics","status":"publish","type":"post","link":"https:\/\/minervadb.com\/index.php\/2019\/10\/01\/database-replication-from-mysql-to-clickhouse-for-high-performance-webscale-analytics\/","title":{"rendered":"Database Replication from MySQL to ClickHouse for High Performance WebScale Analytics"},"content":{"rendered":"

MySQL to ClickHouse Replication\u00a0<\/span><\/h1>\n
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MySQL works great for Online Transaction Processing (OLTP) systems, MySQL performance degrades with analytical queries on very large database infrastructure, I agree you can optimize MySQL query performance with InnoDB compressions but why then combine OLTP and OLAP (Online Analytics Processing Systems) when you have columnar stores which can deliver high performance analytical queries more efficiently? I have seen several companies building dedicated MySQL servers for Analytics but over the period of time they end spending more money in fine tuning MySQL for Analytics with no significant improvements, There is no point in blaming MySQL for what it is not built for, MySQL \/ MariaDB is any day a bad choice for columnar analytics \/ big data solutions. \u00a0Columnar database systems are best suited for\u00a0handling large quantities of data: data stored in columns typically is easier to compress, it is also easier to access on per column basis – typically you ask for some data stored in a couple of columns – an ability to retrieve just those columns instead of reading all of the rows and filter out unneeded data makes the data accessed faster. So how can you combine the best of both ? Using MySQL purely for Transaction Processing Systems and Archiving MySQL Transactional Data for Data Analytics on a columnar store like ClickHouse, This post is about archiving and replicating data from MySQL to ClickHouse, you can continue reading from here<\/a><\/strong><\/span>.<\/p>\n

Recommended Reading for ClickHouse:\u00a0<\/strong><\/p>\n