Boosting MySQL Performance with Table Partitioning: A Practical Guide

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At my company, we faced a significant performance bottleneck when querying a MySQL table containing millions of rows. Our queries were taking up to 20 minutes to execute. In the world of data processing, such delays are unacceptable. After exploring various solutions, we found that partitioning our table was the key to drastically improving query performance.

In this post, I’ll explain how we implemented table partitioning in MySQL to optimize performance and reduce query execution times.

The Problem

We had a table (market_tick_data) with the following structure, storing millions of rows of stock market tick data:

CREATE TABLE `market_tick_data` (
  `tick_id` bigint(20) NOT NULL AUTO_INCREMENT,
  `bus_date` varchar(50) NOT NULL DEFAULT '',
  `datestamp` datetime(3) NOT NULL,
  ......
  PRIMARY KEY (`tick_id`,`bus_date`,`datestamp`)
) ENGINE=InnoDB AUTO_INCREMENT=35850355 DEFAULT CHARSET=utf8;

The queries were centered around retrieving tick data based on date ranges, making the sheer number of rows a challenge for efficient query execution.

The Solution: Partitioning

By partitioning our table based on the datestamp column, we reduced the amount of data MySQL needed to scan…