EmergencyEMERGENCY? Get 24/7 Help Now!

Gh-ost benchmark against pt-online-schema-change performance

 | July 12, 2017 |  Posted In: Benchmarks, Insight for DBAs, MySQL

gh-ost-benchmark

In this blog post, I will run a gh-ost benchmark against the performance of pt-online-schema-change. When gh-ost came out, I was very excited. As MySQL ROW replication became commonplace, you could use it to track changes instead of triggers. This practice is cleaner and safer compared to Percona Toolkit’s pt-online-schema-change. Since gh-ost doesn’t need triggers, I assumed it would […]

Read More

ClickHouse in a General Analytical Workload (Based on a Star Schema Benchmark)

 | June 22, 2017 |  Posted In: Benchmarks, MySQL, Performance Schema

ClickHouse

In this blog post, we’ll look at how ClickHouse performs in a general analytical workload using the star schema benchmark test. We have mentioned ClickHouse in some recent posts (ClickHouse: New Open Source Columnar Database, Column Store Database Benchmarks: MariaDB ColumnStore vs. Clickhouse vs. Apache Spark), where it showed excellent results. ClickHouse by itself seems […]

Read More

Column Store Database Benchmarks: MariaDB ColumnStore vs. Clickhouse vs. Apache Spark

 | March 17, 2017 |  Posted In: Apache Spark, Big Data, Column Store Database, MySQL

Column Store Database

This blog shares some column store database benchmark results, and compares the query performance of MariaDB ColumnStore v. 1.0.7 (based on InfiniDB), Clickhouse and Apache Spark. I’ve already written about ClickHouse (Column Store database). The purpose of the benchmark is to see how these three solutions work on a single big server, with many CPU cores and large amounts of […]

Read More

ClickHouse: New Open Source Columnar Database

 | February 13, 2017 |  Posted In: Benchmarks

Clickhouse

For this blog post, I’ve decided to try ClickHouse: an open source column-oriented database management system developed by Yandex (it currently powers Yandex.Metrica, the world’s second-largest web analytics platform). In my previous set of posts, I tested Apache Spark for big data analysis and used Wikipedia page statistics as a data source. I’ve used the same data as […]

Read More

Millions of Queries per Second: PostgreSQL and MySQL’s Peaceful Battle at Today’s Demanding Workloads

and  | January 6, 2017 |  Posted In: Benchmarks, InnoDB, MySQL, open source databases, OpenSource Databases on big machines, Percona Live

PostgreSQL and MySQL

This blog compares how PostgreSQL and MySQL handle millions of queries per second. Anastasia: Can open source databases cope with millions of queries per second? Many open source advocates would answer “yes.” However, assertions aren’t enough for well-grounded proof. That’s why in this blog post, we share the benchmark testing results from Alexander Korotkov (CEO of […]

Read More

tpcc-mysql benchmark tool: less random with multi-schema support

 | August 9, 2016 |  Posted In: Benchmarks, MySQL

In this blog post, I’ll discuss changes I’ve made to the tpcc-mysql benchmark tool. These changes make it less random and support multi-schema. This post might only be interesting to performance researchers. The tpcc-mysql benchmark to is what I use to test different hardware (as an example, see my previous post: https://www.percona.com/blog/2016/07/26/testing-samsung-storage-in-tpcc-mysql-benchmark-percona-server/). The first change is support for multiple schemas, […]

Read More

InnoDB vs TokuDB in LinkBench benchmark

 | July 24, 2015 |  Posted In: Benchmarks, MySQL, Percona Server, TokuDB

Previously I tested Tokutek’s Fractal Trees (TokuMX & TokuMXse) as MongoDB storage engines – today let’s look into the MySQL area. I am going to use modified LinkBench in a heavy IO-load. I compared InnoDB without compression, InnoDB with 8k compression, TokuDB with quicklz compression. Uncompressed datasize is 115GiB, and cachesize is 12GiB for InnoDB […]

Read More

Sample datasets for benchmarking and testing

 | February 1, 2011 |  Posted In: Insight for Developers, MySQL

Sometimes you just need some data to test and stress things. But randomly generated data is awful — it doesn’t have realistic distributions, and it isn’t easy to understand whether your results are meaningful and correct. Real or quasi-real data is best. Whether you’re looking for a couple of megabytes or many terabytes, the following […]

Read More