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Displaying posts with tag: benchmark (reset)
Gh-ost benchmark against pt-online-schema-change performance

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 generate lower overhead and work faster. I frequently called it “pt-online-schema-change on steroids” in my talks. …

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

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 to be event-oriented RDBMS, as its name suggests (clicks). Its primary purpose, using Yandex Metrica (the system similar to Google Analytics), also points to an event-based nature. We also can see there is …

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

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 RAM. Both systems are massively parallel (MPP) database systems, so they should use many cores for SELECT queries.

For the benchmarks, I chose …

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

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 Development, Postgres Professional) and Sveta Smirnova (Principal Technical Services Engineer, Percona). The comparative research of PostgreSQL 9.6 and MySQL 5.7 performance will be especially valuable for environments with multiple databases.

The idea behind this research is to provide an honest comparison for the two popular RDBMSs. Sveta and Alexander wanted to test the most recent versions of both MySQL and PostgreSQL with the same tool, under the same challenging …

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RocksDB doesn't support large transactions very well

So I tried to do my first set of benchmarks and testing on RocksDB today, but I ran into a problem and had to file a bug:
https://github.com/facebook/mysql-5.6/issues/365

MySQL @ Facebook RocksDB appears to store at least 2x the size of the volume of changes in a transaction. I don't know how much space for the row + overhead there is in each transcation, so I'm just going to say 2x the raw size of the data changed in the transaction, as approximation. I am not sure how this works for updates either, that is whether old/new row information is maintained. If old/row data is maintained, then a pure update workload you would need 4x the ram for the given transactional changes. My bulk load was 12GB of raw data, so it failed as I have only 12GB of RAM in my test system.

The workaround (as suggested in the bug) is to set two configuration …

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RocksDB doesn't support large transactions very well

So I tried to do my first set of benchmarks and testing on RocksDB today, but I ran into a problem and had to file a bug:
https://github.com/facebook/mysql-5.6/issues/365

MySQL @ Facebook RocksDB appears to store at least 2x the size of the volume of changes in a transaction. I don't know how much space for the row + overhead there is in each transcation, so I'm just going to say 2x the raw size of the data changed in the transaction, as approximation. I am not sure how this works for updates either, that is whether old/new row information is maintained. If old/row data is maintained, then a pure update workload you would need 4x the ram for the given transactional changes. My bulk load was 12GB of raw data, so it failed as I have only 12GB of RAM in my test system.

The workaround (as suggested in the bug) is to set two configuration …

[Read more]
Netflix Data Benchmark: Benchmarking Cloud Data Stores

The Netflix member experience is offered to 83+ million global members, and delivered using thousands of microservices. These services are owned by multiple teams, each having their own build and release lifecycles, generating a variety of data that is stored in different types of data store systems. The Cloud Database Engineering (CDE) team manages those data store systems, so we run benchmarks to validate updates to these systems, perform capacity planning, and test our cloud instances with multiple workloads and under different failure scenarios. We were also interested in a tool that could evaluate and compare new data store systems as they appear in the market or in the open source domain, determine their performance characteristics and limitations, and gauge whether they could be used in production for relevant use cases. For these purposes, we wrote Netflix Data Benchmark

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

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, rather than just one schema. Supporting only one schema creates too much internal locking in MySQL on the same rows or the same index. Locking is …

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MySQL/docker performance report update

Saturday I was in my favourite grocery store, standing in the line, browsing the net on my phone. I read Vadim Tkachenko‘s blog post about Measuring Percona Server Docker CPU/network overhead and his findings were the opposite than mine – he didn’t found any measurable difference. Reading his post, he did found huge impact in networking which I didn’t […]

MySQL in docker or native – performance benchmarks

Back in October I have write about possible ways of running multiple MySQL instances on the same hardware. As the months passing by, the project of splitting our database schemas into standalone instances is closing in, so I started to check the different ways. EDIT: This post is outdated, here is the follow up. I started […]

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