May 25, 2013

Finding an optimal balance of I/O, CPU, and RAM for MySQL

For a long time I’ve wanted to know how MySQL scales as you add more memory to the server. Vadim recently benchmarked the effects of increasing memory and CPU core count. He looked for a balance between utilizing the hardware as much as possible, limiting the system complexity, and lowering the price-to-performance ratio. The outcome [...]

MySQL caching methods and tips

“The least expensive query is the query you never run.” Data access is expensive for your application. It often requires CPU, network and disk access, all of which can take a lot of time. Using less computing resources, particularly in the cloud, results in decreased overall operational costs, so caches provide real value by avoiding [...]

Modeling MySQL Capacity by Measuring Resource Consumptions

There are many angles you can look at the system to predict in performance, the model baron has published for example is good for measuring scalability of the system as concurrency growths. In many cases however we’re facing a need to answer a question how much load a given system can handle when load is [...]

Is VoltDB really as scalable as they claim?

Before I begin, a disclaimer. VoltDB is not a customer, and did not pay Percona or me to investigate VoltDB’s scalability or publish this blog post. More disclaimers at the end. Short version: VoltDB is very scalable; it should scale to 120 partitions, 39 servers, and 1.6 million complex transactions per second at over 300 [...]

Modeling InnoDB Scalability on Multi-Core Servers

Mat Keep’s blog post on InnoDB-vs-MyISAM benchmarks that Oracle recently published prompted me to do some mathematical modeling of InnoDB’s scalability as the number of cores in the server increases. Vadim runs lots of benchmarks that measure what happens under increasing concurrency while holding the hardware constant, but not as many with varying numbers of [...]

High Rate insertion with MySQL and Innodb

I again work with the system which needs high insertion rate for data which generally fits in memory. Last time I worked with similar system it used MyISAM and the system was built using multiple tables. Using multiple key caches was the good solution at that time and we could get over 200K of inserts/sec. [...]

MySQL Limitations Part 1: Single-Threaded Replication

I recently mentioned a few of the big “non-starter” limitations Postgres has overcome for specific use cases. I decided to write a series of blog posts on MySQL’s unsolved severe limitations. I mean limitations that really hobble it for major, important needs — not in areas where it isn’t used, but in areas where it [...]

Percona Server scalability on multi-cores server

We now have hardware in our test lab that represents the next generation of commodity servers for databases. It’s a Cisco UCS C250 server, powered by two Intel Westmere CPUs (X5670 @ 2.93GHz). Each CPU has 6 cores and 12 threads. The most amazing part is the amount of memory. It has 384GB of RAM, which is [...]

Cache Miss Storm

I worked on the problem recently which showed itself as rather low MySQL load (probably 5% CPU usage and close to zero IO) would spike to have hundreds instances of threads running at the same time, causing intense utilization spike and server very unresponsive for anywhere from half a minute to ten minutes until everything [...]

Star Schema Bechmark: InfoBright, InfiniDB and LucidDB

In my previous rounds with DataWarehouse oriented engines I used single table without joins, and with small (as for DW) datasize (see http://www.mysqlperformanceblog.com/2009/10/02/analyzing-air-traffic-performance-with-infobright-and-monetdb/, http://www.mysqlperformanceblog.com/2009/10/26/air-traffic-queries-in-luciddb/, http://www.mysqlperformanceblog.com/2009/11/02/air-traffic-queries-in-infinidb-early-alpha/). Addressing these issues, I took Star Schema Benchmark, which is TPC-H modification, and tried run queries against InfoBright, InfiniDB, LucidDB and MonetDB. I did not get results for MonetDB, will [...]