Can Shard-Query scale to 20 nodes? Peter asked this question in comments to to my previous Shard-Query benchmark. Actually he asked if it could scale to 50, but testing 20 was all I could due to to EC2 and time limits. I think the results at 20 nodes are very useful to understand the performance: [...]
Shard-Query EC2 images available
May 11, 2011 By 2 Comments
Infobright and InnoDB AMI images are now available There are now demonstration AMI images for Shard-Query. Each image comes pre-loaded with the data used in the previous Shard-Query blog post. The data in the each image is split into 20 “shards”. This blog post will refer to an EC2 instances as a node from here [...]
Shard-Query turbo charges Infobright community edition (ICE)
May 6, 2011 By 23 Comments
Shard-Query is an open source tool kit which helps improve the performance of queries against a MySQL database by distributing the work over multiple machines and/or multiple cores. This is similar to the divide and conquer approach that Hive takes in combination with Hadoop. Shard-Query applies a clever approach to parallelism which allows it to [...]
Optimizing slow web pages with mk-query-digest
April 7, 2011 By 14 Comments
I don’t use many tools in my consulting practice but for the ones I do, I try to know them as best as I can. I’ve been using mk-query-digest for almost as long as it exists but it continues to surprise me in ways I couldn’t imagine it would. This time I’d like to share [...]
Flexviews – part 3 – improving query performance using materialized views
April 4, 2011 By 20 Comments
Combating “data drift” In my first post in this series, I described materialized views (MVs). An MV is essentially a cached result set at one point in time. The contents of the MV will become incorrect (out of sync) when the underlying data changes. This loss of synchronization is sometimes called drift. This is conceptually [...]
How well does your table fits in innodb buffer pool ?
December 9, 2010 By 12 Comments
Understanding how well your tables and indexes fit to buffer pool are often very helpful to understand why some queries are IO bound and others not – it may be because the tables and indexes they are accessing are not in cache, for example being washed away by other queries. MySQL Server does not provide [...]
Shard-Query adds parallelism to queries
November 15, 2010 By 18 Comments
Preamble: On performance, workload and scalability: MySQL has always been focused on OLTP workloads. In fact, both Percona Server and MySQL 5.5.7rc have numerous performance improvements which benefit workloads that have high concurrency. Typical OLTP workloads feature numerous clients (perhaps hundreds or thousands) each reading and writing small chunks of data. The recent improvements to [...]
A workaround for the performance problems of TEMPTABLE views
May 19, 2010 By 7 Comments
MySQL supports two different algorithms for views: the MERGE algorithm and the TEMPTABLE algorithm. These two algorithms differ greatly. A view which uses the MERGE algorithm can merge filter conditions into the view query itself. This has significant performance advantages over TEMPTABLE views. A view which uses the TEMPTABLE algorithm will have to compute the [...]
How well do your tables fit in buffer pool
March 26, 2010 By 4 Comments
In XtraDB we have the table INNODB_BUFFER_POOL_PAGES_INDEX which shows which pages belong to which indexes in which tables. Using thing information and standard TABLES table we can see how well different tables fit in buffer pool.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | SELECT d.*, ROUND(100 * cnt * 16384 / ( data_length + index_length ), 2) fit FROM (SELECT schema_name, table_name, COUNT(*) cnt, SUM(dirty), SUM(hashed) FROM INNODB_BUFFER_POOL_PAGES_INDEX GROUP BY schema_name, table_name ORDER BY cnt DESC LIMIT 20) d JOIN TABLES ON ( TABLES.table_schema = d.schema_name AND TABLES.table_name = d.table_name ); +-------------+---------------------+---------+------------+-------------+--------+ | schema_name | table_name | cnt | sum(dirty) | sum(hashed) | fit | +-------------+---------------------+---------+------------+-------------+--------+ | db | table1 | 1699133 | 13296 | 385841 | 87.49 | | db | table2 | 1173272 | 17399 | 11099 | 98.42 | | db | table3 | 916641 | 7849 | 15316 | 94.77 | | db | table4 | 86999 | 1555 | 75554 | 87.42 | | db | table5 | 32701 | 7997 | 30082 | 91.61 | | db | table6 | 31990 | 4495 | 25681 | 102.97 | | db | table7 | 1 | 0 | 0 | 100.00 | +-------------+---------------------+---------+------------+-------------+--------+ 7 rows in set (26.45 sec) |
You can also see in one of the cases the value shown is a bit over 100% – [...]
New OLAP Wikistat benchmark: Introduction and call for feedbacks
January 28, 2010 By 16 Comments
I’ve seen my posts on Ontime Air traffic and Star Schema Benchmark got a lot of interest (links: http://www.mysqlperformanceblog.com/2010/01/07/star-schema-bechmark-infobright-infinidb-and-luciddb/ 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/ ). However benchmarks by itself did not cover all cases I would want, so I was thinking about better scenario. The biggest problem is to get real big enough dataset, and I thank [...]
