http://Flexvie.ws fully implements a method for creating materialized views for MySQL data sets. The tool is for MySQL, but the methods are database agnostic. A materialized view is an analogue of software transactional memory. You can think of this as database transactional memory, or as database state distributed over time, but in an easy way [...]
Checking the subset sum set problem with set processing
May 16, 2011 By Leave a Comment
Hi, Here is an easy way to run the subset sum check from SQL, which you can then distribute with Shard-Query:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | CREATE TABLE `the list` ( `id` bigint(20) NOT NULL AUTO_INCREMENT, `val` bigint(20) NOT NULL DEFAULT '0', PRIMARY KEY (`id`), KEY `id` (`id`) ) ENGINE=MyISAM; SELECT val as `val`, COUNT(DISTINCT (id)) as `cd` FROM test.data as d WHERE val in (-2,-3,-10,15,15,16) GROUP BY val; +-----+----------+----------+ | val | cd | CNT | +-----+----------+----------+ | -10 | 1 | 1 | | -3 | 1 | 1 | | -2 | 1 | 1 | | 15 | 35417088 | 35417088 | +-----+----------+----------+ 5 rows in set (40.20 sec) |
Notice there is no 16 in the list. We did not pass the check. There are enough 15s though. The distinct value count for each item in the output set, must at least [...]
Distributed Set Processing with Shard-Query
May 14, 2011 By 27 Comments
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: [...]
Percona’s Sessions at the O’Reilly MySQL Conference and Expo
April 7, 2011 By Leave a Comment
I just realized that we haven’t blogged a list of our sessions at the O’Reilly MySQL Conference and Expo (#mysqlconf) yet. Here is a hopefully complete list.
MySQL caching methods and tips
April 4, 2011 By 5 Comments
“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 [...]
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 [...]
Using Flexviews – part two, change data capture
March 25, 2011 By 26 Comments
In my previous post I introduced materialized view concepts. This post begins with an introduction to change data capture technology and describes some of the ways in which it can be leveraged for your benefit. This is followed by a description of FlexCDC, the change data capture tool included with Flexviews. It continues with an [...]
Using Flexviews – part one, introduction to materialized views
March 23, 2011 By 3 Comments
If you know me, then you probably have heard of Flexviews. If not, then it might not be familiar to you. I’m giving a talk on it at the MySQL 2011 CE, and I figured I should blog about it before then. For those unfamiliar, Flexviews enables you to create and maintain incrementally refreshable materialized [...]
Friends of Percona Get 20% Off at the MySQL Conference!
February 24, 2011 By 5 Comments
We have a special Friends of Percona discount code that you can use to get 20% off of registration at the MySQL conference in April: mys11pkb. If you click the image to the left, or this special link, it will pre-fill the code for you when you check out. Read on to see the list [...]
Data mart or data warehouse?
July 15, 2010 By 13 Comments
This is part two in my six part series on business intelligence, with a focus on OLAP analysis. Part 1 – Intro to OLAP Identifying the differences between a data warehouse and a data mart. (this post) Introduction to MDX and the kind of SQL which a ROLAP tool must generate to answer those queries. [...]
