|Showing entries 1 to 30 of 30|
We’re proud to announce that Jim Tommaney, CTO of Calpont, has just signed on to speak at the MySQL & Cloud Database Solutions Day, hosted by SkySQL and MariaDB - taking place next Friday, April 26, directly after Percona Live: MySQL Conference & Expo.
In my ongoing efforts to migrate my fun side projects and coding experiments from SVN to Git I’ve come across some of my favorite Python based apps – which are all available in their respective repos on BitBucket, as follows:
As a follow up to the previous post about logstash, here are a couple of related init scripts for anyone implementing the OpenSource Log Analytics setup that is explained over at divisionbyzero. These have been tested on CentOS 6.3 and are based on generic RC functions from Redhat so they will work with Redhat, CentOS, Fedora, Scientific Linux, etc.
"Why the days are numbered for Hadoop as we know it"I know GigaOM like to provoke scandals sometimes, we all remember some other unforgettable piece, but there is something behind it...
So I’ve been doing a fair number of automated load tests these past six months. Primarily with Sysbench, which is a fine, fine tool. First I started using some simple bash based loop controls to automate my overnight testing, but as usually happens with shell scripts they grew unwieldy and I rewrote them in python. Now I have some flexible and easily configurable code for sysbench based MySQL benchmarking to offer the community. I’ve always been a fan of giving back to such a helpful group of people – you’ll never hear me complain about “my time isn’t free”. So, let me know what you want in an ideal testing environment (from a load testing framework automation standpoint) and I’ll integrate it into my existing framework and then release it via the BSD license. The main goal here is to have a standardized modular framework, based on sysbench,[Read more...]
Letting data speak for itself through analysis of entire data sets is eclipsing modeling from subsets. In the past, all too often what were once disregarded as "outliers" on the far edges of a data model turned out to be the telltale signs of a micro-trend that became a major event. To enable this advanced analytics and integrate in real-time with operational processes, companies and public sector organizations are evolving their enterprise architectures to incorporate new tools and approaches.
Whether you prefer "big," "very large," "extremely large," "extreme," "total," or another adjective for the "X" in the "X Data" umbrella term, what's important is accelerated growth in three dimensions: volume, complexity and speed.
Big data is not without its limitations. Many organizations need to revisit business processes, solve data silo[Read more...]
This is a follow up to my previous post titled “MySQL analytics: information_schema polling for table engine percentages”. Here’s an updated query with more output and quicker execution time. What you get: innodb table space utilization percentage, data+index usage total and per innodb/myisam engine, innodb data/index/percentage, myisam data/index/percentages, and overall percentage values. Rather useful for profiling your table engine usage.
If you’ve ever needed to know how the data and index percentages per table engine were laid out on your MySQL server, but didn’t have the time to write out a query… here it is!
select (select (sum(DATA_LENGTH)+sum(INDEX_LENGTH))/(POW(1024,3)) as total_size from tables) as total_size_gb, (select sum(INDEX_LENGTH)/(POW(1024,3)) as index_size from tables) as total_index_gb, (select sum(DATA_LENGTH)/(POW(1024,3)) as data_size from tables) as total_data_gb, (select ((sum(INDEX_LENGTH) / ( sum(DATA_LENGTH) + sum(INDEX_LENGTH)))*100) as perc_index from tables) as perc_index, (select ((sum(DATA_LENGTH) / ( sum(DATA_LENGTH) + sum(INDEX_LENGTH)))*100) as perc_data from tables) as perc_data, (select ((sum(INDEX_LENGTH) / ( sum(DATA_LENGTH) + sum(INDEX_LENGTH)))*100) as perc_index from tables where ENGINE='innodb') as innodb_perc_index, (select ((sum(DATA_LENGTH) / ([Read more...]
Let's take quick look at the performance of the new InfiniDB Subquery processing available with the 1.1.1 Alpha. The arrow was added to be sure our timings weren't confused with the axis.
This was against a relatively small dataset, the Star Schema Benchmark with 6 million rows in the fact table. A base query was run where the outer query...
|Showing entries 1 to 30 of 30|