Global transaction IDs are being considered for a future version of MySQL. A global transaction ID lets you determine a server’s replication position reliably, among other benefits. This is great when you need to switch a replica to another master, or any number of other needs. Sometimes you can’t wait for the real thing, but [...]
As part of work on “High Performance MySQL, 3rd edition”, Baron asked me to compare different MySQL version in some simple benchmark, but on decent hardware. So why not.
Dealing with MySQL you might need to deal with RAID recovery every so often. Sometimes because of client lacking the proper backup or sometimes because recovering RAID might improve recovery, for example you might get point in time recovery while backup setup only takes you to the point where last binary log was backed up. [...]
Often times, from a computing perspective, one must run a function on a large amount of input. Often times, the same function must be run on many pieces of input, and this is a very expensive process unless the work can be done in parallel. Shard-Query introduces set based processing, which on the surface appears [...]
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: [...]
In a recent blog post, I wrote about four fundamental metrics for system performance analysis. These are throughput, residence time, “weighted time” (the sum of all residence times in the observation period — the terminology is mine for lack of a better name), and concurrency. I derived all of these metrics from two “even more [...]
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 [...]
A while back Friendfeed posted a blog post explaining how they changed from storing data in MySQL columns to serializing data and just storing it inside TEXT/BLOB columns. It seems that since then, the technique has gotten more popular with Ruby gems now around to do this for you automatically.
There was a discussion on LinkedIn one month ago that caught my eye: Database search by “within x number of miles” radius? Anyone out there created a zipcode database and created a “search within x numer of miles” function ? Thankful for any tips you can throw my way.. J A few people commented that [...]
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 [...]