Cubulus OLAP / News: Recent posts

New feature: hierarchical charts. The vertical boxes represent hierarchy "on rows", horizontal boxes represent datasets "on columns" scaled to the "on rows" measure. (See the online demo at http://alxtoth.webfactional.com ). Use a SVG-capable browser such as Firefox or Safari.

Currently Cubulus scaling status is:
"scale up approach" = use multiple Python threads for running SQL queries. Works in theory: one single core CPU runs all the threads. Practical result: it does not crash under Apache Bench stress testing
-"scale out approach" = managed to set up a "cluster" of database processes running in same machine, but listening on different ports. It should be possible to run databases on different IP adresses, not just different ports. Fail-over and load balancing works when database processes are stopped & restarted. Again, it does not crash under load on single-core CPU
-"MySQL parallel query" - briefly tested on a friend's Intel Core Duo laptop. Setup : one Python thread, and MySQL 5.1 beta doing automatically parallel query on the partitioned table. The two cores were waiting for each other , most likely because partitions are not equal. ... read more

MonetDB ( http://monetdb.cwi.nl/ ) database is cca 10 to 15 times faster than MySQL (partitioned, no indexes) in regards to Cubulus queries. This is due to Monet being a "column-oriented" database.

I don't mean to start a benchmarking "flamewar". In you have tuning / porting advices for your favourite DB, please share your thoughts.

Since version 0.49 posgres is supported.

Version 0.48 includes all libraries except sqlite (comes with Python 2.5) and MySQLdb (platform dependent, must be installed separately).

Default database is now SQLite

Cubulus understands a basic subset of MDX syntax starting with version 0.46

Brief presentation material at http://cubulus.sourceforge.net/