Category Archives: Releases

The releases tag describes posts that refert to new versions of CoGaDB.

Release of CoGaDB 0.4.2-beta1

By   December 6, 2015

Release of CoGaDB 0.4.2-beta1

We released version 0.4.2-beta1 of CoGaDB today. From this release on, we will provide the source code, an installer, and a debian package for Ubuntu 14.04 LTS. Additionally, we prepared a virtual machine (Virtual Box) which runs a demo of CoGaDB and provides all libraries and tools required to compile and develop CoGaDB.

The major new feature in this release is an SQL to C compiler, which allows us to compile a query to an optimized program that executes the query. A detailed change log will follow soon.

Release of CoGaDB 0.4.1

By   March 29, 2015

Release of CoGaDB 0.4.1

We released version 0.4.1 of CoGaDB today. You can download it here. The release contains many fixes to bugs that came up the last month and improvements of the error checking for the SQL interface.

Release of CoGaDB 0.4

By   February 28, 2015

Release of CoGaDB 0.4

We released version 0.4 of CoGaDB today. You can download it here. The release contains the following changes:

SQL Extensions

  • SQL queries can now be entered without the exec command
  • Added support for having construct (not fully SQL compliant, but usable)
  • Support for nested queries

Genomics Extension

  • We can now import and analyse aligned genome data from sam, bam and fasta files
  • Implemented specialized aggregation functions to support variant calling via SQL
  • Additional Compression Method: Dictionary Compressed Column with bitpacked encoding keys
  • Explicit use of compression in database schema for genome data

Network Support

  • CoGaDB can now accept connections via network by calling the command “listen <port number>”
  • Users can now connect to CoGaDB via any plain text network utility, such as netcat or telnet


  • Added extensive benchmarking suite for the star schema benchmark
  • Added capability to abort and later, resume experiments without redoing finsihed experiments

Query Processor

  • Complete support for GPU acceleration of most queries, including groupby and aggregation, which runs completely on the GPU now
  • Several improvements on GPU Cache and detection of runtime errors
  • Reverse Join Indexes, which significantly speedup the tuple reconstruction phase after an invisible join
  • A Hash based Aggregation for common aggregation functions on CPUs

Query Optimizer

  • Major improvements on optimization heuristics
  • Added new learning method: weighted KNN Regression, which is now used by default by HyPE and supports up to 5 features in a feature vector


  • Primary key/foreign key integrity cosntraints
  • Measurement of energy consumption of queries on CPUs
  • Bugfixes