History

The origin of Vector dates back to the MonetDB project at CWI(Centrum Wiskunde & Informatica) Research Institute in Netherlands. MonetDB was a columnar in-memory RDBMS which adopts column-at-a-time processing model and targets analytical workload. The "MonetDB/X100" project started in 2005 further improves MonetDB with vectorized processing model and other technologies. In 2008, X100 project parted from CWI as a foudnation for a commercial RDBMS product. After that, efforts were put into integrating X100 with Ingres front end and building a commercial product out of X100 project. The first version of Vectorwise DBMS was released in Jun 2010. In 2014, Vectorwise was rebranded as Actian Vector.

Checkpoints[06]

Vector uses blocking consistent checkpoints. The system waits for all running transactions to finish before taking a new checkpoint. Any new transaction is blocked until the checkpoint is completed.

Compression[04][07]

Vector compresses each column on a per-page basis for better compression performance. Thus different pages of the same column may be compressed using different algorithms.

For the page-level naïve compression, Vector uses LZ4 to compress string values.

Vector adopts late decompression approach where columns in memory buffer are only decompressed when they are needed for query processing.

Concurrency Control[08][09]

Data Model[04]

Foreign Keys[10]

Hardware Acceleration[11]

Vector makes use of SIMD operations on modern CPUs to accelerate query processing.

Isolation Levels[12][13]

Vector only supports snapshot isolation.

Joins[14][15][16]

Bloom filter is used to accelerate join processing.

Query Compilation[17]

Query Execution[16]

Query Interface[18]

Vector supports industry-standard ANSI SQL:2003.

Storage Model[04][16]

Vector uses an adaptive storage model based on PAX partitions. By default, Vector stores tables in columnar model. Nullable attribute has a boolean type column which is stored together with the attribute column. However, if there exists an index where the key spans multiple attributes, these columns are grouped together and stored in the same block. For small tables, user can instruct Vector to store data in n-ary model to reduce wasted space.

Views[19]