Actian Vector is a commercial RDBMS targeting analytical workload and decision support application. It adopts columnar storage model and vectorized processing model. To speed up analytical query exectuion, Vector makes use of various technologies including x86 SIMD execution, in-cache execution, parallel execution, data compression, storage index, etc. Actian Vector is available on Windows, Linux, Hadoop, AWS, and Microsoft Azure platforms.
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.
Dictionary Encoding Delta Encoding Run-Length Encoding Naïve (Page-Level) Bit Packing / Mostly Encoding
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.
http://www.actian.com/products/vectorwise
https://docs.actian.com/vector/5.1/index.html
Actian
Vectorwise, MonetDB/X100
Actian
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