Checkpoints[03]

Vector uses non-fuzzy blocking 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][05]

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.

Concurrency Control[06][07]

Data Model[04]

Foreign Keys[08]

Hardware Acceleration[09]

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

Isolation Levels[10]

Joins[11][12]

Storage Model[04]

Because Vector targets analytical workload, Vector stores tables in columnar model by default. However, if there exists an index where the key spans multiple attributes, these columns are grouped together and stored in the same block. User can instruct Vector to store data in n-ary model.

Views[13]