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High Query Performance

Updated at: Sep 17, 2021 GMT+08:00

The following GaussDB(DWS) features help achieve high query performance.

Fully Parallel Query

GaussDB(DWS) is an MPP system with the shared-nothing architecture. It consists of multiple independent logical nodes that do not share system resources, such as the CPU, memory, and storage units. In such a system architecture, service data is separately stored on numerous nodes. Data analysis tasks are executed in parallel on the nodes where data is stored. The massively parallel data processing significantly improves response speed.

In addition, GaussDB(DWS) improves data query performance by executing operators in parallel, executing commands in registers in parallel, and using LLVM to dynamically compile the logical conditions of redundancy prune.

Hybrid Row-Column Storage

GaussDB(DWS) supports both the row and column storage models. You can choose a row- or column-store table as needed.

The hybrid row-column storage engine achieves higher data compression ratio (column storage), index performance (column storage), and point update and point query (row storage) performance.

Data Compression in Column Storage

You can compress old, inactive data to free up space, reducing procurement and O&M costs.

In GaussDB(DWS), data can be compressed using the Delta Value Encoding, Dictionary, RLE, LZ4, and ZLIB algorithms. The system automatically selects a compression algorithm based on data characteristics. The average compression ratio is 7:1. Compressed data can be directly accessed and is transparent to services, greatly reducing the preparation time before accessing historical data.

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