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High Query Performance

Updated at: Dec 30, 2020 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 this system architecture, service data is separately stored on numerous nodes. Data analysis task is executed at the location nearest the data. Massively parallel data processing enables quick response.

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-based storage engine provides users with a better data compression ratio (column-based storage), index performance (column-based storage), and point update and point query (row-based 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 following 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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