Column-Wise Data Compression for Distributed Database Transmission
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Solution Overview
Problem
In distributed databases, data transmission between nodes often involves uncompressed data, leading to high overheads and network load due to the lack of efficient compression methods.
Innovation Solution
A data transmission method where a data node determines the appropriate compression algorithm based on the data type and distribution rule of the data, compresses the data using algorithms like RLE, dictionary, or LZ4, and sends compressed data to target nodes, reducing transmission overheads and network load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If uncompressed data is transmitted between nodes, then data integrity is maintained, but transmission overheads are high and network load is heavy
Solution Approach 1:
The patent applies compression algorithms that transform data from its original uncompressed state to a compressed state, changing the parameter of data size while maintaining data integrity. Different compression algorithms (RLE, dictionary, LZ4, Zlib) are selected based on data characteristics to achieve optimal compression ratios.
Solution Approach 2:
The patent segments data into columns and applies compression algorithms at the column level rather than processing entire datasets uniformly. This allows different compression strategies to be applied to different columns based on their specific distribution rules and data types, improving overall compression efficiency.
2Loss of energy
If compression algorithms are applied to all data, then transmission overhead is reduced, but processing complexity increases
Solution Approach 1:
The patent applies compression algorithms selectively based on local data characteristics such as distribution rules and data types. Columns with suitable compression patterns (e.g., repeated values, sorted data) are compressed, while columns that don't benefit from compression are left uncompressed, avoiding unnecessary processing complexity.
Solution Approach 2:
The patent applies compression only to the extent necessary - selecting and applying appropriate compression algorithms only to columns where they will be effective. This partial application of compression avoids the complexity of processing all data uniformly while still achieving significant overhead reduction.
3Adaptability or versatility
If multiple compression algorithms are supported, then compression flexibility is improved, but system complexity increases
Solution Approach 1:
The patent implements a dynamic algorithm selection mechanism that automatically chooses the most appropriate compression algorithm based on the specific characteristics of each column (data type, distribution rules, value patterns). This dynamic adaptation provides flexibility without requiring manual configuration or complex decision-making processes.
Solution Approach 2:
The system performs self-service by automatically analyzing data characteristics and selecting appropriate compression algorithms without external intervention. The DN autonomously determines which columns to compress and which algorithms to apply, reducing the complexity burden on the coordinator node and users.
Data Source
AI summary
Embodiments of the present invention relate to the database field, and in particular, to a data transmission method and apparatus, so as to reduce overheads for data transmission between nodes in a distributed database and lighten network load. In the embodiments of the present invention, a DN determines a column that has a distribution rule of parameters in the column; the DN determines, according to a data type of the parameters of the column and the distribution rule of the parameters in the column, a compression algorithm corresponding to the column; the DN compresses the column using the compression algorithm; and the DN sends a compressed column to a target node. In this way, the overheads for the data transmission between the nodes are reduced, and the network load is lightened.