4. Compression solutions for all database scenarios
Posted: Thu Jan 23, 2025 9:19 am
Through the above analysis, in the face of the challenge of inherent business storage growth, a comprehensive database internal compression solution is urgently needed. This solution should fully cover various business scenarios and be close to actual business needs from the perspective of compressed data objects and compression strategies/means.
Among them, the compressed data object classification includes user data tables, indexes, streaming logs and other objects. The compression strategies/means include data hot and cold separation strategy (time/expression), multi-level compression levels, table/ database level independent configuration strategy, other data semantic compression, and other aspects of data lifecycle management technology.
If you need to enrich the means of data lifecycle management, you kazakhstan phone number data can collect data usage information, generate data heat maps, and implement hot and cold data evaluation and prediction in business systems. Based on data heat maps, you can expand the diversified use of storage media and enhance system load perception, etc., to provide more accurate guidance for controlling business costs.
5. GaussDB advanced compression, a better option
GaussDB advanced compression is a full-scenario compression solution within the database, designed to break through the difficulties caused by the rapid growth of data volume in business systems.
• Full-scenario coverage: GaussDB’s advanced compression technology covers the compression of user table data , indexes, and other data. It mainly focuses on the modification time of business data and can optionally use judgment expressions to achieve hot and cold data compression separation , effectively reducing the actual impact on business scenarios.
• Dynamic compression rate: In terms of compression rate, the rule-type encoding method is adopted, which will produce different compression effects under different data table models and data value distribution conditions.
• Flexible compression levels: It also provides a variety of optional compression levels, allowing users to independently configure corresponding compression strategies based on the specific needs of different libraries and tables .
Among them, the compressed data object classification includes user data tables, indexes, streaming logs and other objects. The compression strategies/means include data hot and cold separation strategy (time/expression), multi-level compression levels, table/ database level independent configuration strategy, other data semantic compression, and other aspects of data lifecycle management technology.
If you need to enrich the means of data lifecycle management, you kazakhstan phone number data can collect data usage information, generate data heat maps, and implement hot and cold data evaluation and prediction in business systems. Based on data heat maps, you can expand the diversified use of storage media and enhance system load perception, etc., to provide more accurate guidance for controlling business costs.
5. GaussDB advanced compression, a better option
GaussDB advanced compression is a full-scenario compression solution within the database, designed to break through the difficulties caused by the rapid growth of data volume in business systems.
• Full-scenario coverage: GaussDB’s advanced compression technology covers the compression of user table data , indexes, and other data. It mainly focuses on the modification time of business data and can optionally use judgment expressions to achieve hot and cold data compression separation , effectively reducing the actual impact on business scenarios.
• Dynamic compression rate: In terms of compression rate, the rule-type encoding method is adopted, which will produce different compression effects under different data table models and data value distribution conditions.
• Flexible compression levels: It also provides a variety of optional compression levels, allowing users to independently configure corresponding compression strategies based on the specific needs of different libraries and tables .