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Figure 1 shows the performance comparison between the non-compression solution and the compression solution. For example, compression and decompression operations consume CPU resources, which kazakhstan phone number data in turn affects system performance. The difference in the size of data on the hard disk and in memory before and after compression may also bring risks to database memory management. Therefore, from the overall perspective of the business system and database, how to balance the compression rate and system performance has become one of the key difficulties in coping with storage problems caused by rapid business growth.
When facing a very large single table that needs to be transformed into a distributed system, or a heavyweight database table in a distributed system that needs to be frequently expanded, you need to pay attention not only to the above performance indicators and system stability, but also to consider whether the solution to improve resource utilization will bring additional business transformation costs. In addition, considering the life cycle of data, the solution to improve resource utilization in the business system should not have a cross-impact due to the age of data. Since the life cycle characteristics of different database tables are different, the compression strategy should also be treated differently accordingly.