The implementation of data retention strategies requires clarifying business needs and adopting appropriate cleaning mechanisms. 1. First determine the data retention period according to the business type, such as log classes are retained for 90 days and transaction records are saved for a long time; 2. Use partition tables in combination with automatic cleaning, partition according to time dimensions and quickly delete old data through DROP PARTITION; 3. Archive cold data, export to low-performance libraries or files and retain metadata; 4. Set TTL fields to cooperate with timing tasks to achieve simple and automatic deletion, and pay attention to batch execution to avoid locking tables. In addition, you should back up before deletion, avoid peak periods and pay attention to space recycling issues.
The implementation of MySQL data retention policy is essentially a way to control the data life cycle in a database. Many teams tend to ignore this problem in the early stages, and they do not start to pay attention to it until the data bloat leads to performance decline, increase backup time, and even increase in storage costs. In fact, setting up a data retention strategy rationally can not only save resources, but also improve query efficiency.

Here are some practical suggestions and how to do:
1. Clarify business needs and determine retention period
The value of different types of business data varies with time. For example, log data may only be useful in the last few days, while transaction records may need to be kept for several years or even permanently.

- First sort out which table data can be cleaned , such as access logs, operation records, temporary tasks, etc.
- Set reasonable retention periods , such as "the last 90 days", "the last year", or "retain until the next audit".
Don’t blindly delete data in one-size-fits-all manner, otherwise it may cause complaints from business parties or compliance issues.
2. Use partition table automatic cleaning mechanism
If your data has obvious time dimensions (such as logs, orders, behavior records), using a table structure partitioned by time is a very effective approach.

- Data can be partitioned in range by day, month, or year (RANGE or LIST partition).
- Use
DROP PARTITION
to quickly clear historical data instead of using DELETE statements to delete it line by line, which has a smaller impact on performance. - Perform cleanup tasks regularly with the Event Scheduler.
For example, you could create a log table that partitions by month and then automatically delete the oldest partition at the beginning of each month.
3. Regularly archive and delete cold data
For cold data that cannot be deleted directly but is not frequently accessed, you can consider archived .
- Export old data to a separate archive or historical table.
- After exporting, verify the integrity and then delete it from the main table.
- The archive format can be a compressed CSV, JSON file, or another MySQL instance with low performance requirements.
This method is suitable for data that may be used in the future but is very frequent. Be careful to keep metadata information to facilitate subsequent searches.
4. Set the TTL field to cooperate with the timing task
If you don't want to do complex partition management, you can add a field to the table to identify the "expired time", such as expired_at
.
Set the field value to the current time plus the retention period when inserting or updating data.
-
Use timed tasks (such as running once a morning) to perform SQL similar to the following:
DELETE FROM logs WHERE expired_at < NOW();
This method is simple to implement, but it is important to note that DELETE operations may lock tables. It is recommended to execute them in batches to avoid affecting online services.
In addition, no matter which method is used, you should pay attention to the following points:
- It is best to backup or export snapshots before deletion
- Try to avoid peak business periods when deleting actions
- When deleting large tables, you should consider index fragmentation and space recycling issues.
Basically that's it. Data retention is not a technical problem, but it is easy to become a hidden danger due to negligence. As long as you plan it in advance, it will not be complicated to maintain.
The above is the detailed content of Implementing MySQL Data Retention Policies. For more information, please follow other related articles on the PHP Chinese website!

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