When processing time series data, MySQL's RANGE and LIST partitioning strategies can improve query performance and management efficiency. 1. Priority is given to RANGE partitions, and data is divided by time intervals, suitable for queries in continuous time periods; 2. LIST partitions are suitable for discrete time periods, such as quarterly reports, and other scenarios; 3. Avoid using HASH or KEY partitions because they are not suitable for time range queries; 4. The partition field should be of type DATE, DATETIME or TIMESTAMP and be used as common filtering conditions for querying; 5. The partition field must be part of the primary key or unique index; 6. Use partitions to clean or archive old data efficiently, such as using TRUNCATE PARTITION or EXCHANGE PARTITION operations.
When processing time series data, MySQL's partitioning strategy can significantly improve query performance and management efficiency. If your data is growing by time (such as logs, monitoring records, etc.), using partitions reasonably can allow you to retrieve data within the target time period faster, and it can also make it easier to clean up old data.
Select the appropriate partition type
MySQL supports multiple partition types, but the RANGE and LIST partitions are most commonly used when processing time series data, among which RANGE partitions are particularly suitable for time range division.
- RANGE partition : allocates data according to time intervals. For example, you can divide the data before 2023 into one partition and the data for 2024 into another.
- LIST partitioning : It is suitable for partitioning by enumeration value, not very common in time series scenarios, but can also be considered if your time period is discrete and fixed (such as quarterly reports).
Using HASH or KEY partitions to process time data is not recommended because they are not suitable for time-frame-based query optimization.
Partition by time field
To make partitions work, you must make sure that the fields you use to partition are of time type, such as DATE , DATETIME or TIMESTAMP , and that this field is best used as a filtering condition in the query.
For example:
CREATE TABLE logs (
id INT NOT NULL,
log_time DATETIME NOT NULL,
message TEXT
)
PARTITION BY RANGE (YEAR(log_time)) (
PARTITION p2020 VALUES LESS THAN (2021),
PARTITION p2021 VALUES LESS THAN (2022),
PARTITION p2022 VALUES LESS THAN (2023),
PARTITION pmax VALUES LESS THAN MAXVALUE
); After writing this way, when you execute a query like SELECT * FROM logs WHERE log_time BETWEEN '2021-01-01' AND '2021-12-31' , MySQL will only scan the corresponding partitions of 2021, which is more efficient.
Note: The partition field must be the primary key or part of the unique index of the table, otherwise creating the partition table will fail.
Regularly maintain and archive old data
Time series data usually has one feature: old data is rarely accessed, but cannot be deleted easily. At this time, the partition can be used to archive operations very easily.
For example, if you want to delete all logs before 2020, you only need one statement:
ALTER TABLE logs TRUNCATE PARTITION p2020;
Or if you want to migrate the data from a certain partition to another storage system, you can use:
ALTER TABLE logs EXCHANGE PARTITION p2020 WITH TABLE archive_logs_2020;
The premise is that archive_logs_2020 table structure is the same as the original table and there is no partition.
This is much faster than direct DELETE and does not affect data access to other partitions.
Basically that's it. Rationally designing partition structures and regular maintenance can make time series data run faster and easier in MySQL.
The above is the detailed content of MySQL Partitioning Strategies for Time-Series Data. For more information, please follow other related articles on the PHP Chinese website!
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