SQL partitioning strategies can significantly improve performance when processing large-scale data. Divide data suitable for time attributes by time, such as logs and orders, and use range partitioning to improve query efficiency; hash partitioning is suitable for scenarios retrieved through ID, so that the data is distributed evenly and avoids hot spots; list partitioning is suitable for clear classification situations, such as regions, status, etc., which is easy to manage; the selection of partition keys is crucial, and common filtering conditions should be selected and fields should be avoided frequently updated. A rational design can improve performance, otherwise it will increase costs.

When you need to process large-scale data, SQL partitioning strategies become an important means to improve database performance and scalability. It is not a master key, but it is very effective if used correctly.

Dividing by time is the most common way

If your data has obvious time attributes, such as logs, orders, access records, etc., Range Partitioning is almost the default choice based on time. For example, by dividing tables or partitions by month, querying data for the last week or month can bypass historical cold data, which is much more efficient.

• Suitable for scenarios where frequent writing and queries are concentrated on the latest data
• The partition granularity can be days, weeks, and months, and it can be adjusted according to the service frequency.
• Pay attention to whether the time field is increasing monotonically, otherwise it will easily lead to partition confusion

For example: You have an order table, which adds millions of new data every day, but 90% of the queries are for orders in the last 30 days. At this time, split the table into multiple partitions by day, which can greatly reduce the number of scanned rows.

Hash partitions are suitable for evenly distributed data

When your query conditions do not depend on time, but are often retrieved through a certain ID (such as user ID, order ID), hash partitioning is more suitable. It can break the data into multiple partitions to avoid single hotspots.

• Choose the hash key well and try to choose fields with high cardinality and even distribution.
• Not suitable for range query because the data is discrete
• Suitable for scenarios where reading and writing balance and data distribution is not obvious

For example, a user information table, the query entry is the user ID. In this case, using hash partition can effectively distribute the load and improve concurrency capabilities.

List partitions are suitable for clear classification

List Partitioning is also very practical in certain specific scenarios, such as partitioning according to limited sets such as region, state, and type. Its advantages are clear logic and easy management.

• Classification must be clear and cannot be crossed
• Suitable for static classification, such as country, city, equipment type, etc.
• You can combine other partitioning methods to make combination partitions (for example, first partition by state, and then sub-partition by time in each state)

For example, you have an order system, and the order status is divided into "to be paid", "paid", "completed", and "cancel". Then you can use the status to partition the list, so that batch operations of different states will be more efficient.

Partition key selection is more important than policy

No matter which partition strategy you use, the selection of partition keys is the key among the keys . If the wrong key is selected, the partition may not have the expected effect and may even affect performance.

• The partition key is best used filtering conditions in queries
• Avoid using frequently updated fields as partition keys
• If the writing pressure is high, consider using the self-increment primary key partition key separation design

To give a practical example: if your query often uses user ID, but the writing is concentrated on several IDs, the hash partition may cause hot issues. At this time, you can consider combining strategies or add an intermediate buffering mechanism.

Basically that's it. Partitioning is not a one-time solution, and needs to be designed in combination with business models and query habits. If done well, the performance will be significantly improved; if done poorly, the maintenance cost will be increased.

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