How to handle excessive data in MySQL.

When there is a large amount of data in the MySQL database, several processing methods can be considered:

1. Data partitioning: dividing data into multiple regions according to certain rules for storage can improve query performance. Data can be partitioned into multiple partitions based on certain characteristics of the data, such as time range or value range of a specific field.
2. Optimizing data indexing: Creating indexes for frequently queried fields can speed up the querying process. It is important to avoid creating too many indexes, as this can impact the performance of inserting, updating, and deleting data.
3. Data archiving and compression involves moving historical data to other storage mediums such as disks or cloud storage to reduce the load on the database. For data that is not frequently accessed, using compression algorithms can help reduce storage space.
4. Data sharding: When dealing with a large amount of data, splitting the database into multiple logical partitions can improve query performance and concurrency.
5. Data backup and recovery: Regularly back up the database to prevent data loss. Have a recovery plan in place so data can be quickly restored in case of loss.
6. Data cleansing involves removing unused or outdated data to reduce the database’s load and storage space usage.
7. Optimizing database performance, such as adjusting parameters, optimizing query statements, and using cache effectively, can enhance the performance of the database.
8. Database distributed architecture: When a single database cannot meet the demands, it can be considered to change the database architecture to a distributed architecture, distributing data across multiple nodes to enhance the scalability and fault tolerance of the database.
9. Regularly upgrading and optimizing databases to achieve greater performance and functionality.

In conclusion, managing databases with large amounts of data requires considering various factors such as data partitioning, index optimization, data archiving and compression, database sharding, data backup and recovery, data cleansing, database performance optimization, and distributed database architecture. Depending on the specific situation, choose the appropriate solution to improve the performance and availability of the database.