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MySQL 1237: Partition is full.
When encountering the error "1237: Partition is full" in MySQL DB, follow these steps:
- Check Disk Space: First, confirm that the disk where the MySQL database is stored has enough space. You can use the command:
df -h
Look for the partition that MySQL uses and check if it's almost full.
- Identify Large Tables: Identify which tables are taking up the most space, especially if they are partitioned. Run the following query in MySQL to get the size of all tables in a specific database:
SELECT tablename AS `Table`, round(((datalength + index_length) / 1024 / 1024), 2) `Size in MB`
FROM information_schema.TABLES
WHERE tableschema = "database_name>"
ORDER BY (datalength + indexlength) DESC;
- Check Specific Partition Usage: If you have a partitioned table and you know which partition is causing the issue, check its size with:
SELECT partitionname, tablerows, datalength, indexlength,
round(((datalength + indexlength) / 1024 / 1024), 2) as `Size in MB`
FROM information_schema.partitions
WHERE tablename = 'yourtablename' AND tableschema = 'yourdatabasename';
- Clean Up Data: If possible, delete unnecessary or old data from the table or partition that is full. Make sure to take a backup if necessary. Use the `DELETE` command for this, or `TRUNCATE` if you need to empty a whole table/partition.
- Increase Partition Size: If deleting data is not an option and the partition is indeed full, you might need to increase the size of your disk or allocate more space to the database partition. This might involve server administration tasks such as resizing disk volumes or modifying MySQL configuration to use a different partition with more space.
- Optimize Tables: After cleaning up, you can also run the `OPTIMIZE TABLE` command on the affected tables to reclaim unused space and defragment the data file:
OPTIMIZE TABLE yourtablename;
Remember, if these steps involve changes to production data or critical systems, ensure you have appropriate backups and understand the commands you're running to avoid accidental data loss or downtime.
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