How do I troubleshoot local storage issues in my Aurora PostgreSQL-Compatible DB instances?

Short Description

There are two types of storage for Amazon Aurora: Storage for persistent data such as shared cluster volume and local storage for each DB instance.

The instance class determines the local storage size. You can change the storage size only if you move to a larger DB instance class. To store error logs and temporary files, Aurora PostgreSQL-Compatible uses local storage. For non-T class instance types, the system writes logs onto a dedicated volume that the system automatically cleans.

To monitor the local storage space that's associated with the Aurora DB instance or node, use the Amazon CloudWatch metric FreeLocalStorage. This metric reports the amount of local storage that's available in each DB instance.

Resolution

Resolve the "No space left on device" error

You receive the "could not write block ###### of temporary file: No space left on device" error message when there's no temporary storage left. This error can occur for the following operations:

• Alter large tables
• Add indexes on large tables or perform a REINDEX
• Perform large SELECT queries with complex JOINs, GROUP BY, or ORDER BY clause
• Run VACUUM FULL or pg_repack

Check the temporary file size

To identify the details of the temporary file, turn on the log_temp_files parameter in the Aurora PostgreSQL-Compatible DB instance. Then, compare the temporary files to the FreeLocalStorage metric.

The log_temp_files parameter logs each temporary file that's larger than the number of specified kilobytes. A value of 0 logs all temporary file information. A positive value logs only the files that are larger than or equal to the specified number of kilobytes. The default value is -1, which turns off temporary file logging. When you turn on the log_temp_files parameter, it can cause excessive logging on the Aurora PostgreSQL-Compatible DB instance.

It's a best practice to check the size of the Aurora PostgreSQL-Compatible log files before you turn on log_temp_files. If the log files consume the maximum space for the local storage, then reduce the value of rds.log_retention to reclaim space. The default value for rds.log_retention is 3 days.

To review the temporary file size, run the following command multiple times:

maxiops=> select datname, temp_files , pg_size_pretty(temp_bytes) as temp_file_size FROM  pg_stat_database order by temp_bytes desc;

Note: Use the output that changed after subsequent runs.

The temp_files and temp_bytes columns in the pg_stat_database view collect statistics for the accumulated value of all temporary files, regardless of when you created them. To reset the value, use the pg_stat_reset() function or restart the DB instance. For more information, see Additional statistics functions on the PostgreSQL website.

If you use Aurora PostgreSQL-Compatible 10 or later, then you can monitor temp_bytes and temp_files with Performance Insights. Performance Insights provides native counters for your DB engine's internal metrics and wait events. You can also turn on Performance Insights and use Database Insights to monitor the database logs.

To allocate more memory to the processes that perform the operation, increase the maintenance_work_mem and work_mem parameters. This method uses more memory for the operation, and less temporary disk storage. For more information about these parameters, see maintenance_work_mem and work_mem on the PostgreSQL website.

It's a best practice to set the values for maintenance_work_mem and work_mem at a query or session level. Then, your DB instances don't run out of memory. For more information, see Amazon Aurora PostgreSQL reference.

Check the temporary table size

Run the following query:

maxiops=> SELECT n.nspname as SchemaName
,c.relname as RelationName
,CASE c.relkind
WHEN 'r' THEN 'table'
WHEN 'v' THEN 'view'
WHEN 'i' THEN 'index'
WHEN 'S' THEN 'sequence'
WHEN 's' THEN 'special'
END as RelationType
,pg_catalog.pg_get_userbyid(c.relowner) as RelationOwner
,pg_size_pretty(pg_relation_size(n.nspname ||'.'|| c.relname)) as RelationSize
FROM pg_catalog.pg_class c
LEFT JOIN pg_catalog.pg_namespace n
  ON n.oid = c.relnamespace
WHERE c.relkind IN ('r','s')
AND (n.nspname !~ '^pg_toast' and nspname like 'pg_temp%')
ORDER BY pg_relation_size(n.nspname ||'.'|| c.relname) DESC;

It's a best practice to closely monitor your application and see the transactions that create temporary tables. Then, you can manage the usage of the available local storage capacity. You can also change the instance class for your Aurora DB instance so that the DB instance has more available local storage.

Check log files on burstable-performance instance class types

Excessive logging can cause your DB instance to run out of local storage for only burstable-performance instance class types such as db.t2, db.t3, and db.t4g. The following are examples of logging parameters that can consume local storage space. The consumption might occur because of excessive logging or because the system retains the error log for a long time.

To identify the parameter that causes excessive logging, complete the following steps:

1. Analyze the PostgreSQL logs to find the largest logs.
2. Identify the parameter that's responsible for most of the entries in those logs.
3. Modify the parameter that causes excessive logging.

Example parameters:

rds.log_retention_period
auto_explain.log_min_duration
log_connections
log_disconnections
log_lock_waits
log_min_duration_statement
log_statement
log_statement_stats

By default, when you repeatedly run a query that fails with an error, PostgreSQL logs the errors to the PostgreSQL error log. To prevent excessive storage use, review the errors in the log, and then fix the failing query. Alternatively, reduce the 3-day default value for rds.log_retention to reclaim the space that the error logs use.

It's a best practice to change your instance class to a larger instance class to avoid excessive logging. This provides more available local storage for your Aurora DB instance.

Related information

Best practices with Amazon Aurora PostgreSQL