How to Resolve Blocking in Aurora PostgreSQL ?

Blocking in databases including Aurora PostgreSQL can affect performance by slowing down or completely halting queries. Blocking happens when one transaction locks a resource (like a table or row) and prevents other transactions from using that resource until the lock is removed.

This article covers the common causes of blocking in Aurora PostgreSQL, how to identify them, and offers strategies to resolve and prevent blocking issues.

Understanding Blocking in Aurora PostgreSQL

• In Aurora PostgreSQL blocking generally arises when multiple transactions try to access the same resource simultaneously.
  
• When one transaction holds a lock on a resource such as during UPDATE, DELETE or INSERT operations, while other transactions requiring access to that resource are blocked until the first transaction releases its lock.
  
• Prolonged blocking can degrade database performance and lead to deadlocks if not resolved promptly.

Common Causes of Blocking

• Long-Running Transactions: Long-running transactions that hold locks for extended periods are one of the main causes of blocking.
  
• Locking Conflicts: When multiple transactions try to update or modify the same resource concurrently then locking conflicts can arise and causing blocking.
  
• Inefficient Indexing: Lack of proper indexing can result in slower query execution and increase the chances of blocking as transactions wait for locks to be released.
  
• Deadlocks: A deadlock occurs when two or more transactions block each other by holding locks and waiting for the other to release a lock.

How to Detect and Diagnose Blocking in Aurora PostgreSQL?

Before resolving blocking issues it is important to identify which queries or transactions are causing the problem. Aurora PostgreSQL provides several tools and views to help identify blocking situations.

1. Using the pg_stat_activity View

• The pg_stat_activity view in PostgreSQL provides information about the currently active sessions.
• We can use it to check for blocking by examining the wait_event and state columns.

Query:

SELECT
  pid,
  usename,
  state,
  wait_event_type,
  wait_event,
  query,
  query_start
FROM
  pg_stat_activity
WHERE
  state = 'active';

Explanation:

• This query retrieves details about currently active sessions in the PostgreSQL database by querying the `pg_stat_activity` view.
• It returns information such as the process ID (`pid`), username (`usename`), session state (`state`), wait event type, the query being executed (`query`) and the start time of the query (`query_start`). The results are filtered to show only sessions in the `active` state.

2. Identifying Blocking Sessions

• In a PostgreSQL database, blocking occurs when one transaction holds a lock on a resource that another transaction needs which leads to delays and potential performance issues.
• It is important to identify both the blocked and blocking sessions to resolve these conflicts and ensure smooth database operations.

Query:

SELECT blocked_locks.pid AS blocked_pid,
       blocked_activity.usename AS blocked_user,
       blocked_activity.query AS blocked_query,
       blocking_locks.pid AS blocking_pid,
       blocking_activity.usename AS blocking_user,
       blocking_activity.query AS blocking_query
FROM pg_catalog.pg_locks blocked_locks
JOIN pg_catalog.pg_stat_activity blocked_activity
  ON blocked_activity.pid = blocked_locks.pid
JOIN pg_catalog.pg_locks blocking_locks
  ON blocking_locks.locktype = blocked_locks.locktype
  AND blocking_locks.DATABASE IS NOT DISTINCT FROM blocked_locks.DATABASE
  AND blocking_locks.relation IS NOT DISTINCT FROM blocked_locks.relation
  AND blocking_locks.page IS NOT DISTINCT FROM blocked_locks.page
  AND blocking_locks.tuple IS NOT DISTINCT FROM blocked_locks.tuple
  AND blocking_locks.virtualxid IS NOT DISTINCT FROM blocked_locks.virtualxid
  AND blocking_locks.transactionid IS NOT DISTINCT FROM blocked_locks.transactionid
JOIN pg_catalog.pg_stat_activity blocking_activity
  ON blocking_activity.pid = blocking_locks.pid
WHERE NOT blocked_locks.GRANTED;

Explanation:

This query fetches information about both blocked and blocking sessions by examining locks in the pg_locks table. It shows the process ID, username, and query for both the blocked and blocking processes. This helps in identifying the cause of blocking, allowing database administrators to resolve the issue by either terminating or managing the conflicting sessions.

Strategies to Resolve Blocking in Aurora PostgreSQL

To resolve blocking in Aurora PostgreSQL, we can terminate the blocking transaction, optimize queries to minimize lock duration, or adjust transaction isolation levels to reduce lock contention.

Additionally, using indexes and breaking large transactions into smaller ones can help prevent future blocking issues.

1. Optimize Long-Running Transactions

Long-running transactions that hold locks for extended periods are one of the primary causes of blocking. To resolve this:

• Commit Early and Often: Avoid holding locks for longer than necessary by committing transactions early. For example, break down large UPDATE or DELETE statements into smaller batches and commit them periodically.
  
• Use Optimistic Locking: For scenarios involving high concurrency, consider using optimistic locking strategies to reduce lock contention.

2. Kill Blocking Queries

• If a transaction is causing significant blocking and cannot be optimized immediately so we can terminate the blocking query manually using the pg_terminate_backend() function.
• Use this approach cautiously as killing a transaction can result in lost work if the transaction is performing critical operations.

Query:

SELECT pg_terminate_backend(pid)
FROM pg_stat_activity
WHERE pid = <blocking_pid>;

Explanation:

• The query uses the pg_terminate_backend() function to forcefully terminate a blocking transaction identified by its process ID (pid). By querying the pg_stat_activity view, we can locate the process causing the block and end it.
• However, this should be done with caution as terminating a transaction may result in data loss or rollback if critical operations were in progress.

3. Implement Proper Indexing

Inefficient queries or lack of indexing can lead to slower execution times, increasing the chances of blocking. To reduce this risk:

• Create Appropriate Indexes: Ensure that queries involving JOIN, WHERE, or ORDER BY clauses have the appropriate indexes to minimize lock times.
  
• Analyze Query Execution Plans: Use EXPLAIN ANALYZE to examine query execution plans and identify inefficiencies that may lead to blocking.

4. Reduce Lock Contention

• Use NOWAIT or SKIP LOCKED as these options allow transactions to bypass locked rows.
• NOWAIT will return an error if a lock is encountered, while SKIP LOCKED skips the locked rows.

Query:

SELECT * FROM table_name FOR UPDATE NOWAIT;
SELECT * FROM table_name FOR UPDATE SKIP LOCKED;

Explanation:

Partition our Tables can help reduce contention by distributing rows across different partitions. This can lower the chances of multiple transactions trying to access the same resource simultaneously.

5. Set Appropriate Timeout Values

PostgreSQL offers the lock_timeout parameter, which can limit how long a query waits for a lock. This prevents transactions from being blocked indefinitely.

Query:

SET lock_timeout = '2s';

Explanation:

• The SET lock_timeout = '2s'; command configures PostgreSQL to limit how long a query will wait for a lock, in this case, 2 seconds.
• If the query can't acquire the necessary lock within this time frame, it will be canceled automatically, preventing long or indefinite blocking of transactions.
• This helps maintain database performance by ensuring that locked queries don't stall other operations for too long.

How to Prevent Blocking Issues?

To minimize blocking incidents in Aurora PostgreSQL, consider adopting best practices for database management:

• Monitor Regularly: Use automated monitoring tools like Amazon CloudWatch and PostgreSQLs built-in views (pg_stat_activity, pg_locks) to identify blocking issues early.
  
• Analyze Workload Patterns: Understand the read/write patterns of your applications and optimize queries accordingly.
  
• Tune Database Parameters: Adjust Aurora PostgreSQL's configuration parameters, such as max_connections to reduce contention in high-concurrency environments.

Conclusion

Blocking in Aurora PostgreSQL is a common issue that can slow down database performance and degrade user experience. However by identifying the root cause of blocking and using strategies like optimizing long-running transactions, improving indexing, reducing lock contention and setting appropriate timeout values you can resolve and prevent these issues. Continuous monitoring and proactive database management are key to ensuring smooth operations and optimal performance in Aurora PostgreSQL.