“PostgreSQL rocks, except when it blocks”: Understanding Locks in PostgreSQL


PostgreSQL, renowned for its robustness and versatility, often “rocks” as a database management system. However, it’s not without its challenges, particularly when it comes to locking mechanisms. Understanding how PostgreSQL handles locks is crucial for database administrators and developers to ensure smooth operation and avoid the dreaded scenario where “it blocks”.

The Essence of Locking in PostgreSQL

Why Locks are Necessary

  • Data Integrity: Locks prevent data from being read or written inappropriately, ensuring transactional integrity and consistency.
  • Concurrency Control: They manage simultaneous access to data by multiple transactions, essential in a multi-user environment.

Types of Locks in PostgreSQL

  1. Row-level Locks: Applied to individual rows within a table.
  2. Table-level Locks: Applied to entire tables.
  3. Advisory Locks: Custom locks for application-level logic.
  4. Deadlocks: Occur when two or more transactions are stuck waiting for each other to release locks.

Understanding Lock Conflicts and Performance

Symptoms of Locking Issues

  • Slow Query Execution: Excessive locking can lead to increased transaction times.
  • Blocked Transactions: Transactions may become blocked waiting for other transactions to release locks.
  • Deadlocks: Result in transaction failures and can significantly impact performance.

Monitoring and Identifying Locks

  • pg_locks: A PostgreSQL view that provides information on current locks.
  • pg_stat_activity: Helps identify which queries are holding or waiting for locks. Read more here.
  • Logging: Configuring log settings to capture deadlock information and long-running lock waits.

Best Practices for Managing Locks

1. Avoiding Excessive Locking

  • Optimize Transaction Sizes: Smaller, more frequent transactions reduce the likelihood and impact of lock contention.
  • Indexing: Proper indexing can reduce the need for heavy table scans, thus minimizing row-level locks.

2. Dealing with Deadlocks

  • Application Logic: Design applications to handle deadlocks gracefully, such as retrying transactions.
  • Query Ordering: Consistent ordering of operations across transactions can reduce deadlock occurrences.

3. Lock Management in Application Design

  • Use of Advisory Locks: Implement advisory locks for complex application-level locking scenarios.
  • Transaction Isolation Levels: Choose appropriate transaction isolation levels to balance between consistency and locking overhead.

Advanced Locking Mechanisms

1. Lightweight Locks (LWLocks)

  • Internal Mechanism: Used by PostgreSQL to protect shared data structures in memory.
  • Minimizing LWLock Contention: Requires tuning of shared buffers, autovacuum settings, and other server parameters.

2. Partitioning to Reduce Lock Contention

  • Table Partitioning: Distributes rows across multiple partitions, reducing the scope of lock contention.
  • Sharding: In a distributed database setup, sharding can significantly reduce lock-related performance bottlenecks.


While PostgreSQL excels in managing data with high reliability, lock-related issues can be a stumbling block, impacting performance and user experience. Understanding the types of locks, how to monitor and diagnose locking issues, and implementing best practices for lock management are essential skills for anyone working with PostgreSQL. By mastering these aspects, you can ensure that PostgreSQL not only rocks in its capabilities but also runs smoothly without unwanted blocks. You can also learn more about row locks in PostgreSQL here.

About Shiv Iyer 456 Articles
Open Source Database Systems Engineer with a deep understanding of Optimizer Internals, Performance Engineering, Scalability and Data SRE. Shiv currently is the Founder, Investor, Board Member and CEO of multiple Database Systems Infrastructure Operations companies in the Transaction Processing Computing and ColumnStores ecosystem. He is also a frequent speaker in open source software conferences globally.