What are the possible scenarios for deadlock in PostgreSQL?

Deadlock is a situation where two or more transactions are blocked, waiting for each other to release locks that they hold. In PostgreSQL, deadlocks can occur in several scenarios, such as:

  1. Concurrent updates: When two transactions try to update the same set of rows in different orders, they may end up locking each other’s resources and creating a deadlock. For example, Transaction A updates row 1 and then row 2, while Transaction B updates row 2 and then row 1.
  2. Circular dependency: When two or more transactions hold locks on resources that the other transactions need, and these dependencies form a cycle, a deadlock can occur. For example, Transaction A holds a lock on resource X and needs resource Y, while Transaction B holds a lock on resource Y and needs resource X.
  3. Unreleased locks: When a transaction acquires a lock and fails to release it before committing or rolling back, other transactions may be blocked waiting for the lock to be released. This can lead to a deadlock if multiple transactions are waiting for the same lock.
  4. Long-running transactions: When a transaction holds locks for an extended period of time, other transactions may be blocked waiting for those locks to be released. If multiple transactions hold locks for a long time, a deadlock can occur.

To prevent deadlocks in PostgreSQL, it is important to design applications and queries that acquire locks in a consistent order, release locks as soon as they are no longer needed, and avoid long-running transactions. Additionally, PostgreSQL provides a deadlock detection and resolution mechanism that can detect and resolve deadlocks automatically, but it is better to avoid deadlocks altogether if possible.

Here is a Python script that uses the psycopg2 library to monitor PostgreSQL for deadlocks in real-time:

This script connects to the PostgreSQL database using the psycopg2 library, and sets the autocommit mode to True to ensure that transactions are committed automatically. It also enables deadlock detection by setting the deadlock_timeout parameter to 5 seconds.

The script then enters a loop that repeatedly executes a SELECT statement that may cause a deadlock. If a deadlock is detected, the psycopg2.Error exception is caught and the error message is printed to the console.

The time.sleep(1) statement is used to wait for a short time before trying the SELECT statement again, to avoid flooding the database with requests.

You can customize this script to monitor your own PostgreSQL database for deadlocks, by modifying the connection parameters and the SELECT statement to match your own database and tables. Note that this script only detects deadlocks that occur during the execution of the SELECT statement, so it may not detect all deadlocks that occur in your database.

About Shiv Iyer 455 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.