Understanding locks and deadlocks in PostgreSQL

Understanding locks and deadlocks in PostgreSQL

Locks in PostgreSQL are used to ensure that concurrent transactions do not interfere with each other. When a transaction modifies a piece of data, it will take out an exclusive lock on that data, which prevents other transactions from modifying it until the lock is released. PostgreSQL also supports shared locks, which allow multiple transactions to read the same data simultaneously, but not modify it.
Deadlocks occur when two or more transactions are waiting for locks held by the other. To prevent this, PostgreSQL uses a technique called lock escalation, where it releases multiple locks and replaces them with a higher-level lock, such as a table lock. Additionally, PostgreSQL can detect and resolve deadlocks by canceling one of the transactions that is involved in the deadlock.
To troubleshoot locks and deadlocks, you can use the pg_locks and pg_stat_activity views to view the current locks held by each transaction and the queries that are being executed. Additionally, you can use the pg_deadlock_detect() function to manually detect and resolve deadlocks. It's important to note that it's important to understand the application's access pattern and query patterns to prevent and troubleshoot lock and deadlock issues.

Python script for monitoring and troubleshooting PostgreSQL locks and deadlocks in real-time

Here is an example of a Python script that uses the psycopg2 library to connect to a PostgreSQL database and retrieve information about current locks and deadlocks in real-time:
This script will continuously run and print information about current locks and deadlocks in the database. It is using the pg_locks and pg_stat_activity system tables to gather the information. This script can be useful to monitor and troubleshoot performance issues related to locks and deadlocks in your PostgreSQL database. It should be modified according to the specific requirements of your use case.

About Shiv Iyer 72 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.

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