Untangling PostgreSQL Locks: How to Fix Hidden Performance Bottlenecks
Hey folks 👋
If you've ever run into slow queries, blocked transactions, or that dreaded deadlock detected error in PostgreSQL — you're not alone.
I just published a blog post that dives deep into one of the most overlooked causes of database performance issues: row-level locking and contention.
👉 Read the full article:
Untangling PostgreSQL Locks – Your Guide to Conquering Database Contention
🔍 What You’ll Learn
✅ How PostgreSQL handles row-level locks behind the scenes
✅ Different lock modes (FOR UPDATE, FOR SHARE, etc.) and when to use them
✅ Real-world examples of deadlocks and blocked transactions
✅ Query inspection tools like pg_locks and pg_stat_activity
✅ Strategies to prevent and resolve contention, including indexing tips and timeout settings
🛠 Use Cases
If you're working with:
High-concurrency APIs or microservices
Background workers writing to the same tables
Batch jobs or cron processes touching shared rows
OLTP systems with high write volumes
…then this guide will help you detect, debug, and fix locking issues before they hurt your performance.
📈 Why This Matters
Lock contention is one of those problems that creeps in slowly and suddenly becomes a fire in production. Understanding how PostgreSQL locks work is critical if you're scaling your backend or optimizing database performance.
🔗 Read Now
👉 Untangling PostgreSQL Locks – Your Guide to Conquering Database Contention
Would love to hear how you handle lock contention in your systems — reply or drop a comment with your approach.
Thanks for reading 🙏
More deep dives on scaling, observability, and backend engineering coming soon.
—
Shubham Gautam
Backend Engineer | System Design Enthusiast