PostgreSQL is built to perform. However, as workloads increase and systems evolve, even the most robust setups can begin to show signs of strain. Whether you are scaling a product or supporting enterprise SLAs, performance slowdowns tend to surface when you least want them to.

If you are a technology leader overseeing a team of developers who manage PostgreSQL as part of a broader application stack, or you are responsible for uptime and customer satisfaction at scale, knowing where to look first can make all the difference.

Let us walk through a focused checklist of patterns and places that commonly hold the key to unlocking better PostgreSQL performance.

1. Query plans: The hidden blueprint

PostgreSQL’s EXPLAIN ANALYZE is often the first flashlight to pick up. It tells you exactly how the planner interprets your SQL and whether it is using the proper indexes, scanning the correct number of rows, or making inefficient joins.

Look here if:

• A query runs fast in dev but crawls in prod
• You see inconsistent latencies during peak usage

Small changes in filters or joins can flip a plan from efficient to expensive. Understanding the shape of your plan is a good starting point.

2. Indexes: Present but not always useful

Indexes are powerful—but only when they match the access patterns of your application. In production systems, it is common to see:

• Missing indexes on frequently filtered columns
• Unused indexes from past iterations
• Multi-column indexes that do not match the filter order

What to check:

• pg_stat_user_indexes for usage stats
• pg_index for index bloat
• EXPLAIN ANALYZE to check for sequential scans

Right-size your indexes. Not too few, not too many—just enough to serve your access paths cleanly.

3. Autovacuum: Quietly doing important work

Autovacuum keeps your tables bloat-free and performance-ready. But it can fall behind silently, especially on large or high-churn tables. When it does, query times increase, memory pressure rises, and planner estimates become unreliable.

Look out for:

• Tables with long update/delete histories
• High bloat ratios (pg_stat_user_tables)
• Lagging last_autovacuum and n_dead_tup in pg_stat_user_tables

Tuning autovacuum is essential for consistent performance. Never turn it off! 

4. Connection spikes and pooling gaps

Your application may scale in users, but PostgreSQL prefers to keep things lean in terms of concurrent connections. Without a connection pooler like PgBouncer or Pgpool-II, spikes in app traffic can exhaust backend workers and lead to queueing.

Ask your team:

• Are we using connection pooling for all services?
• Is the pooler configured for transaction mode where appropriate?
• Do we have connection spikes during high load?

Connection management often delivers immediate performance gains with minimal code changes.

5. Memory and work settings

PostgreSQL’s memory usage — especially work_mem, shared_buffers, and effective_cache_size — plays a direct role in query performance. When tuned correctly, sorts, joins, and aggregations stay in memory. When undersized, they spill to disk.

Signs to investigate:

• High temp files usage in logs
• Sudden disk I/O spikes during query execution
• Slow sort or hash join operations

Tune based on actual workload patterns instead of using the conservative defaults.

6. Locking and contention

Some performance issues are about blocked queries, not just slow ones. Locking is healthy and expected, but when a long-running transaction holds a critical lock, others pile up behind it.

Where to look:

• pg_stat_activity for blocked sessions
• pg_locks for lock dependencies
• Application logic that wraps writes in long transactions

Encourage the habit of short transactions and use monitoring to catch long blockers early.

7. Logs tell the story

PostgreSQL logs are incredibly rich. With the proper settings (log_min_duration_statement, log_checkpoints, log_temp_files), they tell you what PostgreSQL is struggling with — long queries, lock waits, temporary file usage, and more.

Tip: Start with slow query logging. It almost always manifests low-hanging fruit.

8. Application behavior and query patterns

Performance often hinges on how your app talks to the database:

• Are ORM-generated queries efficient?
• Are N+1 patterns creeping into your workload?
• Are you over-fetching data or issuing too many writes?

Application logic is upstream of PostgreSQL. If the same query runs hundreds of times per second, even small inefficiencies add up quickly.

Tip 1: Delegate the right questions

When your dev team says “Postgres is slow,” steer the conversation here:

• Have you looked at the query plan?
• Are we indexing the columns we filter on?
• What do the logs tell us about the query duration?

These questions help you guide the team to better answers, faster.

Tip 2: Build in room to breathe

When escalations arrive and customer SLAs are on the line, it is tempting to scale hardware as a first fix. But over time, clarity beats over-provisioning.

A repeatable performance triage checklist can help you:

• Tackle the root cause without trial-and-error
• Build a case for long-term tuning and monitoring
• Reduce surprises in production

Start with visibility. Postgres is remarkably predictable when given the right feedback loops.

Closing Thoughts

PostgreSQL performance issues generally follow patterns. Knowing where to look first helps you reduce the time-to-resolution and makes your team more confident in handling production workloads.

The best part? Most slowdowns are fixable with better insight, not bigger machines.