PostgreSQL 18 Performance Tuning Checklist: What Actually Changed in Production

PostgreSQL 18 doesn’t arrive with flashy “50% faster” promises.

What teams usually notice after upgrading from PostgreSQL 14, 15, or 16 is something more valuable in production:

Fewer latency spikes, more predictable behavior, and less emergency tuning.

This is not a theoretical overview or a benchmark recap.
This checklist focuses on what actually changed in PostgreSQL 18 once real traffic hits the database.

For each section, we’ll look at:

• How things behaved in older PostgreSQL versions
• What changed in PostgreSQL 18
• What kind of improvement you can realistically expect
• How to monitor it in production

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1- Query Planner

Fewer Bad Plans, More Stable Latency

Before PostgreSQL 18

In PostgreSQL 12–16, the planner could still struggle with:

• Skewed data distributions
• Highly correlated columns
• Parameterized queries under fluctuating workloads

The result was familiar:

• Sequential scans even when a good index existed
• Queries that were usually fast, but occasionally disastrous

Most DBAs compensated by:

• Disabling planner options
• Adding defensive indexes
• Rewriting queries just to “help” the planner

It worked — but it wasn’t clean.

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What Changed in PostgreSQL 18

PostgreSQL 18 improves:

• Row count estimation accuracy
• Join order stability
• Plan selection consistency for parameterized queries

The biggest difference is not raw speed.
It’s plan predictability.

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Real Production Impact

In OLTP workloads, teams typically see:

• 10–25% fewer latency spikes on index-backed queries
• Flatter p95 and p99 latency curves under load

PostgreSQL 18 feels faster mainly because it stops surprising you.

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How to Monitor It

Averages won’t tell the story. Variance will.

Watch:

• pg_stat_statements
  • stddev_exec_time trending down
  • Fewer sudden plan changes for the same query
• Plan churn over time

If a query was “fast most of the time but slow sometimes,”
PostgreSQL 18 should noticeably reduce that pattern.

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2- Background I/O

Less Noise, Smoother Throughput

Before PostgreSQL 18

Maintenance work often arrived in bursts:

• Autovacuum
• Checkpoints
• Background writer activity

Under load, this caused:

• Sudden I/O spikes
• Latency jumps with no obvious query culprit

Most teams accepted this as normal and tuned around it.

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What Changed in PostgreSQL 18

Background workers are more adaptive:

• Work is spread more evenly over time
• Foreground queries are less disrupted by maintenance

The system behaves less like a series of spikes and more like a steady flow.

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Real Production Impact

On write-heavy systems:

• p95 latency becomes 15–30% more stable
• Checkpoints and vacuum runs fade into the background

This is especially noticeable on cloud-backed storage.

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How to Monitor It

Track correlations, not isolated metrics:

• pg_stat_checkpointer checkpoint activity
• OS-level I/O wait
• Query latency vs vacuum timing

In PostgreSQL 18, maintenance work should no longer dominate your latency graphs.

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3- Memory Tuning

Old Rules Don’t Always Apply

Before PostgreSQL 18

Classic advice still dominated:

• shared_buffers = 25% of RAM
• Keep work_mem conservative
• Avoid memory risk at all costs

These rules were safe — but often overly cautious on modern systems.

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What Changed in PostgreSQL 18

Memory behavior is more controlled:

• The planner avoids overly memory-hungry plans more often
• Temporary file usage is more predictable

This doesn’t remove the need for tuning,
but it reduces how risky tuning mistakes can be.

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Real Production Impact

For sort- and hash-heavy workloads:

• Fewer disk spills
• More stable execution times under concurrency

It’s harder for a single query to monopolize system memory.

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How to Monitor It

Focus on spill signals, not configuration size:

• temp_files and temp_bytes
• Spill frequency per query
• Memory usage vs concurrent query count

Higher work_mem only makes sense if spill metrics go down.

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4- Autovacuum

Less Manual Intervention

Before PostgreSQL 18

Autovacuum tuning was a balancing act:

• Too passive → bloat
• Too aggressive → latency spikes

Table-level overrides were common and often necessary.

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What Changed in PostgreSQL 18

Autovacuum is more workload-aware:

• Less unnecessary vacuuming
• Better balance between cleanup and query performance

The defaults behave better under real-world traffic.

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Real Production Impact

• Update-heavy tables maintain index performance longer
• Fewer “the database suddenly got slow” incidents

Over time, the system stays healthier with less babysitting.

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How to Monitor It

Watch long-term trends:

• Table and index bloat
• Vacuum frequency vs table growth
• Autovacuum runtime consistency

The goal is steady cleanup, not aggressive bursts.

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5- So… How Much Faster Is PostgreSQL 18?

Let’s be honest:

• ❌ Not magically “50% faster”
• ✅ 10–30% improvement in performance stability
• ✅ Less tuning overhead
• ✅ Fewer production surprises

The biggest gain is predictability, not raw throughput.

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Final PostgreSQL 18 Performance Checklist

After upgrading:

• Review old planner workarounds
• Trust default autovacuum behavior more
• Monitor latency variance, not just averages
• Correlate I/O spikes with background activity
• Measure memory tuning by spill reduction, not allocation size

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Final Thoughts

PostgreSQL 18 doesn’t eliminate tuning.
It eliminates panic tuning.

Instead of constantly reacting to performance issues, teams can focus on observing trends and making deliberate, informed adjustments.

That shift alone makes PostgreSQL 18 a meaningful production upgrade.

For a deeper look at what to monitor in PostgreSQL 18 production systems, see our PostgreSQL 18 production performance guide

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