PostgreSQL at 100% CPU with Normal Query Load: The $2M Lesson in Stale

Normal queries. 100% CPU. PostgreSQL’s stale statistics misled the planner costing $2M every 10 minutes.

Friday night.
A Fortune 100 e-commerce platform was running its biggest campaign of the quarter.

Orders were steady. Query volume normal.
Yet dashboards suddenly lit up red: CPU 100% across all PostgreSQL nodes.

Cart and checkout latency jumped from milliseconds to 6 seconds.
And every 10 minutes of slowdown was costing over $2 million in lost sales.

No query storms.
No deadlocks.
No high number of connections.

So what happened?

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Every 10 Minutes = $2M Burn Rate

In cloud-scale systems, performance degradation isn’t just latency it’s money leaking in real time.

When PostgreSQL misjudges its execution plans:

• Compute autoscaling kicks in

• Queries do more work per execution

• Queues back up

• Downstream systems start timing out

• Replication falls behind

This real-world financial model revealed:

• 10 minutes of degraded CPU efficiency → $2 million loss

• Query latency blew up from 120 ms → 6 seconds

• Replication lag crossed 18 minutes, triggering failover alarms

All while query volume stayed normal.

Let’s go step by step to solve this…

Step 1: Don’t Trust the First Narrative

When you see 100% CPU with normal query counts, the default instincts usually go like this:

1. Traffic surge? — Nope, query per second (QPS) is stable.

2. Bad query or missing index? — Plans look consistent, no new deployments.

3. Locks or waits? — pg_locks is quiet.

So what’s left?

If you’ve been in PostgreSQL long enough, you know the planner is both brilliant and dangerous when it has stale information.

The next question that must pop into your head:

Is PostgreSQL guessing wrong about how much data it needs to touch?

That one mental shift changes everything.

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Step 2: Think Like the Planner

The PostgreSQL planner doesn’t know. It guesses based on statistics.

And those stats might be days out of date if your autovacuum isn’t tuned or if bulk updates quietly happened in the background.

When the planner thinks a table has 10 rows but reality holds 10 million, it can pick a nested loop instead of a hash join, or scan an entire table instead of using an index.

Result?
CPU spikes.
Caches thrashed.
Executors doing 100x more work per query all while your dashboards show normal QPS.

Step 3: Ask the Right Question

Not what changed in traffic, but:

When did PostgreSQL last learn the truth about this table?

Run this:

SELECT schemaname,
       relname,
       n_live_tup,
       last_analyze,
       last_autoanalyze
FROM pg_stat_all_tables
WHERE last_analyze IS NULL
   OR last_autoanalyze IS NULL
   OR now() - GREATEST(last_analyze, last_autoanalyze) > interval ‘1 day’
ORDER BY n_live_tup DESC;

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Why this matters

This view is your stats freshness monitor.
If last_analyze or last_autoanalyze is stale, PostgreSQL’s planner is flying blind.

• n_live_tup tells you estimated row counts (what planner thinks).

• last_analyze shows when stats were last refreshed.

• last_autoanalyze shows if autovacuum is doing its job.

If you see large tables with old stats you’ve likely found your CPU culprit.

Step 4: Correct the Guess

You don’t fix stale stats by rebooting or vacuuming everything.
You fix the data the planner depends on.

ANALYZE your_table;

ANALYZE command rebuilds the table’s statistical histogram and MCV (most common value) lists, restoring accurate planner estimates.

CPU load will often drop within seconds of this command.

For high-velocity tables (e.g., orders, events, sessions), the default autovacuum thresholds are too lazy.

You can force more frequent auto-analyze like this:

ALTER TABLE your_table
SET (autovacuum_analyze_scale_factor = 0.005);

For less than the cost of two coffees a month, you’ll gain the knowledge that prevents six-figure outages and protects your sleep.

Why it matters:

By default, PostgreSQL waits until 10% of rows change before analyzing.
That’s fine for archival data, but bad for real-time workloads.
A 0.5% threshold ensures frequent planner updates.

If your workload has highly skewed columns (like status, region, or category), improve the histogram depth:

ALTER TABLE your_table ALTER COLUMN status SET STATISTICS 500;

The planner uses column stats to guess selectivity.
Richer histograms = better cardinality estimates = fewer bad plan choices.

Note:
You should use this specific command and low value (0.005) for high-velocity, volatile tables (e.g., sessions, logs, or primary transaction tables) where statistical drift is a known or likely problem.

• The default scale factor is 0.1 (10%).

• By setting it to 0.005, you instruct PostgreSQL to perform an automatic ANALYZE operation when only 0.5% of the rows in your_table have been inserted, updated, or deleted.

This ensures the Query Planner gets fresh data frequently, preventing the kind of disastrous plan changes that lead to sudden high CPU utilization even when query volume is normal.

Step 5: Prevention Mindset

You can’t afford to hope autovacuum catches up. You have to design for stats freshness.

Checklist:

• Set autovacuum_analyze_scale_factor to 0.005–0.01 for hot tables.

• Use pg_stat_all_tables or monitoring dashboards to track last_autoanalyze lag.

• Periodically ANALYZE large partitioned tables after bulk loads.

• Watch for tables where n_dead_tup grows faster than vacuum_count.

When the CPU suddenly spikes but queries don’t, ask yourself:

Is PostgreSQL guessing wrong again?

Key Takeaway

When CPU spikes without a query surge, don’t chase ghosts.
Ask one question first:

Is PostgreSQL guessing wrong?

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