Translated from the Korean original. Some screenshots show the original Korean interface.
1. Has PostgreSQL suddenly slowed down?
If you run PostgreSQL long enough, at some point query responses slow down, or a specific API lags and drags down the performance of the whole service. The problem is that in most cases you notice that things got slow, but you can't quickly pin down the exact cause. It has become even more complex lately as MSA-based architectures, multi-cloud environments, and many application layers are used together. Just telling whether the slowdown comes from inside the DB, the application, or the infrastructure has become hard.
This isn't simply a lack of operating experience. The official PostgreSQL documentation also explains that query performance can be affected by many system factors. In real production environments, factors such as planner statistics, WAL, VACUUM, and I/O often interact and lead to performance degradation.
Now the ability to analyze "why the whole system slowed down from a certain point" matters more than simply asking "which query is slow." In this post, we cover 5 key areas a DBA must check when PostgreSQL performance degrades.
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1-1. Slow queries and execution plans
The first area to check is which queries are delayed. When you see a slowdown, first suspect the queries themselves and start by identifying them. In PostgreSQL, the following signs point to slow queries.
- A specific query's execution time increases gradually or suddenly
- An N+1 pattern appears, where the same query runs repeatedly against a large number of records
- The execution plan changes inefficiently, for example from Index Scan to Seq Scan
- Joins or sorts on unnecessarily large data sets occur repeatedly
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In particular, if you see the following in
EXPLAIN ANALYZE output, they are likely the direct cause of the slowdown. - Actual execution time is far larger than the estimated cost
- Seq Scan (full table scan) occurs repeatedly instead of Index Scan
In practice, finding why a query got slow from a certain point is much harder than finding which query is slow.
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That's because you also have to look at the context: whether it started after a specific deployment, when traffic increased, or only during certain hours. Also, when statistics don't reflect the actual data distribution, the optimizer often picks an inefficient execution plan. In that case even the same query can suddenly slow down from a certain point, so you need to track the history of execution plan changes as well.
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1-2. Lock and transaction bottlenecks
One of the most common causes of PostgreSQL slowdowns is lock contention. If locks aren't released properly, a delay in a single transaction can spread into a delay across the whole service, so check the following first.
- Long-running transactions
- Increasing lock waits on a specific table
- Whether deadlocks occur
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In production, if one transaction holds a lock for a long time, queries waiting on that resource are delayed in a chain, which can affect the entire service.
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In PostgreSQL, you can check the current lock state with views such as the following.
However, these views only show the current state, and it's hard to trace which locks existed in the past. And more important than "is there a lock" is the blocking relationship. You need to know which session holds which lock and which queries are waiting because of it to find the real bottleneck.
So for long-term lock analysis, you need a separate logging policy or monitoring solution that preserves lock history together with blocking relationships.
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1-3. Connection and session state
Connection management is easy to overlook when a DB slows down. If the number of connections nears its limit or sessions pile up abnormally, responses can be delayed regardless of query performance. Check the following first.
- Current session count relative to max_connections
- Whether a connection pool is used (pgBouncer, etc.)
- Whether there are too many idle sessions
- Whether there are connection leaks
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When connections hit the limit, new requests are put on hold, responses are delayed, and this can lead to an incident. In MSA environments in particular, connections are often exhausted faster than expected as the number of services grows.
Inefficient connection use at the application level, such as delayed connection return or holding transactions longer than needed, is also a frequent cause. So go beyond DB metrics alone and look at the application's connection usage patterns too.
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1-4. I/O and storage bottlenecks
If performance is slow even though queries and indexes are fine, suspect a bottleneck at the I/O and storage level. PostgreSQL performance depends heavily on disk I/O, so check the following metrics first.
- Disk I/O wait time
- Checkpoint frequency
- WAL (Write-Ahead Log) write latency
- An improper checkpoint interval causes write bursts.
If the checkpoint interval is too long, a large amount of dirty buffers is written all at once and causes I/O spikes. If it's too frequent, WAL volume explodes and adds to disk load.
- Delayed WAL flushes delay transaction commits.
PostgreSQL returns a response on commit only after WAL is safely written to disk, so the write speed of the WAL disk effectively sets the upper limit for every write transaction.
These symptoms are likely an infrastructure-level bottleneck rather than a problem inside the DB.
In cloud environments, performance varies widely by storage tier and IOPS/throughput limits. For example, with AWS EBS, allowed IOPS differ by storage type (gp3, io2, etc.), and some types drop sharply in performance once a certain amount of credit is used up. This is one of the traps that make the cause hard to pin down in operation. So don't just look at metrics; also check whether the current workload has hit the infrastructure's limits.
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1-5. VACUUM/AUTOVACUUM state
If queries keep getting slower over time even with normal I/O metrics, check the VACUUM state. Because of PostgreSQL's MVCC (Multi-Version Concurrency Control) design, deleted or updated data isn't removed immediately but remains as dead tuples. VACUUM cleans them up, and if it isn't managed properly, performance degrades directly. Check the following first.
- Whether dead tuples are increasing
- AUTOVACUUM delayed or not running
- Growing table/index bloat
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If this continues, index efficiency drops and more pages must be scanned to read the same data, so overall query performance degrades. The busier the table, the more directly the VACUUM policy affects performance. What matters is not just whether AUTOVACUUM runs. The key is whether it runs at a timing and speed that fit the current workload. If the settings don't fit the real environment, VACUUM falls behind and accumulated bloat causes long-term performance degradation.
Also, if AUTOVACUUM doesn't work properly for a long time, the transaction ID limit can be reached and the DB can stop (wraparound), so this matters for stability as well as performance.
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2. PostgreSQL slowdown checklist at a glance
Here are the five check areas we covered, summarized in a table.
Check area | What to check | Main symptoms | First response |
Slow queries/execution plans | Longer execution time, Seq Scan, execution plan changes | Only certain features slow down. Response time spikes | Check EXPLAIN ANALYZE, review indexes, update statistics |
Locks/transactions | Lock waits, idle in transaction, deadlocks | Service-wide delays. Specific APIs stall | Identify blocking sessions, terminate long transactions |
Connections/sessions | Near max_connections, more idle sessions, connection leaks | Requests waiting, intermittent incidents, new connections refused | Apply connection pooling, manage sessions |
I/O, storage | Higher iowait, WAL latency, improper checkpoint interval | Overall performance degradation | Check disk performance, review storage tier and IOPS |
VACUUM/AUTOVACUUM | More dead tuples, bloat | Gradual performance degradation; in severe cases the DB stops (wraparound) | Tune AUTOVACUUM settings, run VACUUM manually |
These check areas look independent, but in real incidents several of them often show up at the same time or in a chain. And tracking all of them in real time, along with their history, has limits if done by hand.
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3. Why you need integrated monitoring
The 5 areas above are basic check points familiar to any DBA. But in real production environments you run into limits like these.
- The query is slow โ hard to tell whether it's due to locks or I/O
- You can see the lock โ hard to trace which service request caused it
- You know when the slowdown started โ but can't connect it to deployments or traffic
In other words, you can see the metrics but not the cause.
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To close this gap, you need performance analysis from an integrated view. A checklist is useful for checking individual items, but in real environments these metrics affect one another. So you need a cause-centered, integrated analysis system rather than monitoring centered on single metrics.
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EXEM's full-stack observability platform, exemONE, supports monitoring in the following ways.
- Connects query โ transaction โ service flow
You can trace which user request ran which SQL, and why that SQL got slow.
- Analyzes locks, sessions, and I/O from multiple angles
You can intuitively identify the cause of a bottleneck at a given point in time on one screen.
- Intelligent analysis and automation
Custom analysis and AI-based prediction enable proactive decisions and automated responses.
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4. PostgreSQL performance problems call for analysis, not just response
As we've seen, PostgreSQL slowdowns usually come from several factors acting together, not a single cause. So it's important to go beyond checking individual metrics and look at the whole flow connected together.
As services grow, the limits of manual checks become clear. It's time to shift the approach to monitoring โ root cause analysis โ insights from history.
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Cut incident response time with a system that analyzes query, lock, session, and infrastructure metrics together.
๐๐ป Explore exemONE
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