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PostgreSQL connection exhaustion and slow SQL can appear together, but they are different failures. Connection exhaustion means sessions are consuming the server’s available connection slots; a slow query means the planner or executor is taking too long, often because of CPU, I/O, locks, or a poor row-count estimate. Start by capturing the exact error and current activity, then investigate connection capacity and query plans as separate workstreams. Do not raise max_connections or add an index until the evidence shows that change addresses the bottleneck.
Start by confirming which failure you have
Record the following before changing configuration:
- The complete client or server error, such as a connection-slot error, a timeout, or a statement-cancellation message.
- The time window, affected application or job, database name, user, and whether the problem is continuous or comes in bursts.
- The PostgreSQL major version, hosting model, and whether you can restart the server.
- The configured connection and reservation settings.
- Whether new connections are refused, merely delayed, or accepted while SQL requests remain slow.
- Recent changes to traffic, deployments, background jobs, connection-pool settings, schema, or indexes.
| Observed symptom | What it suggests | First evidence to collect |
|---|---|---|
too many clients already or a similar slot error |
The server has reached the usable connection ceiling, or the application is trying to use more sessions than the system can serve. | pg_stat_activity, max_connections, reserved-slot settings, and application pool counts. |
| “Remaining connection slots are reserved …” | Ordinary roles are at the limit while reserved access remains for administrative roles. | The role used by the application and the reserved-connection configuration. |
| Connections succeed, but a statement is slow | A query-plan, resource, lock, or workload problem; increasing the connection ceiling does not explain the slow plan. | The exact SQL, representative parameters, EXPLAIN output, waits, and CPU/I/O evidence. |
| Both occur during traffic spikes | Excess session concurrency may be creating resource pressure that makes individual statements slower, but both symptoms still need separate confirmation. | Time-aligned session counts, query activity, wait events, and operating-system metrics. |
Understand what max_connections actually controls
max_connections is the ceiling on concurrent server connections. PostgreSQL 18 documents a typical default of 100, subject to operating-system and kernel limits; a particular installation or managed service can use a different value. The setting takes effect only when the server starts, so changing it requires a restart (or the hosting provider’s equivalent operation).
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Near the ceiling, reserved slots explain why one connection can succeed while another is rejected. PostgreSQL reserves connections for superusers through its superuser reservation setting. PostgreSQL 18 can also allow roles granted pg_use_reserved_connections to use a separate reserved capacity when that feature is configured. Check the settings and role grants on the actual server rather than assuming every connection has equal access.
SHOW max_connections;
SHOW superuser_reserved_connections;
-- PostgreSQL versions that expose an additional reserved pool:
SHOW reserved_connections;On a managed service, the provider may expose these values in a parameter page instead of allowing direct edits. Verify the running value, the permitted range, and whether a restart is required.
How do I see which PostgreSQL connections are active?
PostgreSQL’s monitoring documentation frames the first question well: “A database administrator frequently wonders, ‘What is the system doing right now?’” Use pg_stat_activity and the cumulative statistics system to answer that question instead of treating a raw connection count as a diagnosis.
Get a state summary
SELECT
count(*) AS total_sessions,
count(*) FILTER (WHERE state = 'active') AS active,
count(*) FILTER (WHERE state = 'idle') AS idle,
count(*) FILTER (WHERE state = 'idle in transaction') AS idle_in_transaction,
count(*) FILTER (WHERE wait_event IS NOT NULL) AS waiting
FROM pg_stat_activity;Compare total_sessions with max_connections, but do not stop there. Idle sessions consume slots even when they are not using CPU, while a small number of active sessions can saturate storage or CPU.
Inspect individual sessions
SELECT
pid,
usename,
datname,
application_name,
client_addr,
state,
wait_event_type,
wait_event,
now() - query_start AS query_age,
now() - xact_start AS transaction_age,
left(query, 500) AS query_text
FROM pg_stat_activity
WHERE pid <> pg_backend_pid()
ORDER BY query_start NULLS LAST;Look for long-running active statements, sessions waiting on locks, and idle in transaction sessions. The latter indicate an open transaction whose client is not currently sending SQL; they can retain locks or prevent cleanup while still occupying a connection. Confirm the application’s transaction behavior before terminating anything.
Group sessions by source
SELECT
usename,
datname,
application_name,
client_addr,
state,
count(*) AS sessions
FROM pg_stat_activity
GROUP BY usename, datname, application_name, client_addr, state
ORDER BY sessions DESC;This often reveals a deployment that created a second pool, a worker fleet whose per-process pool is too large, or a health check that opens sessions repeatedly. Account for non-client backends and administrative sessions when reconciling the total.
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Check the host at the same time
Database views show PostgreSQL’s perspective. Pair them with operating-system evidence:
psortopshows PostgreSQL processes and CPU consumption.iostathelps determine whether storage latency or saturation coincides with slow statements.vmstatshows CPU run queues, memory pressure, and I/O wait indicators.
Collect these measurements during the incident when possible. A high connection count alone cannot tell you whether the limiting resource is CPU, storage, locks, memory, or the application’s own connection handling.
Find why sessions are accumulating
Once you know which sessions exist, classify the cause before changing the ceiling.
Pool or process multiplication
Many application servers create a pool per process, pod, worker, or host. A nominal pool size of 20 can become hundreds of database sessions when multiplied across workers. Compare the configured maximum in every component with the number of running instances and with the server’s usable connection capacity.
Leaked or unreleased connections
If the session count rises after requests complete, inspect error paths, retries, and shutdown handling. A connection that is not returned to the application pool can look like a database-side leak. Check pool wait time and checkout failures alongside pg_stat_activity rather than assuming PostgreSQL created the excess sessions.
Long transactions and blocked work
A transaction can remain open while its client is idle, or many sessions can wait behind one lock holder. To inspect blockers for a waiting backend:
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SELECT
blocked.pid AS blocked_pid,
blocked.usename AS blocked_user,
now() - blocked.query_start AS blocked_for,
blocking.pid AS blocking_pid,
blocking.usename AS blocking_user,
left(blocking.query, 500) AS blocking_query
FROM pg_stat_activity AS blocked
CROSS JOIN LATERAL unnest(pg_blocking_pids(blocked.pid)) AS blocker(pid)
JOIN pg_stat_activity AS blocking ON blocking.pid = blocker.pid
WHERE blocked.wait_event_type = 'Lock';Resolve the transaction or application behavior that holds the lock. Terminating a backend is an emergency action, not a substitute for finding why the transaction stayed open.
Bursts and legitimate concurrency
A batch, migration, or traffic surge can create a temporary demand peak. Record the time series of sessions and requests; a steady leak requires a different fix from a short burst that should be queued or rate-limited.
Should I increase max_connections or use a connection pool?
Use a pool when applications create more database sessions than PostgreSQL can use productively. Pooling lets many short-lived requests share a smaller number of database sessions, but the useful active concurrency depends on query mix, hardware, storage, lock behavior, and the application’s latency goals. There is no universal pool size.
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| Option | When it helps | Risks or work required |
|---|---|---|
Raise max_connections |
There is verified spare CPU, memory, and I/O capacity, and the workload genuinely needs more simultaneous sessions. | Requires a server start; increases resource allocation and can worsen contention or memory pressure. |
| Client or framework pool | The application opens many short-lived sessions or has an existing pool that can cap concurrency. | Each process may create its own pool; pool wait time and timeout behavior must be monitored. |
| External pooler | Several applications or languages need a shared control point, or connection setup overhead is significant. | Adds an operational component and another set of limits; transaction and session state must match the pooling mode. |
| Queue or throttle work | Bursty jobs can wait without requiring every request to hold a database session. | Requires application-level back-pressure and a clear policy for timeouts and retries. |
Evaluate where pooling runs, how it interacts with the framework’s built-in pool, the workload’s transaction pattern, and how administrators and monitoring will retain access. Keep reserved capacity for incident response; a pool that consumes every ordinary slot can prevent diagnosis.
Tune incrementally on the production-like system. Change one limit, observe throughput, latency, errors, CPU, memory, I/O, and lock waits, then decide whether another change is justified. Community PostgreSQL guidance uses batch-size examples to illustrate that useful concurrency varies; those examples are not universal performance results and should not be copied as defaults.
Recover safely when the server is already full
First stop the source of new sessions if possible: pause a runaway job, reduce application replicas, or temporarily lower an application pool limit. Preserve at least one administrative path through reserved access or a superuser session.
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For a confirmed runaway statement, cancellation is less disruptive than disconnecting the client:
SELECT pg_cancel_backend(<pid>);If a session is demonstrably abandoned and must be removed, termination closes the connection and rolls back its transaction:
SELECT pg_terminate_backend(<pid>);Replace <pid> only after checking the user, application, database, query, transaction age, and blocking relationships. Never terminate sessions merely because they are idle; an idle session may belong to a critical application or administrator, and an idle-in-transaction session needs a deliberate rollback decision.
How do I find the slowest PostgreSQL queries?
Identify a specific statement and representative parameter values, then inspect what the planner and executor actually did. A connection ceiling does not explain a poor plan, and reducing connections does not repair stale statistics or an inefficient access path.
Capture a plan
EXPLAIN (ANALYZE, BUFFERS, VERBOSE)
SELECT ...;EXPLAIN without ANALYZE shows the estimate. EXPLAIN ANALYZE executes the statement and reports actual row counts and timing; use it only with safe, representative work. For a data-changing statement, run it in a controlled transaction and roll it back only when the statement and all invoked functions are safe to execute in that environment:
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EXPLAIN (ANALYZE, BUFFERS) UPDATE ...;
ROLLBACK;Compare estimated and actual rows at each major plan node. Large differences point toward planner statistics or data-distribution problems. Check whether the plan spends time scanning, sorting, joining, reading blocks, or waiting on another backend. An index, memory change, or query rewrite is a hypothesis to validate against the plan, not an automatic remedy.
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Refresh and verify planner statistics
Planner statistics describe the data distribution used to estimate costs. After substantial data changes or when estimates are clearly wrong, run ANALYZE on the affected table in a suitable maintenance window and capture a new plan. Also check whether autovacuum and analyze activity are keeping statistics current for the workload.
Separate query time from waiting time
A statement that appears slow may be waiting on a lock or storage rather than consuming CPU. During the event, inspect wait_event_type, wait_event, blockers, host CPU, and I/O. A plan captured after the wait has cleared may look normal, so preserve both the plan and the concurrent activity evidence.
Use pg_stat_statements for aggregate evidence
pg_stat_statements aggregates planning and execution information across normalized statements. It can show calls, total and mean execution time, rows, and block activity, which helps rank recurring work instead of chasing a single slow request.
Install it with the required restart
- Add
pg_stat_statementsto the existingshared_preload_librarieslist in the server configuration. Preserve other libraries already present. - Restart PostgreSQL. Adding or removing a preloaded library cannot be completed with a SQL reload alone.
- In each database where you want the view, run
CREATE EXTENSION IF NOT EXISTS pg_stat_statements;. - Confirm the installed version’s available columns and provider restrictions before relying on a configuration change.
One useful ranking query is:
SELECT
calls,
total_exec_time,
mean_exec_time,
rows,
shared_blks_hit,
shared_blks_read,
left(query, 500) AS query_text
FROM pg_stat_statements
ORDER BY total_exec_time DESC
LIMIT 20;Rank by total execution time to find the workload’s largest aggregate cost, or by mean execution time to find statements that are individually slow. Calls and rows provide the context needed to distinguish a frequently executed moderate query from a rarely executed expensive one.
Decide whether to track planning time
Planning-time tracking is off by default. Enabling pg_stat_statements.track_planning can reveal planning overhead, but the official documentation warns that it may impose a noticeable penalty in some highly concurrent workloads. Weigh that value against setup, restart constraints, and observed overhead. Enable it for a defined diagnostic objective, measure the impact, and turn it off when the evidence is no longer needed if your operational policy permits.
Work through common incident patterns
“Too many connections” appears after a deployment
- Compare the old and new instance counts, worker counts, and per-process pool limits.
- Group
pg_stat_activitybyapplication_name, user, and client address to find the new source. - Restore a safe pool or replica limit, then investigate connection-release and retry paths.
- Only after resource measurements show headroom should you consider a higher server ceiling.
Connections are available, but one endpoint is slow
- Capture the exact statement and parameters used by the endpoint.
- Run a representative
EXPLAINand, where safe,EXPLAIN ANALYZEwith buffers. - Check row-estimate errors, waits, block reads, and concurrent activity.
- Test one targeted change and compare the new plan and latency under the same workload.
Some roles connect while the application is refused
Inspect reserved-slot settings and role membership. Administrative reservations are intentional protection for recovery, not extra capacity for ordinary application traffic. Keep those slots available while correcting the application’s session demand.
Performance degrades only under concurrency
Compare single-session and concurrent behavior. Look for CPU saturation, storage wait, lock queues, pool oversubscription, and planning overhead. A query that is fast in isolation can still create unacceptable contention when multiplied across sessions.
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A repeatable diagnostic checklist
- Save the exact error, timestamp, PostgreSQL version, hosting constraints, and affected workload.
- Check
max_connectionsand reservation settings; note that the ceiling changes only at server start. - Inspect
pg_stat_activityfor active, idle, idle-in-transaction, waiting, and long-running sessions. - Correlate database activity with
ps,top,iostat, andvmstat. - Identify whether session growth comes from pools, worker multiplication, leaks, bursts, or blocked transactions.
- Preserve administrative access and stop the source of runaway sessions before terminating carefully selected backends.
- For slow SQL, capture the statement, plan, waits, statistics state, and concurrent resource pressure.
- Use
pg_stat_statementswhen aggregate rankings are needed, accounting for preload, restart, version, and planning-tracking overhead. - Tune pool limits, query design, statistics, or server capacity one change at a time and measure the result on the target workload.
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