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How to Troubleshoot PostgreSQL Connection Exhaustion and Slow Queries

A diagnostic workflow for PostgreSQL connection exhaustion and slow queries: inspect sessions and resources, evaluate pooling, and investigate plans separately.
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PostgreSQL connection exhaustion and slow queries can happen at the same time, but they are different problems: one is about available server connection slots; the other is about how a statement is planned and executed. Start by recording the exact error, time window, PostgreSQL version, and a slow statement or query plan. Then inspect sessions and system pressure before changing settings. Raising max_connections without that evidence can increase resource use without making a slow query faster.

Why am I getting too many connections in PostgreSQL?

PostgreSQL accepts a limited number of concurrent server connections. The max_connections setting controls that ceiling. PostgreSQL 18 documentation says the default is typically 100, but the usable value can be lower if operating-system kernel limits do not support it. The setting takes effect at server start, and increasing it raises resource allocations, including shared memory. Check the documentation for your installed release and the controls exposed by your hosting provider rather than assuming the PostgreSQL 18 default applies to your server.

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A rejected connection is not the same as a slow SQL statement. First capture the exact client or server error, when it occurred, whether new connections are refused or timing out, and whether existing connections can still run queries. Also record the PostgreSQL version, hosting model, configured connection limits, and whether application traffic or deployment patterns changed around the incident.

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Check the configured connection ceiling and reserved slots

From a session that can still connect, inspect the relevant settings:

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SHOW max_connections;
SHOW superuser_reserved_connections;
SHOW reserved_connections;

reserved_connections is available in PostgreSQL 18; a server running an earlier version may not recognize it, so check that release’s documentation and available settings. Reserved slots affect who can connect as the server approaches its limit. In PostgreSQL 18, slots can be reserved for superusers and, where configured, for roles with the pg_use_reserved_connections privilege. That can explain why one role still connects while another is refused near the ceiling. Do not treat an ordinary application login’s failure as proof that every slot is occupied in the same way.

If no ordinary connection is available, use an existing administrative path, a hosting-provider console, or another access method your operations team has configured. Avoid restarting a production database simply to inspect settings: the immediate priority is to preserve service and gather evidence through an available route.

Distinguish exhaustion from connection churn or a timeout

A connection-limit error points toward server slots, but an application may also report a timeout because it is waiting in its own pool, waiting on a network path, or unable to establish a session promptly. Compare the application’s error text and timestamps with server logs and the database’s observed session count. A burst of short-lived connections can look different from sessions accumulating and remaining open. Check whether the issue began after a traffic increase, an application rollout, a retry storm, a pool configuration change, or a change in the number of application instances.

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Do not infer the cause from a single connection count. A session may be running a query, waiting on a lock, idle, or idle inside a transaction; those states have different operational implications. The next step is to inspect activity at the time of the problem and compare it with CPU, memory, storage, and application-side pool evidence.

How do I see which PostgreSQL connections are active?

PostgreSQL’s pg_stat_activity view shows current sessions and their activity. “A database administrator frequently wonders, ‘What is the system doing right now?’” as the PostgreSQL 18 monitoring documentation puts it. Inspect the database’s current state rather than treating the raw number of sessions as a diagnosis.

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Count sessions by state and database

This query gives a compact snapshot of session state by database. It distinguishes active sessions from idle ones without assuming that all sessions are doing useful work:

SELECT datname, state, count(*) AS sessions
FROM pg_stat_activity
GROUP BY datname, state
ORDER BY datname, state;

For a more detailed view, inspect session identity, state, wait information, transaction age, and the start time of the current query. The query text can contain sensitive data, so restrict access to its output and avoid copying it into broadly visible tickets or logs:

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SELECT pid,
       usename,
       application_name,
       client_addr,
       state,
       wait_event_type,
       wait_event,
       xact_start,
       query_start,
       query
FROM pg_stat_activity
ORDER BY xact_start NULLS LAST, query_start NULLS LAST;

Availability and visibility of session details can depend on role privileges and PostgreSQL version. A null query start or transaction start is not itself proof of a fault. Interpret the rows alongside the incident time, application behavior, and whether a session is active, waiting, or idle.

Look for sessions that remain in transactions

An old transaction can be operationally important even if it is not currently executing a statement. Use xact_start to locate sessions whose transactions began long ago, then identify their owner and application before deciding what to do:

SELECT pid, usename, application_name, state,
       now() - xact_start AS transaction_age,
       wait_event_type, wait_event, query
FROM pg_stat_activity
WHERE xact_start IS NOT NULL
ORDER BY xact_start;

This is an investigative query, not permission to terminate sessions indiscriminately. Confirm what the session belongs to and whether it is blocking work or holding resources. Cancelling a query or ending a session may interrupt an application operation or roll back its transaction; coordinate that action with the service owner and use the controls appropriate to your environment.

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Correlate PostgreSQL activity with operating-system evidence

PostgreSQL’s cumulative statistics and activity views describe database work, while operating-system tools add context about server processes and resource pressure. During the same time window, use tools such as ps and top to inspect processes and CPU use, and iostat and vmstat to examine I/O and broader system behavior. The exact availability and output of these tools depends on the operating system and whether the database is self-managed or hosted.

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Use the combined evidence to ask whether the server is CPU-bound, waiting on storage, constrained by memory, stalled behind locks, or receiving more application connections than it can productively serve. A high session count by itself does not establish which of those conditions applies, nor does it show whether application-side connection handling is responsible.

Should I increase max_connections or use a connection pool?

Do not raise the connection ceiling as a reflex. PostgreSQL documents that increasing max_connections increases resource allocation, and the setting requires a server start to take effect. If sessions are accumulating because clients are opening more connections than the server can use effectively, increasing the limit may shift or worsen resource pressure rather than improve throughput. First establish whether connections are genuinely needed concurrently and what the server is doing with them.

When a pool is worth evaluating

Pooling is worth considering when application clients create more server sessions than the database can use productively. A pool reuses a bounded set of database sessions for application work, but the useful active concurrency depends on the workload, hardware, and the application’s transaction behavior. PostgreSQL community guidance emphasizes incremental tuning on the actual production system; any example size or formula should be treated as a test hypothesis, not a universal default.

There is no pool size prescribed for every application. Test a candidate limit against representative traffic and observe both service throughput and latency, as well as database CPU, I/O, waits, and connection behavior. The community wiki illustrates transaction batches of 10,000 in groups of 5, 10, or 20 versus 500 at a time, but this is an illustration of differing concurrency choices, not a measured promise for your workload.

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Choose where pooling belongs

Approach What to evaluate Trade-off to resolve
Client- or application-side pool How it fits the framework’s existing pool, application instance count, and transaction lifecycle. Each application or process may have its own pool; account for the combined possible sessions across all instances.
External pool Operational ownership, deployment and failure behavior, and how the pool fits existing application connections. Adds a component to operate and monitor; ensure its limits and behavior match the workload rather than creating another bottleneck.

These options are not interchangeable in every architecture. Before introducing or resizing a pool, map how many application processes can connect, what each pool’s maximum means, and whether one layer already pools connections. Keep administrative and monitoring access available when setting application pool limits; a pool that consumes all useful access can make incident response harder.

When increasing the ceiling may be justified

A higher ceiling may be appropriate only after measuring the workload and resource headroom, understanding how connection slots are used, and testing the operational consequences. Account for shared-memory and other resource allocation, startup requirements, kernel limits, and reserved slots. On managed PostgreSQL, confirm whether a setting change is supported and what restart or maintenance behavior the provider applies. Do not assume a larger ceiling is harmless just because the configuration accepts it.

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How do I find the slowest PostgreSQL queries?

Slow SQL needs its own evidence. Identify the statement and inspect its execution plan with EXPLAIN, then review relevant planner statistics. More connections do not explain why a statement has a slow plan; in a resource-constrained system they can add pressure. Similarly, adding an index or changing a memory setting is not a reliable fix without evidence from the plan and workload.

Capture and inspect a statement plan

Run EXPLAIN for the exact statement with representative parameters and conditions. It shows the planner’s proposed plan without running the statement:

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EXPLAIN
SELECT ...;

Replace the ellipsis with the actual statement; it is not executable SQL as written. Compare the plan with what the query is intended to do and inspect the relevant planner statistics. Check whether the plan’s choices make sense for the data and filters involved before changing indexes or settings.

EXPLAIN ANALYZE executes the statement to collect actual timing and row information. Use it only when executing that statement is safe and its side effects are understood; for a modifying statement, execution can modify data. Test carefully in an appropriate environment when production execution could be disruptive. A plan from an unrepresentative parameter set or workload may not explain the incident you are trying to diagnose.

Use aggregate statistics to rank statements

The pg_stat_statements extension can report aggregate planning and execution statistics across statements, including calls, total and mean execution time, rows, and block activity. These aggregates help find statements worth investigating, but they are not a substitute for an individual plan: a statement with many calls may consume substantial total time even when each call is quick, while a low-call statement may have a high mean duration.

Where the extension is already available, a query such as the following can surface statements with high accumulated execution time:

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SELECT queryid, calls, total_exec_time, mean_exec_time,
       rows, shared_blks_hit, shared_blks_read, query
FROM pg_stat_statements
ORDER BY total_exec_time DESC
LIMIT 20;

The view’s available columns and access requirements depend on the installed PostgreSQL and extension version. Query text may contain sensitive information. Restrict access to results, and compare statistics over a known observation interval; cumulative totals are not a per-second ranking unless you calculate the interval correctly.

Understand setup and overhead before enabling it

pg_stat_statements must be configured in shared_preload_libraries. Adding or removing it requires a server restart, which can be a significant constraint on a production or managed service. Check the exact PostgreSQL version and provider controls before changing the configuration; extension availability and permitted settings are not identical on every host.

Planning-time tracking is optional and off by default. PostgreSQL documentation warns that enabling it may impose a noticeable performance penalty in some high-concurrency workloads. Weigh the diagnostic value of planning metrics against restart constraints and possible overhead; enabling every metric is not automatically the best troubleshooting choice.

How to work through an incident without making it worse

  1. Record the failure. Save the exact error, affected clients, time window, PostgreSQL version, and whether connections are rejected, delayed, or established successfully while queries run slowly.
  2. Check the connection configuration. Inspect max_connections and the reserved-slot settings supported by the installed release. Note whether a setting change would require a restart and whether the hosting provider controls it.
  3. Snapshot session activity. Query pg_stat_activity for counts by state and database, then inspect relevant sessions and transaction ages. Protect query text because it can contain sensitive data.
  4. Correlate resource evidence. Compare database activity with available operating-system process, CPU, I/O, and memory information, plus the application’s own connection-pool metrics and recent deployment history.
  5. Separate the remedies. If the issue is excessive session concurrency, assess pool behavior and available resources. If a statement is slow, capture its plan and inspect planner statistics. Treat simultaneous symptoms as separate diagnoses unless evidence shows a shared cause.
  6. Change one thing at a time. Test a pool or configuration adjustment under representative load, observe the resulting database and application behavior, and retain an administrative access path. Avoid combining several speculative changes, which makes the result difficult to interpret.

Common diagnostic mistakes

  • Raising max_connections first: this can increase resource use and requires a server start; it does not identify why sessions accumulated or why SQL is slow.
  • Equating idle sessions with active work: inspect session state, transaction age, and waits rather than treating every connection as an executing query.
  • Assuming the database is the only place a timeout can occur: application pools, retries, network paths, and database limits can all affect connection establishment.
  • Copying a pool-size rule: active concurrency depends on the hardware and workload. Tune incrementally on the target system and preserve operational access.
  • Changing indexes or memory settings without a plan: investigate the statement’s plan and relevant statistics first; generic tuning changes are not a diagnosis.
  • Enabling planning tracking without weighing cost: it is off by default and can have noticeable overhead in some concurrent workloads.
  • Assuming every hosting setup exposes the same controls: provider configuration, restart procedures, extension availability, and monitoring access vary.

What to document after the incident

Keep a concise incident record with the PostgreSQL release, hosting model, error text, timestamps, relevant connection settings, session-state snapshot, resource observations, and any query plan or aggregate statistics used. Record which change was tested and what happened to application latency, database resource use, and connection behavior. This makes later diagnosis more reliable and helps distinguish recurring application connection patterns from a one-off resource event.

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