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PostgreSQL’s configured limit is set by max_connections. In PostgreSQL 18, the documented default is typically 100 concurrent connections, but that is an admission limit—not a promise that every workload will run efficiently with 100 active queries. The practical capacity depends on server resources and workload; when many clients need access, a connection pool can queue work and keep the number of PostgreSQL backend sessions lower.
What does “handle” mean?
There are three different counts to keep separate:
- Concurrent database sessions: the maximum number PostgreSQL is configured to admit, controlled by
max_connections. - Active work: how many queries or transactions can run efficiently at once. This depends on the workload and available resources; adding sessions can increase contention and reduce throughput once the server is saturated.
- Application clients: the number of clients an application can serve. With pooling, many clients can share a smaller set of PostgreSQL backend connections, with excess work waiting in a queue.
The PostgreSQL 18 manual defines max_connections as the maximum number of concurrent connections to the database server. Its typical default is 100, though the effective configured value may be lower when platform or kernel limits require it. See the PostgreSQL 18 connection settings.
Why there is no universal safe connection count
The configured ceiling does not determine how many connections your particular server can run well. Queries differ in duration and resource demand, and a connection that is idle is not equivalent to one running an expensive query or holding an open transaction. When active work exceeds what the server can serve efficiently, contention can make throughput fall rather than rise.
The PostgreSQL Wiki offers broad, qualitative guidance that good hardware may support a few hundred connections and suggests considering pooling for workloads targeting thousands. These are not benchmark results or guarantees for a particular machine. The wiki also describes its older CPU-and-disk-based rule of thumb as approximate and in need of adjustment, including because it did not analyze SSD performance. A workload test is a better basis for sizing than applying that formula. See the PostgreSQL Wiki discussion of connection counts and its server-tuning guidance.
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How connection limits and reserved slots work
In PostgreSQL 18, ordinary roles cannot use every slot up to max_connections. The server reserves a small number of final slots for privileged access so administrators can still connect when ordinary connection capacity is exhausted.
| Setting | PostgreSQL 18 documented default | Who can use the reserved slots |
|---|---|---|
reserved_connections |
0 | Roles granted pg_use_reserved_connections |
superuser_reserved_connections |
3 | Superusers |
These reservations are constrained by the configured connection limit. Consult the official connection settings reference for the exact rules for your server version.
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What happens when you raise max_connections?
The setting can only be changed at server start, so changing it requires a PostgreSQL restart. The official manual also warns that increasing it raises allocation of some resources, including shared memory. A larger limit therefore does not come free, and it does not guarantee improved throughput.
Do not estimate memory by multiplying work_mem by the connection limit as though every connection always uses that amount. Resource use varies with settings and workload; measure memory pressure and query behavior before increasing the cap. PostgreSQL’s resource configuration documentation notes that shared_buffers is typically 128 MB by default and gives 25% of system memory as a reasonable starting point for a dedicated database server with at least 1 GB RAM. That is general memory guidance, not a formula for choosing a connection count.
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How to decide what limit to use
- Check the deployed value. Connect to the server and run
SHOW max_connections;. Confirm the PostgreSQL version and platform constraints rather than assuming the documented default applies. - Inspect current usage. Query
pg_stat_activityto distinguish active sessions from idle ones and identify whether connections are long-lived or holding transactions open. - Measure the workload. Monitor memory, query latency, throughput, and resource pressure under representative traffic. A higher number of admitted sessions is not a success if active-query contention worsens.
- Change cautiously. If measurements show the server has headroom, adjust the limit in measured increments, restart as required, and compare results. If memory pressure or contention is already present, raising the limit is unlikely to solve the underlying problem.
- Review standby configuration. A standby must have
max_connectionsset at least as high as the primary for queries to be allowed on the standby. See the PostgreSQL 18 hot standby documentation.
When pooling is a better fit
If an application has many client sessions but only a smaller number need to run database work at once, pooling can limit active PostgreSQL backend connections and queue requests during bursts. This separates the number of application clients from the number of simultaneous database sessions. The PostgreSQL Wiki recommends considering pooling at very high client counts and emphasizes that persistent connections alone are not pooling; see its connection-count guidance.
Pooling trades direct access for queueing and introduces implementation-specific behavior. Before choosing a pool or mode, verify that it fits the application’s transaction and session requirements, and consider its latency and failure behavior. Those details depend on the pool implementation and configuration, not on PostgreSQL’s connection limit alone. In memory-constrained situations, PostgreSQL’s connection settings documentation recognizes reducing max_connections and using external pooling as an option.
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Practical answer
For PostgreSQL 18, expect a documented typical default of 100 connections, subject to the deployed server’s configuration and platform limits. Treat that as a starting cap, not a universal performance target. Find the number your workload can handle by measuring active work and resource pressure; if client demand is much larger than efficient backend concurrency, use pooling to manage and queue sessions instead of simply raising the limit.
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