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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteSize a database connection pool around useful concurrent database work—not your user count or a copied default. Set a maximum that lets the database make progress without creating harmful contention, then make sure every pool across every application instance fits within the database’s connection budget. Validate the setting under representative load while watching both pool and database metrics.
What a connection pool limit controls
A pool reuses database connections instead of repeatedly opening and closing them, and lets many clients share a smaller number of connections, as the pgJDBC documentation describes. Its maximum is also a concurrency limit: it caps how many operations can hold a connection from that pool at once.
For example, HikariCP’s maximumPoolSize counts both idle and in-use connections. When all connections are occupied, another caller waits up to connectionTimeout for one to become available; if none does, acquisition times out. HikariCP lists 10 as the default maximum, but that is an implementation default, not a recommended size for every application. Check the HikariCP project documentation and the version used by your application framework.
More connections do not necessarily produce more throughput. Additional active transactions can help until the database’s resources are being used effectively; beyond that point, contention can make performance worse. A bounded pool can queue excess demand instead of sending every waiting request to the database at once.
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Build a connection budget for the whole deployment
Calculate the maximum connections that could be opened across the deployment, not just the limit in one process. A service with multiple replicas and possibly several pools per process can multiply a seemingly modest setting into a large server-wide demand.
- List every application instance and each pool it owns. Multiply the pool maximum by the number of instances.
- Add other connection consumers: worker services, scheduled jobs, monitoring, administrative sessions, and maintenance tools.
- Compare the total possible connections with the database’s configured limit, leaving room for operational use and unexpected demand.
- Check your deployed database version and, for a managed service, its platform-specific connection limits before changing the server cap.
In its PostgreSQL 17 documentation, the PostgreSQL Global Development Group says max_connections is typically 100 by default, subject to platform limits. It is a server-wide ceiling, not a pool-size recommendation. Raising it increases resource allocation, including shared memory, and requires a server restart. See the PostgreSQL 17 connection settings documentation.
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Estimate useful concurrency, then test it
Start with the amount of concurrent database work your workload can usefully advance. The answer depends on factors such as CPU, storage, cache behavior, query mix, and how long transactions hold connections. Front-end user count is not a substitute: many users may not be querying at the same time, while a smaller group of busy workers may create high database concurrency.
A PostgreSQL Wiki page offers the rough starting heuristic connections ≈ (core_count × 2) + effective_spindle_count. The PostgreSQL Wiki connection guidance presents it as a starting point, not a rule. The HikariCP pool-sizing wiki also repeats it as a heuristic and notes uncertainty around SSDs. Treat it as one candidate test point, not a guaranteed optimum or a substitute for measurements.
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- Choose a conservative starting maximum that fits the deployment-wide budget.
- Load-test with representative queries, transaction durations, and traffic patterns. Vary concurrency around the starting point.
- Compare useful throughput and tail latency, such as p95 and p99 response times. Do not rely on average response time alone.
- Stop increasing the pool when additional concurrency no longer improves useful throughput or starts worsening latency or database contention.
- Repeat under realistic peak conditions and check that headroom remains for other clients and operational work.
No single numeric pool size is established as best for all databases and applications. A useful setting is the one that performs well for your workload while respecting the total connection budget.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Read pool and database metrics together
Pool metrics show whether callers are waiting for connections; database metrics help establish whether the pool is limiting work or protecting a database that is already busy. Track these signals during load tests and after deployment:
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- Pool: active and idle connections, pending borrowers, acquisition wait time, and acquisition timeouts.
- Database and workload: query latency, transaction duration, database CPU, and total server connections.
- Application outcome: useful throughput and p95/p99 latency under representative traffic.
If borrowers wait while the database still has capacity, the pool limit may be too restrictive. If connections are checked out for a long time while queries or transactions are slow, increasing the pool may add contention rather than solve the underlying bottleneck. Investigate query performance and transaction duration alongside pool settings.
Account for long-running work and idle connections
A connection remains occupied for the time it is leased, so a long transaction reduces the capacity available to other work. Bound background-job concurrency so a burst of workers cannot consume the whole pool. If two workload classes have sharply different needs, separate pools may provide isolation, but only when that isolation justifies splitting the same finite database connection budget.
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