For most long-running application servers, use a properly managed connection pool rather than opening a new database connection for every request. A pool reuses established connections and helps limit how many sessions reach the database. It still needs sensible limits and prompt cleanup: too many pooled connections can consume database resources, while long transactions can keep connections unavailable to other work.
What changes between the two approaches?
Opening a connection for each request means the application repeatedly establishes and tears down database sessions. That can involve network and protocol setup, authentication, TLS negotiation when configured, and session initialization. Amazon RDS Proxy documentation describes pooling as reducing the overhead of opening and closing connections and of keeping many connections open simultaneously. Frequent connection churn can also add authentication overhead and contribute to connection-slot exhaustion, as AWS notes in its troubleshooting guidance.
With an in-process pool, the application borrows an established connection for a unit of database work, then returns it so another request can use it. In JDBC pooling, calling close() on the client-facing connection returns it to the pool rather than necessarily closing the underlying database session. The PostgreSQL JDBC documentation describes this behavior for its pooling data source; pooling implementations differ.
How the approaches compare
| Consideration | New connection per request | Connection pool |
|---|---|---|
| Connection setup | Repeats connection establishment work for each request. | Reuses established connections, reducing repeated setup and teardown. |
| Database sessions | Creates connection churn; bursts can contribute to slot exhaustion. | Can cap simultaneous sessions, but idle connections still consume slots. |
| Concurrency | Does not inherently control how many requests try to connect at once. | Can bound concurrent database work and queue borrowers; an overly small or saturated pool can make requests wait. |
| Operational concerns | Repeated setup and authentication; connection storms can strain the database. | Requires limits, cleanup, stale-connection handling, monitoring, and attention to how state affects reuse. |
There is no universal performance gain or ideal pool size established across databases and workloads. More open connections do not automatically produce more throughput: once the database is saturated, contention can make performance worse. PostgreSQL-specific guidance discusses limiting active transactions and queuing work rather than assuming that more connections will solve the problem.
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When pooling is the better fit
Long-lived application servers
A conventional web application or API server usually benefits from an application-level pool. Configure it in the database layer, borrow a connection for the unit of work, and return it on both success and error paths. Release connections promptly so other requests can use them.
Serverless or bursty clients
Short-lived or numerous application instances can create a large number of simultaneous client connections even when each instance uses a small local pool. An external pooler or managed proxy may let many clients share fewer database connections. For AWS RDS and Aurora, RDS Proxy is one service-specific option; AWS says it can multiplex completed transactions when the application’s session behavior permits reuse. Consult its current workload guidance before relying on multiplexing.
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Cases where per-request connections may be reasonable
A small, low-traffic tool or a process with a genuinely brief lifetime may not benefit enough from maintaining a pool to justify its operational overhead. The choice depends on the driver, database, deployment, and workload. Even in these cases, measure connection pressure and latency rather than assuming new connections are harmless.
Size and operate a pool safely
- Set a limit with the whole deployment in view. Count possible connections across application instances, workers, separate pools, database users, and replicas. A per-process maximum multiplies as the application scales out.
- Keep transactions short. Do not hold a connection open while making unrelated network calls or doing lengthy application work. Long-running transactions tie up capacity and may restrict reuse.
- Return connections on every path. Use the driver or framework’s reliable cleanup mechanism so exceptions do not leave connections checked out.
- Monitor both application and database signals. Track pool waiters, connection-acquisition timeouts, active and idle pooled connections, database connection counts, and idle-in-transaction sessions. Slow acquisition can reflect query saturation or database locks, not simply an undersized pool.
- Test changes under representative load. Observe connection wait time, active and idle sessions, transaction duration, concurrency, and request latency on the actual stack. Avoid choosing a limit from a generic rule.
Choose the right pooling layer
In-process pool
This is the natural starting point for a long-running application: the app’s database library reuses connections locally. Confirm how its limits apply per process and how it detects or replaces unusable connections. For JDBC specifically, PostgreSQL’s documented built-in pooling data source has limitations and is generally not recommended; that warning is about that implementation, not every JDBC pool library.
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External pooler
For PostgreSQL, PgBouncer is an external pooling option. Its pool mode matters: session pooling keeps a client associated with a backend for the session, while transaction pooling can return the backend after a transaction. Check whether the application depends on session-level features before selecting a mode.
Managed proxy
A managed proxy can reduce the number of database connections required by many application clients, but its compatibility, failover behavior, and cost depend on the service and current terms. RDS Proxy pools separately for writer and reader instances and can multiplex transactions when session behavior allows. Do not add a proxy on top of one or more application pools without mapping where connections are held and which layer enforces each limit.
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Database-specific details matter
The general reason to reuse connections applies broadly, but session behavior and connection architecture differ by engine, driver, and hosting environment. In PostgreSQL 17, the documented server model uses a supervisor process that spawns a backend process when a connection is requested; this is a PostgreSQL-specific detail, not a description of every database. Check the documentation for the exact database and client library you run.
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- Amazon RDS Proxy concepts and terminology
- AWS troubleshooting for PostgreSQL performance issues in RDS
- PostgreSQL JDBC connection pools and data sources
- PostgreSQL Wiki: Number of Database Connections
- AWS RDS Proxy workload considerations
- PostgreSQL 17: How Connections Are Established
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