How would you diagnose HikariCP connection pool exhaustion?

Uses Hikari metrics, thread evidence, database activity, transaction scope, and query behavior to separate leaks from slow or excessive work.

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TL;DR

I first confirm the symptom: active connections near the pool maximum, pending acquisition growing, and acquisition timeouts. Then I correlate the start with traffic, deployment, query latency, and database health. Pool exhaustion can come from leaked connections, long transactions, slow or blocked queries, too much parallel database work, or a pool that is too small for a changed workload.

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Pool pending and active metrics, acquisition timeout, leak suspicion, thread dumps, database sessions, transactions, and root cause.

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Core answer
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Overview
Pool exhaustion means demand for connections exceeds their return rate; a leak is only one possible cause.
I first confirm the symptom: active connections near the pool maximum, pending acquisition growing, and acquisition timeouts. Then I correlate the start with traffic, deployment, query latency, and database health. Pool exhaustion can come from leaked connections, long transactions, slow or blocked queries, too much parallel database work, or a pool that is too small for a changed workload.
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I inspect thread dumps for requests waiting on Hikari and for threads holding transactions while doing non-database work. Database activity and lock views show long-running statements, blockers, and sessions by application instance. With Spring Data and transaction management, ordinary managed access should return connections, so I focus on manual JDBC handling, streams or cursors left open, remote calls inside @Transactional, and new fan-out loops. Leak detection can provide clues but is not a substitute for correlation.

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Details

Assuming every exhaustion incident is a leak.
Restarting before collecting thread and database evidence.
Increasing maximumPoolSize as the first fix.

How would you diagnose HikariCP connection pool exhaustion?

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