java.lang.OutOfMemoryError: GC overhead limit exceeded
The JVM spent over 98 percent of recent time collecting and recovered less than 2 percent of the heap, so it stopped rather than crawling on. The heap need not be full: a cache holding strong references keeps everything reachable, and the collector works harder each cycle for less return.
Quick fix
Read the commands before running them. Anything that restarts a service, deletes data or changes permissions should be tried on a non-production system first.
# Capture the evidence before restarting, or you will wait for it again
-XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/var/log/app/
-Xlog:gc*:file=/var/log/app/gc.log:time,uptime:filecount=5,filesize=10M
# Read the dump: the dominator tree names the retaining object
# Eclipse MAT dominator tree: look for the one map holding most of the heap
jcmd <pid> GC.heap_info
jcmd <pid> GC.class_histogram | head -20
# Raising -Xmx buys time and hides a leak: do it only once the histogram
# shows a legitimately large working set
# Common causes, in order: an unbounded cache, a ThreadLocal never cleared,
# a ClassLoader retained by a redeploy, string keys built per request
# The switch exists but silences the symptom rather than the cause
-XX:-UseGCOverheadLimitHow to diagnose Performance errors
Performance failures rarely produce an error message. They produce timeouts elsewhere. The three classic causes are stop-the-world garbage collection, thread pool starvation (all workers blocked on I/O so new requests queue), and event loop blocking in single-threaded runtimes. All three look identical from outside: latency climbs, then upstream timeouts fire. Distinguishing them requires looking inside the process.
If the quick fix above does not resolve it, work through these steps. They apply to this whole class of error, not just to this one message, which is usually what saves the time.
- Measure the p99, not the mean. Averages hide exactly the pauses that cause timeouts.
- For the JVM, enable GC logging (
-Xlog:gc*) and correlate pause durations with latency spikes before tuning anything. - For thread pools, log active versus queued task counts. A queue that grows while CPU is idle is starvation, and the fix is asynchronous I/O, not more threads.
- In Node.js, measure event loop lag directly (
perf_hooks.monitorEventLoopDelay). Any synchronous work over a few milliseconds per request will show up here. - Profile before optimising.
pprof,async-profiler,py-spyand Chrome's profiler all point at the real hot path, which is rarely where intuition suggests.
Tools worth reaching for
async-profilergo tool pprofpy-spyperf_hooks.monitorEventLoopDelay-Xlog:gc*
Authoritative references
Primary documentation for this error, worth reading before applying any fix in production.
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