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Diagnose Cloudflare Workers memory failures by first confirming the platform signal, then plotting memory percentiles against deployments, reproducing representative traffic, and using the profiler that answers your question. A JavaScript or WebAssembly allocation above Cloudflare’s 128 MB per-isolate limit produces Error 1102 (“Worker exceeded resource limits”), an Exceeded Memory status, and the exceededMemory analytics outcome. The limit belongs to the isolate rather than an individual request, so concurrent requests can consume the same budget. DevTools snapshots reveal objects that remain live; production Heap profiles reveal allocation call sites. Streaming bodies, bounding module-scope state, and moving durable data to KV, R2, or D1 reduce the conditions that cause the failure.
What does a Cloudflare Workers out-of-memory error mean?
Each Cloudflare Workers isolate has 128 MB for JavaScript heap and WebAssembly allocations, according to Cloudflare’s 2026 limits documentation. This is a hard ceiling for that isolate; choosing a paid Workers plan changes CPU limits, not the 128 MB memory allowance.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11An isolate can handle multiple requests at the same time. Their request objects, parsed payloads, response buffers, module state, and temporary allocations all count toward the same ceiling. A request that looks small in isolation can therefore fail when it overlaps with other work or when module-scope data has accumulated.
Which failure signals should I look for?
- Error 1102: “Worker exceeded resource limits,” the resource-limit signal for memory exhaustion.
- Dashboard status: Exceeded Memory in the invocation status views.
- Analytics and Logpush: the
exceededMemoryoutcome. - Runtime message: “Memory limit would be exceeded before EOF,” commonly seen when buffering a body would cross the limit.
Error 1101 indicates a JavaScript exception, not the Workers memory-limit signal. Distinguishing 1101 from 1102 prevents you from chasing ordinary application exceptions as if they were leaks.
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How do I confirm the symptom and its scope?
Start with Cloudflare’s native status and log data before changing code. This tells you whether the problem is an actual memory-limit event, which routes are affected, and whether it is isolated to a release or traffic pattern.
- Open Workers & Pages → your Worker → Metrics → Errors → Invocation Statuses.
- Look for Exceeded Memory and note its time range, route, and frequency.
- In Analytics or Logpush, filter outcomes for
exceededMemory. - In Workers Logs, use the filter
$metadata.error EXISTSto find errors. You can also filter$workers.outcomefor outcomes such asexception. - When reproducing locally, run
wrangler tailand inspect theexceptionsfield.
Record whether failures occur on one endpoint, one payload size, one scheduled task, or all traffic. A route-specific pattern points toward request parsing, response construction, or a dependency used only by that route; broad failures suggest shared module state, concurrency, or a deployment-wide change.
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Use the Workers Metrics memory chart to establish a trend before guessing which object is responsible. The chart reports P50, P90, P99, and P999 memory usage from reservoir sampling, and deployment markers let you compare the distribution before and after a code release.
Read the percentiles as a distribution
- P50: the middle of observed usage; useful for the normal request path.
- P90: shows the heavier, but still common, requests.
- P99: exposes high-memory requests that may fail under concurrency.
- P999: highlights the extreme tail where rare payloads or unusual routes can approach the limit.
A step change immediately after a deployment is a strong lead for a regression. A gradual, sustained rise can indicate that state is being retained between requests. Metrics alone cannot identify the retaining object, and a high percentile can also reflect a change in traffic or payload mix, so correlate the chart with routes, deployments, and request characteristics.
Which profiler should I use: a snapshot or a production Heap profile?
Choose the tool according to whether you need to know what remains live or where allocations originate. They measure different questions and should not be treated as interchangeable.
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| Tool | Best question | What it records | Important limitation |
|---|---|---|---|
| DevTools Memory snapshot | Which objects are retained after requests finish? | Object counts, types, and retained memory that can be compared between snapshots. | Interactive local debugging; fidelity depends on how closely local traffic and bindings match production. |
| Production Heap profile | Which code paths allocate memory? | Stack traces sampled every 512 kB of allocation in a deployed Worker. | It measures allocations, not retained liveness; an allocating site may free its objects promptly. |
Use a snapshot when the question is “what is still live?” Use a Heap profile when the question is “which call site is allocating so much?” If both questions matter, pair the tools rather than interpreting an allocation profile as proof of a leak.
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Reproduction is useful only when the workload resembles production. Match the affected routes, request methods, payload sizes, concurrency, authentication path, and any scheduled or background activity that exercises the same code.
- Start the Worker with
wrangler dev. - Press D in the Wrangler terminal to open DevTools.
- In DevTools, open the Memory panel.
- If local bindings do not behave like production, use
wrangler dev --remoteso the Worker can access production-like resources. - Warm the Worker with the normal startup requests before taking a baseline snapshot.
- Send a controlled batch of requests that follows the production route and payload pattern.
- Take a second snapshot after the batch, then repeat the batch and snapshot so a one-off startup allocation is separated from a repeated growth pattern.
Do not rely on a single request in an otherwise idle development session. A leak may be visible only after repeated requests, overlapping requests, or a payload that exercises a rarely used branch.
How do I compare DevTools memory snapshots?
Capture snapshots at comparable points: after warm-up, after a defined workload, and after another identical workload. Compare both the Statistics view and the memory summary grouped by type.
What counts as a useful lead?
- A constructor or object type whose retained count increases after each workload cycle.
- A string, array, map, or response-related object that remains reachable from module or global scope.
- Repeated growth in one route’s data structures while unrelated types remain stable.
Cloudflare’s example identifies a retained string created by repeatedly appending to a module-scope variable. The important clue is not that strings are inherently unsafe; it is that a value reachable from long-lived module state keeps accumulating instead of being replaced or bounded.
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How should I interpret a snapshot difference?
A larger second snapshot is not automatically a leak. Caches, compiled code, connection metadata, and one-time initialization can legitimately grow and then plateau. Repeat the same workload and check whether the same type continues to increase after the application has reached its normal steady state.
How do I capture and interpret a production Heap profile?
For a deployed Worker, start a Heap profile from the Cloudflare dashboard, CLI, or API. The profile samples a stack trace every 512 kB allocated and identifies the code paths responsible for allocation activity.
- Ensure the deployed Worker has a recently active, loaded isolate handling representative traffic.
- Start the Heap profile from the dashboard, CLI, or API interface available for your deployment.
- Generate the workload that produces the elevated memory usage while the profile is active.
- Inspect the allocation stacks and rank the call sites responsible for the largest or most frequent sampled allocations.
- Check those call sites against snapshot evidence or object-lifetime reasoning before labeling one a leak.
A profile can expose a parser, serializer, validation routine, or response builder that allocates heavily even if its objects are short-lived. Conversely, a retained module-scope array may allocate only occasionally but keep old objects alive; snapshots are needed to demonstrate that persistence.
What code changes reduce peak memory in a Worker?
Fix the largest peak first. The practical goal is to avoid having complete request or response bodies, duplicate representations, and unrelated concurrent work resident at the same time.
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Stream request and response bodies
Buffering a complete body requires the whole payload to be resident before processing can continue. Use a TransformStream or the node:stream APIs supported by your Worker instead, processing chunks and forwarding output as it becomes available. Streaming is especially important when the runtime reports “Memory limit would be exceeded before EOF,” because the buffered body itself would cross the limit.
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Bound module and global state
Module-scope variables survive individual requests for as long as the isolate remains loaded. Avoid unbounded arrays, strings, maps, and ad hoc caches there. If a cache is necessary, enforce a maximum entry count or byte budget, define an eviction policy, and make sure failed requests cannot append indefinitely.
Remove duplicate representations
Parsing a body into an object while retaining the original text, creating a second transformed copy, and then buffering the response can multiply peak usage. Release references as soon as a stage is complete, and prefer incremental parsing or transformation where the format and library permit it.
Move durable or bulky data outside the isolate
KV, R2, and D1 are designed to hold data that must outlive a request or exceed a practical in-memory working set. Store durable records, uploaded objects, and large result sets there, retrieving only the portion needed for the current operation. This also prevents a warm isolate from becoming an accidental long-term data store.
Review schema-validation dependencies
Cloudflare recommends Zod 4.5.0 or later; earlier versions can use substantially more memory per schema. Check the version actually bundled into the deployed Worker, not only the version declared in a local package file, and rebuild after upgrading.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What happens after an isolate reaches 128 MB?
When an isolate exceeds 128 MB, the Workers runtime lets in-flight requests complete and creates a new isolate for subsequent requests. Under extremely high load, Cloudflare says it may cancel some incoming requests to maintain platform stability.
This behavior can make a problem look intermittent: requests already inside the old isolate may finish while later requests are routed to a fresh isolate. Recycling does not repair the underlying allocation pattern; it only changes when the accumulated state is discarded. Treat recurring isolate replacement, elevated latency, or canceled requests as operational symptoms while you continue the code-level investigation.
How do I verify that a fix worked?
Verification must use the same workload that exposed the problem. A lower reading during a quiet test is not enough if production traffic includes larger payloads or concurrency.
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- Replay the route, payload, and concurrency pattern used during diagnosis.
- Compare the memory chart’s P50, P90, P99, and P999 with the pre-fix period and deployment marker.
- Repeat DevTools snapshots, when the issue is reproducible locally, and confirm that the suspected retained type reaches a plateau.
- Use a production Heap profile to confirm that the expensive allocation path has decreased or moved to an intentional, bounded operation.
- Keep the Workers Logs and Analytics filters in place long enough to detect renewed
exceededMemoryoutcomes.
Document the payload and traffic conditions used for the comparison. Without that context, a percentile change can be mistaken for an improvement when the test simply exercised less demanding requests.
Which diagnostic approach fits each symptom?
| Observed symptom | First check | Most useful next action |
|---|---|---|
| Error 1102 or Exceeded Memory on one large upload | Body handling and the “before EOF” message | Stream the body and test progressively larger payloads. |
| Memory percentiles rise after every deployment | Deployment markers on the Metrics chart | Compare snapshots before and after the release; inspect new module-scope state. |
| High allocation activity but no sustained memory rise | Production Heap profile | Optimize the allocating call site only if CPU or peak overlap warrants it; allocations may be short-lived. |
| Memory keeps rising through repeated identical requests | DevTools snapshot differences | Find the retaining path and bound, clear, or relocate the object. |
| Failures appear only under concurrent traffic | Per-isolate sharing and P99/P999 metrics | Reduce per-request peaks and test overlapping requests, not just sequential calls. |
What are the main pitfalls when diagnosing Worker memory?
- Confusing allocation with retention: a hot allocator is not necessarily a leak, and a leak may allocate infrequently.
- Testing only one request: retained state and concurrency effects need repeated or overlapping traffic.
- Assuming the limit is per invocation: concurrent requests share the isolate’s budget.
- Blaming the plan: paid CPU allowances do not raise the 128 MB per-isolate memory limit.
- Using a quiet local test as proof: local bindings, routes, payloads, and volume may differ from production.
- Ignoring dependency versions: schema libraries can change memory consumption substantially without changes to your application logic.
Can external error monitoring complement Cloudflare’s tools?
Workers Logs, Metrics, Analytics, Logpush, DevTools, and production Heap profiles provide the first-party evidence needed to diagnose this class of failure. Cloudflare’s documentation also names Sentry as an example of an external error-log destination. An external system can add alert routing, retention, and cross-service correlation, but it does not replace snapshots for retained objects or Heap profiles for allocation stacks.
Frequently Asked Questions
Can a Worker exceed its memory limit because of WebAssembly code?
Yes. The 128 MB isolate budget covers both JavaScript heap and WebAssembly allocations. Measure the combined workload, including buffers exchanged between WebAssembly and JavaScript, rather than treating the two runtimes as separate quotas.
Why did the error disappear after I redeployed without changing code?
A redeploy creates fresh isolates, so accumulated module-scope state is discarded temporarily. If the same request pattern rebuilds that state, the memory trend and failures will return; compare post-deploy percentiles over time instead of treating the reset as a fix.
Do scheduled events and fetch requests have separate memory budgets?
They execute in isolates that can be reused, so state created by one event can affect later work in that isolate. Include scheduled handlers in reproduction when they share modules, caches, parsers, or large data structures with fetch handling.
What should I save when opening a Cloudflare support case?
Provide the affected Worker version, deployment time, route and payload pattern, invocation-status screenshots, relevant exceededMemory records, percentile trends, and a sanitized profile or snapshot comparison. This lets support distinguish a traffic-driven peak from retained application state without requiring sensitive request bodies.
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