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A Stirling-PDF container that runs out of memory, stalls, or returns a broken PDF can be failing at different layers: container or host memory, a large-file pipeline, OCR output, or delivery from the backend to the browser. Start by identifying where the failure occurs; don’t assume every hang or invalid file is an out-of-memory error. The 15 checks below separate those paths and suggest what to verify next.
First, identify where the failure happens
Compare the timing and symptoms with both the application logs and the container or host’s resource and exit information. A memory-related failure points toward the memory branch; a failure tied to one large input or watched-folder pipeline points toward the processing path; an OCR warning about invalid output belongs in the OCR branch. If server-side processing appears to finish but the browser reports a network error or 401, investigate result delivery and authentication separately.
These distinctions matter because the available examples describe different incidents, not one universal Stirling-PDF defect. A pipeline report records an out-of-memory event while Files.readAllBytes reads a large PDF. A separate OCRmyPDF report records the warning “The generated PDF is INVALID” in a trace involving Tesseract and Ghostscript. A report for v2.4.5 describes backend processing followed by UI network errors and 401s. Each is evidence of a reported case, not a guarantee that other failures share its cause.
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15 anti-patterns and what to check instead
1. Treating every hang or broken file as an OOM
Anti-pattern: Increasing memory as the first response to any timeout, stalled job, or invalid PDF. Check instead: Look for evidence of memory pressure or a container termination, then compare it with the application’s last processing messages and the browser’s error. An invalid-output warning or a post-processing 401 suggests a different branch to investigate.
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2. Assuming host RAM is all available to the container
Anti-pattern: Judging capacity from the host’s installed memory alone. Docker configuration can impose a container limit, so the host’s total RAM does not by itself establish what the service can use. Check instead: Review the effective container resource settings alongside host pressure and the application’s logs before deciding whether the host needs more memory.
3. Treating documented example limits as universal requirements
Anti-pattern: Reading the project’s example limits as minimum requirements, tested ceilings, or a recommendation for every workload. The current Stirling-Tools Docker Setup README gives these examples by image variant:
| Image variant | Example memory limit in project documentation | What the documentation establishes |
|---|---|---|
| Ultra-Lite | 2G | An example configuration value; not a universal minimum or workload guarantee. |
| Standard | 4G | An example configuration value; not a universal minimum or workload guarantee. |
| Fat | 6G | An example configuration value; not a universal minimum or workload guarantee. |
Those figures are the project’s configuration examples, accessed in 2026. The documentation does not establish that a given input will succeed at those limits.
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4. Setting a limit below the workload’s demonstrated needs
Anti-pattern: Copying a low limit into production without observing the service under the files and operations it actually handles. Check instead: Reproduce the failure with a representative job, record whether memory pressure coincides with it, and adjust the container limit only if the evidence points there and the host has capacity. A higher limit cannot create host memory that is not available.
5. Changing the Java heap without checking the container limit
Anti-pattern: Changing a JVM heap setting in isolation when the container itself may be constrained. No current official project guidance establishes a universal heap-to-container formula for Stirling-PDF. Check instead: Review the effective container limit and the Java process configuration together; avoid assuming a heap change resolves a limit imposed outside the process.
6. Choosing a heavier image on a constrained host without a feature need
Anti-pattern: Selecting an image variant for convenience and overlooking the resource constraints. The current getting-started guide describes Ultra-Lite as the minimal-size option for resource-constrained environments, while the Docker documentation lists Ultra-Lite, standard, and fat variants. Check instead: Confirm the tools and conversion support your workload needs, then choose a compatible image. The docs describe fat as adding fonts and tools for broader conversion support; they do not provide independent performance benchmarks.
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7. Switching to Ultra-Lite without checking tool availability
Anti-pattern: Treating the smallest image as interchangeable with every other variant. Image variants can differ in available features and tools. Check instead: Verify that the specific operation and conversion support you rely on are available in the chosen image before changing variants. The project documentation does not establish that every tool is present in every image.
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8. Treating one reported file size as a product limit
Anti-pattern: Using an individual issue report’s input size as a maximum supported PDF size—or as proof that every file of that size will fail. In a January 2026 pipeline issue, the reporter described a PDF just over 2.6GB, and the trace reached Files.readAllBytes before an OOM. Check instead: Treat that report as one example of a large-input processing path, not as a verified threshold. Test the affected file and operation in your own environment.
9. Retrying the same large watched-folder job without isolating it
Anti-pattern: Repeatedly resubmitting the same pipeline input while changing unrelated settings. Check instead: Determine whether the issue follows that file or pipeline path, and compare the result with a smaller or otherwise representative input. This is a diagnostic method, not a claim that watched-folder pipelines always cause OOMs.
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10. Treating a healthy status check as proof that conversions work
Anti-pattern: Equating a successful health check with a successful PDF operation and download. The project’s example Compose health check tests an application status endpoint; that checks service availability, not every conversion or file transfer. Check instead: After confirming the service is available, perform a representative transformation and verify that its output can be retrieved.
11. Calling every invalid PDF general file corruption
Anti-pattern: Blaming the input PDF or the entire application based on an invalid output alone. An OCRmyPDF issue report shows the warning “The generated PDF is INVALID” during an OCR workflow, with Tesseract and Ghostscript in the trace. Check instead: Note whether OCR was part of the operation and inspect the relevant log messages. That individual report does not show that OCR dependencies always cause invalid files.
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Anti-pattern: Changing language data or OCR-related components simply because the output is broken. The cited OCR report identifies tools in its trace but does not establish a general root cause or a fix for other installations. Check instead: Confirm that the failure occurs on the OCR path and use the actual error messages to guide any targeted change. Compare with a non-OCR operation when that is practical.
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13. Assuming a browser network error proves the transformation failed
Anti-pattern: Treating a UI error as conclusive evidence that the backend never finished processing. A v2.4.5 issue report describes successful backend processing followed by UI network errors. Check instead: Compare the server-side job messages with the browser’s error and determine whether processing completed before result delivery failed.
14. Ignoring 401 responses after a long-running operation
Anti-pattern: Ignoring authentication messages or repeatedly rerunning the conversion when the response indicates an authorization problem. In the same v2.4.5 report, 401s appeared alongside UI delivery problems. Check instead: Investigate the authentication and result-retrieval path separately from the transformation logs. That report is version-specific evidence, not proof of a current, universal authentication issue.
15. Losing the evidence—or applying an old workaround blindly
Anti-pattern: Recreating a container without retaining useful logs, configuration, or the original input and output; or applying a workaround from an issue report without checking whether it applies to your release. Check instead: Keep relevant configuration and logs in persistent locations where appropriate. The project’s example Compose configuration mounts /configs and /logs. Preserve a minimal input/output pair when safe to do so, and compare the incident’s version and symptoms with the release you run before adopting a version-specific change.
A practical order for narrowing the cause
- Capture the boundary: Record the operation, input, time of failure, application messages, browser response, and container or host resource/exit information.
- Choose the matching branch: Follow memory evidence for a suspected OOM; isolate a large input or pipeline when the failure follows that path; examine OCR logs for an OCR-specific invalid output; and investigate browser delivery or authentication when backend work appears complete.
- Change one relevant variable: Check the effective container limit and image variant for resource symptoms, or test the affected processing path for task-specific symptoms. Avoid changing unrelated settings together.
- Verify the result end to end: Confirm both that the intended transformation completed and that the resulting PDF can be retrieved and opened. A status endpoint alone cannot establish both.
The project’s current documentation presents several image choices and example memory limits, but it does not establish a universal RAM-per-page rule, maximum PDF size, or memory setting that will prevent every failure. Resolve the specific failure boundary before changing hardware or treating one issue report as a product-wide rule.
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