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hey all, i have a workflow that never had memory issues, been running for months without a problem but recently it recently started throwing memory erros. i haven’t made significant changes like adding new nodes or anything, just minor bug fixing
hey all, i have a workflow that never had memory issues, been running for months without a problem but recently it recently started throwing memory erros. i haven’t made significant changes like adding new nodes or anything, just minor bug fixing
I’m sorry you’re running into memory errors with your workflow, especially after it was stable for so long. Here are some steps and considerations to help you troubleshoot and resolve the issue: ### 1. Increase Workflow Memory Pipedream sets a default memory limit for workflows. If your workflow is now exceeding that, you may see “Out of Memory” errors. You can increase the available memory in your workflow settings. Raising the memory limit can often resolve these errors, especially if your data volume or processing needs have grown. ### 2. Check for Memory Leaks or Inefficient Code Even minor bug fixes can sometimes introduce memory leaks or cause code to hold onto large objects longer than needed. Review recent changes for: - Large objects or files being loaded entirely into memory (e.g., reading a whole file into a variable). - Unnecessary data being stored in variables. - Loops or recursive calls that might not exit as expected. If you’re processing files, try to stream them to/from disk instead of loading them fully into memory. See this technique for streaming files. ### 3. Workflow Step Count If your workflow has grown to include many steps (typically more than 8-10), each step adds memory overhead. Even if you haven’t added new nodes, check if any recent changes increased the number of steps or the amount of data passed between them. ### 4. External Factors Sometimes, changes in the data your workflow processes (e.g., larger payloads from an API) or updates to dependencies can increase memory usage, even if your workflow logic hasn’t changed much. ### 5. Debugging Tips - Try running the workflow with test data to see if you can reproduce the error. - Add logging to steps to identify where memory usage spikes. - Temporarily comment out or disable steps to isolate the problematic area. ### 6. Support If you continue to experience issues or need more help, please visit Support - Pipedream for more support options. Let me know if you need help with any specific step or want to share more details about your workflow!
Also want to point out that making a new deploy will update the installed packages and the execution environment, which could potentially affect the memory usage