Workflows can fail for a lot of different reasons. If you're lucky, you have the wrong inputs plugged into the configuration, which is typically fast to fail and fast to fix (to learn more about how to avoid these sorts of errors, see this article). More complex errors come from the workflow code: bugs, or limitations in the analysis software package you're calling on. And of course you can fall victim to transient errors when something goes wrong on the Google Cloud itself (even giants sometimes stumble).
In this document we'll go over some basic strategies to investigate failed workflows on Terra. This isn’t a guide for solving all errors but a doc to help diagnose failed submissions. The information below is to help guide you as you drill down to find the root cause of these more complex errors, so you can be up and running quickly. Descriptions of more complicated errors are always welcomed on the Terra Forum, where our team is happy to help.
Overview
At this point we will assume that you have a workspace set up with a data table and workflow configuration loaded. You’ve launched your workflow, but your submission has failed. Don’t despair! There is some information you can gather that will be helpful in getting your workflow up and running.
Note: If you are looking for general information about how submissions work, see How to monitor and troubleshoot in the Job History Tab.
High-level submissions status
When you click on Job History, you’ll see a list of all the workflows within the submission along with high-level submission status (succeeded, failed, running, etc.) and a few columns of metadata (such as the submission ID). For details about a particular submission, click on the link in the "Submission" column.
Note that if your workflow failed immediately, the problem is almost certainly because it could not find the input file. If you are using inputs from the data table, this could be because the attribute name - in the inputs section of the workflow card - did not match the column header in the data table. Or it could mean that you don't have access to the bucket where the data is stored.
For additional guidance, please see the section titled 6. Configure Inputs in How to set up a workflow analysis.
Workflow-level status
The next level contains further details about each workflow within the submission and its status (failed, queued, etc.). From here you can access more detail by selecting one of the three icons at the right.
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If your job failed because it never started, you won't see these options. |
Job manager (far left icon)
Job Manager is your go-to location for a more thorough breakdown of your workflow. Here you can find information about each individual task in the workflow, including log files, links to Google Cloud executions directories, error messages, and a timing diagram.
If Job Manager won’t load
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Job Manager may fail to load if your job produced huge amounts of metadata. In these cases, skip to using the Workflow Dashboard (described below)! |
1. Error message
The message listed under Failure Messages isn't always short and sweet, and, if interpreted incorrectly, will lead you down the wrong debugging path. Instead, use the message to identify and investigate which task failed.
Error Message Examples
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The job was stopped before the command finished. PAPI error code 4. User specified operation timeout reachedThe maximum time a non-preemptible PAPI VM can survive is 1 week (168 hours). This is a default set by Google Pipelines API. If you are running into this error, we recommend increasing the CPU or memory, using larger disks, or using SSDs rather than HDDs in order to speed up the work so that it doesn't run out of time. You can alternatively try to chunk the work into smaller, separate workflows.
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PAPI error code 9Check stderr/stdout because the “command block” component of a task ended up failing which generated a non-zero return code. Consult the stderr/stdout file for a stacktrace/exception. To learn more about this error, see this article.
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Job exit code 3 [See PAPI error code 9]PAPI error code 9. Please check the log file for more details. To learn more about this error, see this article.
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PAPI Error code 10This error means the job exited in such an abrupt way that the machine where the command was running has essentially crashed. It can also mean that the job was preempted (this is less common). To rule out this last possibility, check whether the requested machine was preemptible. To learn more about this error, see this article.
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The task run request has exceeded the maximum PAPI request size (146800064 bytes)When localizing lots of files at once, the command length in physical characters can get too long and you will see an error message similar to the one shown above. For this example, the user had 1000s of inputs attempting to be localized to a task when the workflow failed. To fix an issue like this, you can create a tar.gz of all the input files and provide it as an input to the workflow, then localize and unzip within the task.
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Job ____ exited with return code _ which has not been declared as a valid return code
This error message is just saying that your command or tool failed in some way, returning a non-zero return code which Cromwell considers a failure. To troubleshoot, we recommend searching for “error” in the log file for your task. If your command/tool produced a useful error message, you may find a solution by searching for that message in your search engine of choice.
2. Log files
If it's not immediately obvious what failed, the best sources of information are log files, which you can access directly from the Job manager interface by clicking on the icon at the left. These files are generated by Cromwell when executing any task and are placed in the task's folder along with its output. In Terra, we add quick links to these files to make troubleshooting easier.
Task log
You can also see this in Google Cloud Platform console by clicking the link at the bottom.
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Some log files seem to stop abruptly, not yet having reached the delocalization stage. This is almost certainly because the task has run out of memory. We recommend retrying with more memory to see if your job gets farther. |
Execution directory
1. taskname.log
A log file tracking the events that occurred in performing the task such as downloading docker, localizing files, etc. This is the same log mentioned in the previous section. Occasionally a workflow will fail without a stderr and stdout files, leaving you with only a task log.
2. stderr and stdout
Standard Error (stderr): A file containing error messages produced by the commands executed in the task. A good place to start for a failed task, as many common task level errors are indicated in the stderr file.
Standard Out (stdout): A file containing log outputs generated by commands in the task. Not all commands generate log outputs and so this file may be empty.
Compute details
Workflow Dashboard (middle icon)
Includes 1) error message and 2) links to Job Manager (above) and the Execution directory (see section below).
Execution directory (icon at right)
The Execution directory, which is on Google Cloud Platform console, includes a wealth of details on the API side of things. For more information about what goes on under the hood, see this article.
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The error examples discussed above are pretty simple and very common. Be sure to Remember that when troubleshooting, you should automatically head towards In cases where there isn’t a stdout or stderr file, use the task log and the Of course, if you are having any trouble with Terra troubleshooting, you can ask your |