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  • Troubleshooting Timeout Issue Running GA4 on Spark / Databricks

    Posted by Jack on 22 January 2023 at 6:41 pm

    “Hi there, I’m running the GA4 Java libraries and have encountered a problem when running it in Databricks, even though the same code is running perfectly fine locally on my machine. It seems that this issue arises from going about it within the Spark cluster environment. I tried installing it on the cluster as a normal JAR and as a fat JAR using SBT in the clustered environment, but both methods resulted in a DEADLINE_EXCEEDED error, which appears to be a timeout after about 60 seconds. The odd thing is that the same cluster has successfully fetched data from the universal analytics API, which means the cluster has an internet connection. The same credentials are used for both my local machine and the GA4 that’s working. Also, I got an error when simply fetching metadata of a property, so it doesn’t seem to tie back to the size of the query. Can’t seem to understand the issue here. Do you think it might have something to do with dependency issues? Appreciate your input on this. Thanks! Eric”

    George replied 10 months, 3 weeks ago 3 Members · 2 Replies
  • 2 Replies
  • Mason

    Member
    23 February 2023 at 8:42 am

    Your problem may be related to dependency issues or how your network calls are being handled in the Spark cluster environment. Since your code is running fine locally but encountering the DEADLINE_EXCEEDED error in Spark cluster environment on Databricks, it could be a result of different handling of network calls, where network operations are taking longer than expected or are getting blocked altogether. There might be a need for additional configuration for your Spark Cluster to ensure it works with the GA4 libraries, or it might be a matter of increasing timeouts values if possible. The fact that it’s also happening when just fetching metadata suggests it’s not related to data load, but more to how the network calls are handled. I recommend reaching out to Spark or Databricks support with this issue or see if there is any known difference in network call handling between the two environments that might be causing this behavior.

  • George

    Member
    13 June 2023 at 12:37 am

    It definitely sounds like a complex issue, Eric. The error you’re seeing, DEADLINE_EXCEEDED, is generally indicative of a task that is taking too long, which as you’ve noted, isn’t due to the size of the query. Your cluster demonstrating successful communication with the universal analytics API would also suggest it’s not an inherent connectivity issue. Although, it could be caused by network latency or a server-side issue at Google. It might also be a threading issue in how the requests are being made. The first thing I would suggest is increasing the timeout setting to see if that resolves your issue. However, dependency issues could also be a potential cause in cases like this. Checking your cluster logs for BEGIN dependency errors or conflicts should give you a better understanding. Another avenue to investigate would be any differences in environmental conditions between your local setup and your Databricks environment. Lastly, given the unique combination of technologies and APIs, you might be encountering a less common, undocumented issue for which reaching out to the Databricks and/or Google Analytics 4 support teams may yield the fastest resolution.

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