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Possible memory leak in NodeJS / Python services #538
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Was unable to find the root cause of the NodeJS memory leak after a few weeks of testing. Needs a Node expert or someone else to further investigate. Internal doc with my notes so far: https://docs.google.com/document/d/1gyc8YvfKwMr86wzY_cz1NICQU48VE-wXifqjDprAafI/edit?resourcekey=0-g04_Kba4MQjeXDFzsp-Bqw |
According to the profiler data for the The same behaviour is reported in the I have created for PRs to stage 4 clusters with different settings to observe how the memory usage is over time.
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So the issue clearly seems like it's with any library that uses |
One more thing that was noticed is that the This is because the I have created a new cluster This takes care of all the |
I create two PRs two generate some profiler data in the CI project for this repo.
These PR had different version tags for the
You can view the profiler data for these versions under the profiler view in the CI project. Filter by the following criteria and you use it to understand the differences. |
Hi @Shabirmean, Please correct me if I'm wrong. Let me know if there is any action we need to take in the meantime. |
Hello @NimJay There isn't much we can do from our side. I have communicated with Ben and seeing if we can work with the debug team to get that issue (googleapis/cloud-debug-nodejs#811) fixed. Until then, no action is needed/possible from our side. I suggest we keep this issue open! |
This is still an issue, but bumping priority down to p3 |
Now that #1281 is merged into the |
Uptime checks for the production deployment of OnlineBoutique have been failing once every few weeks. Looking at
kubectl events
timed with an uptime check failure --It looks like memory requests are exceeding their limit. There seems to be plenty of allocatable memory across the prod GKE nodes
But as observed by @bourgeoisor, it seems that three of the workloads are using steadily increasing amounts of memory until the pods are killed by GKE.
Currency and payment (NodeJS):
Recommendation: (Python)
TODO - investigate possible memory leaks starting with the NodeJS services. Investigate why the services use an increasing amount of memory over time rather than a constant amount. Then investigate the Python services + see if other python services (emailservice, for instance) show the same behavior as recommendation service.
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