• Memory usage (JVM pressure) increased steadily during the morning, reaching 75% at 12:30 UTC:
  

  

• Once JVM memory pressure reached 75%, then Amazon ES triggered the Concurrent Mark Sweep (CMS) garbage collector. Some memory was reclaimed, but JVM memory pressure again reached 75% at 13:05 UTC, triggering another garbage collection. Garbage collection is a CPU-intensive process, pushing CPU utilisation to 100% between 12:50 - 13:15 UTC.
  

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• It is possible that during this period the cluster was encountering ClusterBlockException and/or JVM OutOfMemoryError; there were definitely cluster performance issues (as per https://aws.amazon.com/premiumsupport/knowledge-center/high-jvm-memory-pressure-elasticsearch/). Error logging has now been enabled on the cluster to provide this level of detail in future.

• There are a range of possible reasons for the steady increase in JVM memory pressure. In a general sense, the cluster may be configured sub-optimally. In particular, the number of shards (5) may be too high for the persons / masterdoctorindex indices, given these both comprise less than 300mb total size. AWS recommends shard size between 10–50 GiB as “too many small shards can cause performance issues and out of memory errors”. Elastic recommends “average shard size between at least a few GB and a few tens of GB”. Benchmarking performance with fewer shards could confirm whether redesigning the cluster would be advantageous.

• During normal usage, the ElasticSearch cluster shows gradual increases in JVM memory pressure, followed by garbage reclamation, in the normal saw-tooth pattern. As JVM memory pressure is a measure of the fill rate of the old generation pool, this reflects the accumulation of long-lived objects (e.g. cached searches?) in memory, and is not inherently problematic.

• Nightly sync runs cause a spike in indexing activity, and JVM memory pressure commonly increases at that point; however, this incident occurred outside of that timeframe. There were no apparent significant sync jobs being run at the time of cluster failure. Given that JVM memory pressure had been steadily increasing during the course of the morning, it is more likely that slightly higher than normal usage, or unusually diverse searches, were causing an accumulation of long-lived objects. (Would Google Analytics confirm this?)

ES being over utilised.

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Given it normally idles away at around 10-15% it would take something REALLY BIG to push it up to 100% CPU utilisation across 3 nodes.

We were having issues with number of servers in a cluster. We settled on 3.

Might there be something wrong with the “Elected master”?

Any routines that could have been triggered (even by accident) to run a load of data in, that may have screwed up.

What services are heavily associated with ES? Can we investigate each and discount them as culprits (in the absence of logging)?

•  TCS - People, Programme Membership, GMC details, Programme []
•  Reval (when updates to Programme Membership, GMC details, Programme in TIS occur) [Not enough logging to tell whether this might have anything to do with things]
•  Bulk upload - People
•  Sync service

Enable slow logs to figure out faulty requests

John Simmons (Deactivated) Review cluster configuration (e.g. shards) and consider benchmark testing alternatives.

In place

Someone investigate any spikes in TCS logging

Reuben Roberts

Nothing significant to report

Someone investigate spikes in bulk upload

Joseph (Pepe) Kelly

Someone investigate spikes in sync service

Marcello Fabbri (Unlicensed)

Check whether AWS have notified us of any changes?

John Simmons (Deactivated)

TBC

Review cluster configuration (e.g. shards) and consider benchmark testing alternatives

Might be worthwhile (but it could be a bunch of effort - should be coordinated and probably time-boxed).

Investigate how to restart clusters without having to do an update?

Apply auto-tuning to the cluster