From 01-13, search performance has slowed down, maybe some metrics has reached a critical value, e.g the index doc count or store size.
From official document, I got
Elasticsearch heavily relies on the filesystem cache in order to make search fast. In general, you should make sure that at least half the available memory goes to the filesystem cache so that Elasticsearch can keep hot regions of the index in physical memory.
And now the maxed used heap is 11.72GB and elasticsearch app specified 16G (-Xms16g -Xmx16g)
So if I changed elasticsearch heap size to 12GB(-Xms12g -Xmx12g), does filesystem cache could used more memory and the search performance could improve?
Related
I know when elastic search start it take %50 of OS memory,
The stored size of my index in elastic search is about 100 MB, But when I call the API "_nodes/stats" I found the "heap_used_in_bytes" is about 3GB
I know that elastic search use some memory for indexing and caching process, but that is very high for the small size of stored index !
Why elatic search is use all of these memory, although stored index size is very small ?
And there are any way to avoid these high memory usage ?
I have following scenario:
A single machine with 32GB of ram runs Elasticsearch 2.4, there is one index with 5 shards that is 25gb in size.
On that index we are constantly indexing new data, plus doing full-text search queries that check about 95% documents - no aggregations. The instance generates a lot of CPU load - there is no swapping.
My question is: how should I tweak elasticsearch memory usage? (I don't have an option to add another machine at this moment)
Should I assign more memory to ES HEAP like 25GB (going over 50% memory that readme advises to not do do), or should I assign minimal HEAP like 1GB-2GB and assume Lucene will cache all the index in memory since its full-text searches?
Right now 50% of server memory so 16GB in this case seems to work best for us.
Assuming a single machine system with an in-memory indexing schema.
I am not able to find this info in ES docs. Does ES start swapping out the overflowing data, loads it when needed and continue working or it gives an error?
In-memory indices provide better performance at the cost of limiting the index size to the amount of available physical memory.
Via the 1.7 documentation. Memory stores are no longer available in 2.0+.
Under the hood it uses the Lucene RAMDirectory, which will just consume RAM (and eventually swap) until either you hit Java heap limits and ES crashes with out-of-memory errors, or the system gives up and oomkills the Elasticsearch process. Don't use in-memory indexes for large indexes, or for any situation where persistence is important.
I have one question about the following quota from ES official doc:
But if you give all available memory to Elasticsearch’s heap,
there won’t be any left over for Lucene.
This can seriously impact the performance of full-text search.
If my server has 80G memory, I issued the following command to start ES node: bin/elasticsearch -xmx 30g
That means I only give the process of ES 30g memory maximum. How can Lucene use the left 50G, since Lucene is running in ES process, it's just part of the process.
The Xmx parameter simply indicates how much heap you allocate to the ES Java process. But allocating RAM to the heap is not the only way to use the available memory on a server.
Lucene does indeed run inside the ES process, but Lucene doesn't only make use of the allocated heap, it also uses memory by heavily leveraging the file system cache for managing index segment files.
There were these two great blog posts (this one and this other one) from Lucene's main committer which explain in greater details how Lucene leverages all the available remaining memory.
The bottom line is to allocate 30GB heap to the ES process (using -Xmx30g) and then Lucene will happily consume whatever is left to do what needs to be done.
Lucene uses the off heap memory via the OS. It is described in the Elasticsearch guide in the section about Heap sizing and swapping.
Lucene is designed to leverage the underlying OS for caching in-memory data structures. Lucene segments are stored in individual files. Because segments are immutable, these files never change. This makes them very cache friendly, and the underlying OS will happily keep hot segments resident in memory for faster access.
We have a ES 1.6 cluster with 4 nodes used to store mostly logging data (~500 documents a second).
ES is configured with 10G of Heap but after numerous OutOfMemoryExceptions and stop-the-world GCs we limited the Field data cache to 10%.
My question is, why are all nodes' JVM constantly using ~9Gb Heap when field data (which i understand to be one of the primary users of Heap) is limited to 1Gb.
Some graphs:
It's worth pointing out our filter cache size is much smaller (~200Mb) and yes the aggressively limited Field data cache size does cause a lot of Field data cache evictions.
What else is using so much heap?
Thanks