Understanding Memory Discrepancy in Elasticsearch

Hi everyone,

I'm relatively new to Elasticsearch and I'm encountering some issues with memory management. Specifically, I've noticed that Elasticsearch frequently triggers circuit breaking due to high JVM heap usage, particularly in the old generation. However, when I calculate the memory usage based on internal statistics such as fielddataMemory, queryCacheMemory, requestCacheMemory, and segmentsMemory, I find that the total doesn't match the heap usage (heapCurrent).

I suspect that there are other memory-consuming components within Elasticsearch that are not explicitly covered by these statistics. For instance:

  1. Field Data Circuit Breaker: Could the fielddata be consuming more memory than reported by fielddataMemory?
  2. Filters Cache: Is the memory usage for caching filter results included in the statistics I mentioned?
  3. Aggregations and Scripts: Do aggregations and scripted fields contribute significantly to memory usage?
  4. Translog and Shard Overhead: Are there additional memory overheads associated with translog and Lucene segments per shard?

I would appreciate any insights or guidance on where to look to better understand how memory is allocated and utilized within Elasticsearch. Additionally, any resources or documentation recommendations on memory optimization and troubleshooting would be greatly helpful.

Thank you in advance for your assistance!

Best regards,

Yes there's quite a lot of memory usage that isn't tracked in the metrics you're looking at. The only reliable way to investigate further is to take a heap dump.

Hey there, I read your reply about memory usage and heap dumps. As a beginner, I'm curious about how to analyze memory consumption from a heap dump. Do you have any recommended articles or resources for someone just starting out?

Not really I'm afraid, it needs some knowledge of the code and its expected memory usage. Sometimes there's just an obvious memory hog, perhaps a particularly heavyweight query or aggregation, although even tracing that back from a heap dump to the client's request can take some effort.

Seeking insights on memory optimization for Elasticsearch: I've recently conducted a memory analysis of my Elasticsearch setup and encountered the following stack trace. It seems that a significant portion of memory is occupied by instances of org.apache.lucene.codecs.compressing.CompressingStoredFieldsReader, referenced mainly from a java.util.concurrent.ConcurrentHashMap$Node[] instance. This, in turn, is referenced by an org.elasticsearch.search.SearchService instance. Additionally, there's a thread with local variables pointing to these instances.

I'd appreciate any insights or suggestions on how to interpret and optimize this stack trace to reduce memory consumption and improve performance. Thanks in advance!