I'm using Elasticsearch with Kibana as a searchable/aggregatable event log, so a kind of ELK installation although no logstash. Recently we made the jump to Elasticsearch 0.20 all the way up to 1.5 and added Kibana. Things have been good. The memory profile less so for smaller machines. These machines also share memory with a cassandra installation, and both are memory-hungry.
- Newer Elasticsearch uses mmapfs for some parts. How does this affect memory usage? Are there alternatives to this setting which use less memory if we can sacrifice search performance?
- How does index.number_of_replicas affect memory usage?
Changes we are evaluating, please comment on these:
a. ES_HEAP_SIZE tuning. It's at 25% of physical RAM right now to accomodate the neighbouring cassandra process which also is set to have a max heap of 25%. I think that is reasonable but we have to measure more.
b. Modifying term_index settings to: index.term_index_interval: 256 and index.term_index_divisor: 5
c. We have set indices.fielddata.cache.size to 40%, considering to lower it to 20%