Memory pressure and implications

Aggregations are awesome! However, they bring a lot of memory pressure on Elasticsearch. They work on an in-memory data structure called fielddata, which is the biggest consumer of HEAP memory in a Elasticsearch cluster. Fielddata is not only used for aggregations, but also used for sorting and scripts. The in-memory fielddata is slow to load, as it has to read the whole inverted index and un-invert it. If the fielddata cache fills up, old data is evicted causing heap churn and bad performance (as fielddata is reloaded and evicted again.)

The more unique terms exist in the index, the more terms will be loaded into memory and the more pressure it will have. If you are using an Elasticsearch version below 2.0.0 and above 1.0.0, then you can use the doc_vlaues parameter inside the mapping while creating the index to avoid the use of fielddata using the following syntax:

PUT /index_name/_mapping/index_type
{
  "properties": {
    "field_name": {
      "type": ...