Closing Thoughtsedit

This section covered a lot of ground, and a lot of deeply technical issues. Aggregations bring a power and flexibility to Elasticsearch that is hard to overstate. The ability to nest buckets and metrics, to quickly approximate cardinality and percentiles, to find statistical anomalies in your data, all while operating on near-real-time data and in parallel to full-text search—these are game-changers to many organizations.

It is a feature that, once you start using it, you’ll find dozens of other candidate uses. Real-time reporting and analytics is central to many organizations (be it over business intelligence or server logs).

But with great power comes great responsibility, and for Elasticsearch that often means proper memory stewardship. Memory is often the limiting factor in Elasticsearch deployments, particularly those that heavily utilize aggregations. Because aggregation data is loaded to fielddata—and this is an in-memory data structure—managing efficient memory usage is important.

The management of this memory can take several forms, depending on your particular use-case:

  • At a data level, by making sure you analyze (or not_analyze) your data appropriately so that it is memory-friendly
  • During indexing, by configuring heavy fields to use disk-based doc values instead of in-memory fielddata
  • At search time, by utilizing approximate aggregations and data filtering
  • At a node level, by setting hard memory and dynamic circuit-breaker limits
  • At an operations level, by monitoring memory usage and controlling slow garbage-collection cycles, potentially by adding more nodes to the cluster

Most deployments will use one or more of the preceding methods. The exact combination is highly dependent on your particular environment. Some organizations need blisteringly fast responses and opt to simply add more nodes. Other organizations are limited by budget and choose doc values and approximate aggregations.

Whatever the path you take, it is important to assess the available options and create both a short- and long-term plan. Decide how your memory situation exists today and what (if anything) needs to be done. Then decide what will happen in six months or one year as your data grows. What methods will you use to continue scaling?

It is better to plan out these life cycles of your cluster ahead of time, rather than panicking at 3 a.m. because your cluster is at 90% heap utilization.