Advanced tuning: finding and fixing slow Elasticsearch queries

Elasticsearch is a very flexible and feature-rich application that provides many different ways to query your data. But have you ever experienced query speeds that were less than you hoped for? With a distributed system like Elasticsearch, there can be various possible factors contributing to query performance, including external factors such as load-balancer settings, network latency (bandwidth, NIC cards/drivers), etc.


Improving the performance of high-cardinality terms aggregations

An Elasticsearch terms aggregation is used to create buckets corresponding to unique values of a given field. For example, a terms aggregation on a field containing country names would create a bucket for USA, a bucket for Canada, a bucket for Spain, and so on. Under normal circumstances, terms aggregations are very fast, however in some exceptional cases they may be slow. One reason for slow terms aggregations may be a mis-configured cluster.


Distance Feature Query: Time and Geo in Elasticsearch Result Ranking

In Elasticsearch, you will soon be able to use proximity (geographical or time) as part of your ranking score. The new way for combining proximity within the ranking score is easy to configure, performs well, and will likely yield superior ranking in many scenarios. This blog explains why and how.


Benchmarking Elasticsearch cross-cluster replication

One of the highly anticipated features of Elasticsearch 6.7 and 7.0 was cross-cluster replication (CCR), or the ability to replicate indices across clusters, e.g., for disaster recovery planning or geographically distributed deployments. Since this feature introduces a lot of changes to the Elasticsearch code base, we need our users to be confident about its performance, resilience and stability.


Open Distro for Elasticsearch: What it Means and Why it’s Important

Recently Amazon launched Open Distro for Elasticsearch, a distribution of Elasticsearch with a number of additional features. The project was created out of concern that Elasticsearch was starting to include proprietary features, and that Elastic was straying from its open source roots.