WARNING: This documentation covers Elasticsearch 2.x. The 2.x versions of Elasticsearch have passed their EOL dates. If you are running a 2.x version, we strongly advise you to upgrade.
This documentation is no longer maintained and may be removed. For the latest information, see the current Elasticsearch documentation.
Elasticsearch is used by some companies to index and search petabytes of data every day, but most of us start out with something a little more humble in size. Even if we aspire to be the next Facebook, it is unlikely that our bank balance matches our aspirations. We need to build for what we have today, but in a way that will allow us to scale out flexibly and rapidly.
Elasticsearch is built to scale. It will run very happily on your laptop or in a cluster containing hundreds of nodes, and the experience is almost identical. Growing from a small cluster to a large cluster is almost entirely automatic and painless. Growing from a large cluster to a very large cluster requires a bit more planning and design, but it is still relatively painless.
Of course, it is not magic. Elasticsearch has its limitations too. If you are aware of those limitations and work with them, the growing process will be pleasant. If you treat Elasticsearch badly, you could be in for a world of pain.
The default settings in Elasticsearch will take you a long way, but to get the most bang for your buck, you need to think about how data flows through your system. We will talk about two common data flows: time-based data (such as log events or social network streams, where relevance is driven by recency), and user-based data (where a large document collection can be subdivided by user or customer).
This chapter will help you make the right decisions up front, to avoid nasty surprises later.