| 1234567891011121314151617181920212223242526272829 |
- [[scale]]
- == Designing for scale
- 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>> like log
- events or social network streams where relevance is driven by recency, and
- <<user-based>> where a large document corpus can be subdivided by user or
- customer.
- This chapter will help you to make the right decisions up front, to avoid
- nasty surprises later on.
|
