Elasticsearch 核心概念

Let's master Elasticsearch's core concepts: clusters, nodes, indices, documents, and the shards and replicas that make it scale.

An Elasticsearch cluster is one or more nodes that together hold all your data and serve indexing and search as a single, unified system.

A node is one server in your cluster, running an Elasticsearch instance and storing part of your data. Each has a unique name and can take on different roles.

Nodes sharing a cluster name discover each other and form a cluster. One becomes the master node, managing cluster-wide changes; the rest hold data.

An index is a logical grouping of similar documents, like a database in the relational world — say a products index and a customers index.

A document is the basic indexed unit: a single JSON record with a unique ID inside its index. The example shows one product document.

A mapping defines how each field is stored and indexed. It shapes how you can search — text for full-text, float for price, and so on.

Elasticsearch splits an index into shards — self-contained sub-indexes spread across nodes for horizontal scaling and parallel processing.

A replica shard is a copy of a primary shard. If a node fails, a replica takes over with no data loss — and it boosts search throughput too. 🛡️

Putting it together: a cluster of nodes hosts shards, shards are slices of an index, an index holds documents, and replicas add redundancy.

Which of the following statements about Elasticsearch core concepts are TRUE?

Recap: clusters, nodes, indices, documents, mappings, shards, and replicas are the building blocks of Elasticsearch. Next, you'll spin one up.