Elasticsearch is built to be always available and to scale with your needs. It does this by being distributed by nature. You can add servers (nodes) to a cluster to increase capacity and Elasticsearch automatically distributes your data and query load across all of the available nodes. No need to overhaul your application, Elasticsearch knows how to balance multi-node clusters to provide scale and high availability. The more nodes, the merrier.

How does this work? Under the covers, an Elasticsearch index is really just a logical grouping of one or more physical shards, where each shard is actually a self-contained index. By distributing the documents in an index across multiple shards, and distributing those shards across multiple nodes, Elasticsearch can ensure redundancy, which both protects against hardware failures and increases query capacity as nodes are added to a cluster. As the cluster grows (or shrinks), Elasticsearch automatically migrates shards to rebalance the cluster.

There are two types of shards: primaries and replicas. Each document in an index belongs to one primary shard. A replica shard is a copy of a primary shard. Replicas provide redundant copies of your data to protect against hardware failure and increase capacity to serve read requests like searching or retrieving a document.

The number of primary shards in an index is fixed at the time that an index is created, but the number of replica shards can be changed at any time, without interrupting indexing or query operations.

It depends…​edit

There are a number of performance considerations and trade offs with respect to shard size and the number of primary shards configured for an index. The more shards, the more overhead there is simply in maintaining those indices. The larger the shard size, the longer it takes to move shards around when Elasticsearch needs to rebalance a cluster.

Querying lots of small shards makes the processing per shard faster, but more queries means more overhead, so querying a smaller number of larger shards might be faster. In short…​it depends.

As a starting point:

The best way to determine the optimal configuration for your use case is through testing with your own data and queries.

In case of disasteredit

For performance reasons, the nodes within a cluster need to be on the same network. Balancing shards in a cluster across nodes in different data centers simply takes too long. But high-availability architectures demand that you avoid putting all of your eggs in one basket. In the event of a major outage in one location, servers in another location need to be able to take over. Seamlessly. The answer? Cross-cluster replication (CCR).

CCR provides a way to automatically synchronize indices from your primary cluster to a secondary remote cluster that can serve as a hot backup. If the primary cluster fails, the secondary cluster can take over. You can also use CCR to create secondary clusters to serve read requests in geo-proximity to your users.

Cross-cluster replication is active-passive. The index on the primary cluster is the active leader index and handles all write requests. Indices replicated to secondary clusters are read-only followers.

Care and feedingedit

As with any enterprise system, you need tools to secure, manage, and monitor your Elasticsearch clusters. Security, monitoring, and administrative features that are integrated into Elasticsearch enable you to use Kibana as a control center for managing a cluster. Features like data rollups and index lifecycle management help you intelligently manage your data over time.