In Elasticsearch, data is distributed across shards using a process called sharding. When an index is created, it is divided into a specified number of primary shards, and each primary shard is stored on a separate node in the Elasticsearch cluster.
The sharding process is responsible for distributing the data across the primary shards in a way that balances the workload and maximizes performance. Elasticsearch uses a hashing algorithm to determine which primary shard a document should be stored on based on its document ID.
The hashing algorithm generates a unique hash value for each document ID, which is used to determine the routing value for the document. The routing value is then used to determine which primary shard the document should be stored on. By default, Elasticsearch uses the document ID as the routing value, but this can be customized to use a different field if necessary.
Replica shards are also used to distribute the data across the nodes in the Elasticsearch cluster. Each replica shard is a copy of a primary shard and is stored on a different node than the primary shard. Elasticsearch uses a process called replica allocation to determine which nodes should store the replica shards. Replica allocation is based on factors such as node availability, disk usage, and network latency.
When a search request is made, Elasticsearch distributes the request across all the primary and replica shards that contain the relevant data, and then combines the results to return the requested information. This allows for efficient search and retrieval of data across the Elasticsearch cluster.
Overall, data is distributed across shards in Elasticsearch using a process called sharding, which balances the workload and maximizes performance. By using primary and replica shards, Elasticsearch provides fault tolerance and high availability of data, ensuring that multiple copies of the data are available in case of node failures or other issues.