What is shards in Elastic Search index?

Shards in Elasticsearch are logical partitions of an index. Each shard is a self-contained Lucene index, which means that it can be searched and indexed independently of the other shards in the index. Shards are used to distribute data horizontally across the nodes in an Elasticsearch cluster. This allows Elasticsearch to scale horizontally to handle large volumes of data and traffic.

When a document is indexed in Elasticsearch, it is assigned to a shard based on its routing value. The routing value is a field in the document that is used to distribute the document across the shards in the index. By default, Elasticsearch uses the _id field as the routing value.

Once a document is assigned to a shard, it is stored on the node that contains the shard. When a query is executed in Elasticsearch, it is broadcast to all of the shards in the index. Each shard executes the query and returns the results to the node that coordinated the query. The coordinating node then merges the results from all of the shards and returns the final results to the client.

Shards also provide redundancy and fault tolerance. If a node fails, the shards on that node are automatically rebalanced to the other nodes in the cluster. This ensures that the data remains available even if a node fails.

The number of shards in an index is determined by two settings: number_of_shards and number_of_replicas. The number_of_shards setting determines the number of primary shards in the index. The number_of_replicas setting determines the number of replicas of each primary shard.

For example, if you create an index with number_of_shards set to 5 and number_of_replicas set to 1, then Elasticsearch will create 5 primary shards and 5 replica shards. This means that each document in the index will be stored on 2 nodes.

You should choose the number of shards in an index based on the following factors:

• The size of the index
• The volume of traffic to the index
• The desired redundancy and fault tolerance

If you are unsure how many shards to create, it is generally a good idea to start with a small number of shards and increase the number of shards as needed.

Here are some of the benefits of using shards in Elasticsearch:

• Scalability: Shards allow Elasticsearch to scale horizontally to handle large volumes of data and traffic.
• Redundancy and fault tolerance: Shards provide redundancy and fault tolerance by storing multiple copies of each document on different nodes in the cluster.
• Performance: Shards can improve performance by distributing the search and indexing load across multiple nodes in the cluster.

However, there are also some drawbacks to using shards:

• Overhead: Shards add some overhead to the Elasticsearch cluster. This is because Elasticsearch needs to track the state of each shard and rebalance shards when nodes fail.
• Complexity: Shards can add complexity to Elasticsearch deployments. For example, you need to consider how to distribute data across the shards and how to handle routing documents to the correct shards.

Overall, the benefits of using shards in Elasticsearch outweigh the drawbacks. Shards are essential for scaling Elasticsearch to handle large volumes of data and traffic.