索引被分解成碎片，以便分布它们和扩展它们。

副本是碎片的副本。

节点是弹性搜索的一个运行实例，属于一个集群。

集群由一个或多个具有相同集群名称的节点组成。每个集群都有一个由集群自动选择的主节点，如果当前的主节点发生故障，可以将其替换。

碎片:

Being distributed search server, ElasticSearch uses concept called Shard to distribute index documents across all nodes. An index can potentially store a large amount of data that can exceed the hardware limits of a single node For example, a single index of a billion documents taking up 1TB of disk space may not fit on the disk of a single node or may be too slow to serve search requests from a single node alone. To solve this problem, Elasticsearch provides the ability to subdivide your index into multiple pieces called shards. When you create an index, you can simply define the number of shards that you want. Documents are stored in shards, and shards are allocated to nodes in your cluster As your cluster grows or shrinks, Elasticsearch will automatically migrate shards between nodes so that the cluster remains balanced. A shard can be either a primary shard or a replica shard. Each document in your index belongs to a single primary shard, so the number of primary shards that you have determines the maximum amount of data that your index can hold A replica shard is just a copy of a primary shard.

副本:

Replica shard is the copy of primary Shard, to prevent data loss in case of hardware failure. Elasticsearch allows you to make one or more copies of your index’s shards into what are called replica shards, or replicas for short. An index can also be replicated zero (meaning no replicas) or more times. The number of shards and replicas can be defined per index at the time the index is created. After the index is created, you may change the number of replicas dynamically anytime but you cannot change the number of shards after-the-fact. By default, each index in Elasticsearch is allocated 5 primary Shards and 1 replica which means that if you have at least two nodes in your cluster, your index will have 5 primary shards and another 5 replica shards (1 complete replica) for a total of 10 shards per index.

Elasticsearch is superbly scalable with all the credit goes to its distributed architecture. It is made possible due to Sharding. Now, before moving further into it, let us consider a simple and very common use case. Let us suppose, you have an index which contains a hell lot of documents, and for the sake of simplicity, consider that the size of that index is 1 TB (i.e, Sum of sizes of each and every document in that index is 1 TB). Also, assume that you have two Nodes each with 512 GB of space available for storing data. As can be seen clearly, our entire index cannot be stored in any of the two nodes available and hence we need to distribute our index among these Nodes.

在这种情况下，索引的大小超过了单个节点的硬件限制，Sharding就可以发挥作用。Sharding通过将索引划分为更小的块来解决这个问题，这些块被命名为Shards。

I will explain this using a real word scenarios. Imagine you are a running a ecommerce website. As you become more popular more sellers and products add to your website. You will realize the number of products you might need to index has grown and it is too large to fit in one hard disk of one node. Even if it fits in to hard disk, performing a linear search through all the documents in one machine is extremely slow. one index on one node will not take advantage of the distributed cluster configuration on which the elasticsearch works.

So elasticsearch splits the documents in the index across multiple nodes in the cluster. Each and every split of the document is called a shard. Each node carrying a shard of a document will have only a subset of the document. suppose you have 100 products and 5 shards, each shard will have 20 products. This sharding of data is what makes low latency search possible in elasticsearch. search is conducted parallel on multiple nodes. Results are aggregated and returned. However the shards doesnot provide fault tolerance. Meaning if any node containing the shard is down, the cluster health becomes yellow. Meaning some of the data is not available.

To increase the fault tolerance replicas come in to picture. By deault elastic search creates a single replica of each shard. These replicas are always created on a other node where the primary shard is not residing. So to make the system fault tolerant, you might have to increase the number of nodes in your cluster and it also depends on number of shards of your index. The general formula to calculate the number of nodes required based on replicas and shards is "number of nodes = number of shards*(number of replicas + 1)".The standard practice is to have atleast one replica for fault tolerance.

设置碎片数量是一个静态操作，这意味着您必须在创建索引时指定它。在此之后的任何改变都需要完全重新索引数据，并且需要时间。但是，副本数量的设置是一个动态操作，也可以在索引创建后的任何时间完成。

您可以使用下面的命令为索引设置碎片和副本的数量。

curl -XPUT 'localhost:9200/sampleindex?pretty' -H 'Content-Type: application/json' -d '
{
  "settings":{
    "number_of_shards":2,
    "number_of_replicas":1
  }
}'

索引被分解成碎片，以便分布它们和扩展它们。

副本是分片的副本，在节点丢失时提供可靠性。这个数字经常会引起混淆，因为副本计数== 1意味着集群必须有可用的分片的主副本和复制副本才能处于绿色状态。

为了创建副本，您的集群中必须至少有2个节点。

你可能会发现这里的定义更容易理解: http://www.elasticsearch.org/guide/reference/glossary/

不是答案，而是ElasticSearch的核心概念的另一个参考，我认为它们非常清楚地补充了@javanna的答案。

碎片

An index can potentially store a large amount of data that can exceed the hardware limits of a single node. For example, a single index of a billion documents taking up 1TB of disk space may not fit on the disk of a single node or may be too slow to serve search requests from a single node alone. To solve this problem, Elasticsearch provides the ability to subdivide your index into multiple pieces called shards. When you create an index, you can simply define the number of shards that you want. Each shard is in itself a fully-functional and independent "index" that can be hosted on any node in the cluster. Sharding is important for two primary reasons: It allows you to horizontally split/scale your content volume. It allows you to distribute and parallelize operations across shards (potentially on multiple nodes) thus increasing performance/throughput.

副本

In a network/cloud environment where failures can be expected anytime, it is very useful and highly recommended to have a failover mechanism in case a shard/node somehow goes offline or disappears for whatever reason. To this end, Elasticsearch allows you to make one or more copies of your index’s shards into what are called replica shards, or replicas for short. Replication is important for two primary reasons: It provides high availability in case a shard/node fails. For this reason, it is important to note that a replica shard is never allocated on the same node as the original/primary shard that it was copied from. It allows you to scale out your search volume/throughput since searches can be executed on all replicas in parallel.