（二）Basic Concepts 基本概念

Basic Concepts

There are a few concepts that are core to Elasticsearch. Understanding these concepts from the outset will tremendously help ease the learning process.

有一些概念是Elasticsearch的核心。从一开始就理解这些概念将极大地帮助简化学习过程。

Near Realtime (NRT)

Elasticsearch is a near real time search platform. What this means is there is a slight latency (normally one second) from the time you index a document until the time it becomes searchable.

Elasticsearch是一个近乎实时的搜索平台。这意味着从索引文档到可搜索文档的时间有一点延迟（一般为一秒）。

Cluster

A cluster is a collection of one or more nodes (servers) that together holds your entire data and provides federated indexing and search capabilities across all nodes. A cluster is identified by a unique name which by default is "elasticsearch". This name is important because a node can only be part of a cluster if the node is set up to join the cluster by its name.

集群是一个或多个节点（服务器）的集合，它们共同保存您的整个数据，并提供跨全部节点的联合索引和搜索功能。群集由惟一名称标识，默认状况下为“elasticsearch”。此名称很重要，由于若是节点设置为按名称加入群集，则该节点只能是群集的一部分。

 

Make sure that you don’t reuse the same cluster names in different environments, otherwise you might end up with nodes joining the wrong cluster. For instance you could use  logging-dev,  logging-stage, and  logging-prod for the development, staging, and production clusters.

确保不要在不一样的环境中重用相同的群集名称，不然最终会致使节点加入错误的群集。例如，您能够将logging-dev，logging-stage和logging-prod用于开发，登台和生产集群。

 

Note that it is valid and perfectly fine to have a cluster with only a single node in it. Furthermore, you may also have multiple independent clusters each with its own unique cluster name.

请注意，若是群集中只有一个节点，那么它是彻底正常的。此外，您还能够拥有多个独立的集群，每一个集群都有本身惟一的集群名称。

Node

A node is a single server that is part of your cluster, stores your data, and participates in the cluster’s indexing and search capabilities. Just like a cluster, a node is identified by a name which by default is a random Universally Unique IDentifier (UUID) that is assigned to the node at startup. You can define any node name you want if you do not want the default. This name is important for administration purposes where you want to identify which servers in your network correspond to which nodes in your Elasticsearch cluster.

节点是做为群集一部分的单个服务器，存储数据并参与群集的索引和搜索功能。就像集群同样，节点由名称标识，默认状况下，该名称是在启动时分配给节点的随机通用惟一标识符（UUID）。若是不须要默认值，能够定义所需的任何节点名称。此名称对于管理目的很是重要，您能够在其中识别网络中哪些服务器与Elasticsearch集群中的哪些节点相对应。

 

A node can be configured to join a specific cluster by the cluster name. By default, each node is set up to join a cluster named  elasticsearch which means that if you start up a number of nodes on your network and—assuming they can discover each other—they will all automatically form and join a single cluster named  elasticsearch.

能够将节点配置为按群集名称加入特定群集。默认状况下，每一个节点都设置为加入名为elasticsearch的集群，这意味着若是您在网络上启动了许多节点而且假设它们能够相互发现 - 它们将自动造成并加入名为elasticsearch的单个集群。

 

In a single cluster, you can have as many nodes as you want. Furthermore, if there are no other Elasticsearch nodes currently running on your network, starting a single node will by default form a new single-node cluster named  elasticsearch.

在单个群集中，您能够拥有任意数量的节点。此外，若是您的网络上当前没有其余Elasticsearch节点正在运行，则默认状况下，启动单个节点将造成名为elasticsearch的新单节点集群。

Index

An index is a collection of documents that have somewhat similar characteristics. For example, you can have an index for customer data, another index for a product catalog, and yet another index for order data. An index is identified by a name (that must be all lowercase) and this name is used to refer to the index when performing indexing, search, update, and delete operations against the documents in it.

In a single cluster, you can define as many indexes as you want.

索引是具备某些相似特征的文档集合。例如，您能够拥有客户数据的索引，产品目录的另外一个索引以及订单数据的另外一个索引。索引由名称标识（必须所有小写），此名称用于在对其中的文档执行索引，搜索，更新和删除操做时引用索引。

在单个群集中，您能够根据须要定义任意数量的索引。

 

Type

Deprecated in 6.0.0.  See Removal of mapping types

A type used to be a logical category/partition of your index to allow you to store different types of documents in the same index, eg one type for users, another type for blog posts. It is no longer possible to create multiple types in an index, and the whole concept of types will be removed in a later version. See Removal of mapping types for more.

一种类型，曾经是索引的逻辑类别/分区，容许您在同一索引中存储不一样类型的文档，例如，一种类型用于用户，另外一种类型用于博客帖子。再也不可能在索引中建立多个类型，而且将在更高版本中删除类型的整个概念。请参阅删除映射类型以获取更多信息。

Document

A document is a basic unit of information that can be indexed. For example, you can have a document for a single customer, another document for a single product, and yet another for a single order. This document is expressed in JSON (JavaScript Object Notation) which is a ubiquitous internet data interchange format.

文档是能够编制索引的基本信息单元。例如，您能够为单个客户提供文档，为单个产品提供另外一个文档，为单个订单提供另外一个文档。该文档以JSON（JavaScript Object Notation）表示，JSON是一种广泛存在的互联网数据交换格式。

 

Within an index/type, you can store as many documents as you want. Note that although a document physically resides in an index, a document actually must be indexed/assigned to a type inside an index.

在索引/类型中，您能够根据须要存储任意数量的文档。请注意，尽管文档实际上驻留在索引中，但实际上必须将文档编入索引/分配给索引中的类型。

Shards & Replicas

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.

索引可能存储大量可能超过单个节点的硬件限制的数据。例如，占用1TB磁盘空间的十亿个文档的单个索引可能不适合单个节点的磁盘，或者可能太慢而没法单独从单个节点提供搜索请求。

 

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.

为了解决这个问题，Elasticsearch提供了将索引细分为多个称为分片的功能。建立索引时，只需定义所需的分片数便可。每一个分片自己都是一个功能齐全且独立的“索引”，能够托管在集群中的任何节点上。

 

Sharding is important for two primary reasons:

分片很重要，主要有两个缘由：

      一、It allows you to horizontally split/scale your content volume

       它容许您水平拆分/缩放内容量

  　二、It allows you to distribute and parallelize operations across shards (potentially on multiple nodes) thus increasing performance/throughput

      它容许您跨分片（可能在多个节点上）分布和并行化操做，从而提升性能/吞吐量

 

The mechanics of how a shard is distributed and also how its documents are aggregated back into search requests are completely managed by Elasticsearch and is transparent to you as the user.

分片的分布方式以及如何将其文档聚合回搜索请求的机制彻底由Elasticsearch管理，对用户而言是透明的。

 

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.

在能够随时发生故障的网络/云环境中，很是有用，强烈建议使用故障转移机制，以防分片/节点以某种方式脱机或因任何缘由消失。为此，Elasticsearch容许您将索引的分片的一个或多个副本制做成所谓的副本分片或简称副本。

 

Replication is important for two primary reasons:

复制很重要，主要有两个缘由：

    一、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.

    它在碎片/节点发生故障时提供高可用性。所以，请务必注意，副本分片永远不会在与从中复制的原始/主分片相同的节点上分配。

    二、It allows you to scale out your search volume/throughput since searches can be executed on all replicas in parallel.

 　它容许您扩展搜索量/吞吐量，由于能够在全部副本上并行执行搜索。

 

To summarize, each index can be split into multiple shards. An index can also be replicated zero (meaning no replicas) or more times. Once replicated, each index will have primary shards (the original shards that were replicated from) and replica shards (the copies of the primary shards).

总而言之，每一个索引能够拆分为多个分片。索引也能够复制为零（表示没有副本）或更屡次。复制后，每一个索引都将具备主分片（从中复制的原始分片）和副本分片（主分片的副本）。

 

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 also change the number of replicas dynamically anytime. You can change the number of shards for an existing index using the  _shrink and  _split APIs, however this is not a trivial task and pre-planning for the correct number of shards is the optimal approach.

能够在建立索引时为每一个索引定义分片和副本的数量。建立索引后，您还能够随时动态更改副本数。您可使用_shrink和_split API更改现有索引的分片数，但这不是一项简单的任务，预先计划正确数量的分片是最佳方法。

 

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.

默认状况下，Elasticsearch中的每一个索引都分配了5个主分片和1个副本，这意味着若是群集中至少有两个节点，则索引将包含5个主分片和另外5个副本分片（1个完整副本），总计为每一个索引10个分片。

 

Each Elasticsearch shard is a Lucene index. There is a maximum number of documents you can have in a single Lucene index. As of LUCENE-5843, the limit is 2,147,483,519 (= Integer.MAX_VALUE - 128) documents. You can monitor shard sizes using the _cat/shards API.

每一个Elasticsearch分片都是Lucene索引。单个Lucene索引中能够包含最大数量的文档。自LUCENE-5843起，限制为2,147,483,519（= Integer.MAX_VALUE - 128）个文件。您可使用_cat / shards API监视分片大小。

 

With that out of the way, let’s get started with the fun part…

有了这个，让咱们开始有趣的部分......