elasticsearch 6.x 基本概念解读

官方网址

Elasticsearch最新基本请参考官方介绍：
https://www.elastic.co/guide/en/elasticsearch/reference/current/getting-started-concepts.html

https://www.elastic.co/guide/en/elasticsearch/reference/7.x/getting-started-concepts.html
备注：以下是官方文档+谷歌翻译（机器翻译效果还可以，加上个别人工调整）

Basic Concepts/基本概念

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)/近实时

Elasticsearch is a near-realtime 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是一个近实时搜索平台。 这意味着从索引文档到可搜索文档的时间有一点延迟（通常是一秒）。

Cluster/集群

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，并提供跨所有节点的联合索引和搜索功能。 群集由唯一名称标识，默认情况下为“elasticsearch”。 此名称很重要，因为如果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.
确保不要在不同的环境中重用相同的群集名称，否则最终会导致节点加入错误的群集。 例如，您可以将logging-dev，logging-stage和logging-prod用于开发、测试和生产集群。

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，如果集群中只有一个节点，那么它是完全正常的。 此外，您还可以拥有多个独立的集群，每个集群都有自己唯一的集群名称。

Node/节点

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.
节点是作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。 就像集群一样，节点由名称标识，默认情况下，该名称是在启动时分配给节点的随机通用唯一标识符（UUID）。 如果不需要默认值，可以定义所需的任何节点名称。 此名称对于管理目的非常重要，您可以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.
可以将节点配置为按群集名称加入特定群集。 默认情况下，每个节点都设置为加入名为elasticsearch的群集，这意味着如果您在网络上启动了许多节点并且 - 假设他们可以相互发现 - 他们将自动形成并加入名为elasticsearch的单个群集。

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，您可以拥有任意数量的节点。 此外，如果您的网络上当前没有其他Elasticsearch节点正在运行，则默认情况下，启动单个节点将形成名为elasticsearch的新单节点集群。

Index/索引

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.
索引是具有某些类似特征的文档集合。 例如，您可以拥有客户数据的索引，产品目录的另一个索引以及订单数据的另一个索引。 索引由名称标识（必须全部小写），此名称用于在对其中的文档执行索引，搜索，更新和删除操作时引用索引。

In a single cluster, you can define as many indexes as you want.
在单个群集中，您可以根据需要定义任意数量的索引。

Type/类型

tpye从6.0.0开始弃用。
最明显的变化就是一个index只能有一个type，推荐的type名是 _doc，7.0版本以后就完全抛弃type了

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, e.g. 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，允许您在同一索引中存储不同类型的文档，例如 一种用户类型，另一种用于博客帖子。现在已经不可以在一个索引中创建多个类型了，并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。
（Indices created in Elasticsearch 6.0.0 or later may only contain a single mapping type. Indices created in 5.x with multiple mapping types will continue to function as before in Elasticsearch 6.x. Mapping types will be completely removed in Elasticsearch 7.0.0.
在Elasticsearch 6.0.0或更高版本中创建的索引可能只包含单个映射类型。 在具有多种映射类型的5.x中创建的索引将继续像以前一样在Elasticsearch 6.x中运行。 映射类型将在Elasticsearch 7.0.0中完全删除。）

Document/文档

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.
文档是可以被索引到的基本信息单元。 例如，您可以为单个客户提供文档，为单个产品提供另一个文档，为单个订单提供另一个文档。 该文档以JSON（JavaScript Object Notation）表示，JSON是一种普遍存在的互联网数据交换格式。
（简单地说，ES中的文档可以是一篇新闻报道、一份DOC文档或一份PDF文件内容）

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，您可以根据需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。

为什么要去除类型（type）？

在Elasticsearch 6.0之前的版本中，有个著名的比喻：
Elasticsearch是文档数据库，与关系型数据库对应。索引index对应database，type对应table，document对应数据记录

类别	库	表	记录
关系型数据库	数据库	数据表	数据记录
文档数据库	索引（index）	类型（type）	文档

Elasticsearch的开发者们逐渐认为这是一个糟糕的认识。例如：关系型数据库中两个数据表示是独立的，即使他们里面有相同名称的列也不影响使用，但Elasticsearch中不是这样的。
Elasticsearch是基于Lucene开发的搜索引擎，而Elasticsearch中不同type下名称相同的字段最终在Lucene中的处理方式是一样的。例如，两个不同type下的两个username，在Elasticsearch同一个索引下其实被认为是同一个字段，出现冲突情况，导致Lucene处理效率下降。
去掉type后，文档数据存储在独立的index中，这样不会出现相同字段名称冲突问题。

除此之外，在同一个索引的不同type下存储字段数不一样的文档会导致存储中出现稀疏数据，影响Lucene压缩文档的能力，导致Elasticsearch查询效率的降低。

Shards & Replicas/分片和副本

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，或者可能太慢而无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。 创建索引时，只需定义所需的分片数即可。 每个分片本身都是一个功能齐全且独立的“索引”，可以托管在集群中的任何节点上。

Sharding is important for two primary reasons:
分片很重要，主要有两个原因

• It allows you to horizontally split/scale your content volume . 它允许您水平拆分/缩放内容量
• It allows you to distribute and parallelize operations across shards (potentially on multiple nodes) thus increasing performance/throughput . 它允许您跨分片（可能在多个节点上）分发和并行化操作，从而提高性能/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完全由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.
在可以随时发生故障的网络/云环境中，非常有用，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原因消失。 为此，Elasticsearch允许您将索引的分片的一个或多个副本制作成所谓的副本分片或简称副本。

Replication is important for two primary reasons: 副本很重要，主要有两个原因：

• 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/节点出现故障时提供高可用性。 因此，请务必注意，副本分片永远不要和原始/主分片位于相同的节点上。
• It allows you to scale out your search volume/throughput since searches can be executed on all replicas in parallel.它允许您扩展搜索量/吞吐量，因为可以在所有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，每个索引可以拆分为多个分片。 索引也可以复制为零（表示没有副本）或更多次。 复制后，每个索引都将具有主分片（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.
可以在创建索引时为每个索引定义分片和副本的数量。 创建索引后，您还可以随时动态更改副本数。 您可以使用_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.
默认情况下，Elasticsearch中的每个索引都分配了5个主分片和1个副本，这意味着如果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 每个索引10个分片。