#分布式搜索的执行方式
在继续之前,我们将绕道讲一下搜索是如何在分布式环境中执行的。 <!--"distributed search execution"--> 它比我们之前讲的基础的_增删改查_(_create-read-update-delete_ ,CRUD)<!--"CRUD (create-read-update-delete) operations"-->请求要复杂一些。
> ####注意:
> 本章的信息只是出于兴趣阅读,使用Elasticsearch并不需要理解和记住这里的所有细节。
> 阅读这一章只是增加对系统如何工作的了解,并让你知道这些信息以备以后参考,所以别淹没在细节里。
一个CRUD操作只处理一个单独的文档。文档的唯一性由`_index`, `_type`和`routing-value`(通常默认是该文档的`_id`)的组合来确定。这意味着我们可以准确知道集群中的哪个分片持有这个文档。
由于不知道哪个文档会匹配查询(文档可能存放在集群中的任意分片上),所以搜索需要一个更复杂的模型。一个搜索不得不通过查询每一个我们感兴趣的索引的分片副本,来看是否含有任何匹配的文档。
但是,找到所有匹配的文档只完成了这件事的一半。在搜索(`search`)API返回一页结果前,来自多个分片的结果必须被组合放到一个有序列表中。因此,搜索的执行过程分两个阶段,称为_查询然后取回_(_query then fetch_)。
<!--
[[distributed-search]]
== Distributed Search Execution
Before moving on, we are going to take a detour and talk about how search is
executed in a distributed environment.((("distributed search execution"))) It is a bit more complicated than the
basic _create-read-update-delete_ (CRUD) requests((("CRUD (create-read-update-delete) operations"))) that we discussed in
<<distributed-docs>>.
.Content Warning
****
The information presented in this chapter is for your interest. You are not required to
understand and remember all the detail in order to use Elasticsearch.
Read this chapter to gain a taste for how things work, and to know where the
information is in case you need to refer to it in the future, but don't be
overwhelmed by the detail.
****
A CRUD operation deals with a single document that has a unique combination of
`_index`, `_type`, and <<routing-value,`routing` values>> (which defaults to the
document's `_id`). This means that we know exactly which shard in the cluster
holds that document.
Search requires a more complicated execution model because we don't know which
documents will match the query: they could be on any shard in the cluster. A
search request has to consult a copy of every shard in the index or indices
we're interested in to see if they have any matching documents.
But finding all matching documents is only half the story. Results from
multiple shards must be combined into a single sorted list before the `search`
API can return a ``page'' of results. For this reason, search is executed in a
two-phase process called _query then fetch_.
-->
- Introduction
- 入门
- 是什么
- 安装
- API
- 文档
- 索引
- 搜索
- 聚合
- 小结
- 分布式
- 结语
- 分布式集群
- 空集群
- 集群健康
- 添加索引
- 故障转移
- 横向扩展
- 更多扩展
- 应对故障
- 数据
- 文档
- 索引
- 获取
- 存在
- 更新
- 创建
- 删除
- 版本控制
- 局部更新
- Mget
- 批量
- 结语
- 分布式增删改查
- 路由
- 分片交互
- 新建、索引和删除
- 检索
- 局部更新
- 批量请求
- 批量格式
- 搜索
- 空搜索
- 多索引和多类型
- 分页
- 查询字符串
- 映射和分析
- 数据类型差异
- 确切值对决全文
- 倒排索引
- 分析
- 映射
- 复合类型
- 结构化查询
- 请求体查询
- 结构化查询
- 查询与过滤
- 重要的查询子句
- 过滤查询
- 验证查询
- 结语
- 排序
- 排序
- 字符串排序
- 相关性
- 字段数据
- 分布式搜索
- 查询阶段
- 取回阶段
- 搜索选项
- 扫描和滚屏
- 索引管理
- 创建删除
- 设置
- 配置分析器
- 自定义分析器
- 映射
- 根对象
- 元数据中的source字段
- 元数据中的all字段
- 元数据中的ID字段
- 动态映射
- 自定义动态映射
- 默认映射
- 重建索引
- 别名
- 深入分片
- 使文本可以被搜索
- 动态索引
- 近实时搜索
- 持久化变更
- 合并段
- 结构化搜索
- 查询准确值
- 组合过滤
- 查询多个准确值
- 包含,而不是相等
- 范围
- 处理 Null 值
- 缓存
- 过滤顺序
- 全文搜索
- 匹配查询
- 多词查询
- 组合查询
- 布尔匹配
- 增加子句
- 控制分析
- 关联失效
- 多字段搜索
- 多重查询字符串
- 单一查询字符串
- 最佳字段
- 最佳字段查询调优
- 多重匹配查询
- 最多字段查询
- 跨字段对象查询
- 以字段为中心查询
- 全字段查询
- 跨字段查询
- 精确查询
- 模糊匹配
- Phrase matching
- Slop
- Multi value fields
- Scoring
- Relevance
- Performance
- Shingles
- Partial_Matching
- Postcodes
- Prefix query
- Wildcard Regexp
- Match phrase prefix
- Index time
- Ngram intro
- Search as you type
- Compound words
- Relevance
- Scoring theory
- Practical scoring
- Query time boosting
- Query scoring
- Not quite not
- Ignoring TFIDF
- Function score query
- Popularity
- Boosting filtered subsets
- Random scoring
- Decay functions
- Pluggable similarities
- Conclusion
- Language intro
- Intro
- Using
- Configuring
- Language pitfalls
- One language per doc
- One language per field
- Mixed language fields
- Conclusion
- Identifying words
- Intro
- Standard analyzer
- Standard tokenizer
- ICU plugin
- ICU tokenizer
- Tidying text
- Token normalization
- Intro
- Lowercasing
- Removing diacritics
- Unicode world
- Case folding
- Character folding
- Sorting and collations
- Stemming
- Intro
- Algorithmic stemmers
- Dictionary stemmers
- Hunspell stemmer
- Choosing a stemmer
- Controlling stemming
- Stemming in situ
- Stopwords
- Intro
- Using stopwords
- Stopwords and performance
- Divide and conquer
- Phrase queries
- Common grams
- Relevance
- Synonyms
- Intro
- Using synonyms
- Synonym formats
- Expand contract
- Analysis chain
- Multi word synonyms
- Symbol synonyms
- Fuzzy matching
- Intro
- Fuzziness
- Fuzzy query
- Fuzzy match query
- Scoring fuzziness
- Phonetic matching
- Aggregations
- overview
- circuit breaker fd settings
- filtering
- facets
- docvalues
- eager
- breadth vs depth
- Conclusion
- concepts buckets
- basic example
- add metric
- nested bucket
- extra metrics
- bucket metric list
- histogram
- date histogram
- scope
- filtering
- sorting ordering
- approx intro
- cardinality
- percentiles
- sigterms intro
- sigterms
- fielddata
- analyzed vs not
- 地理坐标点
- 地理坐标点
- 通过地理坐标点过滤
- 地理坐标盒模型过滤器
- 地理距离过滤器
- 缓存地理位置过滤器
- 减少内存占用
- 按距离排序
- Geohashe
- Geohashe
- Geohashe映射
- Geohash单元过滤器
- 地理位置聚合
- 地理位置聚合
- 按距离聚合
- Geohash单元聚合器
- 范围(边界)聚合器
- 地理形状
- 地理形状
- 映射地理形状
- 索引地理形状
- 查询地理形状
- 在查询中使用已索引的形状
- 地理形状的过滤与缓存
- 关系
- 关系
- 应用级别的Join操作
- 扁平化你的数据
- Top hits
- Concurrency
- Concurrency solutions
- 嵌套
- 嵌套对象
- 嵌套映射
- 嵌套查询
- 嵌套排序
- 嵌套集合
- Parent Child
- Parent child
- Indexing parent child
- Has child
- Has parent
- Children agg
- Grandparents
- Practical considerations
- Scaling
- Shard
- Overallocation
- Kagillion shards
- Capacity planning
- Replica shards
- Multiple indices
- Index per timeframe
- Index templates
- Retiring data
- Index per user
- Shared index
- Faking it
- One big user
- Scale is not infinite
- Cluster Admin
- Marvel
- Health
- Node stats
- Other stats
- Deployment
- hardware
- other
- config
- dont touch
- heap
- file descriptors
- conclusion
- cluster settings
- Post Deployment
- dynamic settings
- logging
- indexing perf
- rolling restart
- backup
- restore
- conclusion