[[multiple-indices]]
=== Multiple Indices
Finally, remember that there is no rule that limits your application to using
only a single index.((("scaling", "using multiple indices")))((("indices", "multiple"))) When we issue a search request, it is forwarded to a
copy (a primary or a replica) of all the shards in an index. If we issue the
same search request on multiple indices, the exact same thing happens--there
are just more shards involved.
TIP: Searching 1 index of 50 shards is exactly equivalent to searching
50 indices with 1 shard each: both search requests hit 50 shards.
This can be a useful fact to remember when you need to add capacity on the
fly. Instead of having to reindex your data into a bigger index, you can
just do the following:
* Create a new index to hold new data.
* Search across both indices to retrieve new and old data.
In fact, with a little forethought, adding a new index can be done in a
completely transparent way, without your application ever knowing that
anything has changed.
In <<index-aliases>>, we spoke about using an index alias to point to the
current version of your index. ((("index aliases")))((("aliases, index"))) For instance, instead of naming your index
`tweets`, name it `tweets_v1`. Your application would still talk to `tweets`,
but in reality that would be an alias that points to `tweets_v1`. This allows
you to switch the alias to point to a newer version of the index on the fly.
A similar technique can be used to expand capacity by adding a new index. It
requires a bit of planning because you will need two aliases: one for
searching and one for indexing:
[source,json]
---------------------------
PUT /tweets_1/_alias/tweets_search <1>
PUT /tweets_1/_alias/tweets_index <1>
---------------------------
<1> Both the `tweets_search` and the `tweets_index` alias point to
index `tweets_1`.
New documents should be indexed into `tweets_index`, and searches should be
performed against `tweets_search`. For the moment, these two aliases point to
the same index.
When we need extra capacity, we can create a new index called `tweets_2` and
update the aliases as follows:
[source,json]
---------------------------
POST /_aliases
{
"actions": [
{ "add": { "index": "tweets_2", "alias": "tweets_search" }}, <1>
{ "remove": { "index": "tweets_1", "alias": "tweets_index" }}, <2>
{ "add": { "index": "tweets_2", "alias": "tweets_index" }} <2>
]
}
---------------------------
<1> Add index `tweets_2` to the `tweets_search` alias.
<2> Switch `tweets_index` from `tweets_1` to `tweets_2`.
A search request can target multiple indices, so having the search alias point
to `tweets_1` and `tweets_2` is perfectly valid. However, indexing requests can
target only a single index. For this reason, we have to switch the index alias
to point to only the new index.
[TIP]
==================================================
A document `GET` request, like((("HTTP methods", "GET")))((("GET method"))) an indexing request, can target only one index.
This makes retrieving a document by ID a bit more complicated in this
scenario. Instead, run a search request with the
http://bit.ly/1C4Q0cf[`ids` query], or do a((("mget (multi-get) API")))
http://bit.ly/1sDd2EX[`multi-get`] request on `tweets_1` and `tweets_2`.
==================================================
Using multiple indices to expand index capacity on the fly is of particular
benefit when dealing with time-based data such as logs or social-event
streams, which we discuss in the next section.
- 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