[[user-based]]
=== User-Based Data
Often, users start using Elasticsearch because they need to add full-text
search or analytics to an existing application.((("scaling", "user-based data")))((("user-based data"))) They create a single index
that holds all of their documents. Gradually, others in the company realize
how much benefit Elasticsearch brings, and they want to add their data to
Elasticsearch as well.
Fortunately, Elasticsearch supports
http://en.wikipedia.org/wiki/Multitenancy[multitenancy] so each new user can
have her own index in the same cluster.((("mulltitenancy"))) Occasionally, somebody will want to
search across the documents for all users, which they can do by searching
across all indices, but most of the time, users are interested in only their
own documents.
Some users have more documents than others, and some users will have heavier
search loads than others, so the ability to specify the number of primary shards
and replica shards that each index should have fits well with the index-per-user
model.((("indices", "index-per-user model")))((("primary shards", "number per-index"))) Similarly, busier indices can be allocated to stronger boxes with shard
allocation filtering. (See <<migrate-indices>>.)
TIP: Don't just use the default number of primary shards for every index.
Think about how much data that index needs to hold. It may be that all you
need is one shard--any more is a waste of resources.
Most users of Elasticsearch can stop here. A simple index-per-user approach
is sufficient for the majority of cases.
In exceptional cases, you may find that you need to support a large number of
users, all with similar needs. An example might be hosting a search engine
for thousands of email forums. ((("forums, resource allocation for"))) Some forums may have a huge amount of traffic,
but the majority of forums are quite small. Dedicating an index with a single
shard to a small forum is overkill--a single shard could hold the data for
many forums.
What we need is a way to share resources across users, to give the impression
that each user has his own index without wasting resources on small users.
- Introduction
- 入門
- 是什么
- 安裝
- API
- 文檔
- 索引
- 搜索
- 聚合
- 小結
- 分布式
- 結語
- 分布式集群
- 空集群
- 集群健康
- 添加索引
- 故障轉移
- 橫向擴展
- 更多擴展
- 應對故障
- 數據
- 文檔
- 索引
- 獲取
- 存在
- 更新
- 創建
- 刪除
- 版本控制
- 局部更新
- Mget
- 批量
- 結語
- 分布式增刪改查
- 路由
- 分片交互
- 新建、索引和刪除
- 檢索
- 局部更新
- 批量請求
- 批量格式
- 搜索
- 空搜索
- 多索引和多類型
- 分頁
- 查詢字符串
- 映射和分析
- 數據類型差異
- 確切值對決全文
- 倒排索引
- 分析
- 映射
- 復合類型
- 結構化查詢
- 請求體查詢
- 結構化查詢
- 查詢與過濾
- 重要的查詢子句
- 過濾查詢
- 驗證查詢
- 結語
- 排序
- 排序
- 字符串排序
- 相關性
- 字段數據
- 分布式搜索
- 查詢階段
- 取回階段
- 搜索選項
- 掃描和滾屏
- 索引管理
- 創建刪除
- 設置
- 配置分析器
- 自定義分析器
- 映射
- 根對象
- 元數據中的source字段
- 元數據中的all字段
- 元數據中的ID字段
- 動態映射
- 自定義動態映射
- 默認映射
- 重建索引
- 別名
- 深入分片
- 使文本可以被搜索
- 動態索引
- 近實時搜索
- 持久化變更
- 合并段
- 結構化搜索
- 查詢準確值
- 組合過濾
- 查詢多個準確值
- 包含,而不是相等
- 范圍
- 處理 Null 值
- 緩存
- 過濾順序
- 全文搜索
- 匹配查詢
- 多詞查詢
- 組合查詢
- 布爾匹配
- 增加子句
- 控制分析
- 關聯失效
- 多字段搜索
- 多重查詢字符串
- 單一查詢字符串
- 最佳字段
- 最佳字段查詢調優
- 多重匹配查詢
- 最多字段查詢
- 跨字段對象查詢
- 以字段為中心查詢
- 全字段查詢
- 跨字段查詢
- 精確查詢
- 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
- 地理坐標點
- 地理坐標點
- 通過地理坐標點過濾
- 地理坐標盒模型過濾器
- 地理距離過濾器
- 緩存地理位置過濾器
- 減少內存占用
- 按距離排序
- Geohashe
- Geohashe
- Geohashe映射
- Geohash單元過濾器
- 地理位置聚合
- 地理位置聚合
- 按距離聚合
- Geohash單元聚合器
- 范圍(邊界)聚合器
- 地理形狀
- 地理形狀
- 映射地理形狀
- 索引地理形狀
- 查詢地理形狀
- 在查詢中使用已索引的形狀
- 地理形狀的過濾與緩存
- 關系
- 關系
- 應用級別的Join操作
- 扁平化你的數據
- Top hits
- Concurrency
- Concurrency solutions
- 嵌套
- 嵌套對象
- 嵌套映射
- 嵌套查詢
- 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