[[retiring-data]]
=== Retiring Data
As time-based data ages, it becomes less relevant.((("scaling", "retiring data"))) It's possible that we
will want to see what happened last week, last month, or even last year, but
for the most part, we're interested in only the here and now.
The nice thing about an index per time frame ((("indices", "index per-timeframe", "deleting old data and")))((("indices", "deleting")))is that it enables us to easily
delete old data: just delete the indices that are no longer relevant:
[source,json]
-------------------------
DELETE /logs_2013*
-------------------------
Deleting a whole index is much more efficient than deleting individual
documents: Elasticsearch just removes whole directories.
But deleting an index is very _final_. There are a number of things we can
do to help data age gracefully, before we decide to delete it completely.
[[migrate-indices]]
==== Migrate Old Indices
With logging data, there is likely to be one _hot_ index--the index for
today.((("indices", "migrating old indices"))) All new documents will be added to that index, and almost all queries
will target that index. It should use your best hardware.
How does Elasticsearch know which servers are your best servers? You tell it,
by assigning arbitrary tags to each server. For instance, you could start a
node as follows:
./bin/elasticsearch --node.box_type strong
The `box_type` parameter is completely arbitrary--you could have named it
whatever you like--but you can use these arbitrary values to tell
Elasticsearch where to allocate an index.
We can ensure that today's index is on our strongest boxes by creating it with
the following settings:
[source,json]
-------------------------
PUT /logs_2014-10-01
{
"settings": {
"index.routing.allocation.include.box_type" : "strong"
}
}
-------------------------
Yesterday's index no longer needs to be on our strongest boxes, so we can move
it to the nodes tagged as `medium` by updating its index settings:
[source,json]
-------------------------
POST /logs_2014-09-30/_settings
{
"index.routing.allocation.include.box_type" : "medium"
}
-------------------------
[[optimize-indices]]
==== Optimize Indices
Yesterday's index is unlikely to change.((("indices", "optimizing"))) Log events are static: what
happened in the past stays in the past. If we merge each shard down to just a
single segment, it'll use fewer resources and will be quicker to query. We
can do this with the <<optimize-api>>.
It would be a bad idea to optimize the index while it was still allocated to
the `strong` boxes, as the optimization process could swamp the I/O on those
nodes and impact the indexing of today's logs. But the `medium` boxes aren't
doing very much at all, so we are safe to optimize.
Yesterday's index may have replica shards.((("replica shards", "index optimization and"))) If we issue an optimize request, it
will optimize the primary shard and the replica shards, which is a waste.
Instead, we can remove the replicas temporarily, optimize, and then restore the
replicas:
[source,json]
-------------------------
POST /logs_2014-09-30/_settings
{ "number_of_replicas": 0 }
POST /logs_2014-09-30/_optimize?max_num_segments=1
POST /logs_2014-09-30/_settings
{ "number_of_replicas": 1 }
-------------------------
Of course, without replicas, we run the risk of losing data if a disk suffers
catastrophic failure. You may((("snapshot-restore API"))) want to back up the data first, with the
http://bit.ly/14ED13A[`snapshot-restore` API].
[[close-indices]]
==== Closing Old Indices
As indices get even older, they reach a point where they are almost never
accessed.((("indices", "closing old indices"))) We could delete them at this stage, but perhaps you want to keep
them around just in case somebody asks for them in six months.
These indices can be closed. They will still exist in the cluster, but they
won't consume resources other than disk space. Reopening an index is much
quicker than restoring it from backup.
Before closing, it is worth flushing the index to make sure that there are no
transactions left in the transaction log. An empty transaction log will make
index recovery faster when it is reopened:
[source,json]
-------------------------
POST /logs_2014-01-*/_flush <1>
POST /logs_2014-01-*/_close <2>
POST /logs_2014-01-*/_open <3>
-------------------------
<1> Flush all indices from January to empty the transaction logs.
<2> Close all indices from January.
<3> When you need access to them again, reopen them with the `open` API.
[[archive-indices]]
==== Archiving Old Indices
Finally, very old indices ((("indices", "archiving old indices")))can be archived off to some long-term storage like a
shared disk or Amazon's S3 using the
http://bit.ly/14ED13A[`snapshot-restore` API], just in case you may need
to access them in the future. Once a backup exists, the index can be deleted
from the cluster.
- 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