ES doc_values的来源,field data——就是doc->terms的正向索引啊,不过它是在查询阶段通过读取倒排索引loading segments放在内存而得到的?
Support in the Wild: My Biggest Elasticsearch Problem at Scale
Java Heap Pressure
Elasticsearch has so many wildly different use cases that I could not write a reasonably short blog post describing what can and cannot consume memory. However, there is one thing that constantly stands out above all of the other concerns that you might have while running an Elasticsearch cluster at scale.
For the users that I help, fielddata is the problem that is the most likely to cause their cluster's instability. Fielddata is the bane of my existence and it's the most frequent cause of the highest severity issues that I handle with our customers.
Understanding Fielddata
The inverted index is the magic that makes Elasticsearch queries so fast. This data structure holds a sorted list of all the unique terms that appear in a field, and each term points to the list of documents that contain that term:
Term: Docs: 1 2 3 4 5
----------------------------
brown X X X
fox X X
quick X X
----------------------------
Search asks the question: What documents contain term brown
in the foo
field? The inverted index is the perfect structure to answer this question: look up the term of interest in the sorted list and you immediately know which documents match your query.
Sorting or aggregations, however, need to be able to answer this question: What terms does Document 1 contain in the foo
field? To answer this, we need a data structure that is the opposite of the inverted index:
Docs: Terms:
----------------------------
1 [ brown ]
2 [ quick ]
3 [ brown ]
4 [ brown, fox, quick ]
5 [ fox ]
----------------------------
This is the purpose of fielddata. Fielddata can be generated at query time by reading the inverted index, inverting the term <-> doc data structure, and storing the results in memory.(就是doc->terms的正向索引啊,不过它是在查询阶段通过读取倒排索引得到的?如果真是这样,那么如何能够比doc values更快?) The two major downsides of this approach should be obvious:
- Loading fielddata can be slow, especially with big segments.
- It consumes a lot of valuable heap space.
Because loading fielddata is costly, we try to do it as seldom as possible. Once loaded, we keep it in memory for as long as possible.
By default, fielddata is loaded on demand, which means that you will not see it until you are using it. Also, by being loaded per segment, it means that new segments that get created will slowly add to your overall memory usage until the field's fielddata is evicted from memory. Eviction happens in only a few ways:
- Deleting the index or indices that contains it.
- Closing the index or indices that contains it.
- Segment fielddata is removed when segments are removed (e.g., background merging).
- This usually just means that the problem is moving rather than going away.
- Restarting the node containing the fielddata.
- Clearing the relevant fielddata cache.
- Automatically evicting the fielddata to make room for other fielddata.
- This will not happen with default settings.
While the first two ways will cause the memory to be evicted, they're not useful in terms of solving the problem because they make the index unusable. Segment merging is happening in the background and it is not a way to clear fielddata. The fourth and fifth ways are unlikely to be a long term solution because they do not prevent fielddata from being reloaded.
The sixth option, evicting fielddata when the cache is full, leads to different issues: one request triggers fielddata loading for one field and the next request triggers loading for another, causing the first field to be evicted. This causes memory thrashing and slow garbage collections, and your users suffer from very slow queries while they wait for their fielddata to be loaded.
Simply put, once fielddata becomes a problem, then it stays a problem.
Why Fielddata is Bad
At small scales, you can generally get away with fielddata usage without even realizing that you are using it. In highly controlled environments, you may even enjoy that specific fields are being loaded into memory for theoretically faster access.
However, almost without fail, you are bound to run into a problem with it eventually. Whether it's because someone ran a test request on the production system without thinking that it would be a problem (it's just one query, right?), your queries changed to match new data, or you just finally reached a scale where it no longer works: you will eventually run into memory pressure that does not go away.
As I noted earlier, fielddata does not go away on its own. In Elasticsearch 1.3 and later, we allow up to 60% of your Java heap's memory to be consumed by fielddata per node. We control this via the Fielddata Circuit Breaker, which checks incoming requests for potential fielddata usage and then blocks them if they require more memory than is currently available. Any circuit breaker's purpose is to prevent any bad requests, which means that it never gets the chance to cause a problem (e.g., allocate even more fielddata), but it's important to note that it will not clear any existing fielddata.
For example, if a node has 10 GBs of Java heap, then 60% of that is going to be 6 GBs. If a new request requires 1 GB of fielddata to be loaded for that node that is already using 4 GBs of the heap for fielddata, then it will allow it because 4 GB, plus 1 GB, is less than 6 GB. However, if the next request needed 2 GB for yet another field's fielddata, then the entire request would be rejected because the fielddata is exhausted (5 GB + 2 GB = 7 GB
, which is clearly greater than 6 GB).
Note: for versions prior to Elasticsearch 1.3, we allowed an unlimited amount of your Java heap to be consumed by fielddata.
Finding Your Fielddata
Fortunately, it's not all bad news. Not only do we have a solution to the problem, but we also provide a way to find and understand your problem with it.
$ curl -XGET 'localhost:9200/_cat/fielddata?v&fields=*'
This will provide a list of each node with its fielddata usage. For instance, at startup, my local node is using absolutely no fielddata:
id host ip node total
iExRFXn1Qw23iRzhwor-Wg Chriss-MBP.home 192.168.1.2 WallE 0b
To see it change, it's as simple as sorting, scripting, or aggregating any field. So let's do all three!
$ curl -XGET localhost:9200/test-index/_search -d '{
"query": {
"filtered": {
"filter": {
"script": {
"script": "doc[\"percentage\"].value > 0.5"
}
}
}
},
"aggs": {
"max_number": {
"max": {
"field": "number"
}
}
},
"sort": [
{
"@timestamp": {
"order": "desc"
}
}
]
}'
Although order is irrelevant for this, the first field that will be impacted will be the percentage
field that is accessed inside of the scripted filter. The second field used will be the number
field from the aggregation. Finally, the last field is the @timestamp
field used to sort the filtered results. Taking another look at the _cat/fielddata
command above confirms this:
id host ip node total number @timestamp percentage
iExRFXn1Qw23iRzhwor-Wg Chriss-MBP.home 192.168.1.2 WallE 49.9kb 24.8kb 24.8kb 192b
Use Doc Values
The solution to this fielddata problem is to avoid it altogether. Fortunately, you can avoid the use of fielddata bymanually mapping all of your fields to use doc values. Without repeating too much from the guide, doc values offload this burden by writing the fielddata to disk at index time, thereby allowing Elasticsearch to load the values outside of your Java heap as they are needed.
By taking the burden out of your heap, you get fast access to the on-disk fielddata through the file system cache, which gives in-memory performance without the cost of garbage collections coming into play. This also frees up a lot of headroom for the Elasticsearch heap so that more operations (e.g., bulk indexing and concurrent searches) can use the heap without placing the node under memory pressure, which leads to garbage collection that will slow it down.
ES doc_values的来源,field data——就是doc->terms的正向索引啊,不过它是在查询阶段通过读取倒排索引loading segments放在内存而得到的?的更多相关文章
- ES系列八、正排索Doc Values和Field Data
1.Doc Values 聚合使用一个叫Doc Values的数据结构.Doc Values使聚合更快.更高效且内存友好. Doc Values的存在是因为倒排索引只对某些操作是高效的.倒排索引的优势 ...
- ES doc_values介绍——本质是field value的列存储,做聚合分析用,ES默认开启,会占用存储空间(列存储压缩技巧,除公共除数或者同时减去最小数,字符串压缩的话,直接去重后用数字ID压缩)
doc_values Doc values are the on-disk data structure, built at document index time, which makes this ...
- ES doc_values介绍2——本质是field value的列存储,做聚合分析用,ES默认开启,会占用存储空间
一.doc_values介绍 doc values是一个我们再三重复的重要话题了,你是否意识到一些东西呢? 搜索时,我们需要一个“词”到“文档”列表的映射 排序时,我们需要一个“文档”到“词“列表的映 ...
- 2017 ES GZ Meetup分享:Data Warehouse with ElasticSearch in Datastory
以下是我在2017 ES 广州 meetup的分享 ppt:https://elasticsearch.cn/slides/11#page=22 摘要 ES最多使用的场景是搜索和日志分析,然而ES强大 ...
- Elasticsearch由浅入深(十)搜索引擎:相关度评分 TF&IDF算法、doc value正排索引、解密query、fetch phrase原理、Bouncing Results问题、基于scoll技术滚动搜索大量数据
相关度评分 TF&IDF算法 Elasticsearch的相关度评分(relevance score)算法采用的是term frequency/inverse document frequen ...
- es修改指定的field(partial update)
PUT /index/type/id 创建文档&替换文档,就是一样的语法一般对应到应用程序中,每次的执行流程基本是这样的:1.应用程序发起一个get请求,获取到document,展示到前台界面 ...
- doc_values VS stored field
doc_values 按列存储,按docId排序,在query阶段使用,直接根据docId获取具体field的value,用来排序,聚合等. stored field按文档存储,按docId排序,一条 ...
- Jasper_filter data_pass field data from main to sub to filter some data
main report: 1 add variable <variable name="Variable_rule" class="java.lang.String ...
- FNDLOAD Commands to Download Different Seed Data Types. (DOC ID 274667.1)
In this Document Goal Solution References Applies to: Oracle Application Object Library - Version 11 ...
随机推荐
- maven 常用的环境插件
<build> <finalName>yycgproject</finalName> <plugins> <!-- 修改jdk插件 --> ...
- linux上安装Kafka
写个一篇kafka文章了.但是那都是针对性能来说的,下面看一下完整的,kafka步骤: 安装单机三个 Broker 的 Kafka 集群,使用 Kafka 集群发布和接收消息.学完本课程,对 Kafk ...
- php党 强烈推荐TIPI:深入理解PHP内核
深入理解PHP内核(Thinking In PHP Internals) TIPI项目是一个自发项目, 项目主要关注PHP的内部实现, 以及PHP相关的方方面面, 该项目包括<深入理解PHP内核 ...
- Android 更改项目包名的方法
修改APP包名,即APP的唯一标识. 1.在项目上右键,选择android tools->rename application package,输入需要改为的名称,然后选择需要改的包,有部分包可 ...
- Eclipse搭建C++开发环境
http://jingyan.baidu.com/article/456c463b67b4310a59314468.html
- Solr In Action 中文版 第一章(四、五)
1.1 功能概览1. 4 最后,让我们再依照以下的分类.高速的过一下Solr的主要功能: ·用户体验 ·数据建模 ·Solr 4的新功能 在本书中.为你的用户提供良好的搜索体验 ...
- ArcGIS API for js Legend(图例)
1.说明 有关怎么把ArcGIS API for js部署到IIS上,请参考我上面的写的博客https://www.cnblogs.com/net064/p/10302660.html 2.运行效果 ...
- VS2017生成类库选择Release失效的问题
VS的生成可以选择Debug模式或者Release模式,但是我发现在配置里面选择Release无效. 后来发现应该 在 生成->配置管理器 里面设置.
- Js格式化json字符串
var formatJson = function(json, options) { var reg = null, formatted = '', pad = 0, PADDING = ' '; / ...
- linux c编程:信号(四) sigaction
signal 函数的使用方法简单,但并不属于 POSIX 标准,在各类 UNIX 平台上的实现不尽相同,因此其用途受到了一定的限制.而 POSIX 标准定义的信号处理接口是 sigaction 函数, ...