=== Don't Touch These Settings!
There are a few hotspots in Elasticsearch that people just can't seem to avoid
tweaking. ((("deployment", "settings to leave unaltered"))) We understand: knobs just beg to be turned. But of all the knobs to turn, these you should _really_ leave alone. They are
often abused and will contribute to terrible stability or terrible performance.
Or both.
==== Garbage Collector
As briefly introduced in <<garbage_collector_primer>>, the JVM uses a garbage
collector to free unused memory.((("garbage collector"))) This tip is really an extension of the last tip,
but deserves its own section for emphasis:
Do not change the default garbage collector!
The default GC for Elasticsearch is Concurrent-Mark and Sweep (CMS).((("Concurrent-Mark and Sweep (CMS) garbage collector"))) This GC
runs concurrently with the execution of the application so that it can minimize
pauses. It does, however, have two stop-the-world phases. It also has trouble
collecting large heaps.
Despite these downsides, it is currently the best GC for low-latency server software
like Elasticsearch. The official recommendation is to use CMS.
There is a newer GC called the Garbage First GC (G1GC). ((("Garbage First GC (G1GC)"))) This newer GC is designed
to minimize pausing even more than CMS, and operate on large heaps. It works
by dividing the heap into regions and predicting which regions contain the most
reclaimable space. By collecting those regions first (_garbage first_), it can
minimize pauses and operate on very large heaps.
Sounds great! Unfortunately, G1GC is still new, and fresh bugs are found routinely.
These bugs are usually of the segfault variety, and will cause hard crashes.
The Lucene test suite is brutal on GC algorithms, and it seems that G1GC hasn't
had the kinks worked out yet.
We would like to recommend G1GC someday, but for now, it is simply not stable
enough to meet the demands of Elasticsearch and Lucene.
==== Threadpools
Everyone _loves_ to tweak threadpools.((("threadpools"))) For whatever reason, it seems people
cannot resist increasing thread counts. Indexing a lot? More threads! Searching
a lot? More threads! Node idling 95% of the time? More threads!
The default threadpool settings in Elasticsearch are very sensible. For all
threadpools (except `search`) the threadcount is set to the number of CPU cores.
If you have eight cores, you can be running only eight threads simultaneously. It makes
sense to assign only eight threads to any particular threadpool.
Search gets a larger threadpool, and is configured to `# cores * 3`.
You might argue that some threads can block (such as on a disk I/O operation),
which is why you need more threads. This is not a problem in Elasticsearch:
much of the disk I/O is handled by threads managed by Lucene, not Elasticsearch.
Furthermore, threadpools cooperate by passing work between each other. You don't
need to worry about a networking thread blocking because it is waiting on a disk
write. The networking thread will have long since handed off that work unit to
another threadpool and gotten back to networking.
Finally, the compute capacity of your process is finite. Having more threads just forces
the processor to switch thread contexts. A processor can run only one thread
at a time, so when it needs to switch to a different thread, it stores the current
state (registers, and so forth) and loads another thread. If you are lucky, the switch
will happen on the same core. If you are unlucky, the switch may migrate to a
different core and require transport on an inter-core communication bus.
This context switching eats up cycles simply by doing administrative housekeeping; estimates can peg it as high as 30μs on modern CPUs. So unless the thread
will be blocked for longer than 30μs, it is highly likely that that time would
have been better spent just processing and finishing early.
People routinely set threadpools to silly values. On eight core machines, we have
run across configs with 60, 100, or even 1000 threads. These settings will simply
thrash the CPU more than getting real work done.
So. Next time you want to tweak a threadpool, please don't. And if you
_absolutely cannot resist_, please keep your core count in mind and perhaps set
the count to double. More than that is just a waste.
- 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