BKD Tree 
https://www.elastic.co/blog/lucene-points-6.0
Block k-d trees are a simple yet powerful data structure. At index time, they are built by recursively partitioning the full space of N-dimensional points to be indexed into smaller and smaller rectangular cells, splitting equally along the widest ranging dimension at each step of the recursion. However, unlike an ordinary k-d tree, a block k-d tree stops recursing once there are fewer than a pre-specified (1024 in our case, by default) number of points in the cell.

At that point, all points within that cell are written into one leaf block on disk and the starting file-pointer for that block is saved into an in-heap binary tree structure. In the 1D case, this is simply a full sort of all values, divided into adjacent leaf blocks. There are k-d tree variants that can support removing values, and rebalancing, but Lucene does not need these operations because of its write-once per-segment design.
 
At search time, the same recursion takes place, testing at each level whether the requested query shape intersects the left or right sub-tree of each dimensional split, and recursing if so. In the 1D case, the query shape is simply a numeric range whereas in the 2D and 3D cases, it is a geo-spatial shape (circle, ring, rectangle, polygon, cube, etc.).

测试集合：模拟一亿条

0," nnrIuS","raet","lnsr","inu ","saia",83.405273,73.302012,3991,24,"N"," usA","airport","rra i"

1,"omlritp","aaVe","y Mu","AaVV","NMc ",15.459643,-20.826241,2627,54,"a","eemo","airport","MaArp"

2,"kyaneMr","iasm","raAA"," tnt","inls",16.606066,38.663728,2761,53,"o","arIi","airport","uiron"

1. General Multidimensional Space Points
   Search for points with exact given values. 
  Search for points which has one of the value from a given set of values. 
Search for points within a given range. 
Get the number of points which has exact point.
Get the number of points within a given range. (Ranges are multidimensional ranges. In 3D, they are boxes.)
Divide points into range-buckets and get the count in each buckets. (Range bucket is a range which has a label in it)
 
2. Locations on the planet surface. (Latitude, Longitude)
  Find closest set of airports to a given town.  
  Find the set of airports within a given radius from a particular town.
  Find the set of airports inside a country. (Country can be given as a polygon) 
  Find the set of airports within a given range of Latitudes and Longitudes. It is a Latitude, Longitude box query. (For a examples: Airports closer to the equatorial) 
  Find the set of airports closer to a given path. (Path can be something like a road. Find the airports which are less than 50km away from a given highway)
  Count the airports in each country by giving country maps as polygons.
 
search  result:
 
Loading Data is finished ----------------------------------------------------------------------
建索引花费时间：982ms
LatLon - Box Query Example------------------------------------------------------------------------------
search_LatLon_Box 花费时间：69ms

LatLon - K Nearest------------------------------------------------------------------------------
search_LatLon_Nearest 花费时间：108ms

DoublePoint 1D Point Exact------------------------------------------------------------------------------
search_Double_1D_Exact 花费时间：10ms

DoublePoint 1D - Range------------------------------------------------------------------------------
search_Double_1D_range 花费时间：8ms

DoublePoint 1D - Range Buckets -----------------------------------------------------------------------------
search_Double_1D_range_bucket 花费时间：58ms

DoublePoint multi dimensional - Range------------------------------------------------------------------------------
search_Double_MiltiDimensional_Range 花费时间：1ms
 
 
 

1. X-Pack Alternatives http://t.cn/RaFzzv1

如果你看重了 elastic 付费套件 X-Pack 中的某个功能但又囊中羞涩，不妨来看看社区的其他选择方案。当然，还是推荐你去买 X-Pack ，官方出品，质有保障！

2.Elasticsearch as a Graph Database  http://t.cn/R9Xgj2X


听说过图数据库吧？你知道 es 也可以在这个领域发挥能力吗？快来看看吧！请自备梯子哦！

3.Scaling Elasticsearch  http://t.cn/R9Xev3r

听说你的es集群频繁GC，压力巨大，要扩容了？来看看这篇文章，科学扩容有保障！请自备梯子哦！


招聘：

阿里云近期会推出ES云产品，正在组建ES专家小组，工作地点北京、杭州，薪资待遇优厚。详情请看如下链接：https://elasticsearch.cn/article/209





编辑：rockybean

归档：https://elasticsearch.cn/article/210

订阅：https://tinyletter.com/elastic-daily

岗位描述：
1. 负责阿里云上搜索云产品的设计和研发工作，确保项目质量和进度 
2. 能深入理解产品和业务，推动技术不断升级，解决客户和平台问题。
岗位要求：
1. 编程基本功扎实，熟悉常用数据结构和算法，擅长Java编程语言，熟悉JVM机制，熟悉shell、python等脚本语言； 
2. 学习能力较强，有较好的逻辑思维能力，较强的抽象、概括和总结能力，有较好的沟通交流能力，善于主动思考，对技术有强烈激情； 
3. 熟悉ElasticSearch/Lucene开源系统
4. 熟悉分布式系统，例如hadoop、spark、flink，有云计算相关开发经验者优先 
5. 具有敏捷开发经验者优先，具有完整产品生命周期开发者优先
 
阿里云近期会推出ES云产品，正在组建ES专家小组，工作地点北京、杭州，薪资待遇优厚，简历请发送至ruijie.guo@alibaba-inc.com