quick intro to elastic search

1. ElasticSearch Introduction and quick startup medcl 9-29

2. introduction ElasticSearch,a distributed search solution ， domain driven schema free anything pluggable open source, distributed, RESTful Author:shay.banon (expert in search and analytics) Compass GigaSpaces Current Version 0.11.0

3. Features Reliable, Asynchronous Write Behind for long term persistency. (Near) Real Time Search. Built on top of Lucene. shard is a fully functional Lucene index. All the power of Lucene easily exposed through simple configuration / plugins. Per operation consistency Single document level operations are atomic, consistent, isolated and durable. Open Source under Apache 2 License.

4. Distributed and Highly Available Each index is fully sharded with a configurable number of shards. Each shard can have zero or more replicas. Read / Search operations performed on either replica shard.

5. Multi Tenant with Multi Types. Support for more than one index. Support for more than one type per index. Index level configuration (number of shards, index storage, ...).

6. Document oriented No need for upfront schema definition. Schema can be defined per type for customization of the indexing process.

7. Various set of APIs. HTTP RESTful API. Native Java API. 3rd Clients perl、python、php、ruby、groovy、erlang、.NET All APIs perform automatic node operation rerouting.

8. Up and run

9. install Zero Conf

10. index $ curl -XPUT http://localhost:9200/twitter/user/kimchy -d '{ "name" : "Shay Banon" }'$ curl -XPUT http://localhost:9200/twitter/tweet/1 -d '{ "user": "kimchy", "post_date": "2009-11-15T13:12:00", "message": "Trying out Elastic Search, so far so good?" }'$ curl -XPUT http://localhost:9200/twitter/tweet/2 -d '{ "user": "kimchy", "post_date": "2009-11-15T14:12:12", "message": "You know, for Search" }'

11. Schema mapping $ curl -XPUT http://localhost:9200/twitter$ curl -XPUT http://localhost:9200/twitter/user/_mapping -d '{ "properties" : { "name" : { "type" : "string" } }}'

12. GET $ curl -XPUT http://localhost:9200/twitter/tweet/2 -d '{ "user": "kimchy", "postDate": "2009-11-15T14:12:12", "message": "You know, for Search" }'$ curl -XGET http://localhost:9200/twitter/tweet/2

13. Search $ curl -XPUT http://localhost:9200/twitter/tweet/2 -d '{ "user": "kimchy", "postDate": "2009-11-15T14:12:12", "message": "You know, for Search" }'$ curl -XGET http://localhost:9200/twitter/tweet/_search?q=user:kimchy$ curl -XGET http://localhost:9200/twitter/tweet/_search -d '{ "query" : { "term" : { "user": "kimchy" } } }'$ curl -XGET http://localhost:9200/twitter/_search?pretty=true -d '{ "query" : { "range" : { "post_date" : { "from" : "2009-11-15T13:00:00", "to" : "2009-11-15T14:30:00" } } } }'

14. multenancy $ curl -XPUT http://localhost:9200/kimchy$ curl -XPUT http://localhost:9200/elasticsearch$ curl -XPUT http://localhost:9200/elasticsearch/tweet/1 -d apos;{ "post_date": "2009-11-15T14:12:12", "message": "Zug Zug", "tag": "warcraft" }'$ curl -XPUT http://localhost:9200/kimchy/tweet/1 -d apos;{ "post_date": "2009-11-15T14:12:12", "message": "Whatyouwant?", "tag": "warcraft" }'$ curl -XGET http://localhost:9200/kimchy,elasticsearch/tweet/_search?q=tag:warcraft$ curl -XGET http://localhost:9200/_all/tweet/_search?q=tag:warcraft

15. Setting $ curl -XPUT http://localhost:9200/kimchy/ -d apos;index : store: type: memory'$ curl -XPUT http://localhost:9200/elasticsearch/ -d apos; { "index" : { "number_of_shards" : 2, "number_of_replicas" : 3 }}'

16. Behind ElasticSearch

17. Modules

18. Zen Discovery Zen is used for both discovery and master election. A master in elasticsearch is responsible for handling nodes coming and going and allocation of shards. Note, the master is not a single point of failure, if it fails, then another node will be elected as master. that nodes do not need to communicate with the master on each request, so its not a single point of bottleneck The readiness of nodes is done using the shard allocation algorithm. A shard allocated to a node is considered “ready” to receive requests only once it has fully initialized.

19. scalability nodes that can hold data, and nodes that do not. There is no need for a load balancer in elasticsearch, each node can receive a request, and if it can’t handle it, it will automatically delegate it to the appropriate node(s). If you want to scale out search, you can simply have more shard replicas per shard.

20. automatic shard allocation From:http://www.slideshare.net/elasticsearch/elasticsearch-at-berlinbuzzwords-2010#

21. BASE support Each document you index is there once the index operation is done. No need to commit or something similar to get everything persisted. A shard can have 1 or more replicas for HA. Gateway persistency is done in the background in an async manner.

22. The River A river is a pluggable service running within elasticsearch cluster pulling data (or being pushed with data) that is then indexed into the cluster.

23. Geo Location and Search 1. make your data geo enabled { "pin" : { "location" : { "lat" : 40.12, "lon" : -71.34 }, "tag" : ["food", "family"], "text" : "my favorite family restaurant" }} Find By Location Sorting Faceting … …

24. More details in http://www.elasticsearch.com/docs/

25. comparison

26. Compare with solr Though support dynamic schema,but it sucks *i ,name_i,age_i,…. Distribute ,just do many replica,Master-Slave,and with a dirty query like this: http://localhost:9080/solr/select/?q=xxx:xxx&shards=localhost:8080/solr,localhost:9080/solr WTF! Does it really RESTful?anyway, doesn’t matter

27. Compare with katta Featrures Makes serving large or high load indices easy Serves very large Lucene or HadoopMapfile indices as index shards on many servers Replicate shards on different servers for performance and fault-tolerance Supports pluggable network topologies Master fail-over Fast, lightweight, easy to integrate Plays well with Hadoop clusters May heavy to us(may be not) Master-Node,complex and ops will killed us？can’t be a little easy？ Lack of Client and documents Inactivity Community Lake of Some Search Features

28. Resources

29. Link： http://www.elasticsearch.com http://www.elasticsearch.com/blog http://www.elasticsearch.com/docs/ http://www.elasticsearch.com/community/mailinglist/user/ http://github.com/elasticsearch References: http://highscalability.com/blog/2010/2/10/elasticsearch-open-source-distributed-restful-search-engine.html http://blog.sematext.com/2010/05/03/elastic-search-distributed-lucene/ http://mail-archives.apache.org/mod_mbox/hbase-user/201006.mbox/%3C149150.78881.qm@web50304.mail.re2.yahoo.com%3E http://www.slideshare.net/elasticsearch/elasticsearch-at-berlinbuzzwords-2010#

30. Thanks/