learning

Elasticsearch


garygaowork@gmail.com

Agenda


  •     Introduction
  •     Setup & Run
  •     Data Indexing(REST API)
  •     Data Searching(REST API)
  •     Cluster
  •     Plugins
  •     Monitoring
  •     Internals
  •     Optimization
Introduction

Elasticsearch

Based on Lucene, Written in Java
Real time analytics
Full Text Search Engine
Distributed, Easy to Scale
High Availability
Multi tenant architecture
Document oriented(Json)
Schema Free
Restful API,Json over HTTP
Open Source:Apache License 2.0
Easy to Configure
Plugins & Community Support
Introduction

Scenario & Use Case

(1)     Analytics engine

(2)     Aggregate repository

Introduction

Use Case : Github Search


 

  • search repositories, users, issues, pull requests
  • search 130 billion lines of code
  • track all alerts, events, logs

Introduction

Use Case : Stackoverflow



  • combines full text search with geolocation
  • uses more-like-this to find related questions and anwsers

Sina


Sina Edge

Sina S3

TODO

Introduction

Elasticsearch Basic Concepts


  • What is a Document?
  • What is a Document Type?
  • What is an Index ?
  • What is Analysis ?
  • What is a Shard?
  • What is a Replica?
  • What is a Mapping?
  • What is a Node?
  • What is a Cluster?
Introduction
What is a Document ?
 {
	"@timestamp": "2014-02-27T08:42:13.000+08:00",
	"sip": "172.16.8.51",
	"domain": "paikeapp.video.sina.com.cn",
	"cip": "114.244.55.67",
	"taketime": "0.042",
	"hit": "TCP_MISS:DIRECT",
	"verb": "GET",
	"uripath": "/8/paikeapp.video.sina.com.cn/sport/img/884.png",
	"httpversion": "1.1",
	"httpcode": "404",
	"bytes": "278",
	"agent": "livesport/1.3 CFNetwork/672.0.8 Darwin/14.0.0",
}
What is a Document Type ?

A type is like a table in a relational  database. Each type has a list of fields that can be specified for documents of that type. The mapping defines how each field in the document is analyzed.

Introduction
What is an Index ?
  • ElasticSearch stores its data in logical Indices.It has a mapping which defines multiple types.Think of a table, collection or a database.
  • An Index has at least 1 primary Shard, and 0 or more Replicas.
  • Under the hood, ElasticSearch uses Apache Lucene library to write and read the data from the index.
Introduction
What is a Shard?
  • a single Lucene index
  • automatically managed  by elasticsearch
  • distributed  amongst all nodes in the cluster

What is a Replica?
  • a copy of the primary shard
  • Each primary shard can have zero or more replicas
Introduction
What is  Analysis ?
Analysis is the process of converting full text to terms.
Analysis in Indexing
Analysis in Query

           

             "FOO BAR"

             "Foo-Bar"            ------>   "foo" , "bar"

             "foo,bar"


Introduction
What is a Mapping?
Each index has a mapping, which defines each type within the index, plus a number of index-wide settings. A mapping can either be defined explicitly, or it will be generated automatically when a document is indexed.
 curl es_host:9200/my_index_name/_mapping
"tweet" : {
    "properties" : {
         "message" : {
              "type" : "string"
         }
    }
}
Introduction
Analogy of Elasticsearch and Relational Database

                                                        Index    <--->   Database
                                                 Mapping   <---> Schema
                                             Index.Type   <---> Table
                                             Document   <---> Table.Row
                                Document.Field   <---> Table.Column




Introduction

Lucene basics

full text search library

Inverted Index


ElasticSearch uses Lucene to

    (1)  handle document indexing

    (2)  perform search against the indexed documents.

Introduction

Inverted Index


Introduction

Lucene query language

Lucene 4.6 Query Parser Syntax

TODO

Setup & Run

Elasticsearch Setup & Run

Download and unzip the latest Elasticsearch distribution

$ ./bin/elasticsearch
$ curl -XGET http://es_host:9200/
 
{
    "ok" : true,
    "status" : 200,
    "name" : "Slasher",
    "version" : {
        "number" : "0.90.11",
        "build_hash" : "11da1bacf39cec400fd97581668acb2c5450516c",
        "build_timestamp" : "2014-02-03T15:27:39Z",
        "build_snapshot" : false,
        "lucene_version" : "4.6"
    },
    "tagline" : "You Know, for Search"
}
Index Processing

Mapping

  • define how each field of a document should be mapped to the Search Engine and how you will eventually search your data
  • dynamic mapping : automatically created and registered when a new type or new field is introduced
Index Processing

Mapping - Field Types

Mapping:
{
    "type" : "string"
    ...
}

Core Field Types:

  •     Strings :             string
  •     Datetimes:     date
  •     Numbers :       byte, short, integer, log
  •     Floats :               float, double
  •     Booleans :       boolean
  •     Objects :           object


Index Processing

Mapping - Type Detection


  • "Trying out Elasticsearch"      string
  • "2014-01-09"                                  date
  • 10                                                               byte, short, integer, long
  • 3.14                                                          float, double
  • true                                                           boolean
  • {corp:"sina"}                                       object
  • ["stefen", "elena"]                           No special mapping.Any field can have multi values
Index Processing

Mapping - Full Text vs Exact String

 Mapping:
{
    "type" : "string"
    "index" : "analyzed"
    ...
}# full text(default) : "Trying out Elasticsearch" => ["Trying",  "out", "Elasticsearch"]

 Mapping:
{
    "type" : "string"
    "index" : "not_analyzed"
    ...
}# exact string : "Trying out Elasticsearch" => ["Trying out Elasticsearch"]

 Mapping:
{
    "type" : "string"
    "index" : "no"
    ...
}# Not Searchable : "Trying out Elasticsearch" => []

Index Processing

Mapping - Analyzer


Mapping:
{
    "type" : "string"
    "index" : "analyzed"
    "analyzer" : "default"
    ...
}

or

Mapping:
{
    "type" : "string"
    "index" : "analyzed"
    "index_analyzer" : "default"
    "search_analyzer" : "default"
    ...
}

Index Processing

Mapping - Analyzer



Index Processing

Mapping - Analyzer


  • Standard Analyzer
  • Simple Analyzer
  • Whitespace Analyzer
  • Stop Analyzer
  • Keyword Analyzer
  • Pattern Analyzer
  • Language Analyzers
  • Snowball Analyzer
  • Custom Analyzer(customize your analyzer)
Index Processing

Mapping - Analyzer

"The quick brown fox jumped over the lazy dogs,123-456"


Analyze API:
 $ curl -XGET  'elasticsearch_host:9200/_analyze?analyzer=standard' -d 'this is a test'

Data Indexing

Basic CRUD

Create an index named 'log':

 $ curl -XPUT 'http://es_host:9200/log'
{
   "ok": true,
   "acknowledged": true
}

Delete the index 'log':

 $ curl -XDELETE 'http://es_host:9200/log'
{
   "ok": true,
   "acknowledged": true
}
Data Indexing

Basic CRUD

Create a type named 'http_log':

 $ curl -XPUT 'http://es_host:9200/log/http_log/_mapping' -d '
{
  "http_log": {
    "properties": {
      "httpcode": {
        "type": "string"
      },
      "verb": {
      	"type": "string"
      },
      "timestamp": {
      	"type": "date"
      },
      "domain": {
      	"type": "string"
      },
      "uripathparam": {
      	"type": "string"
      }
    }
  }
}'
{
   "ok": true,
   "acknowledged": true
}
Data Indexing

Basic CRUD


        Also you can put a document without:

                     (1) putting an index

                              or

                     (2) putting a mapping


dynamic mapping

Data Indexing

Basic CRUD

PUT /index/type/id

Create a document(Indexing):

 $ curl -XPUT 'http://es_host:9200/log/http_log/1' -d '
{
  "domain":"www.sina.com.cn",
  "httpcode":"404",
  "verb":"GET",
  "uripathparam":"/logo.png",
  "timestamp":"2014-02-27T12:20:05.000+08:00"
}'
{
   "ok": true,
   "_index": "log",
   "_type": "http_log",
   "_id": "1",
   "_version": 1
}
Data Indexing

Basic CRUD

Bulk Indexing:

$ curl -XPOST 'http://es_host:9200/log/_bulk' -d '
{
	"index" :{
		"_index": "log", 
		"_type":"http_log", 
		"_id":"1"
	}
}
{
	"domain": "weibo.com", 
	"httpcode": "404",
	"verb":"GET",
	"uripathparam": "/logo.png", 
	"timestamp": "2014-02-27T12:20:05.000+08:00"
}
{
	"index" : {
		"_index":"log", 
		"_type":"http_log", 
		"_id":"2"
	}
}
{
	"domain": "weibo.com", 
	"httpcode": "404", 
	"verb": "GET", 
	"uripathparam": "/logo_48_48.png", 
	"timestamp": "2014-02-27T12:20:05.000+08:00"
}'
Data Indexing

Basic CRUD

You get:

{
   "took": 12,
   "items": [
      {
         "index": {
            "_index": "log",
            "_type": "http_log",
            "_id": "1",
            "_version": 1,
            "ok": true
         }
      },
      {
         "index": {
            "_index": "log",
            "_type": "http_log",
            "_id": "2",
            "_version": 1,
            "ok": true
         }
      }
   ]
}
Data Indexing

Basic CRUD

Retrieve the document:

 $ curl -XGET 'http://es_host:9200/log/http_log/1'
{
   "_index": "log",
   "_type": "http_log",
   "_id": "1",
   "_version": 1,
   "exists": true,
   "_source": {
      "domain": "www.sina.com.cn",
      "httpcode": "404",
      "verb": "GET",
      "uripathparam": "/logo.png",
      "timestamp": "2014-02-27T12:20:05.000+08:00"
   }
}
Data Indexing

Basic CRUD

Update the document's 'domain' field:

 $ curl -XPOST 'http://es_host:9200/log/http_log/1/_update' -d '
{
  "doc": {
    "domain": "weibo.com"
  }
}'
{
   "ok": true,
   "_index": "log",
   "_type": "http_log",
   "_id": "1",
   "_version": 2
}
Data Indexing

Basic CRUD

Delete the document:

 $ curl -XDELETE 'http://es_host:9200/log/http_log/1'
{
   "ok": true,
   "found": true,
   "_index": "log",
   "_type": "http_log",
   "_id": "1",
   "_version": 3
}
Data Searching

Search Data:Search API & Query DSL


  • How to query ElasticSearch using its Query DSL ?
  • How to use basic queries ?
  • How to use compound queries ?
  • How to filter your results and why it is important ?
  • How to change the sorting of your results ?
  • How to use scripts in ElasticSearch ?

Data Searching

Search Data:Search API & Query DSL


Search Endpoint:

GET elasticsearch_host:9200/ index/type/ _search


Data Searching

Search Data:Search API & Query DSL

Query DSL:

           queries:                                                           

term query          terms query      match query           multi match query

query string query       prefix query        fuzzy query

wildcard query          range query              ......

           Filters:

query filter   range filter  term filter   terms filter 
and filter   or filter    not filter   bool filter
missing filter  prefix filter   exists filter    ......

Data Searching

Search Data:Search API & Query DSL




Basic Queries

Data Searching

Search Data:Search API & Query DSL

Simplest Query:

 $ curl -XGET 'localhost:9200/twitter/tweet/_search?q=user:linken

This is acturally term query :

$ curl -XGET 'localhost:9200/twitter/tweet/_search -d '
{
    "query" : {
        "term" : { "user" : "linken" }
    }
}
'
       /\
       ||
       ||
    Query DSL

Term Query:
        Matches documents that have fields that contain a term (not analyzed).
Data Searching

Search Data:Search API & Query DSL

Terms Query:

       A query that match on any (configurable) of the provided terms. 

$ curl -XGET 'localhost:9200/twitter/tweet/_search -d '
{
    "query" : {
        "terms" : {
            "tags" : [ "novel", "book" ],
            "minimum_should_match" : 1
        }
    }
}
'
# returns all the documents that have one or both of the searched terms in the "tags" field.

"minimum_should_match" : 1 # one term should be matched
"minimum_should_match" : 2 # both term should be matched

Data Searching

Search Data:Search API & Query DSL

Match Query:

        accepts text/numerics/dates, analyzes it, and constructs a query out of it.
 

$ curl -XGET 'localhost:9200/twitter/tweet/_search -d '
{
    "query" : {
        "match" : {
            "tweet" : "duck dog cat"
        }
    }
}
'
# match all the documents that have the terms "duck" or "dog" or "cat" in the "title" field.


Data Searching

Search Data:Search API & Query DSL

Multi Match Query:

        The multi_match query builds on the match query to allow multi-field queries.
 

$ curl -XGET 'localhost:9200/twitter/tweet/_search -d '
{
    "query" : {
        "multi_match" : {
          "query":    "this is a test", 
          "fields": [ "subject", "message" ] 
        }
    }
}
'
# query on "subject" and "message" fields.

Warning : Deprecated in 1.1.0. Replaced by multi_query .

Data Searching

Search Data:Search API & Query DSL

TODO,    Query String Query:

        A query that uses a query parser in order to parse its content.
 

$ curl -XGET 'localhost:9200/twitter/tweet/_search -d '
{
    "query" : {
        "multi_match" : {
          "query":    "this is a test", 
"fields": [ "subject", "message" ] 
        }
    }
}
'
# query on "subject" and "message" fields.

Data Searching

Search Data:Search API & Query DSL

Prefix Query:

        Matches documents that have fields containing terms with a specified prefix (not analyzed).
 

$ curl -XGET 'localhost:9200/twitter/tweet/_search -d '
{
    "query" : {
        "prefix" : { 
            "user" : "ki"
        }
    }
}
'
# matches documents where the "user" field contains a term that starts with "ki"

Data Searching

Search Data:Search API & Query DSL

Match All Query:

        matches all documents.
 

$ curl -XGET 'localhost:9200/twitter/tweet/_search -d '
{
    "query" : {
        "match_all" : { }
    }
}
'
# matches all documents of "tweet" type of "twitter" index.

Data Searching

Search Data:Search API & Query DSL

Wildcard Query:

        allows us to use the  *  and  ?  wildcards in the values we search for(not analyzed).
 

$ curl -XGET 'localhost:9200/twitter/tweet/_search -d '
{
    "query" : {
        "wildcard" : {
            "user" : "kimi*"
        }
    }
}
'
# matches documents start with "kimi".
* : 0-n character sequence
? : any single character


Data Searching

Search Data:Search API & Query DSL

Range Query:

         Matches documents with fields that have terms within a certain range. 

$ curl -XGET 'localhost:9200/twitter/tweet/_search -d '
{
    "query" : {
        "range" : {
            "age" : {
                "gte" : 10,
                "lte" : 20
            }
        }
    }
}
'
# matches all documents where age is between 10 and 20.
gte       Greater-than or equal to
gt        Greater-than
lte       Less-than or equal to
lt        Less-than
Data Searching

Search Data:Search API & Query DSL




Filtering your results 

Data Searching

Search Data:Search API & Query DSL

Range Filter:

       Filters documents with fields that have terms within a certain range. 

$ curl -XGET 'localhost:9200/twitter/tweet/_search -d '
{
    "filter" : {
        "range" : {
            "age" : {
                "gte": 10,
                "lte": 20
            }
        }
    }
}
'
# matches all documents where age is between 10 and 20.
gte       Greater-than or equal to
gt        Greater-than
lte       Less-than or equal to
lt        Less-than
Data Searching

Search Data:Search API & Query DSL

Script Filter:

       A filter allowing to define scripts as filters. 

$ curl -XGET 'localhost:9200/twitter/tweet/_search -d '
{
    "filter" : {
        "script" : {
            "script" : "doc['year'].value = param1"
            "params" : {
                "param1" : 2014
            }
        }
    }
}
'
# matches all documents where "year" field is 2014.# "param1" is a custom parameter.

Data Searching

Search Data:Search API & Query DSL

Type Filter:

       Filters documents matching the provided document / mapping  type.

$ curl -XGET 'localhost:9200/twitter/tweet/_search -d '
{
    "filter" : {
        "type" : {
            "value" : "my_type"
        }
    }
}
'
# matches all documents where mapping type is "my_type".

Data Searching

Search Data:Search API & Query DSL

Ids Filter:

       Filters documents that only have the provided ids.

$ curl -XGET 'localhost:9200/twitter/tweet/_search -d '
{
    "filter" : {
        "ids" : {
            "type" : "my_type",
            "values" : ["1", "4", "100"]
        }
    }
}
'
# matches documents where mapping type is "my_type" and value of "_uid" field in "1", "4", "100".

Data Searching

Search Data:Search API & Query DSL

Query Filter:

       Wraps any query to be used as a filter. 

$ curl -XGET 'localhost:9200/twitter/tweet/_search -d '
{
    "filter" : {
        "query" : {
            "query_string" : {
                "query" : "this AND that OR thus"
            }
        }
    }
}
'
# query "query_string" acts as a filter.
You don't always have to use a wrapped query syntax because we have  a dedicated version in filter.
Data Searching

Search Data:Search API & Query DSL


    You don't have to wrap

Term Query/Range Query

    because we have

Term Filter/Range Filter

Data Searching

Search Data:Search API & Query DSL

Term Filter:

       Filters documents that have fields that contain a term (not analyzed). 

$ curl -XGET 'localhost:9200/twitter/tweet/_search -d '
{
    "filter" : {
        "term" : {
            "user" : "kimchy"
        }
    }
}
'
# matches documents where "user" field is "kimchy".
           Similar to term query, except that it acts as a filter: 
                        (1)  cached by default
                        (2)  no scoring
Data Searching

Search Data:Search API & Query DSL



Range Query     vs     Range Filter ?
Term Query     vs     Term Filter ?


  • The only difference in the result will be in the scoring.
  •  Every document returned by a filter will have a score of 1.0. 

Data Searching

Search Data:Search API & Query DSL


To make it even more complicated:

        Queries can contain other queries.

        Some queries can contain filters, and others can contain both queries and filters.

Data Searching

Search Data:Search API & Query DSL




Compound queries & filters

Data Searching

Search Data:Search API & Query DSL

Bool Query:

       A query that matches documents matching boolean combinations of other queries.

           It is built using one or more boolean clauses, each clause with a typed occurrence: 
must        The clause (query) must appear in matching documents.

should      The clause (query) may or may not appear in the matching document.

must_not    The clause (query) must not appear in the matching documents.

      For example, if you want to 

  • find all the documents that have the term "crime" in the "title" field.  
  • In addition, they may or may not have a range of 1900 to 2000 in the "year" field  
  • and must not have the term "nothing" in the "otitle" field.  
Data Searching

Search Data:Search API & Query DSL

          The query may looks like: 

$ curl -XGET 'localhost:9200/library/books/_search -d '
{
    "query" : {
        "bool" : {
            "must" : {
                "term" : {
                    "title" : "crime"
                }
            },
            "should" : {
                "range" : {
                    "year" : {
                        "from" : 1900,
                        "to" : 2000
                    }
                }
            },
            "must_not" : {
                "term" : {
                    "otitle" : "nothing"
                }
            }
        }
    }
}
'
Data Searching

Search Data:Search API & Query DSL

Constant Score Query:

        Wraps another query (or filter) and return a constant score for each document returned by the wrapped query (or filter).  

$ curl -XGET 'localhost:9200/twitter/tweet/_search -d '
{
    "query" : {
        "constant_score" : {
            "filter" : {
                "term" : { 
                    "user" : "kimchy"
                }
            },
            "boost" : 1.2
        }
    }
}
'
# if we want to have a score of 1.2 for all the documents that have the term "kimchy" in the "user" field.
Data Searching

Search Data:Search API & Query DSL


       bool filter:   Similar in concept to Boolean query, except that the clauses are other filters.

    and filter:  Take an array of filters and return every document that matches all of them.   

    or filter:   Take an array of filters and return every document that matches at least one of them.  

       not filter:  Returned documents are the ones that were not matched by the enclosed filter

     

     All these filters are not cached by default.

Data Searching

Search Data:Search API & Query DSL

These filters also can be combined together:

{
    "filter": {
        "not": {
            "and": [
                {
                    "term": {
                        "title": "Catch-22"
                    }
                },  
                {
                    "or": [
                        {
                            "range": {
                                "year": {
                                    "from": 1930,
                                    "to": 1990
                                }
                            }
                        },
                        {
                            "term": {
                                "available": true
                            }
                        }
                    ]
                }
            ]
        }
    }
}

Data Searching

Search Data:Search API & Query DSL


Queries VS Filters

TODO
relationships among Query ,Filter,Facets?
Data Searching

Search Data:Search API & Query DSL


See more queries and filters at:
    http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/query-dsl.html

Data Searching

Search Data:Search API & Query DSL

Paging:

$ curl -XGET 'localhost:9200/twitter/tweet/_search -d '
{
    "from" : 9,
    "size" : 20,
    "query" : {
        "term" : { "user" : "linken" }
    }
}
'
       /\
       ||
       ||
    Query DSL

  •      from : the offset from the first result you want to fetch.
  •      size : the maximum amount of hits to be returned.

Data Searching

Search Data:Search API & Query DSL

Choose the fields we want to return:

$ curl -XGET 'localhost:9200/twitter/tweet/_search -d '
{
"fields" : [ "content", "upvotes" ],
    "query" : {
        "term" : { "user" : "linken" }
    }
}
'
       /\
       ||
       ||
    Query DSL


      Data Searching

    Search Data:Multi-Index, Multi-Type

    • we can search on all documents across all types within the twitter index:

               $ curl -XGET 'http://localhost:9200/twitter/_search?q=user:kimchy'

    • We can also search within specific types:

              $ curl -XGET 'http://localhost:9200/twitter/tweet,user/_search?q=user:kimchy'

    • We can also search all tweets with a certain tag across several indices:

              $ curl -XGET 'http://localhost:9200/kimchy,elasticsearch/tweet/_search?q=tag:wow'

    • Or we can search all tweets across all available indices using _all placeholder:

              $ curl - XGET 'http://localhost:9200/_all/tweet/_search?q=tag:wow'

    • Or even search across all indices and all types:

              $ curl -XGET 'http://localhost:9200/_search?q=tag:wow'

      Data Searching

    Search Data:Facets


    • Terms - counts by distinct terms (values) in a field    
    • Range - counts for a given set of ranges in a field    
    • Histogram and Date Histogram - counts by constant interval ranges    
    • Statistical - statistical summary of a field (mean, sum etc)    
    • Terms Stats - statistical summary on one field (stats field) for distinct terms in another field. For example, spending stats per department or per region.    
    • Geo Distance: counts by distance ranges from a given point

    Distributed Elasticsearch

    Elasticsearch Cluster

         TODO:Setup, Configuration, Deployment,Upgrade Cluster

         Routing

    Elasticsearch  Plugins

    What can plugins do ?

                                 
                                                       Node discovery

                                    Analysis            Monitoring
                                                                                  River
                          Transport
                                                                            Scripting
                                          Snapshot/Restore
     

    Elasticsearch  Plugins

    Popular Plugins


    AWS Cloud Plugin         Redis River Plugin

    BigDesk Plugin         Elasticsearch Head Plugin  

     Inquisitor Plugin         Paramedic Plugin



    See more plugins:
    http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/modules-plugins.html

    Elasticsearch  Plugins

    Install & Remove Plugin

    #####INSTALL
# elasticsearch_dir/bin/plugin --install  <org>/<user/component>/<version>

#For example:
$ cd elasticsearch_dir
$ bin/plugin --install mobz/elasticsearch-head 
$ bin/plugin --install lukas-vlcek/bigdesk

##installed elasticsearch-head plugin and bigdesk plugin.


#####REMOVE
# plugin --remove <pluginname>

#For example:
$ bin/plugin --remove head
$ bin/plugin --remove bigdesk

    Elasticsearch Monitoring

    We are monitoring:

    Cluster State

    CPU,Memory,Disk

    Index Size

    Request Rate

    ......

    TODO

    Elasticsearch Monitoring

    Useful Tools

    Elasticsearch Marvel

    Bigdesk

    Head

    Curator:https://github.com/elasticsearch/curator

    ......

    TODO

    Elasticsearch Monitoring

    Elasticsearch Marvel

    metrics, screenshots.....

    TODO

    Internals

    Lucene

    ElasticSearch is based on Apache’s Lucene and borrows many concepts.

    Lucene:

                Segments and merge

                Analysis

                Indexing and querying

    Internals

    Analysis


    References

    [1] Elasticsearch References
          http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/index.html

    [2] Your Data, Your Search
          http://www.slideshare.net/karmi/your-data-your-search-elasticsearch-euruko-2011

    [2] Getting Down and dirty with Elasticsearch
           http://www.slideshare.net/clintongormley/down-and-dirty-with-elasticsearch

    [3] Elastic Search Training#1 (brief tutorial)-ESCC#1
          http://www.slideshare.net/medcl/elastic-search-training1-brief-tutorial
    [4] Elasticsearch :Search made easy for (web) developers (some contents are out dated)
    http://spinscale.github.io/elasticsearch/2012-03-jugm.html#/
    [5] Elasticsearch :Pluggable architecture under the hood
          http://spinscale.github.io/elasticsearch-intro-plugins/#/

    [6] Learning ElasticSearch — Fifth Elephant 2013, Bangalore.Anurag Patel Red Hat
          http://www.slideshare.net/gnurag/workshop-learning-elasticsearch

    References

    [7] Elasticsearch References: Glossary of terms
          http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/glossary.html#glossary
    [8] Exploring Elasticsearch
    http://exploringelasticsearch.com/
    [9] Beginner's guide to ElasticSearch
          http://stackoverflow.com/questions/11593035/beginners-guide-to-elasticsearch
    [10] What is an ElasticSearch Index?
          http://euphonious-intuition.com/2013/02/what-is-an-elasticsearch-index/
    [11] An introduction to mapping in elasticsearch
          http://euphonious-intuition.com/2012/07/an-introduction-to-mapping-in-elasticsearch/
    [12] Elasticsearch Aggregations Overview
          http://chrissimpson.co.uk/elasticsearch-aggregations-overview.html

    References

    [13] Querying ElasticSearch - A Tutorial and Guide
         http://okfnlabs.org/blog/2013/07/01/elasticsearch-query-tutorial.html

    [13] Terms of endearment - the ElasticSearch Query DSL explained
          http://www.slideshare.net/clintongormley/terms-of-endearment-the-elasticsearch-query-dsl-explained
    [14] Elasticsearch Facets
          http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/search-facets.html
    [15] Mastering Elasticserach
          http://www.PacktPub.com
    [16] Elasticsearch Search API
          http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/search.html
    [17] Lucene Scoring and elasticsearch’s _all Field
          http://jontai.me/blog/2012/10/lucene-scoring-and-elasticsearch-_all-field/
    [18] Advanced Scoring in elasticsearch
          http://jontai.me/blog/2013/01/advanced-scoring-in-elasticsearch/

    References

    [19] Collect & visualize your logs with Logstash, Elasticsearch & Redis
          http://michael.bouvy.net/blog/en/2013/11/19/collect-visualize-your-logs-logstash-elasticsearch-redis-kibana/
    [20] elasticsearch resources
          http://www.elasticsearch.org/resources/
    [21] Apache Lucene - Query Parser Syntax
          http://lucene.apache.org/core/4_6_0/queryparser/org/apache/lucene/queryparser/classic/package-summary.html#Overview
    The following articles are about Elasticsearch Optimization:
    [22] elasticsearch configuration
          http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/setup-configuration.html#setup-configuration-memory
    [23] Bigdesk - Live charts and statistics for elasticsearch cluster.
          http://bigdesk.org/
    [24] ElasticSearch and Logstash Tuning
          http://jablonskis.org/2013/elasticsearch-and-logstash-tuning/

    References

    [25] Elasticsearch Configuration and Performance Tuning
          http://weiweiwang.github.io/elasticsearch-configuration-and-performance-tuning.html
    [26] Scaling Massive Elasticsearch Clusters
          http://www.slideshare.net/sematext/scaling-massive-elasticsearch-clusters
    [27] Elasticsearch Java Virtual Machine settings explained
          http://jprante.github.io/2012/11/28/Elasticsearch-Java-Virtual-Machine-settings-explained.html
    [28] ElasticSearch Training#2 (advanced concepts)-ESCC#1
          http://www.slideshare.net/medcl/elastic-search-training2-advanced-concepts
    [29] All about Elasticsearch Filter BitSets
          http://euphonious-intuition.com/2013/05/all-about-elasticsearch-filter-bitsets/



    Q  &  A

    Learning Elasticsearch

    By Gary Gao

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    Learning Elasticsearch

    Learn Elasticsearch basic concepts,setup & conf,rest api and optimization.

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