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README.asciidoc

Elasticsearch

A Distributed RESTful Search Engine

https://www.elastic.co/products/elasticsearch

Elasticsearch is a distributed RESTful search engine built for the cloud. Features include:

  • Distributed and Highly Available Search Engine.

    • Each index is fully sharded with a configurable number of shards.

    • Each shard can have one or more replicas.

    • Read / Search operations performed on any of the replica shards.

  • Multi Tenant.

    • Support for more than one index.

    • Index level configuration (number of shards, index storage, …​).

  • Various set of APIs

    • HTTP RESTful API

    • All APIs perform automatic node operation rerouting.

  • Document oriented

    • No need for upfront schema definition.

    • Schema can be defined for customization of the indexing process.

  • Reliable, Asynchronous Write Behind for long term persistency.

  • (Near) Real Time Search.

  • Built on top of Apache Lucene

    • Each 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.

Getting Started

First of all, DON’T PANIC. It will take 5 minutes to get the gist of what Elasticsearch is all about.

Installation

  • Download and unpack the Elasticsearch official distribution.

  • Run bin/elasticsearch on Linux or macOS. Run bin\elasticsearch.bat on Windows.

  • Run curl -X GET http://localhost:9200/.

  • Start more servers …​

Indexing

Let’s try and index some twitter like information. First, let’s index some tweets (the twitter index will be created automatically):

curl -XPUT 'http://localhost:9200/twitter/_doc/1?pretty' -H 'Content-Type: application/json' -d '
{
    "user": "kimchy",
    "post_date": "2009-11-15T13:12:00",
    "message": "Trying out Elasticsearch, so far so good?"
}'

curl -XPUT 'http://localhost:9200/twitter/_doc/2?pretty' -H 'Content-Type: application/json' -d '
{
    "user": "kimchy",
    "post_date": "2009-11-15T14:12:12",
    "message": "Another tweet, will it be indexed?"
}'

curl -XPUT 'http://localhost:9200/twitter/_doc/3?pretty' -H 'Content-Type: application/json' -d '
{
    "user": "elastic",
    "post_date": "2010-01-15T01:46:38",
    "message": "Building the site, should be kewl"
}'

Now, let’s see if the information was added by GETting it:

curl -XGET 'http://localhost:9200/twitter/_doc/1?pretty=true'
curl -XGET 'http://localhost:9200/twitter/_doc/2?pretty=true'
curl -XGET 'http://localhost:9200/twitter/_doc/3?pretty=true'

Searching

Mmm search…​, shouldn’t it be elastic? Let’s find all the tweets that kimchy posted:

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

We can also use the JSON query language Elasticsearch provides instead of a query string:

curl -XGET 'http://localhost:9200/twitter/_search?pretty=true' -H 'Content-Type: application/json' -d '
{
    "query" : {
        "match" : { "user": "kimchy" }
    }
}'

Just for kicks, let’s get all the documents stored (we should see the tweet from elastic as well):

curl -XGET 'http://localhost:9200/twitter/_search?pretty=true' -H 'Content-Type: application/json' -d '
{
    "query" : {
        "match_all" : {}
    }
}'

We can also do range search (the post_date was automatically identified as date)

curl -XGET 'http://localhost:9200/twitter/_search?pretty=true' -H 'Content-Type: application/json' -d '
{
    "query" : {
        "range" : {
            "post_date" : { "from" : "2009-11-15T13:00:00", "to" : "2009-11-15T14:00:00" }
        }
    }
}'

There are many more options to perform search, after all, it’s a search product no? All the familiar Lucene queries are available through the JSON query language, or through the query parser.

Multi Tenant and Indices

Man, that twitter index might get big (in this case, index size == valuation). Let’s see if we can structure our twitter system a bit differently in order to support such large amounts of data.

Elasticsearch supports multiple indices. In the previous example we used an index called twitter that stored tweets for every user.

Another way to define our simple twitter system is to have a different index per user (note, though that each index has an overhead). Here is the indexing curl’s in this case:

curl -XPUT 'http://localhost:9200/kimchy/_doc/1?pretty' -H 'Content-Type: application/json' -d '
{
    "user": "kimchy",
    "post_date": "2009-11-15T13:12:00",
    "message": "Trying out Elasticsearch, so far so good?"
}'

curl -XPUT 'http://localhost:9200/kimchy/_doc/2?pretty' -H 'Content-Type: application/json' -d '
{
    "user": "kimchy",
    "post_date": "2009-11-15T14:12:12",
    "message": "Another tweet, will it be indexed?"
}'

The above will index information into the kimchy index. Each user will get their own special index.

Complete control on the index level is allowed. As an example, in the above case, we might want to change from the default 1 shard with 1 replica per index, to 2 shards with 1 replica per index (because this user tweets a lot). Here is how this can be done (the configuration can be in yaml as well):

curl -XPUT http://localhost:9200/another_user?pretty -H 'Content-Type: application/json' -d '
{
    "settings" : {
        "index.number_of_shards" : 2,
        "index.number_of_replicas" : 1
    }
}'

Search (and similar operations) are multi index aware. This means that we can easily search on more than one index (twitter user), for example:

curl -XGET 'http://localhost:9200/kimchy,another_user/_search?pretty=true' -H 'Content-Type: application/json' -d '
{
    "query" : {
        "match_all" : {}
    }
}'

Or on all the indices:

curl -XGET 'http://localhost:9200/_search?pretty=true' -H 'Content-Type: application/json' -d '
{
    "query" : {
        "match_all" : {}
    }
}'

And the cool part about that? You can easily search on multiple twitter users (indices), with different boost levels per user (index), making social search so much simpler (results from my friends rank higher than results from friends of my friends).

Distributed, Highly Available

Let’s face it, things will fail…​.

Elasticsearch is a highly available and distributed search engine. Each index is broken down into shards, and each shard can have one or more replicas. By default, an index is created with 1 shard and 1 replica per shard (1/1). There are many topologies that can be used, including 1/10 (improve search performance), or 20/1 (improve indexing performance, with search executed in a map reduce fashion across shards).

In order to play with the distributed nature of Elasticsearch, simply bring more nodes up and shut down nodes. The system will continue to serve requests (make sure you use the correct http port) with the latest data indexed.

Where to go from here?

We have just covered a very small portion of what Elasticsearch is all about. For more information, please refer to the elastic.co website. General questions can be asked on the Elastic Forum or on Slack. The Elasticsearch GitHub repository is reserved for bug reports and feature requests only.

Building from Source

Elasticsearch uses Gradle for its build system.

In order to create a distribution, simply run the ./gradlew assemble command in the cloned directory.

The distribution for each project will be created under the build/distributions directory in that project.

See the TESTING for more information about running the Elasticsearch test suite.

Upgrading from older Elasticsearch versions

In order to ensure a smooth upgrade process from earlier versions of Elasticsearch, please see our upgrade documentation for more details on the upgrade process.

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