Adrien Grand d84c643f58 Use the new points API to index numeric fields. #17746
This makes all numeric fields including `date`, `ip` and `token_count` use
points instead of the inverted index as a lookup structure. This is expected
to perform worse for exact queries, but faster for range queries. It also
requires less storage.

Notes about how the change works:
 - Numeric mappers have been split into a legacy version that is essentially
   the current mapper, and a new version that uses points, eg.
   LegacyDateFieldMapper and DateFieldMapper.
 - Since new and old fields have the same names, the decision about which one
   to use is made based on the index creation version.
 - If you try to force using a legacy field on a new index or a field that uses
   points on an old index, you will get an exception.
 - IP addresses now support IPv6 via Lucene's InetAddressPoint and store them
   in SORTED_SET doc values using the same encoding (fixed length of 16 bytes
   and sortable).
 - The internal MappedFieldType that is stored by the new mappers does not have
   any of the points-related properties set. Instead, it keeps setting the index
   options when parsing the `index` property of mappings and does
   `if (fieldType.indexOptions() != IndexOptions.NONE) { // add point field }`
   when parsing documents.

Known issues that won't fix:
 - You can't use numeric fields in significant terms aggregations anymore since
   this requires document frequencies, which points do not record.
 - Term queries on numeric fields will now return constant scores instead of
   giving better scores to the rare values.

Known issues that we could work around (in follow-up PRs, this one is too large
already):
 - Range queries on `ip` addresses only work if both the lower and upper bounds
   are inclusive (exclusive bounds are not exposed in Lucene). We could either
   decide to implement it, or drop range support entirely and tell users to
   query subnets using the CIDR notation instead.
 - Since IP addresses now use a different representation for doc values,
   aggregations will fail when running a terms aggregation on an ip field on a
   list of indices that contains both pre-5.0 and 5.0 indices.
 - The ip range aggregation does not work on the new ip field. We need to either
   implement range aggs for SORTED_SET doc values or drop support for ip ranges
   and tell users to use filters instead. #17700

Closes #16751
Closes #17007
Closes #11513
2016-04-14 17:56:23 +02:00
2016-04-13 17:59:59 -04:00
2015-11-30 14:47:03 +01:00
2016-01-29 18:41:31 -08:00
2015-11-25 09:33:12 -05:00
2011-12-06 13:41:49 +02:00
2016-03-20 22:11:30 -04:00

h1. Elasticsearch

h2. A Distributed RESTful Search Engine

h3. "https://www.elastic.co/products/elasticsearch":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 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, ...).
* Various set of APIs
** HTTP RESTful API
** Native Java API.
** All APIs perform automatic node operation rerouting.
* Document oriented
** No need for upfront schema definition.
** Schema can be defined per type for customization of the indexing process.
* Reliable, Asynchronous Write Behind for long term persistency.
* (Near) Real Time Search.
* Built on top of 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.
* Open Source under the Apache License, version 2 ("ALv2")

h2. Getting Started

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

h3. Requirements

You need to have a recent version of Java installed. See the "Setup":http://www.elastic.co/guide/en/elasticsearch/reference/current/setup.html#jvm-version page for more information.

h3. Installation

* "Download":https://www.elastic.co/downloads/elasticsearch and unzip the Elasticsearch official distribution.
* Run @bin/elasticsearch@ on unix, or @bin\elasticsearch.bat@ on windows.
* Run @curl -X GET http://localhost:9200/@.
* Start more servers ...

h3. Indexing

Let's try and index some twitter like information. First, let's create a twitter user, and add some tweets (the @twitter@ index will be created automatically):

<pre>
curl -XPUT 'http://localhost:9200/twitter/user/kimchy' -d '{ "name" : "Shay Banon" }'

curl -XPUT 'http://localhost:9200/twitter/tweet/1' -d '
{
    "user": "kimchy",
    "postDate": "2009-11-15T13:12:00",
    "message": "Trying out Elasticsearch, so far so good?"
}'

curl -XPUT 'http://localhost:9200/twitter/tweet/2' -d '
{
    "user": "kimchy",
    "postDate": "2009-11-15T14:12:12",
    "message": "Another tweet, will it be indexed?"
}'
</pre>

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

<pre>
curl -XGET 'http://localhost:9200/twitter/user/kimchy?pretty=true'
curl -XGET 'http://localhost:9200/twitter/tweet/1?pretty=true'
curl -XGET 'http://localhost:9200/twitter/tweet/2?pretty=true'
</pre>

h3. Searching

Mmm search..., shouldn't it be elastic?
Let's find all the tweets that @kimchy@ posted:

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

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

<pre>
curl -XGET 'http://localhost:9200/twitter/tweet/_search?pretty=true' -d '
{
    "query" : {
        "match" : { "user": "kimchy" }
    }
}'
</pre>

Just for kicks, let's get all the documents stored (we should see the user as well):

<pre>
curl -XGET 'http://localhost:9200/twitter/_search?pretty=true' -d '
{
    "query" : {
        "matchAll" : {}
    }
}'
</pre>

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

<pre>
curl -XGET 'http://localhost:9200/twitter/_search?pretty=true' -d '
{
    "query" : {
        "range" : {
            "postDate" : { "from" : "2009-11-15T13:00:00", "to" : "2009-11-15T14:00:00" }
        }
    }
}'
</pre>

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.

h3. Multi Tenant - Indices and Types

Maan, 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, as well as multiple types per index. In the previous example we used an index called @twitter@, with two types, @user@ and @tweet@.

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:

<pre>
curl -XPUT 'http://localhost:9200/kimchy/info/1' -d '{ "name" : "Shay Banon" }'

curl -XPUT 'http://localhost:9200/kimchy/tweet/1' -d '
{
    "user": "kimchy",
    "postDate": "2009-11-15T13:12:00",
    "message": "Trying out Elasticsearch, so far so good?"
}'

curl -XPUT 'http://localhost:9200/kimchy/tweet/2' -d '
{
    "user": "kimchy",
    "postDate": "2009-11-15T14:12:12",
    "message": "Another tweet, will it be indexed?"
}'
</pre>

The above will index information into the @kimchy@ index, with two types, @info@ and @tweet@. Each user will get their own special index.

Complete control on the index level is allowed. As an example, in the above case, we would want to change from the default 5 shards with 1 replica per index, to only 1 shard with 1 replica per index (== per twitter user). Here is how this can be done (the configuration can be in yaml as well):

<pre>
curl -XPUT http://localhost:9200/another_user/ -d '
{
    "index" : {
        "numberOfShards" : 1,
        "numberOfReplicas" : 1
    }
}'
</pre>

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

<pre>
curl -XGET 'http://localhost:9200/kimchy,another_user/_search?pretty=true' -d '
{
    "query" : {
        "matchAll" : {}
    }
}'
</pre>

Or on all the indices:

<pre>
curl -XGET 'http://localhost:9200/_search?pretty=true' -d '
{
    "query" : {
        "matchAll" : {}
    }
}'
</pre>

{One liner teaser}: 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).

h3. 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 replica. By default, an index is created with 5 shards and 1 replica per shard (5/1). 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.

h3. 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":http://www.elastic.co/products/elasticsearch website.

h3. Building from Source

Elasticsearch uses "Gradle":http://gradle.org for its build system. You'll need to have a modern version of Gradle installed - 2.8 should do.

In order to create a distribution, simply run the @gradle build@ command in the cloned directory.

The distribution for each project will be created under the @target/releases@ directory in that project.

See the "TESTING":TESTING.asciidoc file for more information about
running the Elasticsearch test suite.

h3. Upgrading from Elasticsearch 1.x?

In order to ensure a smooth upgrade process from earlier versions of
Elasticsearch (1.x), it is required to perform a full cluster restart. Please
see the "setup reference":
https://www.elastic.co/guide/en/elasticsearch/reference/current/setup-upgrade.html
for more details on the upgrade process.

h1. License

<pre>
This software is licensed under the Apache License, version 2 ("ALv2"), quoted below.

Copyright 2009-2015 Elasticsearch <https://www.elastic.co>

Licensed under the Apache License, Version 2.0 (the "License"); you may not
use this file except in compliance with the License. You may obtain a copy of
the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
License for the specific language governing permissions and limitations under
the License.
</pre>
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🔎 Open source distributed and RESTful search engine.
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