OpenSearch/core
Boaz Leskes 80b59e0d66 Discovery: Add a dedicate queue for incoming ClusterStates
The initial implementation of two phase commit based cluster state publishing (#13062) relied on a single in memory "pending" cluster state that is only processed by ZenDiscovery once committed by the master. While this is fine on it's own, it resulted in an issue with acknowledged APIs, such as the open index API, in the extreme case where a node falls behind and receives a commit message after a new cluster state has been published. Specifically:

1) Master receives and acked-API call and publishes cluster state CS1
2) Master waits for a min-master nodes to receives CS1 and commits it.
3) All nodes that have responded to CS1 are sent a commit message, however, node N didn't respond yet
4) Master waits for publish timeout (defaults to 30s) for all nodes to process the commit. Node N fails to do so.
5) Master publishes a cluster state CS2. Node N responds to cluster state CS1's publishing but receives cluster state CS2 before the commit for CS1 arrives.
6) The commit message for cluster CS1 is processed on node N, but fails because CS2 is pending. This caused the acked API in step 1 to return (but CS2 , is not yet processed).

In this case, the action indicated by CS1 is not yet executed on node N and therefore the acked API calls return pre-maturely. Note that once CS2 is processed but the change in CS1 takes effect (cluster state operations are safe to batch and we do so all the time).

An example failure can be found on: http://build-us-00.elastic.co/job/es_feature_two_phase_pub/314/

This commit extracts the already existing pending cluster state queue (processNewClusterStates) from ZenDiscovery into it's own class, which serves as a temporary container for in-flight cluster states. Once committed the cluster states are transferred to ZenDiscovery as they used to before. This allows "lagging" cluster states to still be successfully committed and processed (and likely to be ignored as a newer cluster state has already been processed).

As a side effect, all batching logic is now extracted from ZenDiscovery and is unit tested.
2015-09-11 09:23:41 +02:00
..
src Discovery: Add a dedicate queue for incoming ClusterStates 2015-09-11 09:23:41 +02:00
LICENSE.txt create core module 2015-06-05 13:12:03 +02:00
NOTICE.txt Packaging: Add LICENSE, NOTICE, and sha1 files and tests for all core dependencies 2015-06-17 18:06:00 +02:00
README.textile Fix network binding for ipv4/ipv6 2015-08-17 15:43:07 -04:00
pom.xml Drop commons-lang dependency 2015-08-18 22:59:31 +02:00

README.textile

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 either one of the replica shard.
* 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 his 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 "Maven":http://maven.apache.org for its build system.

In order to create a distribution, simply run the @mvn clean package
-DskipTests@ command in the cloned directory.

The distribution will be created under @target/releases@.

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

h3. Upgrading to Elasticsearch 1.x?

In order to ensure a smooth upgrade process from earlier versions of Elasticsearch (< 1.0.0), it is recommended 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>