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Today, when a primary shard is not allocated we go to all the nodes to find where it is allocated (listing its started state). When we allocate a replica shard, we head to all the nodes and list its store to allocate the replica on a node that holds the closest matching index files to the primary. Those two operations today execute synchronously within the GatewayAllocator, which means they execute in a sync manner within the cluster update thread. For large clusters, or environments with very slow disk, those operations will stall the cluster update thread, making it seem like its stuck. Worse, if the FS is really slow, we timeout after 30s the operation (to not stall the cluster update thread completely). This means that we will have another run for the primary shard if we didn't find one, or we won't find the best node to place a shard since it might have timed out (listing stores need to list all files and read the checksum at the end of each file). On top of that, this sync operation happen one shard at a time, so its effectively compounding the problem in a serial manner the more shards we have and the slower FS is... This change moves to perform both listing the shard started states and the shard stores to an async manner. During the allocation by the GatewayAllocator, if data needs to be fetched from a node, it is done in an async fashion, with the response triggering a reroute to make sure the results will be taken into account. Also, if there are on going operations happening, the relevant shard data will not be taken into account until all the ongoing listing operations are done executing. The execution of listing shard states and stores has been moved to their own respective thread pools (scaling, so will go down to 0 when not needed anymore, unbounded queue, since we don't want to timeout, just let it execute based on how fast the local FS is). This is needed sine we are going to blast nodes with a lot of requests and we need to make sure there is no thread explosion. This change also improves the handling of shard failures coming from a specific node. Today, those nodes were ignored from allocation only for the single reroute round. Now, since fetching is async, we need to keep those failures around at least until a single successful fetch without the node is done, to make sure not to repeat allocating to the failed node all the time. Note, if before the indication of slow allocation was high pending tasks since the allocator was waiting for responses, not the pending tasks will be much smaller. In order to still indicate that the cluster is in the middle of fetching shard data, 2 attributes were added to the cluster health API, indicating the number of ongoing fetches of both started shards and shard store. closes #9502 closes #11101
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>
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