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Following the discussion in #11744, move boost and query _name to base class AbstractQueryBuilder with their getters and setters. Unify their serialization code and equals/hashcode handling in the base class too. This guarantess that every query supports both _name and boost and nothing needs to be done around those in subclasses besides properly parsing the fields in the parsers and printing them out as part of the doXContent method in the builders. More specifically, these are the performed changes: - Introduced printBoostAndQueryName utility method in AbstractQueryBuilder that subclasses can use to print out _name and boost in their doXContent method. - readFrom and writeTo are now final methods that take care of _name and boost serialization. Subclasses have to implement doReadFrom and doWriteTo instead. - toQuery is a final method too that takes care of properly applying _name and boost to the lucene query. Subclasses have to implement doToQuery instead. The query returned will have boost and queryName applied automatically. - Removed BoostableQueryBuilder interface, given that every query is boostable after this change. This won't have any negative effect on filters, as the boost simply gets ignored in that case. - Extended equals and hashcode to handle queryName and boost automatically as well. - Update the query test infra so that queryName and boost are tested automatically, and whenever they are forgotten in parser or doXContent tests fail, so this makes things a lot less error-prone - Introduced DEFAULT_BOOST constant to make sure we don't repeat 1.0f all the time for default boost values. SpanQueryBuilder is again a marker interface only. The convenient toQuery that allowed us to override the return type to SpanQuery cannot be supported anymore due to a clash with the toQuery implementation from AbstractQueryBuilder. We have to go back to castin lucene Query to SpanQuery when dealing with span queries unfortunately. Note that this change touches not only the already refactored queries but also the untouched ones, by making sure that we parse _name and boost whenever we need to and that we print them out as part of QueryBuilder#doXContent. This will result in printing out the default boost all the time rather than skipping it in non refactored queries, something that we would have changed anyway as part of the query refactoring. The following are the queries that support boost now while previously they didn't (parser now parses it and builder prints it out): and, exists, fquery, geo_bounding_box, geo_distance, geo_distance_range, geo_hash_cell, geo_polygon, indices, limit, missing, not, or, script, type. The following are the queries that support _name now while previously they didn't (parser now parses it and builder prints it out): boosting, constant_score, function_score, limit, match_all, type. Range query parser supports now _name at the same level as boost too (_name is still supported on the outer object though for bw comp). There are two exceptions that despite have getters and setters for queryName and boost don't really support boost and queryName: query filter and span multi term query. The reason for this is that they only support a single inner object which is another query that they wrap, no other elements. Relates to #11744 Closes #10776 Closes #11974
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>