How to use the ElasticSearch Query DSL

I'm also in the process of developing an ElasticSearch Client based in Objective-C using RestKit, feel free to have a look & use it ... and shoot me an email if you want to contribute: https://github.com/pulkitsinghal/ElasticSearchClient

	// 1) Lets start with the simplest query that you can run in
	// the head plugin for ElasticSearch located at the url:
	// http://localhost:9200/_plugin/head/
	{
	"query": {
	"match_all": {}
	}
	}
	// 2) Before jumping into forming queries with the ES Query DSL
	// syntax alone, lets try out a few queries written within
	// the comfort zone of those who are already familiar with
	// Lucene and/or Solr
	{
	"query": {
	"query_string": {
	"query": "+camera +laptop",
	"use_dis_max": true
	}
	}
	}
	// 3) Lets try it again but with slightly different syntax, it gives
	// exactly the same number of total hits as the previous query.
	{
	"query": {
	"query_string": {
	"query": "camera AND laptop",
	"use_dis_max": true
	}
	}
	}
	// 4) What if we want to specify the exact fields or terms, which should
	// be searched for the query? Well, here's how:
	{
	"query": {
	"query_string": {
	"fields": [
	"name",
	"shortDescription"
	],
	"query": "+camera +laptop",
	"use_dis_max": true
	}
	}
	}
	// 5) Someone who is not yet well-versed with JSON, may ask: what if I just want
	// to search inside one field exactly for a match and not multiple ones?
	// Simple, don't specify multiple fields:
	{
	"query": {
	"query_string": {
	"fields": [
	"name"
	],
	"query": "+camera +laptop",
	"use_dis_max": true
	}
	}
	}
	// 6) That's all good but now we want to limit the fields coming back
	// in the response. It would be nice to specify exactly the fields
	// that we want to retrieve:
	{
	"fields": [
	"name",
	"shortDescription",
	"longDescription"
	],
	"query": {
	"query_string": {
	"fields": [
	"name"
	],
	"query": "+camera +laptop",
	"use_dis_max": true
	}
	}
	}
	// 7) Now lets try something different like a range query to try and
	// group our results so far into price buckets.
	//
	// WARNING: Make sure to POST to the exact index:
	// http://localhost:9200/my_index/_search/
	// And not against any & all indices
	// http://localhost:9200/_search/
	// Otherwise you may run into an exception like the following:
	// org.elasticsearch.search.facet.FacetPhaseExecutionException:
	// Facet [range1]: No mapping found for key_field [regularPrice]
	// from other indices that don't share the same fields:
	{
	"fields": [
	"name",
	"upc",
	"salePrice",
	"regularPrice"
	],
	"query": {
	"query_string": {
	"fields": [
	"name"
	],
	"query": "+camera +laptop",
	"use_dis_max": true
	}
	},
	"facets": {
	"range1": {
	"range": {
	"regularPrice": [
	{
	"to": 100
	},
	{
	"from": 100,
	"to": 200
	},
	{
	"from": 200,
	"to": 300
	},
	{
	"from": 300
	}
	]
	}
	}
	}
	}
	// 8) The total # of results is simple to find out but what about getting
	// all the results back? Well both getting the results back and doing so
	// in an orderly and paginated manner is easy to do like so:
	//
	// 8.1) Get the results starting from offset 0 and get a total of 2 entries
	{
	"from": 0,
	"size": 2,
	"fields": [
	"name"
	],
	"query": {
	"match_all": {}
	}
	}
	// 8.2) Get the results starting from offset 2 and get a total of 2 entries
	{
	"from": 2,
	"size": 2,
	"fields": [
	"name"
	],
	"query": {
	"match_all": {}
	}
	}
	// 9) If your index doesn't change often, then neither will the position of results
	// being returned by the query. These results are positioned by the score allotted
	// to each one of them in terms of how likely Lucene/ES thinks they are to be
	// the closest-fit / best-answer to your query.
	//
	// Lets say you want random results, what then? How can you ask ES to change the
	// score and return different results for every query? Here's how:
	{
	"from": 0,
	"size": 2,
	"fields": [
	"name",
	"upc"
	],
	"query": {
	"custom_score": {
	"query": {
	"match_all": {}
	},
	"script": "random()"
	}
	}
	}
	// In this example by separating out the key and value facet fields,
	// the range buckets are filled based on the key field ... while the
	// averages etc. are calculated based on the the value field ... the
	// assumption here (I suppose) is that the documents which have the
	// key fields present, will also have the value field present (optimistic?)
	{
	"fields": [
	"name",
	"upc",
	"salePrice",
	"regularPrice"
	],
	"query": {
	"query_string": {
	"fields": [
	"name"
	],
	"query": "+camera +laptop",
	"use_dis_max": true
	}
	},
	"facets": {
	"range1": {
	"range": {
	"key_field": "regularPrice",
	"value_field": "salePrice",
	"ranges": [
	{
	"to": 100
	},
	{
	"from": 100,
	"to": 200
	},
	{
	"from": 200,
	"to": 300
	},
	{
	"from": 300
	}
	]
	}
	}
	}
	}
	// Good example of using histogram facets if the ranges that you specify
	// happen to be at regular intervals anyway.
	{
	"fields": [
	"name",
	"upc",
	"salePrice",
	"regularPrice"
	],
	"query": {
	"query_string": {
	"fields": [
	"name"
	],
	"query": "+camera +laptop",
	"use_dis_max": true
	}
	},
	"facets": {
	"histo1": {
	"histogram": {
	"field": "regularPrice",
	"interval": 100
	}
	}
	}
	}
	// The following is supposed to be the same but seems to give more metrics
	{
	"fields": [
	"name",
	"upc",
	"salePrice",
	"regularPrice"
	],
	"query": {
	"query_string": {
	"fields": [
	"name"
	],
	"query": "+camera +laptop",
	"use_dis_max": true
	}
	},
	"facets": {
	"histo1": {
	"histogram": {
	"key_field": "regularPrice",
	"value_field": "salePrice",
	"interval": 100
	}
	}
	}
	}
	// This is a really exciting example where we take our query results and
	// bucket them by making a date histogram facet, therefore, shirking all
	// responsibility of figuring out which items fall in which year by startDate
	// All work is done by ES! Can you say $profit$ :)
	{
	"fields": [
	"name",
	"upc",
	"salePrice",
	"regularPrice"
	],
	"query": {
	"query_string": {
	"query": "+camera +laptop",
	"use_dis_max": true
	}
	},
	"facets": {
	"histo1": {
	"date_histogram": {
	"key_field": "startDate",
	"value_field": "startDate",
	"interval": "year"
	}
	}
	}
	}
	// What if you have a scenario where out want to figure out if there are
	// really good sales going on in the area that you are interested in?
	// Then statistical facet queries should get your motor started.
	{
	"query": {
	"query_string": {
	"fields": [
	"name",
	"description"
	],
	"query": "+camera +laptop",
	"use_dis_max": true
	}
	},
	"facets": {
	"stat1": {
	"statistical": {
	"field": "regularPrice"
	}
	},
	"stat2": {
	"statistical": {
	"field": "salePrice"
	}
	}
	}
	}

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