elasticsearch/docs/reference/getting-started.asciidoc

[[getting-started]]
= Getting started with {es}

[partintro]
--
Ready to take {es} for a test drive and see for yourself how you can use the
REST APIs to store, search, and analyze data?

Follow this getting started tutorial to:

. Get an {es} cluster up and running
. Index some sample documents
. Search for documents using the {es} query language
. Analyze the results using bucket and metrics aggregations


Need more context?

Check out the <<elasticsearch-intro,
Elasticsearch Introduction>> to learn the lingo and understand the basics of
how {es} works. If you're already familiar with {es} and want to see how it works
with the rest of the stack, you might want to jump to the
{stack-gs}/get-started-elastic-stack.html[Elastic Stack
Tutorial] to see how to set up a system monitoring solution with {es}, {kib},
{beats},  and {ls}.

TIP: The fastest way to get started with {es} is to
https://www.elastic.co/cloud/elasticsearch-service/signup[start a free 14-day
trial of Elasticsearch Service] in the cloud.
--

[[getting-started-install]]
== Get {es} up and running

To take {es} for a test drive, you can create a one-click cloud deployment
on the https://www.elastic.co/cloud/elasticsearch-service/signup[Elasticsearch Service],
or <<run-elasticsearch-local, set up a multi-node {es} cluster>> on your own
Linux, macOS, or Windows machine.


[float]
[[run-elasticsearch-local]]
=== Run {es} locally on Linux, macOS, or Windows

When you create a cluster on the Elasticsearch Service, you automatically
get a three-node cluster. By installing from the tar or zip archive, you can
start multiple instances of {es} locally to see how a multi-node cluster behaves.

To run a three-node {es} cluster locally:

. Download the Elasticsearch archive for your OS:
+
Linux: https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-{version}-linux-x86_64.tar.gz[elasticsearch-{version}-linux-x86_64.tar.gz]
+
["source","sh",subs="attributes,callouts"]
--------------------------------------------------
curl -L -O https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-{version}-linux-x86_64.tar.gz
--------------------------------------------------
// NOTCONSOLE
+
macOS: https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-{version}-darwin-x86_64.tar.gz[elasticsearch-{version}-darwin-x86_64.tar.gz]
+
["source","sh",subs="attributes,callouts"]
--------------------------------------------------
curl -L -O https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-{version}-darwin-x86_64.tar.gz
--------------------------------------------------
// NOTCONSOLE
+
Windows:
https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-{version}-windows-x86_64.zip[elasticsearch-{version}-windows-x86_64.zip]

. Extract the archive:
+
Linux:
+
["source","sh",subs="attributes,callouts"]
--------------------------------------------------
tar -xvf elasticsearch-{version}-linux-x86_64.tar.gz
--------------------------------------------------
+
macOS:
+
["source","sh",subs="attributes,callouts"]
--------------------------------------------------
tar -xvf elasticsearch-{version}-darwin-x86_64.tar.gz
--------------------------------------------------
+
Windows PowerShell:
+
["source","sh",subs="attributes,callouts"]
--------------------------------------------------
Expand-Archive elasticsearch-{version}-windows-x86_64.zip
--------------------------------------------------

. Start elasticsearch from the `bin` directory:
+
Linux and macOS:
+
["source","sh",subs="attributes,callouts"]
--------------------------------------------------
cd elasticsearch-{version}/bin
./elasticsearch
--------------------------------------------------
+
Windows:
+
["source","sh",subs="attributes,callouts"]
--------------------------------------------------
cd %PROGRAMFILES%\Elastic\Elasticsearch\bin
.\elasticsearch.exe
--------------------------------------------------
+
You now have a single-node {es} cluster up and running!

. Start two more instances of {es} so you can see how a typical multi-node
cluster behaves. You need to specify unique data and log paths
for each node.
+
Linux and macOS:
+
["source","sh",subs="attributes,callouts"]
--------------------------------------------------
./elasticsearch -Epath.data=data2 -Epath.logs=log2
./elasticsearch -Epath.data=data3 -Epath.logs=log3
--------------------------------------------------
+
Windows:
+
["source","sh",subs="attributes,callouts"]
--------------------------------------------------
.\elasticsearch.exe -Epath.data=data2 -Epath.logs=log2
.\elasticsearch.exe -Epath.data=data3 -Epath.logs=log3
--------------------------------------------------
+
The additional nodes are assigned unique IDs. Because you're running all three
nodes locally, they automatically join the cluster with the first node.

. Use the `cat health` API to verify that your three-node cluster is up running.
The `cat` APIs return information about your cluster and indices in a
format that's easier to read than raw JSON.
+
You can interact directly with your cluster by submitting HTTP requests to
the {es} REST API. Most of the examples in this guide enable you to copy the
appropriate cURL command and submit the request to your local {es} instance from
the command line. If you have Kibana installed and running, you can also
open Kibana and submit requests through the Dev Console.
+
TIP: You'll want to check out the
https://www.elastic.co/guide/en/elasticsearch/client/index.html[{es} language
clients] when you're ready to start using {es} in your own applications.
+
[source,js]
--------------------------------------------------
GET /_cat/health?v
--------------------------------------------------
// CONSOLE
+
The response should indicate that the status of the _elasticsearch_ cluster
is _green_ and it has three nodes:
+
[source,txt]
--------------------------------------------------
epoch      timestamp cluster       status node.total node.data shards pri relo init unassign pending_tasks max_task_wait_time active_shards_percent
1565052807 00:53:27  elasticsearch green           3         3      6   3    0    0        0             0                  -                100.0%
--------------------------------------------------
// TESTRESPONSE[s/1565052807 00:53:27  elasticsearch/\\d+ \\d+:\\d+:\\d+ integTest/]
// TESTRESPONSE[s/3         3      6   3/\\d+         \\d+      \\d+   \\d+/]
// TESTRESPONSE[s/0             0                  -/0             \\d+                  -/]
// TESTRESPONSE[non_json]
+
NOTE: The cluster status will remain yellow if you are only running a single
instance of {es}. A single node cluster is fully functional, but data
cannot be replicated to another node to provide resiliency. Replica shards must
be available for the cluster status to be green. If the cluster status is red,
some data is unavailable.

[float]
[[gs-other-install]]
=== Other installation options

Installing {es} from an archive file enables you to easily install and run
multiple instances locally so you can try things out. To run a single instance,
you can  run {es} in a Docker container, install {es} using the DEB or RPM
packages on Linux, install using Homebrew on macOS, or install using the MSI
package installer on Windows. See <<install-elasticsearch>> for more information.

[[getting-started-index]]
== Index some documents

Once you have a cluster up and running, you're ready to index some data.
There are a variety of ingest options for {es}, but in the end they all
do the same thing: put JSON documents into an {es} index.

You can do this directly with a simple POST request that identifies
the index you want to add the document to and specifies one or more
`"field": "value"` pairs in the request body:

[source,js]
--------------------------------------------------
PUT /customer/_doc/1
{
  "name": "John Doe"
}
--------------------------------------------------
// CONSOLE

This request automatically creates the _customer_ index if it doesn't already
exist, adds a new document that has an ID of `1`, and stores and
indexes the _name_ field.

Since this is a new document, the response shows that the result of the
operation was that version 1 of the document was created:

[source,js]
--------------------------------------------------
{
  "_index" : "customer",
  "_type" : "_doc",
  "_id" : "1",
  "_version" : 1,
  "result" : "created",
  "_shards" : {
    "total" : 2,
    "successful" : 2,
    "failed" : 0
  },
  "_seq_no" : 26,
  "_primary_term" : 4
}
--------------------------------------------------
// TESTRESPONSE[s/"_seq_no" : \d+/"_seq_no" : $body._seq_no/]
// TESTRESPONSE[s/"successful" : \d+/"successful" : $body._shards.successful/]
// TESTRESPONSE[s/"_primary_term" : \d+/"_primary_term" : $body._primary_term/]


The new document is available immediately from any node in the cluster.
You can retrieve it with a GET request that specifies its document ID:

[source,js]
--------------------------------------------------
GET /customer/_doc/1
--------------------------------------------------
// CONSOLE
// TEST[continued]

The response indicates that a document with the specified ID was found
and shows the original source fields that were indexed.

[source,js]
--------------------------------------------------
{
  "_index" : "customer",
  "_type" : "_doc",
  "_id" : "1",
  "_version" : 1,
  "_seq_no" : 26,
  "_primary_term" : 4,
  "found" : true,
  "_source" : {
    "name": "John Doe"
  }
}
--------------------------------------------------
// TESTRESPONSE[s/"_seq_no" : \d+/"_seq_no" : $body._seq_no/ ]
// TESTRESPONSE[s/"_primary_term" : \d+/"_primary_term" : $body._primary_term/]


[float]
[[getting-started-batch-processing]]
=== Batch processing

In addition to being able to index, update, and delete individual documents, Elasticsearch also provides the ability to perform any of the above operations in batches using the {ref}/docs-bulk.html[`_bulk` API]. This functionality is important in that it provides a very efficient mechanism to do multiple operations as fast as possible with as few network roundtrips as possible.

As a quick example, the following call indexes two documents (ID 1 - John Doe and ID 2 - Jane Doe) in one bulk operation:

[source,js]
--------------------------------------------------
POST /customer/_bulk?pretty
{"index":{"_id":"1"}}
{"name": "John Doe" }
{"index":{"_id":"2"}}
{"name": "Jane Doe" }
--------------------------------------------------
// CONSOLE

This example updates the first document (ID of 1) and then deletes the second document (ID of 2) in one bulk operation:

[source,sh]
--------------------------------------------------
POST /customer/_bulk
{"update":{"_id":"1"}}
{"doc": { "name": "John Doe becomes Jane Doe" } }
{"delete":{"_id":"2"}}
--------------------------------------------------
// CONSOLE
// TEST[continued]

Note above that for the delete action, there is no corresponding source document after it since deletes only require the ID of the document to be deleted.

The Bulk API does not fail due to failures in one of the actions. If a single action fails for whatever reason, it will continue to process the remainder of the actions after it. When the bulk API returns, it will provide a status for each action (in the same order it was sent in) so that you can check if a specific action failed or not.

[float]
=== Sample dataset

Now that we've gotten a glimpse of the basics, let's try to work on a more realistic dataset. I've prepared a sample of fictitious JSON documents of customer bank account information. Each document has the following schema:

[source,js]
--------------------------------------------------
{
    "account_number": 0,
    "balance": 16623,
    "firstname": "Bradshaw",
    "lastname": "Mckenzie",
    "age": 29,
    "gender": "F",
    "address": "244 Columbus Place",
    "employer": "Euron",
    "email": "bradshawmckenzie@euron.com",
    "city": "Hobucken",
    "state": "CO"
}
--------------------------------------------------
// NOTCONSOLE

For the curious, this data was generated using http://www.json-generator.com/[`www.json-generator.com/`], so please ignore the actual values and semantics of the data as these are all randomly generated.

You can download the sample dataset (accounts.json) from https://github.com/elastic/elasticsearch/blob/master/docs/src/test/resources/accounts.json?raw=true[here]. Extract it to our current directory and let's load it into our cluster as follows:

[source,sh]
--------------------------------------------------
curl -H "Content-Type: application/json" -XPOST "localhost:9200/bank/_bulk?pretty&refresh" --data-binary "@accounts.json"
curl "localhost:9200/_cat/indices?v"
--------------------------------------------------
// NOTCONSOLE

////
This replicates the above in a document-testing friendly way but isn't visible
in the docs:

[source,js]
--------------------------------------------------
GET /_cat/indices?v
--------------------------------------------------
// CONSOLE
// TEST[setup:bank]
////

And the response:

[source,txt]
--------------------------------------------------
health status index uuid                   pri rep docs.count docs.deleted store.size pri.store.size
yellow open   bank  l7sSYV2cQXmu6_4rJWVIww   5   1       1000            0    128.6kb        128.6kb
--------------------------------------------------
// TESTRESPONSE[s/128.6kb/\\d+(\\.\\d+)?[mk]?b/]
// TESTRESPONSE[s/l7sSYV2cQXmu6_4rJWVIww/.+/ non_json]

Which means that we just successfully bulk indexed 1000 documents into the bank index.

[[getting-started-search]]
== Start searching

Now let's start with some simple searches. There are two basic ways to run searches: one is by sending search parameters through the {ref}/search-uri-request.html[REST request URI] and the other by sending them through the {ref}/search-request-body.html[REST request body]. The request body method allows you to be more expressive and also to define your searches in a more readable JSON format. We'll try one example of the request URI method but for the remainder of this tutorial, we will exclusively be using the request body method.

The REST API for search is accessible from the `_search` endpoint. This example returns all documents in the bank index:

[source,js]
--------------------------------------------------
GET /bank/_search?q=*&sort=account_number:asc&pretty
--------------------------------------------------
// CONSOLE
// TEST[continued]

Let's first dissect the search call. We are searching (`_search` endpoint) in the bank index, and the `q=*` parameter instructs Elasticsearch to match all documents in the index. The `sort=account_number:asc` parameter indicates to sort the results using the `account_number` field of each document in an ascending order. The `pretty` parameter, again, just tells Elasticsearch to return pretty-printed JSON results.

And the response (partially shown):

[source,js]
--------------------------------------------------
{
  "took" : 63,
  "timed_out" : false,
  "_shards" : {
    "total" : 5,
    "successful" : 5,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
        "value": 1000,
        "relation": "eq"
    },
    "max_score" : null,
    "hits" : [ {
      "_index" : "bank",
      "_type" : "_doc",
      "_id" : "0",
      "sort": [0],
      "_score" : null,
      "_source" : {"account_number":0,"balance":16623,"firstname":"Bradshaw","lastname":"Mckenzie","age":29,"gender":"F","address":"244 Columbus Place","employer":"Euron","email":"bradshawmckenzie@euron.com","city":"Hobucken","state":"CO"}
    }, {
      "_index" : "bank",
      "_type" : "_doc",
      "_id" : "1",
      "sort": [1],
      "_score" : null,
      "_source" : {"account_number":1,"balance":39225,"firstname":"Amber","lastname":"Duke","age":32,"gender":"M","address":"880 Holmes Lane","employer":"Pyrami","email":"amberduke@pyrami.com","city":"Brogan","state":"IL"}
    }, ...
    ]
  }
}
--------------------------------------------------
// TESTRESPONSE[s/"took" : 63/"took" : $body.took/]
// TESTRESPONSE[s/\.\.\./$body.hits.hits.2, $body.hits.hits.3, $body.hits.hits.4, $body.hits.hits.5, $body.hits.hits.6, $body.hits.hits.7, $body.hits.hits.8, $body.hits.hits.9/]

As for the response, we see the following parts:

* `took` – time in milliseconds for Elasticsearch to execute the search
* `timed_out` – tells us if the search timed out or not
* `_shards` – tells us how many shards were searched, as well as a count of the successful/failed searched shards
* `hits` – search results
* `hits.total` – an object that contains information about the total number of documents matching our search criteria
** `hits.total.value` - the value of the total hit count (must be interpreted in the context of `hits.total.relation`).
** `hits.total.relation` - whether `hits.total.value` is the exact hit count, in which case it is equal to `"eq"` or a
                           lower bound of the total hit count (greater than or equals), in which case it is equal to `gte`.
* `hits.hits` – actual array of search results (defaults to first 10 documents)
* `hits.sort` - sort value of the sort key for each result (missing if sorting by score)
* `hits._score` and `max_score` - ignore these fields for now

The accuracy of `hits.total` is controlled by the request parameter `track_total_hits`, when set to true
the request will track the total hits accurately (`"relation": "eq"`). It defaults to `10,000`
which means that the total hit count is accurately tracked up to `10,000` documents.
You can force an accurate count by setting `track_total_hits` to true explicitly.
See the <<request-body-search-track-total-hits, request body>> documentation
for more details.

Here is the same exact search above using the alternative request body method:

[source,js]
--------------------------------------------------
GET /bank/_search
{
  "query": { "match_all": {} },
  "sort": [
    { "account_number": "asc" }
  ]
}
--------------------------------------------------
// CONSOLE
// TEST[continued]

The difference here is that instead of passing `q=*` in the URI, we provide a JSON-style query request body to the `_search` API. We'll discuss this JSON query in the next section.

////
Hidden response just so we can assert that it is indeed the same but don't have
to clutter the docs with it:

[source,js]
--------------------------------------------------
{
  "took" : 63,
  "timed_out" : false,
  "_shards" : {
    "total" : 5,
    "successful" : 5,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
       "value": 1000,
       "relation": "eq"
    },
    "max_score": null,
    "hits" : [ {
      "_index" : "bank",
      "_type" : "_doc",
      "_id" : "0",
      "sort": [0],
      "_score": null,
      "_source" : {"account_number":0,"balance":16623,"firstname":"Bradshaw","lastname":"Mckenzie","age":29,"gender":"F","address":"244 Columbus Place","employer":"Euron","email":"bradshawmckenzie@euron.com","city":"Hobucken","state":"CO"}
    }, {
      "_index" : "bank",
      "_type" : "_doc",
      "_id" : "1",
      "sort": [1],
      "_score": null,
      "_source" : {"account_number":1,"balance":39225,"firstname":"Amber","lastname":"Duke","age":32,"gender":"M","address":"880 Holmes Lane","employer":"Pyrami","email":"amberduke@pyrami.com","city":"Brogan","state":"IL"}
    }, ...
    ]
  }
}
--------------------------------------------------
// TESTRESPONSE[s/"took" : 63/"took" : $body.took/]
// TESTRESPONSE[s/\.\.\./$body.hits.hits.2, $body.hits.hits.3, $body.hits.hits.4, $body.hits.hits.5, $body.hits.hits.6, $body.hits.hits.7, $body.hits.hits.8, $body.hits.hits.9/]

////

It is important to understand that once you get your search results back, Elasticsearch is completely done with the request and does not maintain any kind of server-side resources or open cursors into your results. This is in stark contrast to many other platforms such as SQL wherein you may initially get a partial subset of your query results up-front and then you have to continuously go back to the server if you want to fetch (or page through) the rest of the results using some kind of stateful server-side cursor.

[float]
[[getting-started-query-lang]]
=== Introducing the Query Language

Elasticsearch provides a JSON-style domain-specific language that you can use to execute queries. This is referred to as the {ref}/query-dsl.html[Query DSL]. The query language is quite comprehensive and can be intimidating at first glance but the best way to actually learn it is to start with a few basic examples.

Going back to our last example, we executed this query:

[source,js]
--------------------------------------------------
GET /bank/_search
{
  "query": { "match_all": {} }
}
--------------------------------------------------
// CONSOLE
// TEST[continued]

Dissecting the above, the `query` part tells us what our query definition is and the `match_all` part is simply the type of query that we want to run. The `match_all` query is simply a search for all documents in the specified index.

In addition to the `query` parameter, we also can pass other parameters to
influence the search results. In the example in the section above we passed in
`sort`, here we pass in `size`:

[source,js]
--------------------------------------------------
GET /bank/_search
{
  "query": { "match_all": {} },
  "size": 1
}
--------------------------------------------------
// CONSOLE
// TEST[continued]

Note that if `size` is not specified, it defaults to 10.

This example does a `match_all` and returns documents 10 through 19:

[source,js]
--------------------------------------------------
GET /bank/_search
{
  "query": { "match_all": {} },
  "from": 10,
  "size": 10
}
--------------------------------------------------
// CONSOLE
// TEST[continued]

The `from` parameter (0-based) specifies which document index to start from and the `size` parameter specifies how many documents to return starting at the from parameter. This feature is useful when implementing paging of search results. Note that if `from` is not specified, it defaults to 0.

This example does a `match_all` and sorts the results by account balance in descending order and returns the top 10 (default size) documents.

[source,js]
--------------------------------------------------
GET /bank/_search
{
  "query": { "match_all": {} },
  "sort": { "balance": { "order": "desc" } }
}
--------------------------------------------------
// CONSOLE
// TEST[continued]

Now that we have seen a few of the basic search parameters, let's dig in some more into the Query DSL. Let's first take a look at the returned document fields. By default, the full JSON document is returned as part of all searches. This is referred to as the source (`_source` field in the search hits). If we don't want the entire source document returned, we have the ability to request only a few fields from within source to be returned.

This example shows how to return two fields, `account_number` and `balance` (inside of `_source`), from the search:

[source,js]
--------------------------------------------------
GET /bank/_search
{
  "query": { "match_all": {} },
  "_source": ["account_number", "balance"]
}
--------------------------------------------------
// CONSOLE
// TEST[continued]

Note that the above example simply reduces the `_source` field. It will still only return one field named `_source` but within it, only the fields `account_number` and `balance` are included.

If you come from a SQL background, the above is somewhat similar in concept to the `SQL SELECT FROM` field list.

Now let's move on to the query part. Previously, we've seen how the `match_all` query is used to match all documents. Let's now introduce a new query called the {ref}/query-dsl-match-query.html[`match` query], which can be thought of as a basic fielded search query (i.e. a search done against a specific field or set of fields).

This example returns the account numbered 20:

[source,js]
--------------------------------------------------
GET /bank/_search
{
  "query": { "match": { "account_number": 20 } }
}
--------------------------------------------------
// CONSOLE
// TEST[continued]

This example returns all accounts containing the term "mill" in the address:

[source,js]
--------------------------------------------------
GET /bank/_search
{
  "query": { "match": { "address": "mill" } }
}
--------------------------------------------------
// CONSOLE
// TEST[continued]

This example returns all accounts containing the term "mill" or "lane" in the address:

[source,js]
--------------------------------------------------
GET /bank/_search
{
  "query": { "match": { "address": "mill lane" } }
}
--------------------------------------------------
// CONSOLE
// TEST[continued]

This example is a variant of `match` (`match_phrase`) that returns all accounts containing the phrase "mill lane" in the address:

[source,js]
--------------------------------------------------
GET /bank/_search
{
  "query": { "match_phrase": { "address": "mill lane" } }
}
--------------------------------------------------
// CONSOLE
// TEST[continued]

Let's now introduce the {ref}/query-dsl-bool-query.html[`bool` query]. The `bool` query allows us to compose smaller queries into bigger queries using boolean logic.

This example composes two `match` queries and returns all accounts containing "mill" and "lane" in the address:

[source,js]
--------------------------------------------------
GET /bank/_search
{
  "query": {
    "bool": {
      "must": [
        { "match": { "address": "mill" } },
        { "match": { "address": "lane" } }
      ]
    }
  }
}
--------------------------------------------------
// CONSOLE
// TEST[continued]

In the above example, the `bool must` clause specifies all the queries that must be true for a document to be considered a match.

In contrast, this example composes two `match` queries and returns all accounts containing "mill" or "lane" in the address:

[source,js]
--------------------------------------------------
GET /bank/_search
{
  "query": {
    "bool": {
      "should": [
        { "match": { "address": "mill" } },
        { "match": { "address": "lane" } }
      ]
    }
  }
}
--------------------------------------------------
// CONSOLE
// TEST[continued]

In the above example, the `bool should` clause specifies a list of queries either of which must be true for a document to be considered a match.

This example composes two `match` queries and returns all accounts that contain neither "mill" nor "lane" in the address:

[source,js]
--------------------------------------------------
GET /bank/_search
{
  "query": {
    "bool": {
      "must_not": [
        { "match": { "address": "mill" } },
        { "match": { "address": "lane" } }
      ]
    }
  }
}
--------------------------------------------------
// CONSOLE
// TEST[continued]

In the above example, the `bool must_not` clause specifies a list of queries none of which must be true for a document to be considered a match.

We can combine `must`, `should`, and `must_not` clauses simultaneously inside a `bool` query. Furthermore, we can compose `bool` queries inside any of these `bool` clauses to mimic any complex multi-level boolean logic.

This example returns all accounts of anybody who is 40 years old but doesn't live in ID(aho):

[source,js]
--------------------------------------------------
GET /bank/_search
{
  "query": {
    "bool": {
      "must": [
        { "match": { "age": "40" } }
      ],
      "must_not": [
        { "match": { "state": "ID" } }
      ]
    }
  }
}
--------------------------------------------------
// CONSOLE
// TEST[continued]

[float]
[[getting-started-filters]]
=== Executing filters

In the previous section, we skipped over a little detail called the document score (`_score` field in the search results). The score is a numeric value that is a relative measure of how well the document matches the search query that we specified. The higher the score, the more relevant the document is, the lower the score, the less relevant the document is.

But queries do not always need to produce scores, in particular when they are only used for "filtering" the document set. Elasticsearch detects these situations and automatically optimizes query execution in order not to compute useless scores.

The {ref}/query-dsl-bool-query.html[`bool` query] that we introduced in the previous section also supports `filter` clauses which allow us to use a query to restrict the documents that will be matched by other clauses, without changing how scores are computed. As an example, let's introduce the {ref}/query-dsl-range-query.html[`range` query], which allows us to filter documents by a range of values. This is generally used for numeric or date filtering.

This example uses a bool query to return all accounts with balances between 20000 and 30000, inclusive. In other words, we want to find accounts with a balance that is greater than or equal to 20000 and less than or equal to 30000.

[source,js]
--------------------------------------------------
GET /bank/_search
{
  "query": {
    "bool": {
      "must": { "match_all": {} },
      "filter": {
        "range": {
          "balance": {
            "gte": 20000,
            "lte": 30000
          }
        }
      }
    }
  }
}
--------------------------------------------------
// CONSOLE
// TEST[continued]

Dissecting the above, the bool query contains a `match_all` query (the query part) and a `range` query (the filter part). We can substitute any other queries into the query and the filter parts. In the above case, the range query makes perfect sense since documents falling into the range all match "equally", i.e., no document is more relevant than another.

In addition to the `match_all`, `match`, `bool`, and `range` queries, there are a lot of other query types that are available and we won't go into them here. Since we already have a basic understanding of how they work, it shouldn't be too difficult to apply this knowledge in learning and experimenting with the other query types.

[[getting-started-aggregations]]
== Analyze results with aggregations

Aggregations provide the ability to group and extract statistics from your data. The easiest way to think about aggregations is by roughly equating it to the SQL GROUP BY and the SQL aggregate functions. In Elasticsearch, you have the ability to execute searches returning hits and at the same time return aggregated results separate from the hits all in one response. This is very powerful and efficient in the sense that you can run queries and multiple aggregations and get the results back of both (or either) operations in one shot avoiding network roundtrips using a concise and simplified API.

To start with, this example groups all the accounts by state, and then returns the top 10 (default) states sorted by count descending (also default):

[source,js]
--------------------------------------------------
GET /bank/_search
{
  "size": 0,
  "aggs": {
    "group_by_state": {
      "terms": {
        "field": "state.keyword"
      }
    }
  }
}
--------------------------------------------------
// CONSOLE
// TEST[continued]

In SQL, the above aggregation is similar in concept to:

[source,sh]
--------------------------------------------------
SELECT state, COUNT(*) FROM bank GROUP BY state ORDER BY COUNT(*) DESC LIMIT 10;
--------------------------------------------------

And the response (partially shown):

[source,js]
--------------------------------------------------
{
  "took": 29,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "skipped" : 0,
    "failed": 0
  },
  "hits" : {
     "total" : {
        "value": 1000,
        "relation": "eq"
     },
    "max_score" : null,
    "hits" : [ ]
  },
  "aggregations" : {
    "group_by_state" : {
      "doc_count_error_upper_bound": 20,
      "sum_other_doc_count": 770,
      "buckets" : [ {
        "key" : "ID",
        "doc_count" : 27
      }, {
        "key" : "TX",
        "doc_count" : 27
      }, {
        "key" : "AL",
        "doc_count" : 25
      }, {
        "key" : "MD",
        "doc_count" : 25
      }, {
        "key" : "TN",
        "doc_count" : 23
      }, {
        "key" : "MA",
        "doc_count" : 21
      }, {
        "key" : "NC",
        "doc_count" : 21
      }, {
        "key" : "ND",
        "doc_count" : 21
      }, {
        "key" : "ME",
        "doc_count" : 20
      }, {
        "key" : "MO",
        "doc_count" : 20
      } ]
    }
  }
}
--------------------------------------------------
// TESTRESPONSE[s/"took": 29/"took": $body.took/]

We can see that there are 27 accounts in `ID` (Idaho), followed by 27 accounts
in `TX` (Texas), followed by 25 accounts in `AL` (Alabama), and so forth.

Note that we set `size=0` to not show search hits because we only want to see the aggregation results in the response.

Building on the previous aggregation, this example calculates the average account balance by state (again only for the top 10 states sorted by count in descending order):

[source,js]
--------------------------------------------------
GET /bank/_search
{
  "size": 0,
  "aggs": {
    "group_by_state": {
      "terms": {
        "field": "state.keyword"
      },
      "aggs": {
        "average_balance": {
          "avg": {
            "field": "balance"
          }
        }
      }
    }
  }
}
--------------------------------------------------
// CONSOLE
// TEST[continued]

Notice how we nested the `average_balance` aggregation inside the `group_by_state` aggregation. This is a common pattern for all the aggregations. You can nest aggregations inside aggregations arbitrarily to extract pivoted summarizations that you require from your data.

Building on the previous aggregation, let's now sort on the average balance in descending order:

[source,js]
--------------------------------------------------
GET /bank/_search
{
  "size": 0,
  "aggs": {
    "group_by_state": {
      "terms": {
        "field": "state.keyword",
        "order": {
          "average_balance": "desc"
        }
      },
      "aggs": {
        "average_balance": {
          "avg": {
            "field": "balance"
          }
        }
      }
    }
  }
}
--------------------------------------------------
// CONSOLE
// TEST[continued]

This example demonstrates how we can group by age brackets (ages 20-29, 30-39, and 40-49), then by gender, and then finally get the average account balance, per age bracket, per gender:

[source,js]
--------------------------------------------------
GET /bank/_search
{
  "size": 0,
  "aggs": {
    "group_by_age": {
      "range": {
        "field": "age",
        "ranges": [
          {
            "from": 20,
            "to": 30
          },
          {
            "from": 30,
            "to": 40
          },
          {
            "from": 40,
            "to": 50
          }
        ]
      },
      "aggs": {
        "group_by_gender": {
          "terms": {
            "field": "gender.keyword"
          },
          "aggs": {
            "average_balance": {
              "avg": {
                "field": "balance"
              }
            }
          }
        }
      }
    }
  }
}
--------------------------------------------------
// CONSOLE
// TEST[continued]

There are many other aggregations capabilities that we won't go into detail here. The {ref}/search-aggregations.html[aggregations reference guide] is a great starting point if you want to do further experimentation.

[[getting-started-next-steps]]
== Where to go from here

Now that you've set up a cluster, indexed some documents, and run some
searches and aggregations, you might want to:

* {stack-gs}/get-started-elastic-stack.html#install-kibana[Dive in to the Elastic
Stack Tutorial] to install Kibana, Logstash, and Beats and
set up a basic system monitoring solution.

* {kibana-ref}/add-sample-data.html[Load one of the sample data sets into Kibana]
to see how you can use {es} and Kibana together to visualize your data.

* Try out one of the Elastic search solutions:
** https://swiftype.com/documentation/site-search/crawler-quick-start[Site Search]
** https://swiftype.com/documentation/app-search/getting-started[App Search]
** https://swiftype.com/documentation/enterprise-search/getting-started[Enterprise Search]