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[DOCS] Streamline GS search topic. (#45941) 

* Streamline GS search topic.

* Added missing comma.

* Update docs/reference/getting-started.asciidoc

Co-Authored-By: István Zoltán Szabó <istvan.szabo@elastic.co>
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debadair 2019-08-26 13:58:41 -07:00 committed by Deb Adair
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docs/reference/getting-started.asciidoc
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@ -37,6 +37,21 @@ https://www.elastic.co/cloud/elasticsearch-service/signup[hosted deployment] on
the {ess} or set up a multi-node {es} cluster on your own the {ess} or set up a multi-node {es} cluster on your own
Linux, macOS, or Windows machine. Linux, macOS, or Windows machine.
[float]
[[run-elasticsearch-hosted]]
=== Run {es} on Elastic Cloud
When you create a deployment on the {es} Service, the service provisions
a three-node {es} cluster along with Kibana and APM.
To create a deployment:
. Sign up for a https://www.elastic.co/cloud/elasticsearch-service/signup[free trial]
and verify your email address.
. Set a password for your account.
. Click **Create Deployment**.
Once you've created a deployment, you're ready to <<getting-started-index>>.
[float] [float]
[[run-elasticsearch-local]] [[run-elasticsearch-local]]
@ -333,20 +348,30 @@ yellow open bank l7sSYV2cQXmu6_4rJWVIww 5 1 1000 0 12
[[getting-started-search]] [[getting-started-search]]
== Start searching == 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. Once you have ingested some data into an {es} index, you can search it
by sending requests to the `_search` endpoint. To access the full suite of
search capabilities, you use the {es} Query DSL to specify the
search criteria in the request body. You specify the name of the index you
want to search in the request URI.
The REST API for search is accessible from the `_search` endpoint. This example returns all documents in the bank index: For example, the following request retrieves all documents in the `bank`
index sorted by account number:
[source,js] [source,js]
-------------------------------------------------- --------------------------------------------------
GET /bank/_search?q=*&sort=account_number:asc&pretty GET /bank/_search
{
"query": { "match_all": {} },
"sort": [
{ "account_number": "asc" }
]
}
-------------------------------------------------- --------------------------------------------------
// CONSOLE // CONSOLE
// TEST[continued] // 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. By default, the `hits` section of the response includes the first 10 documents
that match the search criteria:
And the response (partially shown):
[source,js] [source,js]
-------------------------------------------------- --------------------------------------------------
@ -387,8 +412,22 @@ And the response (partially shown):
// TESTRESPONSE[s/"took" : 63/"took" : $body.took/] // 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, $body.hits.hits.9/] // TESTRESPONSE[s/\.\.\./$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/]
For example, the following request retrieves all documents in the `bank` The response also provides the following information about the search request:
index sorted by account number:
* `took` – how long it took {es} to run the query, in milliseconds
* `timed_out` – whether or not the search request timed out
* `_shards` – how many shards were searched and a breakdown of how many shards
succeeded, failed, or were skipped.
* `max_score` – the score of the most relevant document found
* `hits.total.value` - how many matching documents were found
* `hits.sort` - the document's sort position (when not sorting by relevance score)
* `hits._score` - the document's relevance score (not applicable when using `match_all`)
Each search request is self-contained: {es} does not maintain any
state information across requests. To page through the search hits, specify
the `from` and `size` parameters in your request.
For example, the following request gets hits 10 through 19:
[source,js] [source,js]
-------------------------------------------------- --------------------------------------------------
@ -397,107 +436,7 @@ GET /bank/_search
"query": { "match_all": {} }, "query": { "match_all": {} },
"sort": [ "sort": [
{ "account_number": "asc" } { "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]
Each search request is self-contained: {es} does not maintain any
state information across requests. To page through the search hits, specify
the `from` and `size` parameters in your request.
This example does a `match_all` and returns documents 10 through 19:
[source,js]
--------------------------------------------------
GET /bank/_search
{
"query": { "match_all": {} },
"from": 10, "from": 10,
"size": 10 "size": 10
} }
@ -505,7 +444,8 @@ GET /bank/_search
// CONSOLE // CONSOLE
// TEST[continued] // 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. Now that you've seen how to submit a basic search request, you can start to
construct queries that are a bit more interesting than `match_all`.
To search for specific terms within a field, you can use a `match` query. To search for specific terms within a field, you can use a `match` query.
For example, the following request searches the `address` field to find For example, the following request searches the `address` field to find
@ -521,7 +461,9 @@ GET /bank/_search
// CONSOLE // CONSOLE
// TEST[continued] // TEST[continued]
This example is a variant of `match` (`match_phrase`) that returns all accounts containing the phrase "mill lane" in the address: To perform a phrase search rather than matching individual terms, you use
`match_phrase` instead of `match`. For example, the following request only
matches addresses that contain the phrase `mill lane`:
[source,js] [source,js]
-------------------------------------------------- --------------------------------------------------
@ -533,74 +475,13 @@ GET /bank/_search
// CONSOLE // CONSOLE
// TEST[continued] // 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. To construct more complex queries, you can use a `bool` query to combine
multiple query criteria. You can designate criteria as required (must match),
desirable (should match), or undesirable (must not match).
This example composes two `match` queries and returns all accounts containing "mill" and "lane" in the address: For example, the following request searches the `bank` index for accounts that
belong to customers who are 40 years old, but excludes anyone who lives in
[source,js] Idaho (ID):
--------------------------------------------------
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] [source,js]
-------------------------------------------------- --------------------------------------------------
@ -621,17 +502,19 @@ GET /bank/_search
// CONSOLE // CONSOLE
// TEST[continued] // TEST[continued]
[float] Each `must`, `should`, and `must_not` element in a Boolean query is referred
[[getting-started-filters]] to as a query clause. How well a document meets the criteria in each `must` or
=== Executing filters `should` clause contributes to the document's _relevance score_. The higher the
score, the better the document matches your search criteria. By default, {es}
returns documents ranked by these relevance scores.
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. The criteria in a `must_not` clause is treated as a _filter_. It affects whether
or not the document is included in the results, but does not contribute to
how documents are scored. You can also explicitly specify arbitrary filters to
include or exclude documents based on structured data.
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. For example, the following request uses a range filter to limit the results to
accounts with a balance between $20,000 and $30,000 (inclusive).
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] [source,js]
-------------------------------------------------- --------------------------------------------------
@ -655,10 +538,6 @@ GET /bank/_search
// CONSOLE // CONSOLE
// TEST[continued] // 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]] [[getting-started-aggregations]]
== Analyze results with aggregations == Analyze results with aggregations