mirror of
https://github.com/elastic/elasticsearch.git
synced 2025-04-25 15:47:23 -04:00
d44e05d2c2
* [DOCS] TEST restore quickstart * Use up to date Docker instructions, minor user-friendly modifications * Use books dataset, update verbiage, add examples * Update verbiage * Updated Elasticsearch 'Getting Started' docs: added SSL, Docker setup, Python resources, and expanded next steps * minor formatting * Collapse responses, TODO comment tests * Add request tests * Edit superfluities * Apply suggestions Co-authored-by: István Zoltán Szabó <istvan.szabo@elastic.co> * Update docs/reference/tab-widgets/quick-start-install.asciidoc Co-authored-by: István Zoltán Szabó <istvan.szabo@elastic.co> --------- Co-authored-by: István Zoltán Szabó <istvan.szabo@elastic.co>
285 lines
No EOL
8.2 KiB
Text
285 lines
No EOL
8.2 KiB
Text
[chapter]
|
|
[[getting-started]]
|
|
= Quick start
|
|
|
|
This guide helps you learn how to:
|
|
|
|
* install and run {es} and {kib} (using {ecloud} or Docker),
|
|
* add simple (non-timestamped) dataset to {es},
|
|
* run basic searches.
|
|
|
|
[TIP]
|
|
====
|
|
If you're interested in using {es} with Python, check out Elastic Search Labs. This is the best place to explore AI-powered search use cases, such as working with embeddings, vector search, and retrieval augmented generation (RAG).
|
|
|
|
* https://www.elastic.co/search-labs/tutorials/search-tutorial/welcome[Tutorial]: this walks you through building a complete search solution with {es}, from the ground up.
|
|
* https://github.com/elastic/elasticsearch-labs[`elasticsearch-labs` repository]: it contains a range of Python https://github.com/elastic/elasticsearch-labs/tree/main/notebooks[notebooks] and https://github.com/elastic/elasticsearch-labs/tree/main/example-apps[example apps].
|
|
====
|
|
|
|
[discrete]
|
|
[[run-elasticsearch]]
|
|
=== Run {es}
|
|
|
|
The simplest way to set up {es} is to create a managed deployment with {ess} on
|
|
{ecloud}. If you prefer to manage your own test environment, install and
|
|
run {es} using Docker.
|
|
|
|
include::{es-repo-dir}/tab-widgets/code.asciidoc[]
|
|
include::{es-repo-dir}/tab-widgets/quick-start-install-widget.asciidoc[]
|
|
|
|
[discrete]
|
|
[[send-requests-to-elasticsearch]]
|
|
=== Send requests to {es}
|
|
|
|
You send data and other requests to {es} using REST APIs. This lets you interact
|
|
with {es} using any client that sends HTTP requests, such as
|
|
https://curl.se[curl]. You can also use {kib}'s Console to send requests to
|
|
{es}.
|
|
|
|
include::{es-repo-dir}/tab-widgets/api-call-widget.asciidoc[]
|
|
|
|
[discrete]
|
|
[[add-data]]
|
|
=== Add data
|
|
|
|
You add data to {es} as JSON objects called documents. {es} stores these
|
|
documents in searchable indices.
|
|
|
|
[discrete]
|
|
[[add-single-document]]
|
|
==== Add a single document
|
|
|
|
Submit the following indexing request to add a single document to the
|
|
`books` index.
|
|
The request automatically creates the index.
|
|
|
|
////
|
|
[source,console]
|
|
----
|
|
PUT books
|
|
----
|
|
// TESTSETUP
|
|
////
|
|
|
|
[source,console]
|
|
----
|
|
POST books/_doc
|
|
{"name": "Snow Crash", "author": "Neal Stephenson", "release_date": "1992-06-01", "page_count": 470}
|
|
----
|
|
// TEST[s/_doc/_doc?refresh=wait_for/]
|
|
|
|
The response includes metadata that {es} generates for the document including a unique `_id` for the document within the index.
|
|
|
|
.Expand to see example response
|
|
[%collapsible]
|
|
===============
|
|
[source,console-result]
|
|
----
|
|
{
|
|
"_index": "books",
|
|
"_id": "O0lG2IsBaSa7VYx_rEia",
|
|
"_version": 1,
|
|
"result": "created",
|
|
"_shards": {
|
|
"total": 2,
|
|
"successful": 2,
|
|
"failed": 0
|
|
},
|
|
"_seq_no": 0,
|
|
"_primary_term": 1
|
|
}
|
|
----
|
|
// TEST[skip:TODO]
|
|
===============
|
|
|
|
[discrete]
|
|
[[add-multiple-documents]]
|
|
==== Add multiple documents
|
|
|
|
Use the `_bulk` endpoint to add multiple documents in one request. Bulk data
|
|
must be newline-delimited JSON (NDJSON). Each line must end in a newline
|
|
character (`\n`), including the last line.
|
|
|
|
[source,console]
|
|
----
|
|
POST /_bulk
|
|
{ "index" : { "_index" : "books" } }
|
|
{"name": "Revelation Space", "author": "Alastair Reynolds", "release_date": "2000-03-15", "page_count": 585}
|
|
{ "index" : { "_index" : "books" } }
|
|
{"name": "1984", "author": "George Orwell", "release_date": "1985-06-01", "page_count": 328}
|
|
{ "index" : { "_index" : "books" } }
|
|
{"name": "Fahrenheit 451", "author": "Ray Bradbury", "release_date": "1953-10-15", "page_count": 227}
|
|
{ "index" : { "_index" : "books" } }
|
|
{"name": "Brave New World", "author": "Aldous Huxley", "release_date": "1932-06-01", "page_count": 268}
|
|
{ "index" : { "_index" : "books" } }
|
|
{"name": "The Handmaids Tale", "author": "Margaret Atwood", "release_date": "1985-06-01", "page_count": 311}
|
|
----
|
|
// TEST[continued]
|
|
|
|
You should receive a response indicating there were no errors.
|
|
|
|
.Expand to see example response
|
|
[%collapsible]
|
|
===============
|
|
[source,console-result]
|
|
----
|
|
{
|
|
"errors": false,
|
|
"took": 29,
|
|
"items": [
|
|
{
|
|
"index": {
|
|
"_index": "books",
|
|
"_id": "QklI2IsBaSa7VYx_Qkh-",
|
|
"_version": 1,
|
|
"result": "created",
|
|
"_shards": {
|
|
"total": 2,
|
|
"successful": 2,
|
|
"failed": 0
|
|
},
|
|
"_seq_no": 1,
|
|
"_primary_term": 1,
|
|
"status": 201
|
|
}
|
|
},
|
|
{
|
|
"index": {
|
|
"_index": "books",
|
|
"_id": "Q0lI2IsBaSa7VYx_Qkh-",
|
|
"_version": 1,
|
|
"result": "created",
|
|
"_shards": {
|
|
"total": 2,
|
|
"successful": 2,
|
|
"failed": 0
|
|
},
|
|
"_seq_no": 2,
|
|
"_primary_term": 1,
|
|
"status": 201
|
|
}
|
|
},
|
|
{
|
|
"index": {
|
|
"_index": "books",
|
|
"_id": "RElI2IsBaSa7VYx_Qkh-",
|
|
"_version": 1,
|
|
"result": "created",
|
|
"_shards": {
|
|
"total": 2,
|
|
"successful": 2,
|
|
"failed": 0
|
|
},
|
|
"_seq_no": 3,
|
|
"_primary_term": 1,
|
|
"status": 201
|
|
}
|
|
},
|
|
{
|
|
"index": {
|
|
"_index": "books",
|
|
"_id": "RUlI2IsBaSa7VYx_Qkh-",
|
|
"_version": 1,
|
|
"result": "created",
|
|
"_shards": {
|
|
"total": 2,
|
|
"successful": 2,
|
|
"failed": 0
|
|
},
|
|
"_seq_no": 4,
|
|
"_primary_term": 1,
|
|
"status": 201
|
|
}
|
|
},
|
|
{
|
|
"index": {
|
|
"_index": "books",
|
|
"_id": "RklI2IsBaSa7VYx_Qkh-",
|
|
"_version": 1,
|
|
"result": "created",
|
|
"_shards": {
|
|
"total": 2,
|
|
"successful": 2,
|
|
"failed": 0
|
|
},
|
|
"_seq_no": 5,
|
|
"_primary_term": 1,
|
|
"status": 201
|
|
}
|
|
}
|
|
]
|
|
}
|
|
----
|
|
// TEST[skip:TODO]
|
|
===============
|
|
|
|
[discrete]
|
|
[[qs-search-data]]
|
|
=== Search data
|
|
|
|
Indexed documents are available for search in near real-time.
|
|
|
|
[discrete]
|
|
[[search-all-documents]]
|
|
==== Search all documents
|
|
|
|
Run the following command to search the `books` index for all documents:
|
|
[source,console]
|
|
----
|
|
GET books/_search
|
|
----
|
|
// TEST[continued]
|
|
|
|
The `_source` of each hit contains the original
|
|
JSON object submitted during indexing.
|
|
|
|
[discrete]
|
|
[[qs-match-query]]
|
|
==== `match` query
|
|
|
|
You can use the `match` query to search for documents that contain a specific value in a specific field.
|
|
This is the standard query for performing full-text search, including fuzzy matching and phrase searches.
|
|
|
|
Run the following command to search the `books` index for documents containing `brave` in the `name` field:
|
|
[source,console]
|
|
----
|
|
GET books/_search
|
|
{
|
|
"query": {
|
|
"match": {
|
|
"name": "brave"
|
|
}
|
|
}
|
|
}
|
|
----
|
|
// TEST[continued]
|
|
|
|
[discrete]
|
|
[[whats-next]]
|
|
=== Next steps
|
|
|
|
Now that {es} is up and running and you've learned the basics, you'll probably want to test out larger datasets, or index your own data.
|
|
|
|
[discrete]
|
|
[[whats-next-search-learn-more]]
|
|
==== Learn more about search queries
|
|
|
|
* <<search-with-elasticsearch>>. Jump here to learn about exact value search, full-text search, vector search, and more, using the <<search-search,search API>>.
|
|
|
|
[discrete]
|
|
[[whats-next-more-data]]
|
|
==== Add more data
|
|
|
|
* Learn how to {kibana-ref}/sample-data.html[install sample data] using {kib}. This is a quick way to test out {es} on larger workloads.
|
|
* Learn how to use the {kibana-ref}/connect-to-elasticsearch.html#upload-data-kibana[upload data UI] in {kib} to add your own CSV, TSV, or JSON files.
|
|
* Use the https://www.elastic.co/guide/en/elasticsearch/reference/current/docs-bulk.html[bulk API] to ingest your own datasets to {es}.
|
|
|
|
[discrete]
|
|
[[whats-next-client-libraries]]
|
|
==== {es} programming language clients
|
|
|
|
* Check out our https://www.elastic.co/guide/en/elasticsearch/client/index.html[client library] to work with your {es} instance in your preferred programming language.
|
|
* If you're using Python, check out https://www.elastic.co/search-labs[Elastic Search Labs] for a range of examples that use the {es} Python client. This is the best place to explore AI-powered search use cases, such as working with embeddings, vector search, and retrieval augmented generation (RAG).
|
|
** This extensive, hands-on https://www.elastic.co/search-labs/tutorials/search-tutorial/welcome[tutorial]
|
|
walks you through building a complete search solution with {es}, from the ground up.
|
|
** https://github.com/elastic/elasticsearch-labs[`elasticsearch-labs`] contains a range of executable Python https://github.com/elastic/elasticsearch-labs/tree/main/notebooks[notebooks] and https://github.com/elastic/elasticsearch-labs/tree/main/example-apps[example apps]. |