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pr: 102184 | ||
summary: Track ESQL enrich memory | ||
area: ES|QL | ||
type: enhancement | ||
issues: [] |
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pr: 102472 | ||
summary: Expose the `invalidation` field in Get/Query `ApiKey` APIs | ||
area: Security | ||
type: enhancement | ||
issues: [ ] |
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pr: 102492 | ||
summary: Ensure datafeed previews with no start or end time don't search the cold or frozen tiers | ||
area: Machine Learning | ||
type: bug | ||
issues: [] |
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pr: 102562 | ||
summary: Track blocks of intermediate state of aggs | ||
area: ES|QL | ||
type: enhancement | ||
issues: [] |
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pr: 102570 | ||
summary: Added `beat.stats.libbeat.pipeline.queue.max_events` | ||
area: Monitoring | ||
type: enhancement | ||
issues: [] |
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pr: 102602 | ||
summary: Consider search context missing exceptions as recoverable | ||
area: Transform | ||
type: bug | ||
issues: [] |
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[chapter] | ||
[[getting-started]] | ||
= Quick start | ||
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||
This guide helps you learn how to: | ||
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* install and run {es} and {kib} (using {ecloud} or Docker), | ||
* add simple (non-timestamped) dataset to {es}, | ||
* run basic searches. | ||
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[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]. | ||
==== | ||
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[discrete] | ||
[[run-elasticsearch]] | ||
=== Run {es} | ||
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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. | ||
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include::{es-repo-dir}/tab-widgets/code.asciidoc[] | ||
include::{es-repo-dir}/tab-widgets/quick-start-install-widget.asciidoc[] | ||
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[discrete] | ||
[[send-requests-to-elasticsearch]] | ||
=== Send requests to {es} | ||
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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}. | ||
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include::{es-repo-dir}/tab-widgets/api-call-widget.asciidoc[] | ||
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[discrete] | ||
[[add-data]] | ||
=== Add data | ||
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You add data to {es} as JSON objects called documents. {es} stores these | ||
documents in searchable indices. | ||
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[discrete] | ||
[[add-single-document]] | ||
==== Add a single document | ||
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Submit the following indexing request to add a single document to the | ||
`books` index. | ||
The request automatically creates the index. | ||
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//// | ||
[source,console] | ||
---- | ||
PUT books | ||
---- | ||
// TESTSETUP | ||
//// | ||
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[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/] | ||
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The response includes metadata that {es} generates for the document including a unique `_id` for the document within the index. | ||
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.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] | ||
=============== | ||
|
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[discrete] | ||
[[add-multiple-documents]] | ||
==== Add multiple documents | ||
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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. | ||
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[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] | ||
|
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You should receive a response indicating there were no errors. | ||
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.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] | ||
=============== | ||
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[discrete] | ||
[[qs-search-data]] | ||
=== Search data | ||
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Indexed documents are available for search in near real-time. | ||
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[discrete] | ||
[[search-all-documents]] | ||
==== Search all documents | ||
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Run the following command to search the `books` index for all documents: | ||
[source,console] | ||
---- | ||
GET books/_search | ||
---- | ||
// TEST[continued] | ||
|
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The `_source` of each hit contains the original | ||
JSON object submitted during indexing. | ||
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[discrete] | ||
[[qs-match-query]] | ||
==== `match` query | ||
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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. | ||
|
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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] | ||
|
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[discrete] | ||
[[whats-next]] | ||
=== Next steps | ||
|
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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. | ||
|
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[discrete] | ||
[[whats-next-search-learn-more]] | ||
==== Learn more about search queries | ||
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* <<search-with-elasticsearch>>. Jump here to learn about exact value search, full-text search, vector search, and more, using the <<search-search,search API>>. | ||
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[discrete] | ||
[[whats-next-more-data]] | ||
==== Add more data | ||
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* 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}. | ||
|
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[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]. |
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