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[[getting-started]]
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== Quick start guide
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== Quick start: Add data using Elasticsearch APIs
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<titleabbrev>Basics: Add data using APIs</titleabbrev>
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This guide helps you learn how to:
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In this quick start guide, you'll learn how to do the following tasks:
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* Run {es} and {kib} (using {ecloud} or in a local Docker dev environment),
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* add simple (non-timestamped) dataset to {es},
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* run basic searches.
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[TIP]
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====
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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).
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* 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.
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* 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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====
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[discrete]
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[[run-elasticsearch]]
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=== Run {es}
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The simplest way to set up {es} is to create a managed deployment with {ess} on
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{ecloud}. If you prefer to manage your own test environment, install and
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[[qs-match-query]]
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==== `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.
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You can use the <<query-dsl-match-query,`match` query>> to search for documents that contain a specific value in a specific field.
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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:
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[source,console]
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----
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GET books/_search
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}
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}
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----
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// TEST[continued]
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[discrete]
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[[whats-next]]
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=== 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]
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[[whats-next-search-learn-more]]
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==== 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]
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[[whats-next-more-data]]
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==== 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.
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* 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.
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* 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]
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[[whats-next-client-libraries]]
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==== {es} programming language clients
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* 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.
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* 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).
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** This extensive, hands-on https://www.elastic.co/search-labs/tutorials/search-tutorial/welcome[tutorial]
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walks you through building a complete search solution with {es}, from the ground up.
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** 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].
Use these quick starts to get hands-on experience with the {es} APIs.
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Unless otherwise noted, these examples will use queries written in <<query-dsl,Query DSL>> syntax.
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* Learn how to run {es} (and {kib}) for <<run-elasticsearch-locally,local development>>.
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* Follow our <<getting-started,Quickstart guide>> to add data to {es} and query it.
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[discrete]
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[[quickstart-requirements]]
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== Requirements
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include::run-elasticsearch-locally.asciidoc[]
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include::getting-started.asciidoc[]
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You'll need a running {es} cluster, together with {kib} to use the Dev Tools API Console.
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Get started <<run-elasticsearch-locally,locally in Docker>> , or see our <<elasticsearch-intro-deploy,other deployment options>>.
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[discrete]
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[[quickstart-list]]
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== Hands-on quick starts
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* <<getting-started,Basics: Add data using APIs>>. Learn how to add data to {es} and perform basic searches.
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[discrete]
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[[quickstart-python-links]]
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== Working in Python
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If you're interested in using {es} with Python, check out Elastic Search Labs:
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* https://github.com/elastic/elasticsearch-labs[`elasticsearch-labs` repository]: 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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* 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 using Flask.
Copy file name to clipboardExpand all lines: docs/reference/run-elasticsearch-locally.asciidoc
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[[run-elasticsearch-locally]]
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== Run {es} locally in Docker (without security)
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== Run {es} locally in Docker
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<titleabbrev>Local dev setup (Docker)</titleabbrev>
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<titleabbrev>Run {es} locally</titleabbrev>
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[WARNING]
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====
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*DO NOT USE THESE INSTRUCTIONS FOR PRODUCTION DEPLOYMENTS*
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The instructions on this page are for *local development only*. Do not use these instructions for production deployments, because they are not secure.
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While this approach is convenient for experimenting and learning, you should never run the service in this way in a production environment.
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While this approach is convenient for experimenting and learning, you should never run Elasticsearch in this way in a production environment.
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====
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The following commands help you very quickly spin up a single-node {es} cluster, together with {kib} in Docker.
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Note that if you don't need the {kib} UI, you can skip those instructions.
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Follow this tutorial if you want to quickly set up {es} in Docker for local development or testing.
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[discrete]
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[[local-dev-why]]
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=== When would I use this setup?
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Use this setup if you want to quickly spin up {es} (and {kib}) for local development or testing.
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For example you might:
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* Want to run a quick test to see how a feature works.
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* Follow a tutorial or guide that requires an {es} cluster, like our <<getting-started,quick start guide>>.
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* Experiment with the {es} APIs using different tools, like the Dev Tools Console, cURL, or an Elastic programming language client.
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* Quickly spin up an {es} cluster to test an executable https://github.com/elastic/elasticsearch-labs/tree/main/notebooks#readme[Python notebook] locally.
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This tutorial also includes instructions for installing {kib}.
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If you don't need access to the {kib} UI, then you can skip those instructions.
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[discrete]
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[[local-dev-prerequisites]]
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[NOTE]
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====
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The service is started with a trial license. The trial license enables all features of Elasticsearch for a trial period of 30 days. After the trial period expires, the license is downgraded to a basic license, which is free forever. If you prefer to skip the trial and use the basic license, set the value of the `xpack.license.self_generated.type` variable to basic instead. For a detailed feature comparison between the different licenses, refer to our https://www.elastic.co/subscriptions[subscriptions page].
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The service is started with a trial license. The trial license enables all features of Elasticsearch for a trial period of 30 days. After the trial period expires, the license is downgraded to a basic license, which is free forever.
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====
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[discrete]
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[[local-dev-connecting-clients]]
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== Connecting to {es} with language clients
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=== Connect to {es} with language clients
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To connect to the {es} cluster from a language client, you can use basic authentication with the `elastic` username and the password you set in the environment variable.
Use our <<getting-started,quick start guide>> to learn the basics of {es}: how to add data and query it.
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Use our <<quickstart,quick start guides>> to learn the basics of {es}.
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[discrete]
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[[local-dev-production]]
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=== Moving to production
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This setup is not suitable for production use. For production deployments, we recommend using our managed service on Elastic Cloud. https://cloud.elastic.co/registration[Sign up for a free trial] (no credit card required).
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Otherwise, refer to https://www.elastic.co/guide/en/elasticsearch/reference/current/install-elasticsearch.html[Install {es}] to learn about the various options for installing {es} in a self-managed production environment, including using Docker.
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This setup is not suitable for production use.
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Refer to <<elasticsearch-intro-deploy, deployment options>> for more information.
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