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[DOCS] Add local dev setup instructions (#107913)
* [DOCS] Add local dev setup instructions - Replace existing Run ES in Docker locally page, with simpler no-security local dev setup - Move this file into Quickstart folder, along with existing quickstart guide - Update self-managed instructions in Quickstart guide to use local dev approach
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Liam Thompson
GitHub
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12 changed files with 202 additions and 254 deletions
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@ -20,7 +20,7 @@ If you want to install and manage {es} yourself, you can:
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* Run {es} in a <<elasticsearch-docker-images,Docker container>>.
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* Set up and manage {es}, {kib}, {agent}, and the rest of the Elastic Stack on Kubernetes with {eck-ref}[{eck}].
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TIP: To try out Elasticsearch on your own machine, we recommend using Docker and running both Elasticsearch and Kibana. For more information, see <<run-elasticsearch-locally,Run Elasticsearch locally>>.
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TIP: To try out Elasticsearch on your own machine, we recommend using Docker and running both Elasticsearch and Kibana. For more information, see <<run-elasticsearch-locally,Run Elasticsearch locally>>. Please note that this setup is *not suitable for production use*.
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[discrete]
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[[elasticsearch-install-packages]]
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@ -8,6 +8,12 @@ https://github.com/elastic/elasticsearch/blob/{branch}/distribution/docker[GitHu
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include::license.asciidoc[]
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[TIP]
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====
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If you just want to test {es} in local development, refer to <<run-elasticsearch-locally>>.
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Please note that this setup is not suitable for production environments.
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====
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[[docker-cli-run-dev-mode]]
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==== Run {es} in Docker
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@ -1,183 +0,0 @@
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[[run-elasticsearch-locally]]
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== Run Elasticsearch locally
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////
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IMPORTANT: This content is replicated in the Elasticsearch repo
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README.ascidoc file. If you make changes, you must also update the
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Elasticsearch README.
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+
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GitHub renders the tagged region directives when you view the README,
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so it's not possible to just include the content from the README. Darn.
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+
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Also note that there are similar instructions in the Kibana guide:
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https://www.elastic.co/guide/en/kibana/current/docker.html
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////
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To try out Elasticsearch on your own machine, we recommend using Docker
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and running both Elasticsearch and Kibana.
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Docker images are available from the https://www.docker.elastic.co[Elastic Docker registry].
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NOTE: Starting in Elasticsearch 8.0, security is enabled by default.
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The first time you start Elasticsearch, TLS encryption is configured automatically,
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a password is generated for the `elastic` user,
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and a Kibana enrollment token is created so you can connect Kibana to your secured cluster.
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For other installation options, see the
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https://www.elastic.co/guide/en/elasticsearch/reference/current/install-elasticsearch.html[Elasticsearch installation documentation].
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[discrete]
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=== Start Elasticsearch
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. Install and start https://www.docker.com/products/docker-desktop[Docker
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Desktop]. Go to **Preferences > Resources > Advanced** and set Memory to at least 4GB.
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. Start an Elasticsearch container:
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ifeval::["{release-state}"=="unreleased"]
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+
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WARNING: Version {version} of {es} has not yet been released, so no
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Docker image is currently available for this version.
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endif::[]
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+
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[source,sh,subs="attributes"]
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----
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docker network create elastic
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docker pull docker.elastic.co/elasticsearch/elasticsearch:{version}
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docker run --name elasticsearch --net elastic -p 9200:9200 -p 9300:9300 -e "discovery.type=single-node" -t docker.elastic.co/elasticsearch/elasticsearch:{version}
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----
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When you start Elasticsearch for the first time, the generated `elastic` user password and
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Kibana enrollment token are output to the terminal.
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NOTE: You might need to scroll back a bit in the terminal to view the password
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and enrollment token.
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. Copy the generated password and enrollment token and save them in a secure
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location. These values are shown only when you start Elasticsearch for the first time.
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You'll use these to enroll Kibana with your Elasticsearch cluster and log in.
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[discrete]
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=== Start Kibana
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Kibana enables you to easily send requests to Elasticsearch and analyze, visualize, and manage data interactively.
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. In a new terminal session, start Kibana and connect it to your Elasticsearch container:
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ifeval::["{release-state}"=="unreleased"]
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+
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WARNING: Version {version} of {kib} has not yet been released, so no
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Docker image is currently available for this version.
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endif::[]
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[source,sh,subs="attributes"]
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----
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docker pull docker.elastic.co/kibana/kibana:{version}
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docker run --name kibana --net elastic -p 5601:5601 docker.elastic.co/kibana/kibana:{version}
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----
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When you start Kibana, a unique URL is output to your terminal.
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. To access Kibana, open the generated URL in your browser.
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.. Paste the enrollment token that you copied when starting
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Elasticsearch and click the button to connect your Kibana instance with Elasticsearch.
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.. Log in to Kibana as the `elastic` user with the password that was generated
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when you started Elasticsearch.
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[discrete]
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=== Send requests to Elasticsearch
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You send data and other requests to Elasticsearch through REST APIs.
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You can interact with Elasticsearch using any client that sends HTTP requests,
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such as the https://www.elastic.co/guide/en/elasticsearch/client/index.html[Elasticsearch
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language clients] and https://curl.se[curl].
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Kibana's developer console provides an easy way to experiment and test requests.
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To access the console, go to **Management > Dev Tools**.
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[discrete]
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=== Add data
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You index data into Elasticsearch by sending JSON objects (documents) through the REST APIs.
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Whether you have structured or unstructured text, numerical data, or geospatial data,
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Elasticsearch efficiently stores and indexes it in a way that supports fast searches.
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For timestamped data such as logs and metrics, you typically add documents to a
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data stream made up of multiple auto-generated backing indices.
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To add a single document to an index, submit an HTTP post request that targets the index.
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[source,console]
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----
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POST /customer/_doc/1
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{
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"firstname": "Jennifer",
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"lastname": "Walters"
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}
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----
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This request automatically creates the `customer` index if it doesn't exist,
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adds a new document that has an ID of 1, and
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stores and indexes the `firstname` and `lastname` fields.
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The new document is available immediately from any node in the cluster.
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You can retrieve it with a GET request that specifies its document ID:
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[source,console]
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----
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GET /customer/_doc/1
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----
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// TEST[continued]
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To add multiple documents in one request, use the `_bulk` API.
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Bulk data must be newline-delimited JSON (NDJSON).
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Each line must end in a newline character (`\n`), including the last line.
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[source,console]
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----
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PUT customer/_bulk
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{ "create": { } }
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{ "firstname": "Monica","lastname":"Rambeau"}
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{ "create": { } }
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{ "firstname": "Carol","lastname":"Danvers"}
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{ "create": { } }
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{ "firstname": "Wanda","lastname":"Maximoff"}
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{ "create": { } }
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{ "firstname": "Jennifer","lastname":"Takeda"}
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----
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// TEST[continued]
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[discrete]
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=== Search
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Indexed documents are available for search in near real-time.
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The following search matches all customers with a first name of _Jennifer_
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in the `customer` index.
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[source,console]
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----
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GET customer/_search
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{
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"query" : {
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"match" : { "firstname": "Jennifer" }
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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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=== Explore
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You can use Discover in Kibana to interactively search and filter your data.
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From there, you can start creating visualizations and building and sharing dashboards.
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To get started, create a _data view_ that connects to one or more Elasticsearch indices,
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data streams, or index aliases.
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. Go to **Management > Stack Management > Kibana > Data Views**.
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. Select **Create data view**.
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. Enter a name for the data view and a pattern that matches one or more indices,
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such as _customer_.
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. Select **Save data view to Kibana**.
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To start exploring, go to **Analytics > Discover**.
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