722 lines
23 KiB
Plaintext
Executable File
722 lines
23 KiB
Plaintext
Executable File
[[getting-started]]
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= Getting started with {es}
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[partintro]
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--
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Ready to take {es} for a test drive and see for yourself how you can use the
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REST APIs to store, search, and analyze data?
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Step through this getting started tutorial to:
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. Get an {es} cluster up and running
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. Index some sample documents
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. Search for documents using the {es} query language
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. Analyze the results using bucket and metrics aggregations
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Need more context?
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Check out the <<elasticsearch-intro,
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{es} Introduction>> to learn the lingo and understand the basics of
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how {es} works. If you're already familiar with {es} and want to see how it works
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with the rest of the stack, you might want to jump to the
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{stack-gs}/get-started-elastic-stack.html[Elastic Stack
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Tutorial] to see how to set up a system monitoring solution with {es}, {kib},
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{beats}, and {ls}.
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TIP: The fastest way to get started with {es} is to
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https://www.elastic.co/cloud/elasticsearch-service/signup[start a free 14-day
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trial of {ess}] in the cloud.
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--
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[[getting-started-install]]
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== Get {es} up and running
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To take {es} for a test drive, you can create a
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https://www.elastic.co/cloud/elasticsearch-service/signup[hosted deployment] on
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the {ess} or set up a multi-node {es} cluster on your own
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Linux, macOS, or Windows machine.
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[float]
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[[run-elasticsearch-hosted]]
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=== Run {es} on Elastic Cloud
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When you create a deployment on the {es} Service, the service provisions
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a three-node {es} cluster along with Kibana and APM.
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To create a deployment:
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. Sign up for a https://www.elastic.co/cloud/elasticsearch-service/signup[free trial]
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and verify your email address.
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. Set a password for your account.
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. Click **Create Deployment**.
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Once you've created a deployment, you're ready to <<getting-started-index>>.
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[float]
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[[run-elasticsearch-local]]
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=== Run {es} locally on Linux, macOS, or Windows
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When you create a deployment on the {ess}, a master node and
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two data nodes are provisioned automatically. By installing from the tar or zip
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archive, you can start multiple instances of {es} locally to see how a multi-node
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cluster behaves.
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To run a three-node {es} cluster locally:
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. Download the {es} archive for your OS:
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+
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Linux: https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-{version}-linux-x86_64.tar.gz[elasticsearch-{version}-linux-x86_64.tar.gz]
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+
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["source","sh",subs="attributes,callouts"]
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--------------------------------------------------
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curl -L -O https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-{version}-linux-x86_64.tar.gz
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--------------------------------------------------
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// NOTCONSOLE
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+
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macOS: https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-{version}-darwin-x86_64.tar.gz[elasticsearch-{version}-darwin-x86_64.tar.gz]
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+
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["source","sh",subs="attributes,callouts"]
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--------------------------------------------------
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curl -L -O https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-{version}-darwin-x86_64.tar.gz
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--------------------------------------------------
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// NOTCONSOLE
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+
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Windows:
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https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-{version}-windows-x86_64.zip[elasticsearch-{version}-windows-x86_64.zip]
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. Extract the archive:
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+
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Linux:
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+
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["source","sh",subs="attributes,callouts"]
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--------------------------------------------------
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tar -xvf elasticsearch-{version}-linux-x86_64.tar.gz
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--------------------------------------------------
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+
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macOS:
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+
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["source","sh",subs="attributes,callouts"]
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--------------------------------------------------
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tar -xvf elasticsearch-{version}-darwin-x86_64.tar.gz
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--------------------------------------------------
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+
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Windows PowerShell:
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+
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["source","powershell",subs="attributes,callouts"]
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--------------------------------------------------
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Expand-Archive elasticsearch-{version}-windows-x86_64.zip
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--------------------------------------------------
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. Start {es} from the `bin` directory:
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+
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Linux and macOS:
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+
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["source","sh",subs="attributes,callouts"]
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--------------------------------------------------
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cd elasticsearch-{version}/bin
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./elasticsearch
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--------------------------------------------------
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+
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Windows:
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+
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["source","powershell",subs="attributes,callouts"]
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--------------------------------------------------
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cd elasticsearch-{version}\bin
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.\elasticsearch.bat
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--------------------------------------------------
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+
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You now have a single-node {es} cluster up and running!
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. Start two more instances of {es} so you can see how a typical multi-node
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cluster behaves. You need to specify unique data and log paths
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for each node.
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+
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Linux and macOS:
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+
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["source","sh",subs="attributes,callouts"]
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--------------------------------------------------
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./elasticsearch -Epath.data=data2 -Epath.logs=log2
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./elasticsearch -Epath.data=data3 -Epath.logs=log3
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--------------------------------------------------
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+
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Windows:
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+
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["source","powershell",subs="attributes,callouts"]
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--------------------------------------------------
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.\elasticsearch.bat -E path.data=data2 -E path.logs=log2
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.\elasticsearch.bat -E path.data=data3 -E path.logs=log3
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--------------------------------------------------
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+
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The additional nodes are assigned unique IDs. Because you're running all three
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nodes locally, they automatically join the cluster with the first node.
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. Use the cat health API to verify that your three-node cluster is up running.
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The cat APIs return information about your cluster and indices in a
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format that's easier to read than raw JSON.
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+
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You can interact directly with your cluster by submitting HTTP requests to
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the {es} REST API. Most of the examples in this guide enable you to copy the
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appropriate cURL command and submit the request to your local {es} instance from
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the command line. If you have Kibana installed and running, you can also
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open Kibana and submit requests through the Dev Console.
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+
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TIP: You'll want to check out the
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https://www.elastic.co/guide/en/elasticsearch/client/index.html[{es} language
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clients] when you're ready to start using {es} in your own applications.
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+
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[source,js]
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--------------------------------------------------
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GET /_cat/health?v
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--------------------------------------------------
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// CONSOLE
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+
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The response should indicate that the status of the `elasticsearch` cluster
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is `green` and it has three nodes:
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+
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[source,txt]
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--------------------------------------------------
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epoch timestamp cluster status node.total node.data shards pri relo init unassign pending_tasks max_task_wait_time active_shards_percent
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1565052807 00:53:27 elasticsearch green 3 3 6 3 0 0 0 0 - 100.0%
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--------------------------------------------------
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// TESTRESPONSE[s/1565052807 00:53:27 elasticsearch/\\d+ \\d+:\\d+:\\d+ integTest/]
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// TESTRESPONSE[s/3 3 6 3/\\d+ \\d+ \\d+ \\d+/]
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// TESTRESPONSE[s/0 0 -/0 \\d+ (-|\\d+(micros|ms|s))/]
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// TESTRESPONSE[non_json]
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+
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NOTE: The cluster status will remain yellow if you are only running a single
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instance of {es}. A single node cluster is fully functional, but data
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cannot be replicated to another node to provide resiliency. Replica shards must
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be available for the cluster status to be green. If the cluster status is red,
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some data is unavailable.
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[float]
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[[gs-other-install]]
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=== Other installation options
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Installing {es} from an archive file enables you to easily install and run
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multiple instances locally so you can try things out. To run a single instance,
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you can run {es} in a Docker container, install {es} using the DEB or RPM
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packages on Linux, install using Homebrew on macOS, or install using the MSI
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package installer on Windows. See <<install-elasticsearch>> for more information.
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[[getting-started-index]]
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== Index some documents
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Once you have a cluster up and running, you're ready to index some data.
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There are a variety of ingest options for {es}, but in the end they all
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do the same thing: put JSON documents into an {es} index.
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You can do this directly with a simple PUT request that specifies
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the index you want to add the document, a unique document ID, and one or more
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`"field": "value"` pairs in the request body:
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[source,js]
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--------------------------------------------------
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PUT /customer/_doc/1
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{
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"name": "John Doe"
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}
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--------------------------------------------------
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// CONSOLE
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This request automatically creates the `customer` index if it doesn't already
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exist, adds a new document that has an ID of `1`, and stores and
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indexes the `name` field.
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Since this is a new document, the response shows that the result of the
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operation was that version 1 of the document was created:
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[source,js]
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--------------------------------------------------
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{
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"_index" : "customer",
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"_type" : "_doc",
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"_id" : "1",
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"_version" : 1,
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"result" : "created",
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"_shards" : {
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"total" : 2,
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"successful" : 2,
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"failed" : 0
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},
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"_seq_no" : 26,
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"_primary_term" : 4
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}
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--------------------------------------------------
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// TESTRESPONSE[s/"_seq_no" : \d+/"_seq_no" : $body._seq_no/]
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// TESTRESPONSE[s/"successful" : \d+/"successful" : $body._shards.successful/]
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// TESTRESPONSE[s/"_primary_term" : \d+/"_primary_term" : $body._primary_term/]
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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,js]
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--------------------------------------------------
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GET /customer/_doc/1
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--------------------------------------------------
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// CONSOLE
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// TEST[continued]
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The response indicates that a document with the specified ID was found
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and shows the original source fields that were indexed.
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[source,js]
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--------------------------------------------------
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{
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"_index" : "customer",
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"_type" : "_doc",
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"_id" : "1",
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"_version" : 1,
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"_seq_no" : 26,
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"_primary_term" : 4,
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"found" : true,
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"_source" : {
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"name": "John Doe"
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}
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}
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--------------------------------------------------
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// TESTRESPONSE[s/"_seq_no" : \d+/"_seq_no" : $body._seq_no/ ]
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// TESTRESPONSE[s/"_primary_term" : \d+/"_primary_term" : $body._primary_term/]
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[float]
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[[getting-started-batch-processing]]
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=== Indexing documents in bulk
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If you have a lot of documents to index, you can submit them in batches with
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the {ref}/docs-bulk.html[bulk API]. Using bulk to batch document
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operations is significantly faster than submitting requests individually as it minimizes network roundtrips.
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The optimal batch size depends a number of factors: the document size and complexity, the indexing and search load, and the resources available to your cluster. A good place to start is with batches of 1,000 to 5,000 documents
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and a total payload between 5MB and 15MB. From there, you can experiment
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to find the sweet spot.
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To get some data into {es} that you can start searching and analyzing:
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. Download the https://github.com/elastic/elasticsearch/blob/master/docs/src/test/resources/accounts.json?raw=true[`accounts.json`] sample data set. The documents in this randomly-generated data set represent user accounts with the following information:
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+
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[source,js]
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--------------------------------------------------
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{
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"account_number": 0,
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"balance": 16623,
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"firstname": "Bradshaw",
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"lastname": "Mckenzie",
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"age": 29,
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"gender": "F",
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"address": "244 Columbus Place",
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"employer": "Euron",
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"email": "bradshawmckenzie@euron.com",
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"city": "Hobucken",
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"state": "CO"
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}
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--------------------------------------------------
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// NOTCONSOLE
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. Index the account data into the `bank` index with the following `_bulk` request:
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+
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[source,sh]
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--------------------------------------------------
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curl -H "Content-Type: application/json" -XPOST "localhost:9200/bank/_bulk?pretty&refresh" --data-binary "@accounts.json"
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curl "localhost:9200/_cat/indices?v"
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--------------------------------------------------
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// NOTCONSOLE
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+
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////
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This replicates the above in a document-testing friendly way but isn't visible
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in the docs:
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+
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[source,js]
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--------------------------------------------------
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GET /_cat/indices?v
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--------------------------------------------------
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// CONSOLE
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// TEST[setup:bank]
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////
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+
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The response indicates that 1,000 documents were indexed successfully.
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+
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[source,txt]
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--------------------------------------------------
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health status index uuid pri rep docs.count docs.deleted store.size pri.store.size
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yellow open bank l7sSYV2cQXmu6_4rJWVIww 5 1 1000 0 128.6kb 128.6kb
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--------------------------------------------------
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// TESTRESPONSE[s/128.6kb/\\d+(\\.\\d+)?[mk]?b/]
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// TESTRESPONSE[s/l7sSYV2cQXmu6_4rJWVIww/.+/ non_json]
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[[getting-started-search]]
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== Start searching
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Once you have ingested some data into an {es} index, you can search it
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by sending requests to the `_search` endpoint. To access the full suite of
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search capabilities, you use the {es} Query DSL to specify the
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search criteria in the request body. You specify the name of the index you
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want to search in the request URI.
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For example, the following request retrieves all documents in the `bank`
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index sorted by account number:
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[source,js]
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--------------------------------------------------
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GET /bank/_search
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{
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"query": { "match_all": {} },
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"sort": [
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{ "account_number": "asc" }
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]
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}
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--------------------------------------------------
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// CONSOLE
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// TEST[continued]
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By default, the `hits` section of the response includes the first 10 documents
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that match the search criteria:
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[source,js]
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--------------------------------------------------
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{
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"took" : 63,
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"timed_out" : false,
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"_shards" : {
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"total" : 5,
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"successful" : 5,
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"skipped" : 0,
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"failed" : 0
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},
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"hits" : {
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"total" : {
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"value": 1000,
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"relation": "eq"
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},
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"max_score" : null,
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"hits" : [ {
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"_index" : "bank",
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"_type" : "_doc",
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"_id" : "0",
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"sort": [0],
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"_score" : null,
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"_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"}
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}, {
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"_index" : "bank",
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"_type" : "_doc",
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"_id" : "1",
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"sort": [1],
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"_score" : null,
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"_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"}
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}, ...
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]
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}
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}
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--------------------------------------------------
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// TESTRESPONSE[s/"took" : 63/"took" : $body.took/]
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// TESTRESPONSE[s/\.\.\./$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/]
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The response also provides the following information about the search request:
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* `took` – how long it took {es} to run the query, in milliseconds
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* `timed_out` – whether or not the search request timed out
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* `_shards` – how many shards were searched and a breakdown of how many shards
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succeeded, failed, or were skipped.
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* `max_score` – the score of the most relevant document found
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* `hits.total.value` - how many matching documents were found
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* `hits.sort` - the document's sort position (when not sorting by relevance score)
|
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* `hits._score` - the document's relevance score (not applicable when using `match_all`)
|
||
|
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Each search request is self-contained: {es} does not maintain any
|
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state information across requests. To page through the search hits, specify
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the `from` and `size` parameters in your request.
|
||
|
||
For example, the following request gets hits 10 through 19:
|
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|
||
[source,js]
|
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--------------------------------------------------
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GET /bank/_search
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{
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"query": { "match_all": {} },
|
||
"sort": [
|
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{ "account_number": "asc" }
|
||
],
|
||
"from": 10,
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"size": 10
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||
}
|
||
--------------------------------------------------
|
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// CONSOLE
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// TEST[continued]
|
||
|
||
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.
|
||
For example, the following request searches the `address` field to find
|
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customers whose addresses contain `mill` or `lane`:
|
||
|
||
[source,js]
|
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--------------------------------------------------
|
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GET /bank/_search
|
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{
|
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"query": { "match": { "address": "mill lane" } }
|
||
}
|
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--------------------------------------------------
|
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// CONSOLE
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// TEST[continued]
|
||
|
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To perform a phrase search rather than matching individual terms, you use
|
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`match_phrase` instead of `match`. For example, the following request only
|
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matches addresses that contain the phrase `mill lane`:
|
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|
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[source,js]
|
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--------------------------------------------------
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GET /bank/_search
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{
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"query": { "match_phrase": { "address": "mill lane" } }
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}
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--------------------------------------------------
|
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// CONSOLE
|
||
// TEST[continued]
|
||
|
||
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).
|
||
|
||
For example, the following request searches the `bank` index for accounts that
|
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belong to customers who are 40 years old, but excludes anyone who lives in
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Idaho (ID):
|
||
|
||
[source,js]
|
||
--------------------------------------------------
|
||
GET /bank/_search
|
||
{
|
||
"query": {
|
||
"bool": {
|
||
"must": [
|
||
{ "match": { "age": "40" } }
|
||
],
|
||
"must_not": [
|
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{ "match": { "state": "ID" } }
|
||
]
|
||
}
|
||
}
|
||
}
|
||
--------------------------------------------------
|
||
// CONSOLE
|
||
// TEST[continued]
|
||
|
||
Each `must`, `should`, and `must_not` element in a Boolean query is referred
|
||
to as a query clause. How well a document meets the criteria in each `must` or
|
||
`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.
|
||
|
||
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.
|
||
|
||
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).
|
||
|
||
[source,js]
|
||
--------------------------------------------------
|
||
GET /bank/_search
|
||
{
|
||
"query": {
|
||
"bool": {
|
||
"must": { "match_all": {} },
|
||
"filter": {
|
||
"range": {
|
||
"balance": {
|
||
"gte": 20000,
|
||
"lte": 30000
|
||
}
|
||
}
|
||
}
|
||
}
|
||
}
|
||
}
|
||
--------------------------------------------------
|
||
// CONSOLE
|
||
// TEST[continued]
|
||
|
||
[[getting-started-aggregations]]
|
||
== Analyze results with aggregations
|
||
|
||
{es} aggregations enable you to get meta-information about your search results
|
||
and answer questions like, "How many account holders are in Texas?" or
|
||
"What's the average balance of accounts in Tennessee?" You can search
|
||
documents, filter hits, and use aggregations to analyze the results all in one
|
||
request.
|
||
|
||
For example, the following request uses a `terms` aggregation to group
|
||
all of the accounts in the `bank` index by state, and returns the ten states
|
||
with the most accounts in descending order:
|
||
|
||
[source,js]
|
||
--------------------------------------------------
|
||
GET /bank/_search
|
||
{
|
||
"size": 0,
|
||
"aggs": {
|
||
"group_by_state": {
|
||
"terms": {
|
||
"field": "state.keyword"
|
||
}
|
||
}
|
||
}
|
||
}
|
||
--------------------------------------------------
|
||
// CONSOLE
|
||
// TEST[continued]
|
||
|
||
The `buckets` in the response are the values of the `state` field. The
|
||
`doc_count` shows the number of accounts in each state. For example, you
|
||
can see that there are 27 accounts in `ID` (Idaho). Because the request
|
||
set `size=0`, the response only contains the aggregation results.
|
||
|
||
[source,js]
|
||
--------------------------------------------------
|
||
{
|
||
"took": 29,
|
||
"timed_out": false,
|
||
"_shards": {
|
||
"total": 5,
|
||
"successful": 5,
|
||
"skipped" : 0,
|
||
"failed": 0
|
||
},
|
||
"hits" : {
|
||
"total" : {
|
||
"value": 1000,
|
||
"relation": "eq"
|
||
},
|
||
"max_score" : null,
|
||
"hits" : [ ]
|
||
},
|
||
"aggregations" : {
|
||
"group_by_state" : {
|
||
"doc_count_error_upper_bound": 20,
|
||
"sum_other_doc_count": 770,
|
||
"buckets" : [ {
|
||
"key" : "ID",
|
||
"doc_count" : 27
|
||
}, {
|
||
"key" : "TX",
|
||
"doc_count" : 27
|
||
}, {
|
||
"key" : "AL",
|
||
"doc_count" : 25
|
||
}, {
|
||
"key" : "MD",
|
||
"doc_count" : 25
|
||
}, {
|
||
"key" : "TN",
|
||
"doc_count" : 23
|
||
}, {
|
||
"key" : "MA",
|
||
"doc_count" : 21
|
||
}, {
|
||
"key" : "NC",
|
||
"doc_count" : 21
|
||
}, {
|
||
"key" : "ND",
|
||
"doc_count" : 21
|
||
}, {
|
||
"key" : "ME",
|
||
"doc_count" : 20
|
||
}, {
|
||
"key" : "MO",
|
||
"doc_count" : 20
|
||
} ]
|
||
}
|
||
}
|
||
}
|
||
--------------------------------------------------
|
||
// TESTRESPONSE[s/"took": 29/"took": $body.took/]
|
||
|
||
|
||
You can combine aggregations to build more complex summaries of your data. For
|
||
example, the following request nests an `avg` aggregation within the previous
|
||
`group_by_state` aggregation to calculate the average account balances for
|
||
each state.
|
||
|
||
[source,js]
|
||
--------------------------------------------------
|
||
GET /bank/_search
|
||
{
|
||
"size": 0,
|
||
"aggs": {
|
||
"group_by_state": {
|
||
"terms": {
|
||
"field": "state.keyword"
|
||
},
|
||
"aggs": {
|
||
"average_balance": {
|
||
"avg": {
|
||
"field": "balance"
|
||
}
|
||
}
|
||
}
|
||
}
|
||
}
|
||
}
|
||
--------------------------------------------------
|
||
// CONSOLE
|
||
// TEST[continued]
|
||
|
||
Instead of sorting the results by count, you could sort using the result of
|
||
the nested aggregation by specifying the order within the `terms` aggregation:
|
||
|
||
[source,js]
|
||
--------------------------------------------------
|
||
GET /bank/_search
|
||
{
|
||
"size": 0,
|
||
"aggs": {
|
||
"group_by_state": {
|
||
"terms": {
|
||
"field": "state.keyword",
|
||
"order": {
|
||
"average_balance": "desc"
|
||
}
|
||
},
|
||
"aggs": {
|
||
"average_balance": {
|
||
"avg": {
|
||
"field": "balance"
|
||
}
|
||
}
|
||
}
|
||
}
|
||
}
|
||
}
|
||
--------------------------------------------------
|
||
// CONSOLE
|
||
// TEST[continued]
|
||
|
||
In addition to basic bucketing and metrics aggregations like these, {es}
|
||
provides specialized aggregations for operating on multiple fields and
|
||
analyzing particular types of data such as dates, IP addresses, and geo
|
||
data. You can also feed the results of individual aggregations into pipeline
|
||
aggregations for further analysis.
|
||
|
||
The core analysis capabilities provided by aggregations enable advanced
|
||
features such as using machine learning to detect anomalies.
|
||
|
||
[[getting-started-next-steps]]
|
||
== Where to go from here
|
||
|
||
Now that you've set up a cluster, indexed some documents, and run some
|
||
searches and aggregations, you might want to:
|
||
|
||
* {stack-gs}/get-started-elastic-stack.html#install-kibana[Dive in to the Elastic
|
||
Stack Tutorial] to install Kibana, Logstash, and Beats and
|
||
set up a basic system monitoring solution.
|
||
|
||
* {kibana-ref}/add-sample-data.html[Load one of the sample data sets into Kibana]
|
||
to see how you can use {es} and Kibana together to visualize your data.
|
||
|
||
* Try out one of the Elastic search solutions:
|
||
** https://swiftype.com/documentation/site-search/crawler-quick-start[Site Search]
|
||
** https://swiftype.com/documentation/app-search/getting-started[App Search]
|
||
** https://swiftype.com/documentation/enterprise-search/getting-started[Enterprise Search]
|