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Getting started with {es}

Get {es} up and running

To take {es} for a test drive, you can create a hosted deployment on the {ess} or set up a multi-node {es} cluster on your own Linux, macOS, or Windows machine.

Run {es} on Elastic Cloud

When you create a deployment on the {es} Service, the service provisions a three-node {es} cluster along with Kibana and APM.

To create a deployment:

  1. Sign up for a free trial and verify your email address.

  2. Set a password for your account.

  3. Click Create Deployment.

Once you’ve created a deployment, you’re ready to Index some documents.

Run {es} locally on Linux, macOS, or Windows

When you create a deployment on the {ess}, a master node and two data nodes are provisioned automatically. By installing from the tar or zip archive, you can start multiple instances of {es} locally to see how a multi-node cluster behaves.

To run a three-node {es} cluster locally:

  1. Download the {es} archive for your OS:

    curl -L -O https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-{version}.tar.gz
  2. Extract the archive:

    Linux and macOS:

    tar -xvf elasticsearch-{version}.tar.gz

    Windows PowerShell:

    Expand-Archive elasticsearch-{version}-windows-x86_64.zip
  3. Start {es} from the bin directory:

    Linux and macOS:

    cd elasticsearch-{version}/bin
    ./elasticsearch

    Windows:

    cd %PROGRAMFILES%\Elastic\Elasticsearch\bin
    .\elasticsearch.exe

    You now have a single-node {es} cluster up and running!

  4. Start two more instances of {es} so you can see how a typical multi-node cluster behaves. You need to specify unique data and log paths for each node.

    Linux and macOS:

    ./elasticsearch -Epath.data=data2 -Epath.logs=log2
    ./elasticsearch -Epath.data=data3 -Epath.logs=log3

    Windows:

    .\elasticsearch.exe -Epath.data=data2 -Epath.logs=log2
    .\elasticsearch.exe -Epath.data=data3 -Epath.logs=log3

    The additional nodes are assigned unique IDs. Because you’re running all three nodes locally, they automatically join the cluster with the first node.

  5. Use the cat health API to verify that your three-node cluster is up running. The cat APIs return information about your cluster and indices in a format that’s easier to read than raw JSON.

    You can interact directly with your cluster by submitting HTTP requests to the {es} REST API. Most of the examples in this guide enable you to copy the appropriate cURL command and submit the request to your local {es} instance from the command line. If you have Kibana installed and running, you can also open Kibana and submit requests through the Dev Console.

    Tip
    You’ll want to check out the {es} language clients when you’re ready to start using {es} in your own applications.
    GET /_cat/health?v

    The response should indicate that the status of the elasticsearch cluster is green and it has three nodes:

    epoch      timestamp cluster       status node.total node.data shards pri relo init unassign pending_tasks max_task_wait_time active_shards_percent
    1565052807 00:53:27  elasticsearch green           3         3      6   3    0    0        0             0                  -                100.0%
    Note
    The cluster status will remain yellow if you are only running a single instance of {es}. A single node cluster is fully functional, but data cannot be replicated to another node to provide resiliency. Replica shards must be available for the cluster status to be green. If the cluster status is red, some data is unavailable.

Other installation options

Installing {es} from an archive file enables you to easily install and run multiple instances locally so you can try things out. To run a single instance, you can run {es} in a Docker container, install {es} using the DEB or RPM packages on Linux, install using Homebrew on macOS, or install using the MSI package installer on Windows. See [install-elasticsearch] for more information.

Index some documents

Once you have a cluster up and running, you’re ready to index some data. There are a variety of ingest options for {es}, but in the end they all do the same thing: put JSON documents into an {es} index.

You can do this directly with a simple PUT request that specifies the index you want to add the document, a unique document ID, and one or more "field": "value" pairs in the request body:

PUT /customer/_doc/1
{
  "name": "John Doe"
}

This request automatically creates the customer index if it doesn’t already exist, adds a new document that has an ID of 1, and stores and indexes the name field.

Since this is a new document, the response shows that the result of the operation was that version 1 of the document was created:

{
  "_index" : "customer",
  "_type" : "_doc",
  "_id" : "1",
  "_version" : 1,
  "result" : "created",
  "_shards" : {
    "total" : 2,
    "successful" : 2,
    "failed" : 0
  },
  "_seq_no" : 26,
  "_primary_term" : 4
}

The new document is available immediately from any node in the cluster. You can retrieve it with a GET request that specifies its document ID:

GET /customer/_doc/1

The response indicates that a document with the specified ID was found and shows the original source fields that were indexed.

{
  "_index" : "customer",
  "_type" : "_doc",
  "_id" : "1",
  "_version" : 1,
  "_seq_no" : 26,
  "_primary_term" : 4,
  "found" : true,
  "_source" : {
    "name": "John Doe"
  }
}

Indexing documents in bulk

If you have a lot of documents to index, you can submit them in batches with the {ref}/docs-bulk.html[bulk API]. Using bulk to batch document operations is significantly faster than submitting requests individually as it minimizes network roundtrips.

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 and a total payload between 5MB and 15MB. From there, you can experiment to find the sweet spot.

To get some data into {es} that you can start searching and analyzing:

  1. Download the accounts.json sample data set. The documents in this randomly-generated data set represent user accounts with the following information:

    {
        "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"
    }
  2. Index the account data into the bank index with the following _bulk request:

    curl -H "Content-Type: application/json" -XPOST "localhost:9200/bank/_doc/_bulk?pretty&refresh" --data-binary "@accounts.json"
    curl "localhost:9200/_cat/indices?v"

    The response indicates that 1,000 documents were indexed successfully.

    health status index uuid                   pri rep docs.count docs.deleted store.size pri.store.size
    green open   bank  l7sSYV2cQXmu6_4rJWVIww   5   1       1000            0    128.6kb        128.6kb

Once you have ingested some data into an {es} index, you can search it by sending requests to the _search endpoint. To access the full suite of search capabilities, you use the {es} Query DSL to specify the search criteria in the request body. You specify the name of the index you want to search in the request URI.

For example, the following request retrieves all documents in the bank index sorted by account number:

GET /bank/_search
{
  "query": { "match_all": {} },
  "sort": [
    { "account_number": "asc" }
  ]
}

By default, the hits section of the response includes the first 10 documents that match the search criteria:

{
  "took" : 63,
  "timed_out" : false,
  "_shards" : {
    "total" : 5,
    "successful" : 5,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : 1000,
    "max_score": null,
    "hits" : [ {
      "_index" : "bank",
      "_type" : "_doc",
      "_id" : "0",
      "sort": [0],
      "_score" : null,
      "_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"}
    }, {
      "_index" : "bank",
      "_type" : "_doc",
      "_id" : "1",
      "sort": [1],
      "_score" : null,
      "_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"}
    }, ...
    ]
  }
}

The response also provides the following information about the search request:

  • took – how long it took {es} to run the query, in milliseconds

  • timed_out – whether or not the search request timed out

  • _shards – how many shards were searched and a breakdown of how many shards succeeded, failed, or were skipped.

  • max_score – the score of the most relevant document found

  • hits.total.value - how many matching documents were found

  • hits.sort - the document’s sort position (when not sorting by relevance score)

  • hits._score - the document’s relevance score (not applicable when using match_all)

Each search request is self-contained: {es} does not maintain any state information across requests. To page through the search hits, specify the from and size parameters in your request.

For example, the following request gets hits 10 through 19:

GET /bank/_search
{
  "query": { "match_all": {} },
  "sort": [
    { "account_number": "asc" }
  ],
  "from": 10,
  "size": 10
}

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 customers whose addresses contain mill or lane:

GET /bank/_search
{
  "query": { "match": { "address": "mill lane" } }
}

To perform a phrase search rather than matching individual terms, you use match_phrase instead of match. For example, the following request only matches addresses that contain the phrase mill lane:

GET /bank/_search
{
  "query": { "match_phrase": { "address": "mill lane" } }
}

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 belong to customers who are 40 years old, but excludes anyone who lives in Idaho (ID):

GET /bank/_search
{
  "query": {
    "bool": {
      "must": [
        { "match": { "age": "40" } }
      ],
      "must_not": [
        { "match": { "state": "ID" } }
      ]
    }
  }
}

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).

GET /bank/_search
{
  "query": {
    "bool": {
      "must": { "match_all": {} },
      "filter": {
        "range": {
          "balance": {
            "gte": 20000,
            "lte": 30000
          }
        }
      }
    }
  }
}

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:

GET /bank/_search
{
  "size": 0,
  "aggs": {
    "group_by_state": {
      "terms": {
        "field": "state.keyword"
      }
    }
  }
}

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.

{
  "took": 29,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "skipped" : 0,
    "failed": 0
  },
  "hits" : {
     "total" : 1000,
    "max_score" : 0.0,
    "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
      } ]
    }
  }
}

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.

GET /bank/_search
{
  "size": 0,
  "aggs": {
    "group_by_state": {
      "terms": {
        "field": "state.keyword"
      },
      "aggs": {
        "average_balance": {
          "avg": {
            "field": "balance"
          }
        }
      }
    }
  }
}

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:

GET /bank/_search
{
  "size": 0,
  "aggs": {
    "group_by_state": {
      "terms": {
        "field": "state.keyword",
        "order": {
          "average_balance": "desc"
        }
      },
      "aggs": {
        "average_balance": {
          "avg": {
            "field": "balance"
          }
        }
      }
    }
  }
}

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.

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: