Elasticsearch Index – How to create OpenSearch Index and what it

Average Read Time

2 Mins

Elasticsearch Index – How to create OpenSearch Index and what it

Opster Team

Sep 11, 2022

Average Read Time

2 Mins


In addition to reading this guide, we recommend you run the Elasticsearch Health Check-Up. It will detect issues and improve your Elasticsearch performance by analyzing your shard sizes, threadpools, memory, snapshots, disk watermarks and more.

The Elasticsearch Check-Up is free and requires no installation.

To manage all aspects of your OpenSearch operation, you can use Opster’s Management Console (OMC). The OMC makes it easy to orchestrate and manage OpenSearch in any environment. Using the OMC you can deploy multiple clusters, configure node roles, scale cluster resources, manage certificates and more – all from a single interface, for free. Check it out here.

Run the OpenSearch check-up to receive recommendations like this:

checklist Run Check-Up
error

An indexing burst is affecting the performance of the following nodes

error-img

Description

The node is unable to keep up with indexing requests, and as a result indexing requests are being queued. If the write queue reaches full capacity...

error-img

Recommendation

In order to resolve this issue and prevent it from occurring again, we recommend that you begin by changing your configuration of indices to...

1

X-PUT curl -H "Content-Type: application/json" [customized recommendation]

Overview

In OpenSearch, an index (plural: indices) contains a schema and can have one or more shards and replicas. An OpenSearch index is divided into shards and each shard is an instance of a Lucene index.

Indices are used to store the documents in dedicated data structures corresponding to the data type of fields. For example, text fields are stored inside an inverted index whereas numeric and geo fields are stored inside BKD trees.

Examples

Create index

The following example is based on OpenSearch version 5.x onwards. An index with two shards, each having one replica will be created with the name test_index1

PUT /test_index1?pretty
{
    "settings" : {
        "number_of_shards" : 2,
        "number_of_replicas" : 1
    },
    "mappings" : {
        "properties" : {
            "tags" : { "type" : "keyword" },
            "updated_at" : { "type" : "date" }
        }
    }
}

List indices

All the index names and their basic information can be retrieved using the following command:

GET _cat/indices?v

Index a document

Let’s add a document in the index with the command below:

PUT test_index1/_doc/1
{
  "tags": [
    "opster",
    "OpenSearch"
  ],
  "date": "01-01-2020"
}

Query an index

GET test_index1/_search
{
  "query": {
    "match_all": {}
  }
}

Query multiple indices

It is possible to search multiple indices with a single request. If it is a raw HTTP request, index names should be sent in comma-separated format, as shown in the example below, and in the case of a query via a programming language client such as python or Java, index names are to be sent in a list format.

GET test_index1,test_index2/_search

Delete indices

DELETE test_index1

Common problems

  • It is good practice to define the settings and mapping of an Index wherever possible because if this is not done, OpenSearch tries to automatically guess the data type of fields at the time of indexing. This automatic process may have disadvantages, such as mapping conflicts, duplicate data and incorrect data types being set in the index. If the fields are not known in advance, it’s better to use dynamic index templates.
  • OpenSearch supports wildcard patterns in Index names, which sometimes aids with querying multiple indices, but can also be very destructive too. For example, It is possible to delete all the indices in a single command using the following commands:
DELETE /*

To disable this, you can add the following lines in the OpenSearch.yml:

action.destructive_requires_name: true


Run the Check-Up to get a customized report like this:

Analyze your cluster
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