Failed to put audit trail template – How to solve this Elasticsearch error

If you want to learn more about Elasticsearch templates, check out this guide.

Overview

A template in Elasticsearch falls into one of the two following categories and is indexed inside Elasticsearch using its dedicated endpoint: 

1. Index templates, which are a way to define a set of rules including index settings, mappings and an index pattern. The template is applied automatically whenever a new index is created with the matching pattern. Templates are also used to dynamically apply custom mapping for the fields which are not predefined inside existing mapping.
2. Search templates, which help in defining templates for search queries using mustache scripting language. These templates act as a placeholder for variables defined inside the search queries.

Examples

Create a dynamic index template

PUT /_template/template_1?pretty
{
  "index_patterns": [
    "logs*",
    "api*"
  ],
  "settings": {
    "number_of_shards": 2
  },
  "mappings": {
    "dynamic_templates": [
      {
        "strings": {
          "match_mapping_type": "string",
          "mapping": {
            "type": "keyword"
          }
        }
      }
    ],
    "properties": {
      "host_name": {
        "type": "keyword"
      },
      "created_at": {
        "type": "date"
      }
    }
  }
}

Create a search template

POST /_scripts/search_template_1?pretty
{
    "script": {
        "lang": "mustache",
        "source": {
            "query": {
                "match": {
                    "description": "{{query_string}}"
                }
            }
        }
    }
}

Executing a search query using search template

GET /_search/template?pretty
{
    "id": "search_template_1", 
    "params": {
        "query_string": "hello world"
    }
}

The search request will be executed by default on all the indices available in the cluster and can be limited to particular indices using an index parameter.

Notes

• A dynamic index template is always useful when you do not know the field names in advance and want to control their mapping as per the business use case.

Overview

In Elasticsearch, an index (plural: indices) contains a schema and can have one or more shards and replicas. An Elasticsearch 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 Elasticsearch 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",
    "elasticsearch"
  ],
  "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

DELETE /*

action.destructive_requires_name: true