How To Solve Issues Related to Log – Failed to add mapping ; source

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Updated: Feb-20

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Troubleshooting background

To troubleshoot Elasticsearch log “Failed to add mapping ; source” it’s important to understand common problems related to Elasticsearch concepts: transport. See detailed explanations below complete with common problems, examples and useful tips.

Index in Elasticsearch

What it is

In Elasticsearch, an index (indices in plural) can be thought of as a table inside a database that has 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 below command:

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
  • It is good practice to define the settings and mapping of an Index wherever possible because if this is not done, Elasticsearch 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.
  • Elasticsearch 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 elasticsearch.yml:

action.destructive_requires_name: true

Mapping in Elasticsearch


Mapping is similar to database schemas that define the properties of each field in the index. These properties may contain the data type of each field and how fields are going to be tokenized and indexed. In addition, the mapping may also contain various advanced level properties for each field to define the options exposed by Lucene and Elasticsearch. You can create a mapping of an index using _mappings REST endpoint. The very first time Elasticsearch finds a new field, whose mapping is not pre-defined inside the index, it automatically tries to guess the data type and analyzer of that field and set its default value. For example, if you index an integer field without pre-defining the mapping, Elasticsearch sets the mapping of that field as long.

Examples

Create an index with predefined mapping:

PUT /my_index?pretty
{
  "settings": {
    "number_of_shards": 1
  },
  "mappings": {
    "properties": {
      "name": {
        "type": "text"
      },
      "age": {
        "type": "integer"
      }
    }
  }
}

Create mapping in an existing index:

PUT /my_index/_mapping?pretty
{
  "properties": {
    "email": {
      "type": "keyword"
    }
  }
}

View the mapping of an existing index:

GET my_index/_mapping?pretty

View the mapping of an existing field:

GET /my_index/_mapping/field/name?pretty

Notes
  • It is not possible to update the mapping of an existing field. If the mapping is set to the wrong type, re-creating the index with updated mapping and re-indexing is the only option available.
  • In version 7.0, Elasticsearch has deprecated the document type and the default document type is set to _doc. In future versions of Elasticsearch, the document type will be removed completely.

Common Problems
  • The most common problem in Elasticsearch is incorrectly defined mapping which limits the use functionality of the field. For example, if the data type of string field is set as text, you cannot use that field for aggregations, sorting or exact match filters. Similarly, if a string field is dynamically indexed without predefined mapping, Elasticsearch automatically creates two fields internally. One as a text type for full-text search and another as keyword type, which in most cases is a waste of space. 
  • Elasticsearch automatically creates an _all field inside the mapping and copies values of each field of a document inside the _all field. This field is used to search text without specifying a field name. Make sure to disable the _all field in production environments to avoid wasting space. Please note that support for _all field has been removed in version 7.0
  • In versions lower than 5.0, it was possible to create multiple document types inside an index, similar to creating multiple tables inside a database. In those versions, there were higher chances of getting data types conflicts across different document types if they contained the same field name with different data types. 
  • The mapping of each index is part of the cluster state and is managed by master nodes. If the mapping is too big, meaning there are thousands of fields in the index, the cluster state grows too large to be handled and creates the issue of mapping explosion, resulting in the slowness of the cluster.


To help troubleshoot related issues we have gathered selected Q&A from the community and issues from Github , please review the following for further information :

1 elasticsearch java api putmapping from json file error 2.24 K 

2 Unexpected And Failed Attempts To A  


Log Context

Log ” [{}] failed to add mapping [{}]; source [{}]” classname is IndicesClusterStateService.java
We have extracted the following from Elasticsearch source code to get an in-depth context :

                     logger.debug("[{}] parsed mapping [{}]; and got different sources\noriginal:\n{}\nparsed:\n{}"; index; mappingType; mappingSource; mapperService.documentMapper(mappingType).mappingSource());
                    requiresRefresh = true;
                }
            }
        } catch (Throwable e) {
            logger.warn("[{}] failed to add mapping [{}]; source [{}]"; e; index; mappingType; mappingSource);
            throw e;
        }
        return requiresRefresh;
    }







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