Failed to retrieve mapping version for recreating – How to solve this Elasticsearch error

Overview

A version corresponds to the Elasticsearch built-in tracking system that tracks the changes in each document’s update. When a document is indexed for the first time, it is assigned a version 1 using _version key. When the same document gets a subsequent update, the _version is incremented by 1 with every index, update or delete API call.

What it is used for

A version is used to handle the concurrency issues in Elasticsearch which come into play during simultaneous accessing of an index by multiple users. Elasticsearch handles this issue with an optimistic locking concept using the _version parameter to avoid letting multiple users edit the same document at the same time and protects users from generating incorrect data.

Notes

You cannot see the history of the document using _version. That means Elasticsearch does not use _version to keep a track of original changes that had been performed on the document. For example, if a document has been updated 10 times, it’s _version would be marked by Elasticsearch as 11, but you cannot go back and see what version 5 of the document looked like. This has to be implemented independently.

Common problems

If optimistic locking is not implemented while making updates to a document, Elasticsearch may return a conflict error with the 409 status code, which means that multiple users are trying to update the same version of the document at the same time.

POST /ratings/123?version=50
{
    "name": "Joker",
    "rating": 50
}

Overview

You can create a mapping of an index using the _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.

How to optimize your Elasticsearch mapping to reduce costs

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Common problems

• The most common problem in Elasticsearch is incorrectly defined mapping which limits the functionality of the field. For example, if the data type of a 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 the _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.