Document mapping type name can t start with found mappingType – How to solve this Elasticsearch exception

Document in Elasticsearch

What is an Elasticsearch document?

While an SQL database has rows of data stored in tables, Elasticsearch stores data as multiple documents inside an index. This is where the analogy must end however, since the way that Elasticsearch treats documents and indices differs significantly from a relational database.

For example, documents could be:

• Products in an e-commerce index
• Log lines in a data logging application
• Invoice lines in an invoicing system

Document fields

Each document is essentially a JSON structure, which is ultimately considered to be a series of key:value pairs. These pairs are then indexed in a way that is determined by the document mapping. The mapping defines the field data type as text, keyword, float, time, geo point or various other data types.

Elasticsearch documents are described as schema-less because Elasticsearch does not require us to pre-define the index field structure, nor does it require all documents in an index to have the same structure. However, once a field is mapped to a given data type, then all documents in the index must maintain that same mapping type.

Each field can also be mapped in more than one way in the index. This can be useful because we may want a keyword structure for aggregations, and at the same time be able to keep an analysed data structure which enables us to carry out full text searches for individual words in the field.

For a full discussion on mapping please see here.

Document source

An Elasticsearch document _source consists of the original JSON source data before it is indexed. This data is retrieved when fetched by a search query.

Document metadata

Each document is also associated with metadata, the most important items being:

_index – The index where the document is stored

_id – The unique ID which identifies the document in the index

Documents and index architecture

Note that different applications could consider  a “document” to be a different thing.  For example, in an invoicing system, we could have an architecture which stores invoices as documents (1 document per invoice),  or we could have an index structure which stores multiple documents as “invoice lines” for each invoice. The choice would depend on how we want to store, map and query the data.

Examples:

Creating a document in the user’s index:

POST  /users/_doc 
{
    "name" : "Petey",
    "lastname" : "Cruiser",
    "email" : "petey@gmail.com"
}

In the above request, we haven’t mentioned an ID for the document so the index operation generates a unique ID for the document. Here _doc is the type of document.

POST  /users/_doc/1
{
    "name" : "Petey",
    "lastname" : "Cruiser",
    "email" : "petey@gmail.com"
}

In the above query, the document will be created with ID 1.

You can use the below ‘GET’ query to get a document from the index using ID:

GET  /users/_doc/1

Below is the result, which contains the document (in _source field) as metadata:

{
    "_index": "users",
    "_type": "_doc",
    "_id": "1",
    "_version": 1,    "_seq_no": 1,    "_primary_term": 1,
    "found": true,
    "_source": {
        "name": "Petey",
        "lastname": "Cruiser",
        "email": "petey@gmail.com"
    }
}

Notes

Starting version 7.0 types are deprecated, so for backward compatibility on version 7.x all docs are under type ‘_doc’, starting 8.x type will be completely removed from ES APIs.

Overview

Metadata in Elasticsearch refers to additional information stored for each document. This is achieved using the specific metadata fields available in Elasticsearch. The default behavior of some of these metadata fields can be customized during mapping creation.

Examples

Using _meta meta-field for storing application-specific information with the mapping:

PUT /my_index?pretty
{
  "mappings": {
    "_meta": { 
      "domain": "security",
      "release_information": {
        "date": "18-01-2020",
        "version": "7.5"
      }
    }
  }
}

Notes

• In version 2.x, Elasticsearch had a total 13 meta fields available, which are: _index, _uid, _type, _id, _source, _size, _all, _field_names, _timestamp, _ttl, _parent, _routing, _meta
• In version 5.x, _timestamp and _ttl meta fields were removed.
• In version 6.x, the _parent meta field was removed.
• In version 7.x, _uid and _all meta fields were removed.

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

Watch the video below to learn how to save money on your deployment by optimizing your mapping.

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.