Missing paramName in context mapping – How to solve this Elasticsearch exception

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.

Overview

Search refers to the searching of documents in an index or multiple indices. The simple search is just a GET API request to the _search endpoint. The search query can either be provided in query string or through a request body.

Examples

When looking for any documents in this index, if search parameters are not provided, every document is a hit and by default 10 hits will be returned.

GET my_documents/_search

A JSON object is returned in response to a search query. A 200 response code means the request was completed successfully.

{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 2,
"successful" : 2,
"failed" : 0
},
"hits" : {
"total" : 2,
"max_score" : 1.0,
"hits" : [
...
]
}
}

Notes and good things to know

• Distributed search is challenging and every shard of the index needs to be searched for hits, and then those hits are combined into a single sorted list as a final result.
• There are two phases of search: the query phase and the fetch phase.
• In the query phase, the query is executed on each shard locally and top hits are returned to the coordinating node. The coordinating node merges the results and creates a global sorted list. 
• In the fetch phase, the coordinating node brings the actual documents for those hit IDs and returns them to the requesting client.
• A coordinating node needs enough memory and CPU in order to handle the fetch phase.