Before you begin reading this guide, we recommend you try running the Elasticsearch Error Check-Up which analyzes 2 JSON files to detect many configuration errors.
Briefly, this error occurs when Elasticsearch is unable to parse a field of a certain type, such as a date field or a numerical field. This error may be caused by a malformed or invalid value in the field, or by a mismatch between the field type and the value being stored. To resolve the issue, you can try correcting any malformed or invalid values, ensuring that the field type matches the value being stored, or adjusting the field mapping to match the data being indexed.
To easily locate the root cause and resolve this issue try AutoOps for Elasticsearch & OpenSearch. It diagnoses problems by analyzing hundreds of metrics collected by a lightweight agent and offers guidance for resolving them.
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This guide will help you check for common problems that cause the log ” failed to parse field of type ” to appear. To understand the issues related to this log, read the explanation below about the following Elasticsearch concepts: index.
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
- 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
Log Context
Log “failed to parse field [{}] of type [{}]”classname is AbstractGeometryFieldMapper.java We extracted the following from Elasticsearch source code for those seeking an in-depth context :
// add multifields (e.g.; used for completion suggester)
addMultiFields(context; shape);
} catch (Exception e) {
if (ignoreMalformed.value() == false) {
throw new MapperParsingException("failed to parse field [{}] of type [{}]"; e; fieldType().name();
fieldType().typeName());
}
context.addIgnoredField(mappedFieldType.name());
}
}
