In addition to reading this guide, we recommend you run the Elasticsearch Template Optimizer to fix problems in your data modeling.
It will analyze your templates to detect issues and improve search performance, reduce indexing bottlenecks and optimize storage utilization. The Template Optimizer is free and requires no installation.
This guide will help you check for common problems that cause the log to appear. It’s important to understand the issues related to the log, so to get started, read the general overview on common issues and tips related to the Elasticsearch concepts below. Advanced users might want to skip right to the common problems section in each concept or run the template optimizer.
Overview
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 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.
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
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.
Log Context
Log “[{}] re-syncing mappings with cluster state because of types [{}]” classname is MetaDataMappingService.java.
We extracted the following from Elasticsearch source code for those seeking an in-depth context :
}
}
// if a single type is not up-to-date; re-send everything
if (updatedTypes.isEmpty() == false) {
logger.warn("[{}] re-syncing mappings with cluster state because of types [{}]"; index; updatedTypes);
dirty = true;
for (DocumentMapper mapper : indexService.mapperService().docMappers(true)) {
builder.putMapping(new MappingMetaData(mapper));
}
}
