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This article mainly explains "what is the mapping mapping of ElasticSearch". The content of the explanation is simple and clear, and it is easy to learn and understand. Please follow the editor's train of thought to study and learn "what is the mapping mapping of ElasticSearch".

1. What is mapping

Mapping is a table structure definition similar to that in a database, and its main functions are as follows:

Define the field name under index

Define field types, such as numeric, floating-point, Boolean, etc.

Define settings related to inverted index, such as whether to index, record position, etc.

Before introducing mapping, there is a concept that must be understood: ElasticSearch7- removes the concept of type.

Two data representations in a relational database are independent, even if they have columns with the same name, but this is not the case in ES. Elasticsearch is a search engine based on Lucene, and it has the same name under different type in ES.

Filed ends up being treated the same way in Lucene.

Two user_name under two different type are actually considered to be the same filed under the same index of ES. You must

The same filed mapping must be defined in two different type. Otherwise, the same field names in different type will be found in the

Conflicts occur in processing, resulting in a decline in the efficiency of Lucene processing.

The purpose of removing type is to improve the efficiency of ES in processing data.

Elasticsearch 7.x

The type parameter in URL is optional. For example, indexing a document no longer requires a document type.

Elasticsearch 8.x

The type parameter in URL is no longer supported.

Solution: migrate indexes from multiple types to single types, with a separate index for each type of document

2. Check mappingGET / [index_name] / _ mapping3, and create a mappingPUT / my-index-000001 {"mappings": {"properties": {"age": {"type": "integer"}, "email": {"type": "keyword"}, "name": {"type": "text"}.

Description:

Integer: numeric type. Default is long.

Keyword: can only match exactly when retrieving

Text: full-text search. Word segmentation is performed when saving data.

4. Add a new field mapping PUT / my-index-000001/_mapping {"properties": {"employee-id": {"type": "keyword", "index": false}

Description

Index: the default is true, which is indexed, that is, participating in the retrieval. Here, it is set to false, which means that this field does not participate in the retrieval.

5. Update field mapping

Direct modification is prohibited for mapping fields that already exist. Updates must create new indexes for data migration. Because the inverted index implemented by lucene is not allowed to be modified after generation, the new index should be re-established, and then the reindex operation should be done.

However, you can add a new field, which can be controlled by the parameter dynamic. The value of this parameter is as follows:

True: default value, which means automatically added fields are allowed

False: automatic addition of fields is not allowed, but the document can be written normally, but the fields cannot be queried and other operations

Strict: strict mode, document can not be written, error report

Example

First create an index named my_dynamic_false and set the mapping:

PUT my_dynamic_false {"mappings": {"dynamic": false, "properties": {"title": {"type": "text"}, "name": {"type": "keyword"}, "age": {"type": "integer"}

Then write a document:

PUT my_dynamic_false/_doc/1 {"title": "hello world", "desc": "this is book"}

Note that in the mapping setting, "dynamic": false means that when writing to a document, there will be no error if the write field does not exist. The desc field here is a field that does not exist.

Query the written document:

GET my_dynamic_false/_search {"query": {"match": {"title": "hello"}

Result: the contents of the document can be queried through the title field

{"took": 0, "timed_out": false, "_ shards": {"total": 1, "successful": 1, "skipped": 0, "failed": 0}, "hits": {"total": {"value": 1, "relation": "eq"}, "max_score": 0.2876821 "hits": [{"_ index": "my_dynamic_false", "_ type": "_ doc", "_ id": "1", "_ score": 0.2876821, "_ source": {"title": "hello world" "desc": "this is book"}]}}

Query the document through the desc field

GET my_dynamic_false/_search {"query": {"match": {"desc": "book"}

As a result, it could not be found.

{"took": 0, "timed_out": false, "_ shards": {"total": 1, "successful": 1, "skipped": 0, "failed": 0}, "hits": {"total": {"value": 0, "relation": "eq"}, "max_score": null "hits": []}}

Verify the "dynamic": strict mode:

Create mapping

PUT my_dynamic_strict {"mappings": {"dynamic": "strict", "properties": {"title": {"type": "text"}, "name": {"type": "keyword"}, "age": {"type": "integer"}

Create a document

PUT my_dynamic_strict/_doc/1 {"title": "hello world", "desc": "this is book"}

As a result, it was found that the report was wrong directly.

{"error": {"root_cause": [{"type": "strict_dynamic_mapping_exception", "reason": "mapping set to strict, dynamic introduction of [desc] within [_ doc] is not allowed"}], "type": "strict_dynamic_mapping_exception", "reason": "mapping set to strict, dynamic introduction of [desc] within [_ doc] is not allowed"} "status": 400} 6, data migration 6.1, create a new mapping. PUT / newbank {"mappings": {"properties": {"account_number": {"type": "long"}, "address": {"type": "text"}, "age": {"type": "integer"} "balance": {"type": "long"}, "city": {"type": "keyword"}, "email": {"type": "keyword"}, "employer": {"type": "keyword"} "firstname": {"type": "text"}, "gender": {"type": "text"}, "lastname": {"type": "text"} "state": {"type": "keyword"} 6.2.Migration POST _ reindex {"source": {"index": "bank", "type": "account"}, "dest": {"index": "newbank"} 7, copy_to parameter description

The function is to copy the value of this field to the target field, which does not appear in _ source and can only be used for search.

PUT my_index_copy {"mappings": {"properties": {"first_name": {"type": "text", "copy_to": "full_name"}, "last_name": {"type": "text", "copy_to": "full_name"} Full_name: {"type": "text"}

Then create a new document, which only needs to write first_name and last_name:

PUT my_index_copy/_doc/1 {"first_name": "john", "last_name": "smith"}

Query:

GET my_index_copy/_search {"query": {"match": {"full_name": {"query": "john smith"}

Data can be found in the result, but there is no full_name in the result set.

{"took": 5, "timed_out": false, "_ shards": {"total": 1, "successful": 1, "skipped": 0, "failed": 0}, "hits": {"total": {"value": 1, "relation": "eq"}, "max_score": 0.5753642 "hits": [{"_ index": "my_index_copy", "_ type": "_ doc", "_ id": "1", "_ score": 0.5753642, "_ source": {"first_name": "john" "last_name": "smith"}]}} Thank you for your reading The above is the content of "what is the mapping mapping of ElasticSearch". After the study of this article, I believe you have a deeper understanding of what the mapping mapping of ElasticSearch is, and the specific use needs to be verified in practice. Here is, the editor will push for you more related knowledge points of the article, welcome to follow!

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