Therefore before indexing a date field as an epoch long, take some time to think of the use case and whether you’ll need the date-exclusive queries in the future. This will enable Elasticsearch to perform date-based queries such as range queries. For instance, if a field will only store dates, using the date data type is appropriate. Some other important considerations regarding types are when choosing a data type for a field, consider the type of data that will be stored in the field and the type of queries that will be run on it. One thing to remember is that completion builds an in-memory tree which can be expensive on lots of data (i.e., you don’t want to do that for indexes that store logs). You’ll notice in the response values such as “television” “telephone” as this query helps to find values that have the prefix “tele”. If the data type is keyword, results will include only the category called “dress” (with id=1 from the example dataset)Īnother type that can be used to text is “completion” mainly used for a search with spellcheck, and text auto-completion features.Įxample query: completion data type GET test-completion/_search.If the data type is text, results will include all categories containing the word “dress”.For example, consider these two potential outcomes for our data set and query: You can use the query for both keyword and text fields. ![]() To understand the implications of different data types, consider the data set below: [Īnd this corresponding query: GET my-index/_search For example, you should only use the keyword type to do exact match queries. Data types determine matching results, so getting the data type right is essential. It is easy to choose the wrong data type for a use case. Common data type pitfall to avoid: Using the wrong data type The completion data type is used for indexing terms that support fast autocomplete suggestions. This enables us to use different kinds of queries for full-text search (e.g., simple_query_string, query_string, and match_phrase_prefix) Completion Text fields are analyzed and broken down into individual words. Text fields have analyzers which are the key to full-text search, as we’ll explore later. A typical use for keyword data types is matching unique identifiers for a document. A keyword is not broken down into individual words based on delimiters such as spaces and does not have the full-text search features enabled. Keywords are strings that are only matchable exactly as entered. The sections below explain the different Elasticsearch text data types and when to use them. This data type variety can cause misuse of the data type and lead to performance problems. However, Elasticsearch has more string types than typical databases. This includes string, numeric, boolean, and date data types. Analyzers: Text-processing mechanisms that define how characters are interpreted.Įlasticsearch has many of the same data types supported by the other databases.Mapping types: Define whether mappings are static or dynamic.Field properties: Sub-fields that describe data.Data types: Indicators of the type data stored in a field.In the following sections, we’ll take a look at four key Elasticsearch mapping concepts: When you create an index, you can define the mappings for each field in the index, including its data type and how it is indexed. Static mappings define the data type in advance.Įlasticsearch analyzers are text-processing mechanisms that enable you to break text into tokens.Įlasticsearch mappings are data structure definitions that determine how storage and searching work within an Elasticsearch index. Dynamic Elasticsearch mappings automatically detect data types. One of the significant differences with Elasticsearch data types is the variety of string types.įield properties such as reverse indices and doc_values add more detail to the data model.Įlasticsearch supports dynamic and static mapping types (and templates). ConceptĮlasticsearch data types are comparable to database data types. The table below summarizes the key Elasticsearch mapping concepts this article will explain in detail. Summary of key Elasticsearch mapping concepts Need some help with Elasticsearch? Sematext provides a full range of services for Elasticsearch and its entire ecosystem. In this article, we’ll explore the key concepts of Elasticsearch mappings and common Elasticsearch mapping pitfalls to avoid. Understanding how Elasticsearch mappings work is essential to an effective Elasticsearch deployment. Elasticsearch mappings are the blueprints that define how data is indexed and searched to support these data-related features. ![]()
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