Data architecture

There are various methods for retrieving fields in Elasticsearch, including: _source, stored_fields, fields & docvalue_fields. To retrieve...

In this article, we will delve into the process of creating an index with mapping in Elasticsearch. Mapping is the process of defining how...

In this guide, we'll compare Logstash, the flagship ingestion tool of the ELK & Data Prepper, OpenSearch’s ingestion tool response.

In this guide, we'll go over Elasticsearch cardinality, high & low cardinality fields, how to determine field cardinality, and give examples.

This article guides you through the process of changing the field type in Elasticsearch, including the reasons for doing so...

Elasticsearch provides various compression techniques to optimize storage and network usage. Here's how to compress an index, source and...

The Elasticsearch data stream is an abstraction layer between the names used by applications to facilitate ingestion and search operations...

In this guide, we will discuss the process of deleting documents in Elasticsearch as well as best practices for deleting documents.

This guide explains the purpose of the doc values structure and shows examples of usage of the Elasticsearch doc-values-only fields.

This guide discusses the default document size limit in Elasticsearch, the reasons behind it, and how to handle larger documents effectively.

In this article, we will delve into the advanced aspects of dynamic mapping, its benefits, and best practices for optimizing its usage.

The 3 main methods in Elasticsearch to calculate the storage size of specific fields in an index are: using the _disk_usage API, creating...

Elasticsearch filtered aliases can help you filter data more efficiently. To create a filtered alias, you need to define the criteria for...

Terms aggregations rely on an internal data structure known as global ordinals. The eager_global_ordinals parameter is used to...

This article will discuss the benefits of using a hot-warm architecture and provide a step-by-step guide to setting up...

Learn step-by-step techniques for creating, managing, and leveraging Elasticsearch's index aliases effectively.

Elasticsearch index patterns allow you to define how to match & interact with multiple indices. The best practices for index pattern usage...

Discover how to use Elasticsearch index settings. Learn the difference between static and dynamic settings, methods for updating them & more.

This guide will discuss best practices & performance optimization techniques for inserting documents into Elasticsearch. First, use the API...

Learn how to use JSON Arrays in Elasticsearch with this comprehensive guide on Indexing, Querying, and Dealing with Nested JSON Structures.

The new match_only_text feature in Elasticsearch can save up to 10% of disk space on logging datasets. This field type will set a flat...

Elasticsearch nested aggregation is a powerful technique for analyzing complex data structures that contain nested documents. To use...

This guide focuses on how to handle not indexed fields in Elasticsearch, including use cases, how to configure them, and examples.

Learn how to filter and find null values in Elasticsearch when searching for documents where the field is null

Elasticsearch searchable snapshots allow data exploration. They can be controlled with ILM Policies or be manually mounted. To implement...

By using the Elasticsearch Split Index API, an existing index can be split to create a new index with extra primary shards. To do this...

This guide will explore the use of stop words in Elasticsearch, providing examples and step-by-step instructions.

Learn how stored fields differ from _source field, how to configure them for specific use-cases, their advantages and limitations.

The Elasticsearch sum aggregation allows you to calculate the sum of a numeric field for a set of documents. To implement...

A tokenizer decides how Elasticsearch will take a set of words and divide it into separated terms called “tokens”. To work with synonyms...

Learn how to enable and retrieve Term Vectors, and explore use-cases in information retrieval, text classification, and more.

The text analysis process is tasked with two functions: tokenization and normalization and is carried out by employing analyzers. When you...

Elasticsearch Time Series Data Stream (TSDS), is an optimized way to store time-based metrics. To Implement Time Series Data Streams...

The Elasticsearch Transform APIs can be used to turn existing indices into summarized indices. To create a transform API...

Upsert in Elasticsearch allows you to perform both update and insert actions in a single request. To perform an upsert operation...

This article will discuss the reasons for excluding fields from indexing, how to configure Elasticsearch to exclude specific fields...

In this article, we will discuss advanced techniques and best practices for working with arrays in Elasticsearch. Mapping an array can...

This article will delve into the advanced usage of JSON in Elasticsearch, focusing on its role in document structure, search queries...

Elasticsearch sorts uppercase & lowercase letters separately, impacting results. Normalizers can be used to provide...

This guide will show you how to leverage Logstash to aggregate multiple events that share some common data.

Follow these steps to configure all Elasticsearch node role types (master, data, coordinating, ingest, machine learning, remote eligible...

Mappings are the core element of index creation in Elasticsearch. Defining them correctly can improve performance. Mapping types include...

Ingest pipelines sit within the Elasticsearch node and will perform a set of alterations on your data that you...

The join data type field allows users to establish parent-child relationships between documents in Elasticsearch. To use it, you need to...

Elasticsearch has many methods for defining relationships between documents, such as nested documents. To use the nested field type...

Object types in Elasticsearch can be used to define relationships between documents. Here's how to use the object field type for that purpose.

When you have too many shards in your Elasticsearch cluster, there are a few steps you can take in order to reduce the number of shards...

Index lifecycle management helps automate the creation, management & removal of an Elasticsearch index. Define the index lifecycle policy...

Elasticsearch index templates allow us to create indices with user defined configuration. An index can pull the configuration from these...

This article will delve into the practical applications of script fields in Elasticsearch, offering examples to illustrate their usage.

Dynamic templates in Elasticsearch allows you to define custom mappings that can be applied to dynamically added fields. To create...

An Elasticsearch custom analyzer is defined by combining a single tokenizer with 0 or more token filters and character filters. To create...

Elasticsearch normalizers are a crucial component in the text analysis process. Here is how they work, the best practices for usage...

This guide delves into the intricacies of the Elasticsearch geo_point data type, its use cases & implementation and covers geospatial queries.

This article delves into the advanced aspects of handling timestamps in Elasticsearch, including indexing, querying, and formatting.

Nested is a special object type that is indexed as a separate document. To demonstrate the use of Elasticsearch nested VS. object fields...

In this article, we will discuss some advanced techniques and best practices for optimizing schema design in Elasticsearch.

Here are the similarities and differences between Elasticsearch Snapshot Lifecycle Management (SLM) and OpenSearch Snapshot Management (SM).

There are at least three use cases where you should consider using transforms instead of aggregations in Elasticsearch. First, when the...