With Elasticsearch’s bucket aggregations we can create groups of documents. In this article we will mainly focus on aggregations based on keyword type fields in indexes. We will use several examples to understand the main differences between the available aggregation functions.
In addition to text search, Elasticsearch allows analysis on data using aggregations. Among the various types of aggregation available, the metric ones are aimed precisely at calculating statistics on one or more fields. Through examples we will see what information we can extract with this type of aggregation.
Elasticsearch is a widely used NoSQL database for developing search engines because of its ability to index text appropriately. But it does not stop at just that. Thanks to aggregations, Elasticsearch can be used to analyze data and extract statistics from large masses of data. Let’s learn about this functionality of his that underlies many visualizations used by Kibana.
To date, many companies expose their products on the Web through APIs. Some startups, especially in the fintech sector, base much of their economics and growth solely on APIs. Monetizing an API therefore becomes a critical step for any developer. There are various approaches to making money from one’s code. In this article we will discover how to reach millions of potential users and sell our APIs quickly through the most popular API marketplaces.
Elasticsearch is a very good NoSQL database for performing efficient searches on textual and structured data. Despite this, it does not natively support joins between documents. However, there are queries that, by means of an appropriate schema definition, allow searches on related documents between them. We will find out how to write join queries and also some particular queries that might be useful in our projects.
Elasticsearch offers a very valuable tool for performing simple as well as complex searches. In this article we will understand how to include multiple conditions in the same query and modify the score calculation based on custom functions and data values.
Elasticsearch offers a very valuable tool not only for textual searches, but also for structured data. In this article we will understand how to query structured fields using term queries. The various types of queries will allow us to refine the searches for our future projects.
Elasticsearch offers a very good tool for textual queries. In this article we will begin to understand how to query textual fields using match queries. The various types of queries will allow us to refine the searches for our future projects.
Elastisearch is a NoSQL database used primarily for building search engines. In fact, thanks to the integration of Apache Lucene it allows to properly index text documents and perform very accurate searches. The new release introduces some new features and improvements over version 7.
Developing websites requires not only the generation of interesting content but, more importantly, the development of attractive, intuitive and full responsive interfaces. With Wordpress you can build blogs and e-commerce very quickly, but presentation requires extra effort especially for the uninitiated. Through page builders, such as Elementor and Avada, this task is simplified. Let’s discover in this article their main features to understand which one is the best for our needs.