Textual search engines are a feature that plays an important role in the construction of applications. The user experience is greatly improved if the results requested are truly inherent to the words entered in the search bar. Elasticsearch allows us to integrate a full-text search system and obtain excellent results efficiently. Let us find out how and when it is possible to exploit these features.
Elasticsearch offers the ability to extract statistics and data sets using aggregation functions. In many contexts, however, it is necessary to concatenate the results of these analyses to obtain more refined results. Let us see how aggregation pipelines allow us to obtain the results of our interest.
With Elasticsearch’s bucket aggregations we can create groups of documents. Having seen in the previous article aggregations based on keyword type fields, we will now focus on other functions oriented to other data types. In particular, we will use aggregations for definitions of numeric ranges, dates, or groups based on georeferenced data.
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.
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.