ES|QL and Advanced Kibana Concepts

Elasticsearch Query Language (ES|QL) stands at the forefront of Elasticsearch 8.11, offering a powerful and intuitive means to interact with and extract valuable insights from your data. This query language is designed to provide users with a flexible and expressive way to formulate complex queries and searches. With ES|QL, users can seamlessly navigate and explore their Elasticsearch indices, harnessing their capabilities to filter, aggregate, and sort data efficiently. The latest version introduces enhancements to the query language, emphasizing its role in simplifying the querying process while maintaining the scalability and speed that Elasticsearch is renowned for.

Whether you are a seasoned Elasticsearch user or a newcomer, ES|QL’s user-friendly syntax and advanced features empower you to delve into your data with precision, making it a cornerstone of efficient data exploration and analysis.

It is time to take a deep dive into how...

Technical requirements

As per the basic requirements, we assume that you have data ingested in the cluster and that Elasticsearch and Kibana are set up on the nodes of (any) environment (the cloud or locally).

Once Kibana is up and running, we can navigate to Kibana | Try sample data, select Other sample datasets, click on the Add data for Kibana_sample_data_ecommerce dataset, and then Kibana_sample_data_logs.

Learning the ES|QL building blocks

ES|QL is built upon a foundation of robust query capabilities and an intuitive syntax that empowers users to interact effectively with Elasticsearch data. At its core, ES|QL leverages the Elasticsearch query DSL (short for Domain-Specific Language), providing a comprehensive set of commands to articulate complex queries. The language is designed for flexibility, allowing users to seamlessly navigate the intricate structure of Elasticsearch indices.

ES|QL’s architecture incorporates a deep understanding of Elasticsearch’s distributed nature, ensuring optimal performance across large-scale datasets. It embraces principles of scalability, enabling users to harness its capabilities in diverse and demanding environments. The development of ES|QL involves a collaborative effort from the Elasticsearch community, with continuous enhancements and updates being introduced to adapt to evolving user needs and accommodate the latest features of...

Understanding how ES|QL works

The query language works fundamentally through a source command, and it can be followed by (optional) commands called processing commands. They are separated by a pipe (|); hence, ES|QL is also referred to as Elastic’s piped query language. The source command results into a table formation from the data in Elasticsearch, as shown in the following figure:

Figure 9.1 – The source command concept in Elasticsearch

Three different source commands that are supported, which are FROM, ROW, and SHOW.

Here’s a simple example of how ES|QL works. Imagine you have an index named products that contains product data, and you want to find all products with a price above $50.

With ES|QL, you could write the following query:

FROM products WHERE price > 50;

This query looks just like a standard SQL query, but ES|QL translates it into Elasticsearch’s query language, retrieving the desired results. Refer...

Advanced Kibana concepts

Kibana has several implementations that are designed for very specific use case requirements, and runtime fields is one of them. Let’s explore and understand them.

Runtime fields

Forget static schemas; Elasticsearch runtime fields are your dynamic paintbrushes! They let you create fields on the fly, transforming your data on demand within your queries. Imagine a data sculptor, molding your documents with temporary fields based on your needs.

There’s no need to re-index or restructure your data. Runtime fields are calculated at query time, pulling information from existing fields or even combining them into new ones. Think of it as a temporary data transformation, enriching your documents without changing their core structure. These dynamic fields are versatile. You can extract specific values using Grok patterns, calculate aggregates such as averages or counts, or even perform complex calculations. They’re perfect for adding context...

Summary

In this chapter, we took a deep dive into ES|QL, Elasticsearch’s query language, where we have explored its building blocks such as indices, mappings, and queries to understand how data is structured and retrieved. We also grasped the magic of near-real-time search, which lets us perform analysis on live streams such as sports updates or financial transactions. We saw how we can unlock the hidden potential of Kibana with advanced settings such as custom index patterns, time formatting, and security controls. By mastering these elements, you’ll transform from a data consumer to a real-time data maestro, wielding ES|QL and Kibana to extract hidden patterns and insights from the ever-flowing stream of information.

Remember, advanced settings are powerful tools. However, it is important to use them wisely, backed up by understanding and caution. Well, with ES|QL and Kibana in hand, you’re now ready to conquer the ever-changing world of real-time data, transforming...