Kibana Query Language (KQL)

Kibana Query Language, often abbreviated as KQL, is a powerful query language used in Kibana to filter and search data. KQL is used in conjunction with Elasticsearch, a popular open-source search and analytics engine. In this tutorial, we will go over the fundamentals of this language, including its syntax and functionality, as well as how it can be used to perform complex queries on vast amounts of data stored in Elasticsearch.

KQL Introduction

At its most fundamental level, KQL can be thought of as a way of asking questions to your data. For instance, you might want to find all movies that were directed by Steven Spielberg, or all characters in a movie that are played by a certain actor. This would be trivially easy to do with KQL and the appropriate data in Elasticsearch.

In order to use KQL in Kibana, you first need to enable it. You can do this by going into Kibana's settings and toggling the Kibana Query Language option.

Management > Kibana > Query Features > Enable Kibana Query Language

Once you've enabled KQL, you'll be able to use it in Kibana's search bar. If you're just beginning with KQL, Kibana provides autocomplete suggestions to assist you with your syntax.

Basic Queries

The simplest type of query is a single term. For example, if you want to find all records related to "The Godfather", you merely type the term into the search bar:

The Godfather

But what if you only want to see data related to a specific field? You can easily do that by prefixing the term you're looking for with the name of the field you're interested in. So, for instance, if you want to find all movies directed by Steven Spielberg, you could use:

director: "Steven Spielberg"

Here, the colon (:) is used to indicate that we're specifically looking at the director field.

Compound Queries

Keep in mind that simple queries can be combined to form more complicated queries. This is where the power of KQL truly shines—its ability to put together complex questions to uncover precise insights from our data.

Imagine you wish to find all films directed by Steven Spielberg that were released in 1990. Here's how you could form this compound query:

director: "Steven Spielberg" AND release_year: 1990

In this case, AND ensures that both conditions (the director being Steven Spielberg and the release year being 1990) are satisfied. Conversely, you could use OR to find records that match either condition.

Performing Ranges

Ranges represent another very useful feature of KQL. Let's say you want to find all movies from the 1990s. Instead of manually searching each year, you can simply employ a range query:

release_year >= 1990 AND release_year <= 1999

This query will retrieve all films where the release_year is between 1990 and 1999, inclusive.

Wildcard Queries

KQL also facilitates wildcard searches, useful for those times you only recall a portion of what you're searching for. For example, say you're looking for a movie that you know has 'Shaw' in the title, but you can't remember the whole title:

title: *Shaw*

As a result of this query, movies like 'The Shawshank Redemption' would be returned.

Negation and Grouping

Just as we can specify conditions that have to be met, we can also indicate conditions that should not be met. The NOT operator is used to negate. If we want to find all movies in the 1990s not directed by Steven Spielberg, we could do:

release_year: >= 1990 AND release_year: <= 1999 AND NOT director: "Steven Spielberg"

Additionally, KQL supports parentheses for grouping query clauses, enhancing the clarity and order of execution in complex queries. For example:

(release_year: >= 1990 AND release_year: <= 1999) AND (NOT director: "Steven Spielberg")

To conclude, this has been a beginner's introduction to Kibana Query Language (KQL) where we covered everything from basic to compound queries, performing ranges, wildcard queries, and the use of operators like negation and grouping. As you venture into your analytics journey, remember that KQL is an incredibly powerful tool to retrieve precise insights from your data within the Kibana interface. Practice these examples, and in no time, you will be drawing deep insights from your Elasticsearch data using KQL.