Understanding JQL in Jira can feel like discovering a secret superpower. JQL stands for Jira Query Language and it gives you the ability to search through issues using natural phrases that describe what you want to find. When teams have growing backlogs or when hundreds of issues accumulate over time, scrolling is not enough. JQL lets you filter everything down to the exact set of issues you need. One of the most confusing parts for new users is how JQL handles ideas like null, empty and not empty. Even though they look similar, Jira treats them in specific ways that matter when you run a search.
A good place to start is understanding what it means when a field is empty. Many Jira fields, such as labels, components and versions, can be left completely blank. When users don’t fill in anything, Jira stores that field as having no values. JQL gives you several ways to search for this condition. You can ask for issues where the field is empty, or equals empty, or even where the field is considered null. All of these expressions tell Jira to bring back issues where nothing has been entered into the field. This flexibility is helpful because teams develop their own habits in how they write queries, and Jira tries to accept all the common variations.
Jira also has the idea of a null field. For most people, null and empty look identical because both represent a lack of information. But in Jira’s internal structure, empty often means the user left the field blank, while null means the system has no stored value at all. Although the distinction exists behind the scenes, Jira keeps things simple by letting you use null in a search the same way you use empty. When you say a field equals null, Jira returns the issues where the field has no entry. This is why you will often see null and empty treated as interchangeable when performing searches.
The opposite condition is just as important. Sometimes you want to find issues where a field actually contains information. When you search for items where the field is not empty, Jira returns only the issues that have something stored in that field. This becomes useful when you are checking whether team members are tagging issues correctly or when you want to filter for items that have already been categorized. You can also say the field is not null and Jira interprets that in the same way. Both phrases tell Jira to find issues where the field is filled in with at least one value.
Another variation people encounter is the not equals operator. If you ask for items where the field does not equal empty, Jira gives you the same kind of results as not empty. It shows every issue where the field contains data. It may sound like a small difference, but JQL is designed to be flexible enough that people can phrase their search in the way that feels most natural to them, and Jira will still understand what they mean.
When you actually run a JQL search, Jira checks the underlying data stored for each issue. It reviews whether the field has no entries, has a null representation or has one or more values. Based on what you asked for, Jira then returns only the issues that match the condition. Because Jira tries to be forgiving, several different expressions often lead to the same outcome. For fields like labels, where users can add many values or none at all, Jira gives you multiple ways to search so you never feel locked into a single phrasing.
Once you become comfortable with how JQL handles these conditions, the entire experience becomes much less mysterious. You gain a clearer understanding of how your Jira space is organized. You can search intelligently instead of relying on guesswork. You can validate data quality, filter reports and clean up old issues with confidence. JQL becomes a natural part of working in Jira instead of something technical or intimidating.
Learning how null, empty and not empty behave gives you a stronger foundation for building every other kind of search. It is a small skill that makes a big difference, especially as your projects and backlogs grow more complex. JQL is one of Jira’s greatest strengths, and once you start using it effectively, you will wonder how you ever managed without it.
Cameron McKenzie is an AWS Certified AI Practitioner,Machine Learning Engineer,Solutions Architect and author of many popular books in the software development and Cloud Computing space. His growing YouTube channel has well over 30,000 subscribers.
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