GLDYQL: What It Actually Means and Why It Matters

Introduction

Here’s something confusing. Search for “gldyql” online and you’ll find dozens of articles talking about innovation, identity, technology transformation, and business growth. But here’s the problem. Most of that content doesn’t actually explain what gldyql is.

The real answer is simpler than you think. GLDYQL is likely a misspelling or variation of GLQL, which stands for GitLab Query Language. It’s a query system built into GitLab that lets you filter and display content from your projects, issues, merge requests, and other work items. That’s it. Not some revolutionary philosophy. Not a new tech movement. Just a practical tool for people who use GitLab.

What GLQL Actually Does

GLQL is GitLab’s attempt to create one unified query language across its entire platform. Instead of learning different filtering methods for issues, merge requests, epics, and work items, you use the same syntax everywhere.

You write queries using logical expressions. The format looks like this: field, operator, value. Then you connect multiple conditions using “and” statements.

For example, suppose you want to find all open issues assigned to you in a specific project. In that case, your query might look like: project equals “gitlab-org/gitlab” AND assignee equals currentUser() AND state equals opened.

The system supports different field types. You can filter by assignee, author, labels, milestones, or object types like Issue, MergeRequest, Task, or Objective.

How the Query System Works

The operators available in GLQL give you flexibility. You can use equals, not equals, contained in list, greater than, less than, greater than or equal to, and less than or equal to. The “in” operator works as a substitute for “or” logic when you need to check multiple values.

Values can be strings, numbers, dates (both relative like “-1d” for yesterday and absolute like “2025-01-01”), functions like currentUser() or today(), enum values, booleans, null values, GitLab references (like ~label or %milestone or @username), and lists containing any of these wrapped in parentheses.

You can display results in three formats: Table, list, or ordered list. The default is Table view with 100 items shown at a time.

Embedded Views and Where They Work

One of GLQL’s main features is embedded views. These are code blocks you write in Markdown that render as live, filtered displays of your GitLab data.

You can put these embedded views in group wikis, project wikis, issue descriptions, merge request comments, epic descriptions, work item comments, and repository Markdown files.

The syntax uses YAML formatting with parameters like display type, title, description, fields to show, limit on number of results, and your query statement.

A basic example looks like this:

glql

display: table title: My Open Issues fields: title, state, health, milestone, weight, updated limit: 5 query: project = “gitlab-org/gitlab” AND assignee = currentUser

This creates a table showing your first five open issues with specific columns visible.

Customization Options

You can control how data appears. The “fields” parameter determines which columns show up in your table or what information appears in list items. Default is just the title, but you can add state, health status, epic, milestone, weight, due date, updated date, and more.

The “collapsed” parameter lets you start with the view hidden so it doesn’t take up space until someone clicks to expand it.

Sorting works with the “sort” parameter, where you specify a field name followed by “asc” for ascending or “desc” for descending order. The default sort is by updated date, newest first.

Field functions let you create dynamic columns. You can use label filters that show only specific label patterns, like labels(“workflow::*”) AS “Workflow”, to create a custom column showing workflow labels.

Common Mistakes People Make

The biggest mistake is thinking “gldyql” is something separate from GitLab. It’s not. If you’re not using GitLab, this query language doesn’t apply to you at all.

The second mistake is writing queries that are too complex at first. Start simple. Test with one or two conditions before adding more filters. The system only supports “and” logic natively, so working around that limitation takes practice.

The third mistake is not using the available functions. Functions like currentUser make queries dynamic so they work for whoever is viewing them. Hardcoding usernames defeats the purpose of shared views.

The fourth mistake is ignoring field aliases. If you’re displaying data for non-technical team members, renaming columns with “AS” syntax makes tables easier to understand.

The fifth mistake is setting limits too high. Loading 100 items at once can slow down page rendering. Start with 10 or 20 items and use pagination to load more.

What Happens If You Don’t Use It Correctly

If your query syntax is wrong, the embedded view won’t render. You’ll see the raw code block instead of your filtered data.

If you specify fields that don’t exist or misspell field names, those columns appear empty or cause rendering errors.

If you forget to set appropriate permissions, users viewing your wiki page or issue might see empty results because they don’t have access to the projects you’re querying.

If you use “or” logic incorrectly (by not using the “in” operator), your query fails or returns unexpected results.

The Confusion Around “GLDYQL”

Now here’s where things get weird. Most search results for “gldyql” have nothing to do with GitLab Query Language.

Several websites publish articles describing “gldyql” as a concept related to innovation, identity, creativity, business transformation, and technological advancement. These articles are vague, use lots of buzzwords, and never actually define what gldyql is or does.

One site describes it as “a place where genuine expression and creativity flourish” and talks about combining innovation with staying true to identity. Another site calls it a “dynamic concept” that enhances productivity and creativity across sectors like technology, business, education, culture, communication, science, and healthcare.

These articles share common characteristics: no technical specifications, no concrete examples, no real use cases, heavy SEO optimization, internal links to similar vague articles, and generic stock photos.

This content appears designed to rank in search results rather than provide actual information. It’s content marketing without a clear product behind it.

Why This Matters for GitLab Users

If you work with GitLab, learning GLQL saves time. Instead of manually filtering through hundreds of issues or merge requests, you create reusable query views that always show current data.

Teams benefit because everyone sees the same filtered information. You can embed a query in your project wiki showing all high-priority bugs, and that list updates automatically as issue statuses change.

For project managers, embedded views create live dashboards without building separate reporting tools. You can show sprint progress, blocker issues, upcoming milestones, or team member workloads directly in your documentation.

For developers, quick-access queries reduce context switching. You can see your assigned code reviews, pending deployments, or flagged security issues without leaving your current page.

Setting Up Your First GLQL Query

Start by identifying what information you need to filter. Are you looking for issues, merge requests, epics, or tasks?

Decide on your filter conditions. Who should be assigned? What status should items have? Which project or group are you searching within?

Choose your display format. Tables work best for detailed comparisons. Lists work better for simple overviews.

Write your query using the syntax: field operator value AND field operator value. Test it in a wiki page or issue comment where you can edit easily.

Add display parameters like title, description, and field selection to make the output readable.

Set an appropriate limit based on how much data you expect and how often it changes.

Advanced Query Techniques

You can filter by relative dates to create evergreen queries. Using “updated > -7d” shows items modified in the last seven days. This keeps working regardless of today’s actual date.

Label filtering with wildcards helps categorize items. If your team uses label prefixes like “priority::” or “team::”, you can filter specifically for those patterns.

Combining multiple field types creates specific filters. You might search for merge requests from a specific author with a certain label that haven’t been updated in 14 days.

Using null values helps find incomplete data. Searching for “milestone = none” shows items that haven’t been assigned to any milestone yet.

Comparison with Other Query Systems

GLQL differs from SQL database queries. It’s simpler and more limited in scope. You can’t do joins, subqueries, or complex aggregations. But you also don’t need database access or special permissions.

Compared to Jira’s JQL (Jira Query Language), GLQL is younger and less mature. JQL supports more complex logic, including “or” statements at any level, not just through the “in” operator. But GLQL integrates more tightly with GitLab’s specific features, like embedded views in Markdown.

Compared to GitHub’s search syntax, GLQL offers more structured filtering with consistent operator logic. GitHub search uses keyword-based queries that feel less formal.

Real-World Use Cases

Development teams use GLQL to track code review assignments. A query shows all open merge requests assigned to specific team members, sorted by age, so nothing gets forgotten.

Product managers create milestone tracking views that display all issues and merge requests for upcoming releases, filtered by completion status.

Support teams query for customer-reported bugs with specific severity labels, making sure high-priority items get attention first.

Documentation teams filter for issues labelled as documentation needs, creating a queue of items that need user guide updates.

Technical Specifications You Should Know

GLQL is available in GitLab Free, Premium, and Ultimate tiers on GitLab.com, Self-Managed, and Dedicated instances. It’s not a paid feature.

The query language is in beta as of early 2025, meaning syntax and features might change. GitLab requests user feedback through its issue tracker.

Embedded views load the first page of results by default. Pagination works by clicking “Load more” at the bottom of the list or Table.

View actions include copying source code, copying Table contents, reloading data, and viewing the raw query syntax.

Performance Considerations

Large queries with hundreds of results can slow page loading. Break them into smaller, more focused queries.

Frequently changing data means your embedded views need regular reloads. The system doesn’t auto-refresh, so viewers need to reload to see updates manually.

Complex filter combinations take longer to process. Simple queries with one or two conditions run faster than queries with five or six conditions.

Security and Permission Rules

GLQL respects GitLab’s existing permission system. Users only see data they already have access to in their regular GitLab usage.

If you embed a query referencing a private project, users without access to that project see no results instead of getting an error message.

This means you can safely share wiki pages with embedded queries. Each viewer sees only what they’re authorized to see.

Frequently Asked Questions

Is gldyql the same as GLQL? Most likely yes, if you’re talking about GitLab features. GLDYQL appears to be a misspelling or alternate abbreviation. The official GitLab documentation uses GLQL exclusively.

Can I use gldyql outside of GitLab? No. GLQL only works within GitLab’s platform. It’s not a standalone tool or open standard you can implement elsewhere.

Do I need special permissions to use GLQL? No. Any GitLab user can write queries. You just need read access to the projects and groups you’re querying.

Can GLQL queries update or delete data? No. GLQL is read-only. It displays filtered information but cannot modify issues, merge requests, or other objects.

How often does query data refresh? Embedded views show data from the moment they load or reload. They don’t auto-refresh. Users click the reload action to see updated information.

Can I share GLQL queries with my team? Yes. Queries embedded in wikis, issues, or merge requests are visible to anyone who can view those pages. The syntax is also copyable so team members can reuse and modify queries.

What if I want “or” logic in my query? Use the “in” operator with a list of values. Instead of “author = user1 OR author = user2”, write “author in (user1, user2)”.

Are there query length limits? GitLab hasn’t published specific character limits, but extremely long queries with dozens of conditions may hit practical performance limits.

Conclusion

GLDYQL, or more accurately GLQL, is GitLab’s query language for filtering and displaying project data. It uses simple logical syntax to create embedded views in wikis, issues, and merge requests.

For GitLab users, GLQL offers practical time savings. You create reusable filtered views that update automatically as project data changes. Teams get consistent information displays without manual reporting work.

Most importantly, remember that this tool only exists within GitLab. If you’re not a GitLab user, the term “gldyql” probably doesn’t apply to your work at all. And if you are a GitLab user, stick with the official “GLQL” spelling and documentation to avoid confusion.