Lightdash: Recovering From Invalid Filters

Nov 14, 2025 by Admin 43 views

Hey data enthusiasts! Have you ever been there? You're cruising along, building some awesome dashboards in Lightdash, and then BAM! Your dbt model gets a little makeover – maybe a field type changes – and suddenly, your carefully crafted filters go haywire. The dreaded "Filter type boolean with operator include is not implemented" error pops up, and your chart is basically locked down. Fear not, because we're diving into how Lightdash can improve this experience, ensuring you're not left high and dry when schema changes strike.

The Current Predicament: Invalid Filters and the Lightdash Soft-Lock

So, what's the deal, guys? Currently, when a field's data type undergoes a transformation in dbt (like shifting from a text string to a true/false boolean), any saved charts relying on filters with the old type can become invalid. Picture this: you’ve built a fantastic chart, everything's working perfectly, and then your dbt model gets an update. Perhaps a string field, previously used for filtering, is now a boolean. This change can trigger an error, effectively breaking your filters. The user interface (UI) then enters a sort of "soft-lock" situation. The Filters panel becomes inaccessible, refusing to load, and you're left with no way to remove or update the broken filter. You're stuck in a frustrating loop, unable to adjust the filter or make any changes to the chart. The only solution, as things stand, is to recreate the chart from scratch, which is a major pain, especially if you've spent a lot of time perfecting it.

This is a real problem, especially during the early stages of model development when schema changes are frequent. Imagine the time wasted, the frustration, and the potential for data analysis paralysis! The current situation isn't ideal, to say the least. Lightdash needs a more robust solution to handle these inevitable schema changes, offering users a smoother and more resilient experience. We need a way to recover from these invalid filters, so let's explore some solutions.

The Problem: A Chart is Effectively Unrecoverable

When a filter becomes invalid because of a type change, the existing system prevents the Filters panel from loading. This means there is no way to eliminate or correct the filter. The chart is rendered unusable.

The Expected Solution: User-Friendly Filter Management

So, what's the dream, you ask? The ideal scenario is that Lightdash should be able to handle invalid filters gracefully, providing a path to recovery. Users should be able to open the Filters panel, even if some filters are in an invalid state. The problematic filters should be clearly marked and easily removable.

Think of it like this: your chart is a car, and a filter is a critical component. If the component breaks, you still want to be able to access the engine (the chart's data) and fix the problem (the filter). In this new setup, invalid filters would be treated with a bit more finesse:

Grayed Out: These filters would be visually distinct, immediately signaling that something's not quite right.
Clearly Marked as Invalid: A clear indicator, such as an error message or icon, would pinpoint the problematic filters.
Removable: A simple, straightforward way to delete the filter from the left-hand sidebar or other visible elements.

This approach avoids the dreaded "soft-locking" of charts, reduces frustration, and allows teams to rapidly iterate on their dbt models without constantly losing their work. It's about empowering users to troubleshoot and fix these issues quickly, instead of starting from scratch every time.

The Solution: More Control Over Filters

The expected behavior involves the user being able to open the Filters panel even if some filters are invalid. When invalid filters appear:

They should be grayed out.
Should be clearly marked as invalid.
Should be removable via the left-hand sidebar.

Proposed Solutions: Giving Users Control

Okay, let's get into the nitty-gritty of how we can achieve this improved user experience. There are a couple of proposed solutions, and both aim to give users more control over their filters, even when things go wrong.

Solution 1: Enhanced Filters Panel

First up, we have the idea of rendering the Filters panel even when some filters are invalid. This is about making sure that the core functionality of the filters panel remains accessible, regardless of any errors. Here's what this could look like:

The Filters panel loads as usual.
Invalid filters are displayed in a disabled or errored state.
Each invalid filter has a clear indication of the error (e.g., an error message or icon).
An option to delete the invalid filter is readily available (e.g., a delete button next to the filter).

This approach ensures that users can quickly identify and address the issue without having to rebuild the entire chart. It's all about providing that crucial access to the problem filters, so you can fix them.

Solution 2: Direct Filter Deletion from the Left-Hand Config Pane

The second proposed solution is about making filter management even more intuitive. It suggests allowing users to delete filters directly from the left-hand configuration pane, even if the filter cannot be rendered in the main Filters panel. This gives users a direct path to removing the problematic filters, bypassing the need to even open the Filters panel if it's causing issues.

Users can see the list of filters in the left-hand configuration pane.
Invalid filters are clearly marked (e.g., with an error icon or message).
A delete option is available next to each filter.
Users can click the delete button to remove the invalid filter.

This solution keeps things simple and efficient. It means that even if a filter is broken, the user can still easily remove it and get their chart back on track. It's a quick and dirty way to get your dashboards back up and running.

The Benefits: Why This Matters

Implementing these solutions would bring several key benefits:

Eliminating Soft-Locks: Users can always access their charts, even if a filter is broken.
Reduced Frustration: No more starting from scratch – users can quickly fix the problem.
Faster Iteration: Teams can update their dbt models without constantly losing their work.
Improved User Experience: A smoother, more reliable Lightdash experience.

By empowering users to handle these issues directly, Lightdash can enhance its overall usability and help data teams work more efficiently.

The Call to Action: Making Lightdash Better

So, what do you think? We want Lightdash to be the best it can be, and that means making sure it's resilient to the inevitable changes that come with data modeling. Implementing these features would be a significant step in the right direction, creating a more user-friendly and reliable platform. Let's make sure that Lightdash handles invalid filters with grace, keeping our data analysis workflows smooth and efficient. It's about empowering users, reducing frustration, and helping teams iterate quickly and confidently on their data models. The future of Lightdash is bright, especially if we can tackle those pesky invalid filter issues head-on!