Feature ideas

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Enhanced Dashboard Filtering - Filter by Common Properties Across Multiple Blueprints
Problem Statement Currently, Port dashboards require users to manually configure filters with exact property names and values. This creates several challenges: - Users must understand the underlying data model to create effective filters - No support for filtering by common properties that exist across multiple blueprints - Technical workflow not accessible to non-technical stakeholders (managers, executives, product owners) - Each dashboard requires manual filter setup, reducing reusability Use Case As a dashboard consumer (engineering manager, product owner, executive), I want to easily filter dashboard views using simple drop-down selections, so that I can view relevant metrics without needing to understand property names, data models, or filter mechanics. Example Scenario: A DORA metrics dashboard displays data from multiple blueprint types (repositories, deployments, services, incidents). Users want to filter the entire dashboard by a common organizational dimension (product line, team, business unit) that appears across all these blueprints. Current Limitations - Manual filter addition required for each dashboard - Users must know exact property names (e.g., "product", "team", "owner") - Cannot create filters that work across multiple blueprint types - "Contains" property provides only partial workaround - Poor user experience for business stakeholders Desired Solution - Dashboard-level filters with pre-configured drop-down lists - Single-click filtering that applies across all widgets in the dashboard - Cross-blueprint filtering - filter by common properties across different entity types - No technical knowledge required - intuitive UI for filter selection - Reusable filter configurations that can be templated across multiple dashboards Expected Benefits 1. Improved UX - Business stakeholders can self-serve analytics without technical assistance 2. Faster insights - One-click filtering vs. manual configuration 3. Better adoption - Dashboards accessible to broader audience 4. Consistency - Standardized filtering experience across organization 5. Reduced support burden - Less hand-holding required for dashboard consumers Technical Considerations - Port currently cannot filter by common properties across different blueprints - This affects any dashboard showing data from multiple entity types, or a single entity type - Workarounds exist but compromise user experience - Feature would enhance Port's value proposition for executive reporting and cross-functional analytics
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Dashboards & widgets
Allow the bar order in bar-chart widgets to be configured, instead of always sorting by bar size
What I'm trying to do Render the distribution of answers to a Likert-scale survey question as a bar chart — one bar per point on the scale, in scale order (for example "Never / Rarely / Sometimes / Often / Always"). The bars are produced by grouping entities on a relation, and the target blueprint carries an explicit display_order number property giving the intended position of each option. What happens instead bar-chart always orders its bars by value, largest first. There appears to be no way to override this: the widget schema exposes type , title , blueprint , property , dataset , emptyStateText , icon , description and segmentTableViewConfig , and the sortSettings available inside segmentTableViewConfig configures a table view rather than the chart axis. Why size-ordering is a problem for ordinal data For a categorical breakdown, sorting by size is a sensible default — it puts the biggest category first. For an ordinal scale it removes the only information the chart shape carries, because the result is a descending staircase regardless of the underlying data: | Underlying distribution | Counts in scale order | Rendered | | --- | --- | --- | | Polarised — people at both extremes | 5, 1, 0, 1, 5 | 5, 5, 1, 1, 0 | | Converged — people clustered in the middle | 1, 2, 6, 2, 1 | 6, 2, 2, 1, 1 | Both render identically in shape. Worse, the polarised case puts its two opposite extremes side by side as the two tallest bars, which reads as a consensus cluster — the opposite of what the data says. Telling these two cases apart is usually the entire reason for charting a distribution rather than reporting a mean. What I'd like Any one of these would solve it: an explicit sort option on the widget, e.g. sortBy accepting a property of the grouped-by entity (such as display_order ) with asc / desc an option to preserve the natural order of the grouped-by entities rather than sorting by value the ability to supply an explicit ordered list of group keys, with bars rendered in that order The first is the most useful, since the ordering property already exists on the blueprint. Workaround in use table-entities-explorer , grouped by the same relation and sorted on display_order ascending. It gives correct scale order and the group headers carry the counts, so the data is right — but it is a table where a chart belongs, on a dashboard that is otherwise all charts, and it loses the at-a-glance shape that made a chart worth having.
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Dashboards & widgets
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