Query Builder

Query Builder is the visual, no-SQL tool for constructing data queries inside ActiveManage. Use it anywhere the platform asks “which records?” — list views, reports, dashboards, workflow rules, automation triggers.

Why Visual Querying

• Accessible to non-developers: Operations staff can express queries without SQL.
• Auto-validated: The platform knows your schema; invalid queries can't be saved.
• Reusable: A query designed for a list view can be re-used for a report.
• Permission-aware: The query respects the running user's data permissions automatically.

Where You'll See It

• Datastore list views — filter the rows shown.
• Dashboard widgets — define data for a chart or KPI.
• Workflow triggers — “run this workflow when records match these conditions”.
• Notification rules — “alert when matching records appear”.
• Permission rules — “users in group X can see records matching these conditions”.
• Export configurations — “export only records matching this”.

Worked Examples

• List filter: Show only my open tickets.
• Dashboard chart: Plot revenue for the last 12 months, by month.
• Workflow trigger: When an order exceeds $1000, route to manager approval.
• Permission rule: Regional managers see only customers in their region.
• Notification: Notify the on-call team when any system error event is created with severity = critical.

Filters and conditions are the core of every query. This article shows the full toolkit and the common patterns you'll keep coming back to.

Anatomy of a Filter

• Field: Any field on the source datastore, including custom fields and fields on linked records.
• Operator: Adapted to the field type — date fields get date-specific operators, dropdown fields get is-in/is-not-in.
• Value: Literal text/number/date, a variable (current_user, today), or another field reference.
• Connector: AND or OR with the next condition in the group.

Operators by Field Type

• Text: equals, not equals, contains, does not contain, starts with, ends with, matches regex.
• Number: equals, not equals, greater than, less than, between, is null.
• Date: equals, before, after, between, within (relative).
• Boolean: is true, is false.
• Dropdown/multi-select: is, is not, is in (list), is not in.
• Linked record: is, is not, related-record matches sub-condition.

Useful Variables

• current_user — the running user.
• current_user.group — their primary security group.
• today, now, yesterday, tomorrow.
• this_week_start, this_month_start, this_quarter_start, this_year_start.
• days_ago(n), days_from_now(n).

Worked Examples

• My open tickets: assignee = current_user AND status = open.
• Recently created: created_at > days_ago(7).
• Stale items: last_activity < days_ago(30) AND status = active.
• Date range overlap: start_date ≤ today AND end_date ≥ today.
• Linked record condition: customer.country = UK AND total > 5000.

Once you've filtered down to the right rows, sort and group control how those rows are presented. This article shows the controls and common patterns.

Sort

• Pick a field and a direction (ascending / descending).
• Add multiple sort keys — primary, secondary, tertiary — for tie-breaking.
• Sort on any field including custom and computed fields.

Group

• Pick one field to group by — rows with the same value cluster under a heading.
• Optionally show counts per group (“Customer A (12)”).
• Optionally show aggregates per group (sum, average, max, min).
• Groups can be collapsed by default to keep long lists scannable.

Sort and Group Combined

Sort within groups: groups are shown by group field, and rows inside each group are sorted by the chosen sort key.

Worked Examples

• Tickets by customer, newest first: Group by customer, sort by created_at desc within group.
• Sales by region: Group by region, show sum(amount) per group, sort groups by sum desc.
• Tasks by status: Group by status (Open, In Progress, Done), collapse Done by default.
• Inventory low-stock first: Sort by stock_level asc, secondary sort name asc for alphabetical tiebreak.
• Time-series log: Sort by timestamp desc, group by day, count per group.

Tip: Grouping can be expensive on very large datastores. If a group view feels slow, consider applying tighter filters first.