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Time Series

ONQL handles common time-series patterns with a combination of _asc/_desc, slices, and _date.

Last N days of orders​

shop.orders[
  customer_id = $1
]._desc(placed_at)[0:30]{id, total, placed_at}

Range query​

shop.orders[
  placed_at >= $1 and placed_at <= $2
]._asc(placed_at){id, total, placed_at}

Pass the start and end timestamps via ctxvalues.

Latest order per customer​

A common pattern: fetch a list of customers and tag each with their most recent order.

shop.customers{
  id, name,
  orders._desc(placed_at)[0:1]{id, total, placed_at}
}

Aggregating by day (client-side)​

ONQL doesn't yet have window/group-by aggregation. For "daily totals" reports, fetch the rows and aggregate on the client. For very large windows you'll want a materialized table that the database refreshes periodically.

Timestamp filtering​

shop.events[time >= $1]._asc(time)

Numeric epoch timestamps work directly. For string-formatted dates, use _date to parse:

shop.events[time._date("2006-01-02") = "2026-04-12"]