Uses SQL to compute SaaS pricing metrics—MRR, ARR, ARPU—directly in a PostgreSQL database hosting MariaDB-sourced data.
MariaDB SaaS pricing refers to calculating subscription-based revenue metrics—Monthly Recurring Revenue (MRR), Annual Recurring Revenue (ARR), Average Revenue Per User (ARPU)—from data stored in MariaDB or PostgreSQL. You query order and customer tables to derive accurate, real-time pricing insights.
PostgreSQL offers robust SQL functions, window analytics, and materialized views that let engineering teams calculate complex SaaS metrics quickly without exporting data.Keeping pricing logic in-database simplifies reporting pipelines and supports near-real-time dashboards.
Use a normalized schema: Customers(id, name, email, created_at)
, Products(id, name, price, stock)
, Orders(id, customer_id, order_date, total_amount)
, and OrderItems(id, order_id, product_id, quantity)
. Each order item reflects one product line on a subscription invoice.
Group paid orders by month, sum recurring product prices, and exclude refunds.Use date_trunc('month', order_date)
for monthly buckets.
Multiply the latest month’s MRR by 12. Alternatively, aggregate yearly subscriptions directly when plans bill annually.
Window functions like LAG()
detect plan upgrades; COALESCE()
handles nulls in partial periods. CTEs and materialized views speed up repeated pricing queries.
Refresh after daily billing closes or via a cron job aligned with your finance team’s cut-off time.Use CONCURRENTLY
to avoid locking.
1. Store cents, not floating dollars, to avoid rounding errors. 2. Tag one-time fees separately from recurring lines. 3. Index order_date
and customer_id
for faster aggregations.
Use lag(total_amount)
partitioned by customer to compare current and previous invoices, flagging revenue expansions.
Yes. Embed queries in Galaxy collections, endorse them, and schedule refreshes.Galaxy’s AI copilot can optimize them as schema changes, keeping finance dashboards accurate.
.
Yes. Store usage in a separate table, multiply by unit price, and join back to Orders during aggregation.
Daily is typical. High-growth startups may refresh hourly to track rapid changes.
Galaxy’s AI copilot proposes optimized versions and adapts them automatically when your schema evolves.