How to Calculate MariaDB SaaS Pricing in PostgreSQL

Galaxy Glossary

How do I calculate SaaS pricing metrics in MariaDB with PostgreSQL?

Uses SQL to compute SaaS pricing metrics—MRR, ARR, ARPU—directly in a PostgreSQL database hosting MariaDB-sourced data.

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Description

Example H2

Example H3

What is MariaDB SaaS pricing?

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.

Why run pricing queries in PostgreSQL?

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.

How do I structure my tables?

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.

How to compute Monthly Recurring Revenue (MRR)?

Group paid orders by month, sum recurring product prices, and exclude refunds.Use date_trunc('month', order_date) for monthly buckets.

How to derive Annual Recurring Revenue (ARR)?

Multiply the latest month’s MRR by 12. Alternatively, aggregate yearly subscriptions directly when plans bill annually.

Which SQL functions simplify pricing?

Window functions like LAG() detect plan upgrades; COALESCE() handles nulls in partial periods. CTEs and materialized views speed up repeated pricing queries.

When should I refresh materialized views?

Refresh after daily billing closes or via a cron job aligned with your finance team’s cut-off time.Use CONCURRENTLY to avoid locking.

Best practices for MariaDB SaaS pricing queries

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.

Example PostgreSQL code to track upgrades

Use lag(total_amount) partitioned by customer to compare current and previous invoices, flagging revenue expansions.

Can I automate pricing dashboards?

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.

Why How to Calculate MariaDB SaaS Pricing in PostgreSQL is important

How to Calculate MariaDB SaaS Pricing in PostgreSQL Example Usage


-- Compute current MRR, ARR, and ARPU in one shot
WITH mrr_by_month AS (
  SELECT date_trunc('month', o.order_date) AS month,
         SUM(oi.quantity * p.price)        AS mrr
  FROM Orders o
  JOIN OrderItems oi ON oi.order_id = o.id
  JOIN Products p    ON p.id = oi.product_id
  WHERE o.status = 'paid'
  GROUP BY 1
), current_mrr AS (
  SELECT mrr FROM mrr_by_month ORDER BY month DESC LIMIT 1
)
SELECT cm.mrr                                    AS current_mrr,
       cm.mrr * 12                               AS arr,
       ROUND(cm.mrr / COUNT(DISTINCT c.id), 2)   AS arpu
FROM current_mrr cm, Customers c;

How to Calculate MariaDB SaaS Pricing in PostgreSQL Syntax


SELECT
    date_trunc('month', o.order_date)         AS month,
    SUM(oi.quantity * p.price)::NUMERIC(12,2) AS mrr
FROM Orders o
JOIN OrderItems oi ON oi.order_id = o.id
JOIN Products p   ON p.id = oi.product_id
WHERE o.order_date >= CURRENT_DATE - INTERVAL '12 months'
  AND o.status = 'paid'
GROUP BY 1
ORDER BY 1;

-- Options & parameters
-- status:     Filter out failed/refunded invoices
-- date range: Adjust INTERVAL for longer history
-- currency:   Multiply by fx_rate when multi-currency

Common Mistakes

Mistake: Using floating-point columns for prices causes rounding errors in MRR totals. Fix: Store money in integer cents or NUMERIC(12,2).
Mistake: Double-counting revenue by joining Orders and OrderItems twice in the same query. Fix: Use a single join path and aggregate once.