Aggregation & Grouping

Learn how to summarize data using aggregate functions and GROUP BY clauses.

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Aggregate Functions Overview

Function	Description	Example
COUNT(*)	Count all rows	COUNT(*)
COUNT(column)	Count non-NULL values	COUNT(email)
COUNT(DISTINCT col)	Count unique values	COUNT(DISTINCT customer)
SUM(column)	Sum of values	SUM(amount)
AVG(column)	Average of values	AVG(quantity)
MIN(column)	Minimum value	MIN(trandate)
MAX(column)	Maximum value	MAX(amount)

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Basic Aggregation (No GROUP BY)

When used without GROUP BY, aggregates apply to ALL rows.

Test Case: Overall Statistics

-- Company-wide sales statistics
SELECT
    COUNT(*) AS total_invoices,
    COUNT(DISTINCT entity) AS unique_customers,
    SUM(amount) AS total_revenue,
    AVG(amount) AS average_invoice,
    MIN(amount) AS smallest_invoice,
    MAX(amount) AS largest_invoice,
    MIN(trandate) AS first_invoice,
    MAX(trandate) AS last_invoice
FROM transaction
WHERE type = 'CustInvc'
  AND trandate >= TO_DATE('2024-01-01', 'YYYY-MM-DD')

Sample Result:

total_invoices	unique_customers	total_revenue	average_invoice	smallest_invoice	largest_invoice
1,250	342	2,450,000.00	1,960.00	50.00	125,000.00

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GROUP BY Basics

GROUP BY splits data into groups and applies aggregates to each group.

Illustration

Test Case: Sales by Customer

-- Total sales per customer
SELECT
    c.companyname,
    COUNT(*) AS order_count,
    SUM(t.amount) AS total_sales,
    AVG(t.amount) AS avg_order,
    MAX(t.trandate) AS last_order
FROM transaction t
INNER JOIN customer c ON t.entity = c.id
WHERE t.type = 'SalesOrd'
GROUP BY c.id, c.companyname
ORDER BY total_sales DESC
LIMIT 20

Sample Result:

companyname	order_count	total_sales	avg_order	last_order
Acme Corp	45	125,000.00	2,777.78	2024-12-15
Global Tech	38	98,500.00	2,592.11	2024-12-14
Beta Industries	32	87,250.00	2,726.56	2024-12-10

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Multiple GROUP BY Columns

Group by more than one column for detailed breakdowns.

Test Case: Sales by Customer and Month

-- Monthly sales breakdown by customer
SELECT
    c.companyname,
    TO_CHAR(t.trandate, 'YYYY-MM') AS month,
    COUNT(*) AS order_count,
    SUM(t.amount) AS monthly_sales
FROM transaction t
INNER JOIN customer c ON t.entity = c.id
WHERE t.type = 'SalesOrd'
  AND t.trandate >= TO_DATE('2024-01-01', 'YYYY-MM-DD')
GROUP BY c.id, c.companyname, TO_CHAR(t.trandate, 'YYYY-MM')
ORDER BY c.companyname, month

Sample Result:

companyname	month	order_count	monthly_sales
Acme Corp	2024-01	5	12,500.00
Acme Corp	2024-02	7	18,200.00
Acme Corp	2024-03	4	9,800.00
Beta Industries	2024-01	3	8,400.00

Test Case: Sales by Region and Category

-- Sales breakdown by state and item category
SELECT
    c.state,
    i.class AS category,
    COUNT(DISTINCT t.id) AS order_count,
    SUM(tl.amount) AS total_sales
FROM transaction t
INNER JOIN customer c ON t.entity = c.id
INNER JOIN transactionline tl ON t.id = tl.transaction
INNER JOIN item i ON tl.item = i.id
WHERE t.type = 'SalesOrd'
GROUP BY c.state, i.class
ORDER BY c.state, total_sales DESC

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HAVING Clause

HAVING filters AFTER aggregation (WHERE filters BEFORE).

Illustration

Test Case: High-Value Customers Only

-- Customers with more than $50,000 in sales
SELECT
    c.companyname,
    COUNT(*) AS order_count,
    SUM(t.amount) AS total_sales
FROM transaction t
INNER JOIN customer c ON t.entity = c.id
WHERE t.type = 'SalesOrd'
GROUP BY c.id, c.companyname
HAVING SUM(t.amount) > 50000
ORDER BY total_sales DESC

Test Case: Active Customers (Multiple Orders)

-- Customers with more than 5 orders
SELECT
    c.companyname,
    COUNT(*) AS order_count,
    SUM(t.amount) AS total_sales,
    AVG(t.amount) AS avg_order
FROM transaction t
INNER JOIN customer c ON t.entity = c.id
WHERE t.type = 'SalesOrd'
GROUP BY c.id, c.companyname
HAVING COUNT(*) > 5
ORDER BY order_count DESC

Test Case: Multiple HAVING Conditions

-- High-value, active customers
SELECT
    c.companyname,
    COUNT(*) AS order_count,
    SUM(t.amount) AS total_sales
FROM transaction t
INNER JOIN customer c ON t.entity = c.id
WHERE t.type = 'SalesOrd'
GROUP BY c.id, c.companyname
HAVING COUNT(*) >= 5
   AND SUM(t.amount) >= 25000
ORDER BY total_sales DESC

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COUNT Variations

COUNT(*) vs COUNT(column) vs COUNT(DISTINCT)

SELECT
    -- Count all rows
    COUNT(*) AS total_rows,

    -- Count rows where email is not NULL
    COUNT(email) AS with_email,

    -- Count unique states
    COUNT(DISTINCT state) AS unique_states,

    -- Count unique sales reps
    COUNT(DISTINCT salesrep) AS unique_reps

FROM customer
WHERE isinactive = 'F'

Sample Result:

total_rows	with_email	unique_states	unique_reps
1,250	1,180	48	15

Test Case: Data Quality Report

-- Check data completeness
SELECT
    COUNT(*) AS total_customers,
    COUNT(email) AS has_email,
    COUNT(phone) AS has_phone,
    COUNT(CASE WHEN email IS NULL THEN 1 END) AS missing_email,
    ROUND(COUNT(email) * 100.0 / COUNT(*), 1) AS email_pct
FROM customer
WHERE isinactive = 'F'

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Real-World Aggregation Examples

Example 1: Monthly Sales Trend

-- Monthly sales trend for the year
SELECT
    TO_CHAR(trandate, 'YYYY-MM') AS month,
    COUNT(*) AS invoice_count,
    SUM(amount) AS revenue,
    AVG(amount) AS avg_invoice,
    COUNT(DISTINCT entity) AS unique_customers
FROM transaction
WHERE type = 'CustInvc'
  AND EXTRACT(YEAR FROM trandate) = 2024
GROUP BY TO_CHAR(trandate, 'YYYY-MM')
ORDER BY month

Sample Result:

month	invoice_count	revenue	avg_invoice	unique_customers
2024-01	125	245,000	1,960	78
2024-02	142	278,000	1,958	85
2024-03	138	265,000	1,920	82

Example 2: Top Selling Items

-- Top 10 items by revenue
SELECT
    i.itemid,
    i.displayname,
    COUNT(DISTINCT t.id) AS order_count,
    SUM(tl.quantity) AS total_qty,
    SUM(tl.amount) AS total_revenue,
    AVG(tl.rate) AS avg_price
FROM transactionline tl
INNER JOIN transaction t ON tl.transaction = t.id
INNER JOIN item i ON tl.item = i.id
WHERE t.type = 'SalesOrd'
  AND t.trandate >= TO_DATE('2024-01-01', 'YYYY-MM-DD')
GROUP BY i.id, i.itemid, i.displayname
ORDER BY total_revenue DESC
LIMIT 10

Example 3: Sales Rep Performance

-- Sales rep performance comparison
SELECT
    e.entityid AS sales_rep,
    COUNT(DISTINCT t.entity) AS customers_served,
    COUNT(*) AS orders,
    SUM(t.amount) AS total_sales,
    AVG(t.amount) AS avg_order,
    MAX(t.trandate) AS last_sale
FROM transaction t
INNER JOIN employee e ON t.salesrep = e.id
WHERE t.type = 'SalesOrd'
  AND t.trandate >= TO_DATE('2024-01-01', 'YYYY-MM-DD')
GROUP BY e.id, e.entityid
ORDER BY total_sales DESC

Example 4: Customer Aging Report

-- Accounts receivable aging
SELECT
    c.companyname,
    SUM(CASE WHEN SYSDATE - t.duedate <= 0 THEN t.amountremaining ELSE 0 END) AS current,
    SUM(CASE WHEN SYSDATE - t.duedate BETWEEN 1 AND 30 THEN t.amountremaining ELSE 0 END) AS days_1_30,
    SUM(CASE WHEN SYSDATE - t.duedate BETWEEN 31 AND 60 THEN t.amountremaining ELSE 0 END) AS days_31_60,
    SUM(CASE WHEN SYSDATE - t.duedate BETWEEN 61 AND 90 THEN t.amountremaining ELSE 0 END) AS days_61_90,
    SUM(CASE WHEN SYSDATE - t.duedate > 90 THEN t.amountremaining ELSE 0 END) AS over_90,
    SUM(t.amountremaining) AS total_ar
FROM transaction t
INNER JOIN customer c ON t.entity = c.id
WHERE t.type = 'CustInvc'
  AND t.amountremaining > 0
GROUP BY c.id, c.companyname
HAVING SUM(t.amountremaining) > 0
ORDER BY total_ar DESC

Example 5: Inventory Summary

-- Inventory levels by location
SELECT
    l.name AS location,
    COUNT(DISTINCT ib.item) AS unique_items,
    SUM(ib.quantityonhand) AS total_qty,
    SUM(ib.quantityonhand * i.averagecost) AS inventory_value
FROM inventorybalance ib
INNER JOIN item i ON ib.item = i.id
INNER JOIN location l ON ib.location = l.id
WHERE ib.quantityonhand > 0
GROUP BY l.id, l.name
ORDER BY inventory_value DESC

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Common Aggregation Patterns

Percentage of Total

-- Each customer's percentage of total sales
WITH TotalSales AS (
    SELECT SUM(amount) AS grand_total
    FROM transaction
    WHERE type = 'SalesOrd'
)
SELECT
    c.companyname,
    SUM(t.amount) AS customer_sales,
    ROUND(SUM(t.amount) * 100.0 / ts.grand_total, 2) AS pct_of_total
FROM transaction t
INNER JOIN customer c ON t.entity = c.id
CROSS JOIN TotalSales ts
WHERE t.type = 'SalesOrd'
GROUP BY c.id, c.companyname, ts.grand_total
ORDER BY customer_sales DESC
LIMIT 10

Year-Over-Year Comparison

-- Compare this year vs last year by month
SELECT
    EXTRACT(MONTH FROM trandate) AS month,
    SUM(CASE WHEN EXTRACT(YEAR FROM trandate) = 2024 THEN amount ELSE 0 END) AS sales_2024,
    SUM(CASE WHEN EXTRACT(YEAR FROM trandate) = 2023 THEN amount ELSE 0 END) AS sales_2023,
    ROUND(
        (SUM(CASE WHEN EXTRACT(YEAR FROM trandate) = 2024 THEN amount ELSE 0 END) -
         SUM(CASE WHEN EXTRACT(YEAR FROM trandate) = 2023 THEN amount ELSE 0 END)) /
        NULLIF(SUM(CASE WHEN EXTRACT(YEAR FROM trandate) = 2023 THEN amount ELSE 0 END), 0) * 100,
    2) AS growth_pct
FROM transaction
WHERE type = 'CustInvc'
  AND EXTRACT(YEAR FROM trandate) IN (2023, 2024)
GROUP BY EXTRACT(MONTH FROM trandate)
ORDER BY month

Running Count Check

-- Verify data completeness
SELECT
    TO_CHAR(trandate, 'YYYY-MM') AS month,
    COUNT(*) AS total,
    COUNT(entity) AS has_entity,
    COUNT(salesrep) AS has_salesrep,
    COUNT(amount) AS has_amount
FROM transaction
WHERE type = 'SalesOrd'
GROUP BY TO_CHAR(trandate, 'YYYY-MM')
ORDER BY month

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GROUP BY Rules

Rule 1: Non-Aggregated Columns Must Be in GROUP BY

-- WRONG: companyname not in GROUP BY
SELECT companyname, SUM(amount)
FROM transaction t
JOIN customer c ON t.entity = c.id
GROUP BY t.entity  -- Missing companyname!

-- CORRECT:
SELECT c.companyname, SUM(t.amount)
FROM transaction t
JOIN customer c ON t.entity = c.id
GROUP BY c.id, c.companyname

Rule 2: WHERE vs HAVING

-- WHERE: Filter rows BEFORE grouping
-- HAVING: Filter groups AFTER aggregation

SELECT entity, SUM(amount) AS total
FROM transaction
WHERE type = 'SalesOrd'      -- Before grouping (row filter)
GROUP BY entity
HAVING SUM(amount) > 10000   -- After grouping (aggregate filter)

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Quick Reference

Execution Order

1. FROM / JOIN - Get data from tables
2. WHERE - Filter individual rows
3. GROUP BY - Create groups
4. Aggregate functions - Calculate per group
5. HAVING - Filter groups
6. SELECT - Choose columns
7. ORDER BY - Sort results
8. LIMIT - Restrict output

Common Patterns

-- Basic grouping
SELECT category, COUNT(*), SUM(amount)
FROM table
GROUP BY category

-- Grouping with filter
SELECT category, SUM(amount)
FROM table
WHERE status = 'Active'
GROUP BY category
HAVING SUM(amount) > 1000

-- Multiple group levels
SELECT year, month, category, SUM(amount)
FROM table
GROUP BY year, month, category
ORDER BY year, month, category