Window Functions Guide

March 5, 2026 · View on GitHub

Complete guide to using window functions with Relica's SelectExpr() for advanced analytics

Table of Contents

Introduction

Window functions perform calculations across a set of rows related to the current row, without collapsing rows like GROUP BY. Think of them as "aggregate functions that don't group."

Key Benefits:

  • No grouping: Keep all detail rows while calculating aggregates
  • Rankings: ROW_NUMBER(), RANK(), DENSE_RANK()
  • Running totals: Cumulative sums, moving averages
  • Comparative analysis: Compare row to previous/next rows
  • Top N per group: Without complex subqueries

Note: Relica doesn't have dedicated window function API yet. Use SelectExpr() with raw SQL window function syntax.

Basic Window Functions

Using SelectExpr()

Window functions in Relica are added using SelectExpr() with raw SQL syntax.

package main

import (
    "github.com/coregx/relica"
)

// Add row numbers to query results
func GetNumberedProducts(db *relica.DB) ([]Product, error) {
    type Product struct {
        RowNum int     `db:"row_num"`
        ID     int     `db:"id"`
        Name   string  `db:"name"`
        Price  float64 `db:"price"`
    }
    var products []Product

    err := db.Select("id", "name", "price").
        SelectExpr("ROW_NUMBER() OVER (ORDER BY price DESC)", "row_num").
        From("products").
        All(&products)

    return products, err
}

Generated SQL (PostgreSQL):

SELECT "id", "name", "price", ROW_NUMBER() OVER (ORDER BY price DESC) as row_num
FROM "products"

Result:

row_num | id | name      | price
--------|----|-----------|---------
1       | 42 | Laptop    | 1299.99
2       | 15 | Monitor   | 499.99
3       | 8  | Keyboard  | 89.99

Window Function Syntax

<function_name>(<expression>) OVER (
    [PARTITION BY <columns>]
    [ORDER BY <columns>]
    [ROWS|RANGE <frame_clause>]
)

Components:

  • Function: ROW_NUMBER(), SUM(), AVG(), etc.
  • OVER: Defines the window
  • PARTITION BY: Divide rows into groups (optional)
  • ORDER BY: Order rows within partition (optional)
  • Frame: Define row range for calculation (optional)

Ranking Functions

ROW_NUMBER()

Assigns unique sequential number to each row.

// Number products by price (descending)
func RankProductsByPrice(db *relica.DB) ([]ProductRank, error) {
    type ProductRank struct {
        Rank  int     `db:"rank"`
        Name  string  `db:"name"`
        Price float64 `db:"price"`
    }
    var products []ProductRank

    err := db.Select("name", "price").
        SelectExpr("ROW_NUMBER() OVER (ORDER BY price DESC)", "rank").
        From("products").
        All(&products)

    return products, err
}

Generated SQL:

SELECT "name", "price", ROW_NUMBER() OVER (ORDER BY price DESC) as rank
FROM "products"

Result:

rank | name      | price
-----|-----------|-------
1    | Laptop    | 1299
2    | Monitor   | 499
3    | Tablet    | 399
4    | Keyboard  | 89

RANK()

Assigns rank with gaps for ties.

// Rank products (same price = same rank, with gaps)
func RankProductsWithGaps(db *relica.DB) ([]ProductRank, error) {
    type ProductRank struct {
        Rank  int     `db:"rank"`
        Name  string  `db:"name"`
        Price float64 `db:"price"`
    }
    var products []ProductRank

    err := db.Select("name", "price").
        SelectExpr("RANK() OVER (ORDER BY price DESC)", "rank").
        From("products").
        All(&products)

    return products, err
}

Result (note gaps after ties):

rank | name      | price
-----|-----------|-------
1    | Laptop    | 1299
2    | Monitor   | 499
3    | Tablet    | 399
3    | Phone     | 399  ← Same rank
5    | Keyboard  | 89   ← Gap (skipped 4)

DENSE_RANK()

Assigns rank without gaps for ties.

// Rank products (same price = same rank, no gaps)
func RankProductsDense(db *relica.DB) ([]ProductRank, error) {
    type ProductRank struct {
        Rank  int     `db:"rank"`
        Name  string  `db:"name"`
        Price float64 `db:"price"`
    }
    var products []ProductRank

    err := db.Select("name", "price").
        SelectExpr("DENSE_RANK() OVER (ORDER BY price DESC)", "rank").
        From("products").
        All(&products)

    return products, err
}

Result (no gaps):

rank | name      | price
-----|-----------|-------
1    | Laptop    | 1299
2    | Monitor   | 499
3    | Tablet    | 399
3    | Phone     | 399  ← Same rank
4    | Keyboard  | 89   ← No gap!

NTILE()

Distributes rows into N buckets.

// Divide products into 4 price quartiles
func GetPriceQuartiles(db *relica.DB) ([]ProductQuartile, error) {
    type ProductQuartile struct {
        Quartile int     `db:"quartile"`
        Name     string  `db:"name"`
        Price    float64 `db:"price"`
    }
    var products []ProductQuartile

    err := db.Select("name", "price").
        SelectExpr("NTILE(4) OVER (ORDER BY price)", "quartile").
        From("products").
        All(&products)

    return products, err
}

Result:

quartile | name      | price
---------|-----------|-------
1        | Mouse     | 19
1        | Keyboard  | 89
2        | Tablet    | 399
2        | Phone     | 399
3        | Monitor   | 499
3        | Headset   | 199
4        | Laptop    | 1299
4        | Desktop   | 1499

Aggregate Window Functions

Aggregate functions (SUM, AVG, COUNT, MIN, MAX) can be used as window functions.

Running Total (SUM)

// Calculate running total of sales
func GetRunningTotalSales(db *relica.DB) ([]DailySales, error) {
    type DailySales struct {
        Date         string  `db:"sale_date"`
        DailyTotal   float64 `db:"daily_total"`
        RunningTotal float64 `db:"running_total"`
    }
    var sales []DailySales

    err := db.Select("DATE(created_at) as sale_date", "SUM(total) as daily_total").
        SelectExpr("SUM(SUM(total)) OVER (ORDER BY DATE(created_at))", "running_total").
        From("orders").
        GroupBy("DATE(created_at)").
        OrderBy("sale_date").
        All(&sales)

    return sales, err
}

Generated SQL:

SELECT DATE(created_at) as sale_date,
       SUM(total) as daily_total,
       SUM(SUM(total)) OVER (ORDER BY DATE(created_at)) as running_total
FROM "orders"
GROUP BY DATE(created_at)
ORDER BY sale_date

Result:

sale_date  | daily_total | running_total
-----------|-------------|---------------
2025-01-01 | 1000        | 1000
2025-01-02 | 1500        | 2500
2025-01-03 | 800         | 3300
2025-01-04 | 1200        | 4500

Moving Average (AVG)

// Calculate 7-day moving average
func GetMovingAverage(db *relica.DB) ([]DailySales, error) {
    type DailySales struct {
        Date      string  `db:"sale_date"`
        Total     float64 `db:"daily_total"`
        AvgLast7  float64 `db:"avg_last_7_days"`
    }
    var sales []DailySales

    err := db.Select("DATE(created_at) as sale_date", "SUM(total) as daily_total").
        SelectExpr("AVG(SUM(total)) OVER (ORDER BY DATE(created_at) ROWS BETWEEN 6 PRECEDING AND CURRENT ROW)", "avg_last_7_days").
        From("orders").
        GroupBy("DATE(created_at)").
        OrderBy("sale_date").
        All(&sales)

    return sales, err
}

Result:

sale_date  | daily_total | avg_last_7_days
-----------|-------------|----------------
2025-01-01 | 1000        | 1000.00
2025-01-02 | 1500        | 1250.00
2025-01-03 | 800         | 1100.00
...
2025-01-07 | 1200        | 1150.00  ← Average of last 7 days
2025-01-08 | 900         | 1100.00

COUNT Window Function

// Count orders per customer with total count
func GetCustomerOrderCounts(db *relica.DB) ([]CustomerOrders, error) {
    type CustomerOrders struct {
        CustomerID   int `db:"customer_id"`
        OrderCount   int `db:"order_count"`
        TotalOrders  int `db:"total_orders"`
    }
    var customers []CustomerOrders

    err := db.Select("customer_id", "COUNT(*) as order_count").
        SelectExpr("SUM(COUNT(*)) OVER ()", "total_orders").
        From("orders").
        GroupBy("customer_id").
        All(&customers)

    return customers, err
}

Value Functions

Value functions access values from other rows relative to current row.

LAG() - Previous Row Value

// Compare each month's sales to previous month
func GetMonthlySalesComparison(db *relica.DB) ([]MonthlySales, error) {
    type MonthlySales struct {
        Month       string  `db:"month"`
        Sales       float64 `db:"sales"`
        PrevSales   float64 `db:"prev_month_sales"`
        Growth      float64 `db:"growth_pct"`
    }
    var sales []MonthlySales

    err := db.Select("DATE_TRUNC('month', created_at) as month", "SUM(total) as sales").
        SelectExpr("LAG(SUM(total)) OVER (ORDER BY DATE_TRUNC('month', created_at))", "prev_month_sales").
        SelectExpr("(SUM(total) - LAG(SUM(total)) OVER (ORDER BY DATE_TRUNC('month', created_at))) / LAG(SUM(total)) OVER (ORDER BY DATE_TRUNC('month', created_at)) * 100", "growth_pct").
        From("orders").
        GroupBy("DATE_TRUNC('month', created_at)").
        OrderBy("month").
        All(&sales)

    return sales, err
}

Result:

month      | sales | prev_month_sales | growth_pct
-----------|-------|------------------|------------
2025-01    | 10000 | NULL             | NULL
2025-02    | 12000 | 10000            | 20.00
2025-03    | 11500 | 12000            | -4.17
2025-04    | 15000 | 11500            | 30.43

LEAD() - Next Row Value

// Compare current price to next product's price
func GetPriceComparison(db *relica.DB) ([]PriceComp, error) {
    type PriceComp struct {
        Name      string  `db:"name"`
        Price     float64 `db:"price"`
        NextPrice float64 `db:"next_price"`
    }
    var products []PriceComp

    err := db.Select("name", "price").
        SelectExpr("LEAD(price) OVER (ORDER BY price)", "next_price").
        From("products").
        OrderBy("price").
        All(&products)

    return products, err
}

Result:

name      | price | next_price
----------|-------|------------
Mouse     | 19    | 89
Keyboard  | 89    | 399
Tablet    | 399   | 499
Monitor   | 499   | 1299
Laptop    | 1299  | NULL

FIRST_VALUE() and LAST_VALUE()

// Compare each product price to cheapest and most expensive in category
func GetCategoryPriceRange(db *relica.DB) ([]ProductPrice, error) {
    type ProductPrice struct {
        Category  string  `db:"category"`
        Name      string  `db:"name"`
        Price     float64 `db:"price"`
        MinPrice  float64 `db:"min_price"`
        MaxPrice  float64 `db:"max_price"`
    }
    var products []ProductPrice

    err := db.Select("category", "name", "price").
        SelectExpr("FIRST_VALUE(price) OVER (PARTITION BY category ORDER BY price)", "min_price").
        SelectExpr("LAST_VALUE(price) OVER (PARTITION BY category ORDER BY price ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)", "max_price").
        From("products").
        OrderBy("category", "price").
        All(&products)

    return products, err
}

PARTITION BY

PARTITION BY divides rows into groups for window function calculation.

Basic Partitioning

// Rank products within each category
func RankProductsByCategory(db *relica.DB) ([]ProductRank, error) {
    type ProductRank struct {
        Category string  `db:"category"`
        Name     string  `db:"name"`
        Price    float64 `db:"price"`
        Rank     int     `db:"rank"`
    }
    var products []ProductRank

    err := db.Select("category", "name", "price").
        SelectExpr("ROW_NUMBER() OVER (PARTITION BY category ORDER BY price DESC)", "rank").
        From("products").
        OrderBy("category", "rank").
        All(&products)

    return products, err
}

Generated SQL:

SELECT "category", "name", "price",
       ROW_NUMBER() OVER (PARTITION BY category ORDER BY price DESC) as rank
FROM "products"
ORDER BY "category", rank

Result:

category    | name      | price | rank
------------|-----------|-------|------
Electronics | Laptop    | 1299  | 1
Electronics | Monitor   | 499   | 2
Electronics | Keyboard  | 89    | 3
Furniture   | Desk      | 599   | 1
Furniture   | Chair     | 299   | 2
Furniture   | Lamp      | 49    | 3

Top N Per Group

// Get top 3 products per category
func GetTopProductsPerCategory(db *relica.DB, topN int) ([]Product, error) {
    type Product struct {
        Category string  `db:"category"`
        Name     string  `db:"name"`
        Price    float64 `db:"price"`
        Rank     int     `db:"rank"`
    }

    // Use subquery to filter ranked results
    ranked := db.Select("category", "name", "price").
        SelectExpr("ROW_NUMBER() OVER (PARTITION BY category ORDER BY price DESC)", "rank").
        From("products")

    var products []Product
    err := db.Select("category", "name", "price", "rank").
        FromSelect(ranked, "ranked").
        Where("rank <= ?", topN).
        OrderBy("category", "rank").
        All(&products)

    return products, err
}

ORDER BY in Window Functions

ORDER BY within window functions determines row order for calculations.

Impact on Results

// Different ORDER BY gives different results
func DemonstrateOrderBy(db *relica.DB) {
    // Ascending order
    db.Select("name", "price").
        SelectExpr("ROW_NUMBER() OVER (ORDER BY price ASC)", "rank_asc").
        From("products")
    // rank_asc: 1=cheapest, 2=next cheapest, ...

    // Descending order
    db.Select("name", "price").
        SelectExpr("ROW_NUMBER() OVER (ORDER BY price DESC)", "rank_desc").
        From("products")
    // rank_desc: 1=most expensive, 2=next expensive, ...
}

Multiple ORDER BY Columns

// Order by multiple columns
func RankByMultipleColumns(db *relica.DB) ([]Product, error) {
    type Product struct {
        Category string  `db:"category"`
        Name     string  `db:"name"`
        Sales    int     `db:"sales"`
        Rank     int     `db:"rank"`
    }
    var products []Product

    err := db.Select("category", "name", "sales").
        SelectExpr("ROW_NUMBER() OVER (ORDER BY category ASC, sales DESC)", "rank").
        From("products").
        All(&products)

    return products, err
}

Window Frames

Window frames define which rows are included in window function calculation.

Syntax:

ROWS BETWEEN <start> AND <end>
RANGE BETWEEN <start> AND <end>

Frame bounds:

  • UNBOUNDED PRECEDING: First row of partition
  • N PRECEDING: N rows before current
  • CURRENT ROW: Current row
  • N FOLLOWING: N rows after current
  • UNBOUNDED FOLLOWING: Last row of partition

Default Frame

Without explicit frame:

  • With ORDER BY: RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
  • Without ORDER BY: ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING (entire partition)

ROWS Frame

// Moving average of last 3 orders
func GetMovingAverage3(db *relica.DB) ([]OrderAvg, error) {
    type OrderAvg struct {
        OrderID int     `db:"order_id"`
        Total   float64 `db:"total"`
        Avg3    float64 `db:"avg_last_3"`
    }
    var orders []OrderAvg

    err := db.Select("id as order_id", "total").
        SelectExpr("AVG(total) OVER (ORDER BY id ROWS BETWEEN 2 PRECEDING AND CURRENT ROW)", "avg_last_3").
        From("orders").
        OrderBy("id").
        All(&orders)

    return orders, err
}

Result:

order_id | total | avg_last_3
---------|-------|------------
1        | 100   | 100.00     ← Only 1 row
2        | 150   | 125.00     ← (100+150)/2
3        | 200   | 150.00     ← (100+150+200)/3
4        | 120   | 156.67     ← (150+200+120)/3
5        | 180   | 166.67     ← (200+120+180)/3

RANGE Frame

// Sum of orders within same day
func GetDailyTotals(db *relica.DB) ([]DailyOrder, error) {
    type DailyOrder struct {
        OrderID    int     `db:"order_id"`
        OrderDate  string  `db:"order_date"`
        Total      float64 `db:"total"`
        DailyTotal float64 `db:"daily_total"`
    }
    var orders []DailyOrder

    err := db.Select("id as order_id", "DATE(created_at) as order_date", "total").
        SelectExpr("SUM(total) OVER (ORDER BY DATE(created_at) RANGE BETWEEN CURRENT ROW AND CURRENT ROW)", "daily_total").
        From("orders").
        OrderBy("created_at").
        All(&orders)

    return orders, err
}

Unbounded Windows

// Running total from start to current row
func GetCumulativeSum(db *relica.DB) ([]OrderTotal, error) {
    type OrderTotal struct {
        OrderID int     `db:"order_id"`
        Total   float64 `db:"total"`
        Cumsum  float64 `db:"cumulative_sum"`
    }
    var orders []OrderTotal

    err := db.Select("id as order_id", "total").
        SelectExpr("SUM(total) OVER (ORDER BY id ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)", "cumulative_sum").
        From("orders").
        OrderBy("id").
        All(&orders)

    return orders, err
}

When to Use Window Functions

Decision Tree

Need aggregate calculation?
├─ Keep all detail rows? → Window function
│  ├─ Rank rows? → ROW_NUMBER/RANK/DENSE_RANK
│  ├─ Running total? → SUM() OVER (ORDER BY ...)
│  ├─ Compare to prev/next? → LAG/LEAD
│  └─ Top N per group? → ROW_NUMBER() OVER (PARTITION BY ...)
└─ Collapse rows? → GROUP BY

Use Cases

✅ Window functions excel at:

  • Rankings (top N per category)
  • Running totals / cumulative sums
  • Moving averages
  • Row-to-row comparisons (growth rates)
  • Percentiles and quartiles
  • Gap analysis

❌ Use GROUP BY instead when:

  • Only need aggregated results (not detail rows)
  • Simple totals without ranking
  • Smaller result sets

Performance Considerations

1. Sorting Overhead

Window functions require sorting:

-- This sorts entire table by price
ROW_NUMBER() OVER (ORDER BY price)

Impact: O(n log n) complexity

Optimization: Index ORDER BY columns

CREATE INDEX idx_products_price ON products(price);

2. Partitioning Performance

PARTITION BY can be expensive:

-- Sorts once per partition
ROW_NUMBER() OVER (PARTITION BY category ORDER BY price)

Optimization: Index PARTITION BY + ORDER BY columns

CREATE INDEX idx_products_cat_price ON products(category, price);

3. Frame Size

Large frames are slower:

-- Fast: Small frame
ROWS BETWEEN 3 PRECEDING AND CURRENT ROW

-- Slow: Large frame
ROWS BETWEEN 1000 PRECEDING AND 1000 FOLLOWING

4. Multiple Window Functions

Reuse windows when possible:

// ❌ BAD: Same window defined twice
db.Select("name").
    SelectExpr("ROW_NUMBER() OVER (PARTITION BY category ORDER BY price)", "rank").
    SelectExpr("DENSE_RANK() OVER (PARTITION BY category ORDER BY price)", "dense_rank")
// Computes same window twice

// ✅ GOOD: Define window once (PostgreSQL)
db.Select("name").
    SelectExpr("ROW_NUMBER() OVER w", "rank").
    SelectExpr("DENSE_RANK() OVER w", "dense_rank").
    SelectExpr("WINDOW w AS (PARTITION BY category ORDER BY price)")

Note: Named windows (WINDOW clause) not yet supported.

Benchmark Results

Dataset: 1M products, 1000 categories

OperationTimeIndex Impact
Simple ROW_NUMBER()~800ms50% faster with index
PARTITION BY~1200ms70% faster with index
LAG/LEAD~850ms60% faster with index
Moving average (7 rows)~900ms50% faster with index

Database Compatibility

FeaturePostgreSQLMySQLSQLiteNotes
ROW_NUMBER()✓ 8.4+✓ 8.0+✓ 3.25+
RANK()✓ 8.4+✓ 8.0+✓ 3.25+
DENSE_RANK()✓ 8.4+✓ 8.0+✓ 3.25+
NTILE()✓ 8.4+✓ 8.0+✓ 3.28+
LAG/LEAD✓ 8.4+✓ 8.0+✓ 3.25+
FIRST_VALUE/LAST_VALUE✓ 8.4+✓ 8.0+✓ 3.28+
SUM/AVG/COUNT✓ 8.4+✓ 8.0+✓ 3.25+
Frame clauses (ROWS/RANGE)✓ 8.4+✓ 8.0+✓ 3.28+
Named windows (WINDOW)✓ 8.4+✓ 8.0+✓ 3.25+Not in Relica yet

MySQL Notes:

  • MySQL 5.7: No window function support
  • MySQL 8.0+: Full window function support

Best Practices

✅ DO

  1. Index ORDER BY and PARTITION BY columns

    CREATE INDEX idx_cat_price ON products(category, price);

  2. Use descriptive aliases

    SelectExpr("ROW_NUMBER() OVER (ORDER BY price DESC)", "price_rank")

  3. Use ROW_NUMBER for Top N queries

    SelectExpr("ROW_NUMBER() OVER (PARTITION BY category ORDER BY sales DESC)", "rank")

  4. Limit frame size when possible

    SelectExpr("AVG(total) OVER (ORDER BY date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW)", "avg_7_days")

  5. Use CTEs for complex window queries

    ranked := db.Select().SelectExpr("ROW_NUMBER() OVER (...)", "rn").From("products")
db.Select().FromSelect(ranked, "r").Where("rn <= 10")

❌ DON'T

  1. Don't use window functions when GROUP BY suffices

    // ❌ Overkill
SelectExpr("SUM(total) OVER ()")

// ✅ Simple
Select("SUM(total)")

  2. Don't forget ORDER BY in ranking functions

    // ❌ Random order
SelectExpr("ROW_NUMBER() OVER ()", "rank")

// ✅ Meaningful order
SelectExpr("ROW_NUMBER() OVER (ORDER BY price DESC)", "rank")

  3. Don't use large frames without testing

    // ❌ May be slow
ROWS BETWEEN 10000 PRECEDING AND 10000 FOLLOWING

// ✅ Reasonable frame
ROWS BETWEEN 30 PRECEDING AND CURRENT ROW

  4. Don't mix window functions with incompatible GROUP BY

    // ❌ ERROR: Can't mix non-aggregated columns with GROUP BY
Select("name").
SelectExpr("ROW_NUMBER() OVER (ORDER BY price)", "rank").
GroupBy("category")

Common Patterns

Pattern 1: Top N Per Group

// Top 3 selling products per category
func GetTopSellingProducts(db *relica.DB) ([]Product, error) {
    type Product struct {
        Category string `db:"category"`
        Name     string `db:"name"`
        Sales    int    `db:"sales"`
        Rank     int    `db:"rank"`
    }

    ranked := db.Select("category", "name", "sales").
        SelectExpr("ROW_NUMBER() OVER (PARTITION BY category ORDER BY sales DESC)", "rank").
        From("products")

    var products []Product
    err := db.Select().
        FromSelect(ranked, "r").
        Where("rank <= ?", 3).
        OrderBy("category", "rank").
        All(&products)

    return products, err
}

Pattern 2: Percentage of Total

// Calculate each order's percentage of total sales
func GetOrderPercentages(db *relica.DB) ([]OrderPct, error) {
    type OrderPct struct {
        OrderID    int     `db:"order_id"`
        Total      float64 `db:"total"`
        Percentage float64 `db:"pct_of_total"`
    }
    var orders []OrderPct

    err := db.Select("id as order_id", "total").
        SelectExpr("total / SUM(total) OVER () * 100", "pct_of_total").
        From("orders").
        All(&orders)

    return orders, err
}

Pattern 3: Growth Rate Calculation

// Month-over-month growth rate
func GetMoMGrowth(db *relica.DB) ([]MonthlyGrowth, error) {
    type MonthlyGrowth struct {
        Month      string  `db:"month"`
        Sales      float64 `db:"sales"`
        GrowthRate float64 `db:"growth_rate"`
    }
    var growth []MonthlyGrowth

    err := db.Select("DATE_TRUNC('month', created_at) as month", "SUM(total) as sales").
        SelectExpr("(SUM(total) - LAG(SUM(total)) OVER (ORDER BY DATE_TRUNC('month', created_at))) / NULLIF(LAG(SUM(total)) OVER (ORDER BY DATE_TRUNC('month', created_at)), 0) * 100", "growth_rate").
        From("orders").
        GroupBy("DATE_TRUNC('month', created_at)").
        OrderBy("month").
        All(&growth)

    return growth, err
}

Pattern 4: Running Balance

// Calculate account running balance
func GetAccountBalance(db *relica.DB, accountID int) ([]Transaction, error) {
    type Transaction struct {
        Date    string  `db:"txn_date"`
        Amount  float64 `db:"amount"`
        Balance float64 `db:"balance"`
    }
    var txns []Transaction

    err := db.Select("DATE(created_at) as txn_date", "amount").
        SelectExpr("SUM(amount) OVER (ORDER BY created_at ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)", "balance").
        From("transactions").
        Where("account_id = ?", accountID).
        OrderBy("created_at").
        All(&txns)

    return txns, err
}

Pattern 5: Median Calculation

// Calculate median product price per category (PostgreSQL)
func GetMedianPrices(db *relica.DB) ([]CategoryMedian, error) {
    type CategoryMedian struct {
        Category    string  `db:"category"`
        MedianPrice float64 `db:"median_price"`
    }
    var medians []CategoryMedian

    err := db.Select("category").
        SelectExpr("PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY price)", "median_price").
        From("products").
        GroupBy("category").
        All(&medians)

    return medians, err
}

Troubleshooting

Issue: Window Function in WHERE Clause

Problem: Can't use window functions in WHERE

// ❌ ERROR: Window functions not allowed in WHERE clause
db.Select().
    SelectExpr("ROW_NUMBER() OVER (ORDER BY price)", "rank").
    From("products").
    Where("rank <= ?", 10)

Solution: Use subquery or CTE

// ✅ GOOD: Filter in outer query
ranked := db.Select().
    SelectExpr("ROW_NUMBER() OVER (ORDER BY price)", "rank").
    From("products")

db.Select().
    FromSelect(ranked, "r").
    Where("rank <= ?", 10)

Issue: ORDER BY Required for LAG/LEAD

Problem: LAG/LEAD without ORDER BY gives unpredictable results

// ❌ BAD: Random ordering
SelectExpr("LAG(price) OVER ()", "prev_price")

Solution: Always specify ORDER BY

// ✅ GOOD
SelectExpr("LAG(price) OVER (ORDER BY created_at)", "prev_price")

Issue: Frame Extends Beyond Partition

Problem: Frame calculation at partition boundaries

-- At first row: no "2 PRECEDING" rows exist
AVG(total) OVER (ROWS BETWEEN 2 PRECEDING AND CURRENT ROW)

Solution: Use COALESCE or accept NULL

// Averages whatever rows are available
SelectExpr("AVG(total) OVER (ORDER BY id ROWS BETWEEN 2 PRECEDING AND CURRENT ROW)", "avg_3")
// At row 1: avg of 1 row
// At row 2: avg of 2 rows
// At row 3+: avg of 3 rows

Issue: Performance Degradation

Problem: Window function query is slow

// Slow on large tables
SelectExpr("ROW_NUMBER() OVER (ORDER BY created_at DESC)", "rank")

Solution: Add index

CREATE INDEX idx_created_at ON orders(created_at DESC);

Further Reading

Last Updated: 2025-11-24 Minimum Go Version: 1.25+