# API Call Distribution Fraction

> Not all endpoints are created equal.

Canonical URL: <https://datadriven.io/problems/api_call_distribution_fraction>

Domain: SQL · Difficulty: hard · Seniority: L4

## Problem

The API observability team is building a traffic breakdown dashboard. For each combination of HTTP method and response status in the call logs, compute what share of total call volume that combination represents. Return the method, status, and the fraction as a decimal.

## Worked solution and explanation

### Why this problem exists in real interviews

The core skill being tested is conditional branching with CASE combined with division-safe NULLIF guards over `api_calls`. Candidates must decide how `endpoint`, `method`, `status` interact before choosing a join strategy or aggregation level.

> **Trick to Solving**
>
> Any rate or ratio problem requires **null-safe division**. If the denominator can be zero, the query crashes or returns NULL silently.
> 
> 1. Identify the numerator and denominator conditions
> 2. Use `SUM(CASE WHEN ... THEN 1 ELSE 0 END)` for the numerator
> 3. Wrap the denominator in `NULLIF(..., 0)` to prevent division by zero

---

### Break down the requirements

#### Step 1: Group by `method`

`GROUP BY method` produces one output row per distinct value of `method`.

#### Step 2: Compute the ratio with CASE and NULLIF

The numerator uses `SUM(CASE WHEN condition THEN 1 ELSE 0 END)`. Wrapping the denominator in `NULLIF(COUNT(*), 0)` prevents division by zero.

#### Step 3: Round and order

Use `ROUND(..., 4)` for clean decimal output and sort by rate descending.

---

### The solution

**Case-branch for api call distribution fraction**

```sql
SELECT
    method,
    ROUND(
        1.0 * COUNT(CASE WHEN status = 'success' THEN 1 END)
        / NULLIF(COUNT(*), 0),
        4
    ) AS rate
FROM api_calls
GROUP BY method
ORDER BY rate DESC
```

> **Cost Analysis**
>
> The main table has 1.5B rows (384 GB). Partitioned on `call_time`, so queries filtering on that column skip most partitions. The GROUP BY reduces the row count early, keeping downstream operations cheap.

> **Interviewers Watch For**
>
> Strong candidates state the correct `GROUP BY` grain before writing any SQL, showing they think about the output shape first. Division-by-zero handling is a silent correctness bug; interviewers watch for `NULLIF` or equivalent protection.

> **Common Pitfall**
>
> Selecting a non-aggregated column without including it in `GROUP BY` is the most common error. Some engines reject it; others silently return arbitrary values.

---

## Common follow-up questions

- The `latency` column in `api_calls` has roughly 0% NULLs. How does your query handle those rows, and would the result change if NULLs were replaced with zeros? _(Tests whether the candidate understands how NULLs propagate through aggregation functions and whether their WHERE/JOIN conditions implicitly filter them out.)_
- Your CASE expression branches on `endpoint`. What happens if a new category value appears that none of your WHEN clauses match? _(Tests whether the candidate uses a meaningful ELSE branch or lets unmatched rows silently become NULL.)_
- `call_id` in `api_calls` has ~1500M distinct values. What index strategy keeps your query from doing a full table scan? _(Tests whether the candidate can design indexes for high-cardinality columns and understands selectivity.)_
- Could you express this same logic as a single query without CTEs or subqueries? What readability trade-off does that introduce? _(Tests whether the candidate can flatten nested logic and understands when decomposition aids maintainability.)_

## Related

- [All practice problems](https://datadriven.io/problems)
- [Mock interview mode](https://datadriven.io/interview/api_call_distribution_fraction)
- [SQL Interview Questions](https://datadriven.io/sql-interview-questions)
- [Data Engineering Interview Prep Guide](https://datadriven.io/data-engineer-interview-prep)
- [Daily Challenge](https://datadriven.io/daily)

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