# Overall Average API Latency

> The overall average. Across everything.

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

Domain: SQL · Difficulty: easy · Seniority: L3

## Problem

The SLO team is establishing a platform-wide latency baseline. What is the overall average latency across all API calls?

## Worked solution and explanation

### Why this problem exists in real interviews

Working against api_calls, this problem tests averaging on the endpoint and method columns. Interviewers use it as a fundamentals check because a subtle mis-grouping or filter placement changes the output without raising an error.

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### Break down the requirements

#### Step 1: Compute the average

`SELECT AVG(latency) FROM api_calls` returns the single platform-wide average latency.

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### The solution

**Single AVG aggregate**

```sql
SELECT AVG(latency) AS avg_latency
FROM api_calls
```

> **Cost Analysis**
>
> At `api_calls` (100,000,000 rows), a full table scan is expensive. Partition pruning (if the table is partitioned on the filter column) is the first optimization. A covering index on the `GROUP BY` + filter columns eliminates random I/O. Consider a materialized view for repeated dashboard queries.

> **Interviewers Watch For**
>
> Interviewers evaluate whether you translate the English requirements into the correct SQL clauses on the first attempt. They watch for clean syntax, correct column references, and whether you verify edge cases before declaring the query complete.

> **Common Pitfall**
>
> The most common mistake is misreading the prompt's filtering or grouping requirements. Double-check which columns to group by, which to aggregate, and whether the output should be filtered with `WHERE` (before grouping) or `HAVING` (after grouping).

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## Common follow-up questions

- What happens to your result if api_calls.err_msg contains NULLs for some rows? _(Tests whether the candidate accounts for NULL behavior in aggregates and comparisons on err_msg.)_
- How would you verify that your aggregation on api_calls.call_id is not double-counting due to duplicate rows? _(Tests data quality awareness and deduplication strategies.)_
- With millions of distinct values in api_calls.call_id, what index strategy would you use to keep this query performant? _(Tests indexing knowledge specific to high-cardinality columns like call_id.)_

## Related

- [All practice problems](https://datadriven.io/problems)
- [Mock interview mode](https://datadriven.io/interview/overall_average_api_latency)
- [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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