# Log Volume by Day of Week

> Some days are noisier than others.

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

Domain: SQL · Difficulty: easy · Seniority: L3

## Problem

For each day of the week, count the number of server log entries, sorted from most to fewest. Show the day and its entry count.

## Worked solution and explanation

### Why this problem exists in real interviews

Querying server_logs for server_name data using grouping and date extraction tests whether you can translate a business requirement into the right column references and filter sequence. It shows up as a fundamentals check to verify practical fluency.

---

### Break down the requirements

#### Step 1: Aggregate with COUNT

Group by the output grain and apply `COUNT()` to compute the metric. The `GROUP BY` must match exactly what the output needs: one row per group key.

#### Step 2: Order the final output

Apply `ORDER BY` as specified to produce the expected row sequence. When tied values exist, add a secondary sort column for determinism.

---

### The solution

**Day-of-week extraction with STRFTIME grouping**

```sql
SELECT CASE CAST(STRFTIME('%w', log_timestamp) AS INTEGER)
        WHEN 0 THEN 'Sunday'
        WHEN 1 THEN 'Monday'
        WHEN 2 THEN 'Tuesday'
        WHEN 3 THEN 'Wednesday'
        WHEN 4 THEN 'Thursday'
        WHEN 5 THEN 'Friday'
        WHEN 6 THEN 'Saturday'
    END AS day_of_week,
    COUNT(*) AS log_count
FROM server_logs
GROUP BY STRFTIME('%w', log_timestamp)
ORDER BY STRFTIME('%w', log_timestamp)
```

> **Cost Analysis**
>
> The query scans 70M rows from `server_logs`. The aggregation reduces the row count before any downstream processing, which is the key performance lever.

> **Interviewers Watch For**
>
> Naming the output grain ("one row per X") before writing the GROUP BY shows you think about data shape, not just syntax.

> **Common Pitfall**
>
> Comparing dates stored as TEXT without casting can produce lexicographic instead of chronological ordering. Always confirm the column type.

---

## Common follow-up questions

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

## Related

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