Analyzing Website Traffic with SQL Time-Series Queries

Website traffic analysis is crucial for understanding user behavior and optimizing website performance. This challenge requires you to implement several common time-series queries using SQL to analyze a dataset of website visits. You'll be working with a table containing timestamps and visit counts, and your goal is to retrieve aggregated data over different time intervals.

Problem Description

You are given a table named website_visits with the following schema:

timestamp (TIMESTAMP): The date and time of the website visit.
visit_count (INTEGER): The number of visits recorded at that timestamp.

Your task is to write SQL queries to answer the following questions:

Daily Visit Count: Calculate the total number of visits for each day.
Weekly Visit Count: Calculate the total number of visits for each week (starting on Sunday).
Monthly Visit Count: Calculate the total number of visits for each month.
Moving Average (7-day): Calculate a 7-day moving average of the daily visit count. The moving average for a given day is the average of the visit counts for that day and the previous 6 days. The first 6 days will have incomplete averages.
Peak Hour: Determine the hour of the day with the highest average visit count.

Key Requirements:

Queries must be efficient and performant.
Queries should handle edge cases gracefully (e.g., empty tables, missing data).
The queries should be standard SQL and compatible with most relational database systems (e.g., PostgreSQL, MySQL, SQLite).

Expected Behavior:

Queries should return the correct aggregated data as specified in the problem description.
Queries should handle empty tables by returning an empty result set.
Queries should handle cases where data is missing for certain time periods.

Examples

Example 1: Daily Visit Count

Input:
website_visits table:
timestamp | visit_count
----------|------------
2023-10-26 10:00:00 | 10
2023-10-26 12:00:00 | 15
2023-10-27 08:00:00 | 20
2023-10-27 14:00:00 | 25
2023-10-28 16:00:00 | 30

Output:
date | visit_count
------|------------
2023-10-26 | 25
2023-10-27 | 45
2023-10-28 | 30

Explanation: The query groups the data by date and sums the visit_count for each date.

Example 2: Weekly Visit Count

Input:
website_visits table:
timestamp | visit_count
----------|------------
2023-10-22 10:00:00 | 10
2023-10-23 12:00:00 | 15
2023-10-24 08:00:00 | 20
2023-10-25 14:00:00 | 25
2023-10-26 16:00:00 | 30
2023-10-29 09:00:00 | 35
2023-10-30 11:00:00 | 40

Output:
week_start | visit_count
-----------|------------
2023-10-22 | 65
2023-10-29 | 75

Explanation: The query groups the data by week (starting on Sunday) and sums the visit_count for each week.

Example 3: Moving Average (7-day)

Input:
website_visits table:
timestamp | visit_count
----------|------------
2023-10-26 10:00:00 | 10
2023-10-26 12:00:00 | 15
2023-10-27 08:00:00 | 20
2023-10-27 14:00:00 | 25
2023-10-28 16:00:00 | 30
2023-10-29 18:00:00 | 35
2023-10-30 20:00:00 | 40
2023-10-31 22:00:00 | 45

Output:
date | moving_average
------|----------------
2023-10-26 | 12.5
2023-10-27 | 17.5
2023-10-28 | 22.5
2023-10-29 | 27.5
2023-10-30 | 32.5
2023-10-31 | 37.5

Explanation: The query calculates the 7-day moving average for each day.

Constraints

The website_visits table can contain up to 1,000,000 rows.
timestamp values are guaranteed to be in ascending order.
visit_count is a non-negative integer.
Queries should complete within 5 seconds.

Notes

Consider using window functions for calculating the moving average.
Date and time functions will vary slightly depending on the specific SQL dialect you are using. Ensure your queries are compatible with your chosen database system.
For weekly aggregation, you'll need to extract the Sunday of the week from the timestamp.
Focus on writing clear, concise, and efficient SQL queries. Performance is a key consideration.
Think about how to handle edge cases, such as the first few days when a full 7-day moving average cannot be calculated. Return a partial average in those cases.
For the peak hour query, you'll need to extract the hour from the timestamp and then calculate the average visit count for each hour. Pseudocode for Moving Average:

// Assuming a table named website_visits with timestamp and visit_count columns

SELECT
    date(timestamp) AS date,
    AVG(visit_count) OVER (ORDER BY date ASC ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS moving_average
FROM
    website_visits
GROUP BY
    date
ORDER BY
    date;

Pseudocode for Peak Hour:

// Assuming a table named website_visits with timestamp and visit_count columns

SELECT
    hour(timestamp) AS hour,
    AVG(visit_count) AS average_visit_count
FROM
    website_visits
GROUP BY
    hour
ORDER BY
    average_visit_count DESC
LIMIT 1;