Join clicks and clicks to find average revenue

Question

Write a query joining clicks and transactions to produce the combined average revenue.

Codemia · Accepted Answer

The task involves two main datasets: `clicks` and `transactions`. The `clicks` table contains data regarding user clicks on the DoorDash platform, while the `transactions` table records completed purchases. The goal is to join these two datasets to calculate the average revenue generated per click. This requires understanding the relationships between these datasets, specifically how clicks are associated with transactions, which might be through a common identifier like `user_id` or `order_id`. The output should present the average revenue per click, segmented appropriately (e.g., by date or campaign).

1. **Identify Key Columns**: Determine the primary keys and relevant columns in both tables. For example, `clicks` might have `user_id`, `click_timestamp`, and `campaign_id`, while `transactions` might include `user_id`, `transaction_amount`, and `transaction_timestamp`.
2. **Join Tables**: Use a LEFT JOIN to combine the datasets, ensuring we keep all clicks even if they don't result in a transaction.
3. **Aggregate Revenue**: Group the results by the chosen segment (e.g., date or campaign) and calculate total revenue and total clicks.
4. **Calculate Average Revenue**: Use the aggregated sums to compute the average revenue per click.
5. **Utilize CTEs**: For clarity and organization, use Common Table Expressions (CTEs) to separate the logic for clicks and transactions.

```sql
WITH ClicksData AS (
    SELECT user_id, DATE(click_timestamp) as click_date, COUNT(*) as total_clicks
    FROM clicks
    GROUP BY user_id, DATE(click_timestamp)
),
TransactionsData AS (
    SELECT user_id, DATE(transaction_timestamp) as transaction_date, SUM(transaction_amount) as total_revenue
    FROM transactions
    GROUP BY user_id, DATE(transaction_timestamp)
)
SELECT cd.click_date,
       COALESCE(td.total_revenue, 0) / NULLIF(cd.total_clicks, 0) as avg_revenue_per_click
FROM ClicksData cd
LEFT JOIN TransactionsData td ON cd.user_id = td.user_id AND cd.click_date = td.transaction_date;
```

To optimize for large datasets, consider the following strategies: 1. **Indexing**: Ensure that the `user_id` and date columns in both tables are indexed. This will speed up the JOIN operations significantly. 2. **Selective Columns**: Select only the necessary columns in the initial CTEs to reduce the size of intermediate results. 3. **Materialized Views**: If this query is run frequently, consider creating a materialized view for the aggregated data to avoid recomputation. 4. **Analyze Query Plans**: Use EXPLAIN to analyze the execution plan and identify any bottlenecks in the JOIN process.

Several edge cases should be considered:
1. **NULL Values**: If there are NULL values in `transaction_amount`, they might skew the average revenue calculation. Use COALESCE to manage this.
2. **Duplicated Clicks or Transactions**: Ensure that the aggregation correctly handles duplicates. For example, if a user clicks multiple times but only makes one purchase, the revenue per click should be calculated based on unique transactions.
3. **No Transactions**: Handle scenarios where there are clicks but no corresponding transactions by ensuring the LEFT JOIN retains all click records and defaults the revenue to zero.
4. **Date Gaps**: Ensure that the date grouping handles gaps correctly, so that if certain dates have no clicks or transactions, they are still represented in the final output.

Join clicks and clicks to find average revenue

DoorDash

What the Interviewer Expects

Key Topics to Cover

How to Approach This

Possible Follow-up Questions

Sharpen Your Skills on Codemia

Sample Answer

Problem Understanding

Approach

Submit Your Answer

DoorDash Data Scientist Interview Guide

Related Questions

Optimize a slow query on products

Calculate conversion rate per user from orders

Window function: rank over sessions

Optimize a slow query on orders

Write a query to find time to first action from sessions