Join transactions and rides to find percentile rank

Question

Write a query joining transactions and users to produce the combined percentile rank.

Codemia · Accepted Answer

In this problem, we need to join two tables: `transactions` and `users`. The `transactions` table contains information about transactions made by users, including transaction amounts and timestamps, while the `users` table holds user demographic information. The goal is to compute the combined percentile rank of transaction amounts for each user. This means we will need to calculate the percentile rank of each user's transaction amount compared to all transactions in the dataset to understand how each user's spending compares to others.

1. **Join Tables**: We will first perform an inner join between the `transactions` and `users` tables on the user ID, ensuring we only include transactions associated with valid users.
2. **Calculate Percentile Rank**: We will use a window function to calculate the percentile rank of the transaction amount across all transactions in the dataset. This will provide us with a metric to understand how each user's transaction amount ranks against the total.
3. **Select Required Columns**: Finally, we will select the necessary columns for the output, including user ID and the calculated percentile rank.

```sql
WITH RankedTransactions AS (
    SELECT 
        t.user_id,
        t.amount,
        PERCENT_RANK() OVER (ORDER BY t.amount) AS percentile_rank
    FROM 
        transactions t
    JOIN 
        users u ON t.user_id = u.id
)
SELECT 
    user_id,
    percentile_rank
FROM 
    RankedTransactions;
```

To optimize this query for large datasets, consider the following strategies:
1. **Indexing**: Create indexes on `transactions.user_id` and `users.id` to speed up the join operation.
2. **Partitioning**: If the transactions are time-sensitive, partition the `transactions` table by date or user region to improve query performance when filtering by date ranges.
3. **Materialized Views**: If percentile ranks are frequently queried, consider creating a materialized view that pre-calculates the percentile ranks to avoid recalculating them on every query.

1. **NULL Values**: Ensure to handle NULL values in the `amount` column since they may affect the percentile rank calculation. We may filter out transactions with NULL amounts in the join condition or handle them with a default value.
2. **Duplicates**: If users can have multiple transactions with the same amount, the percentile rank calculation should still account for these duplicates correctly. The `PERCENT_RANK()` function handles this, but we should ensure the data is cleaned beforehand.
3. **No Transactions**: Consider what happens if a user has no transactions. They should not appear in the result set, but if we want to include them with a percentile rank of NULL or 0, we might need to adjust our join type.

Join transactions and rides to find percentile rank

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