Join events and clicks to find percentile rank

Question

Write a query joining events and impressions to produce the combined percentile rank.

Codemia · Accepted Answer

The task involves joining two datasets: `events` and `impressions`. The goal is to compute the combined percentile rank for each event based on a certain metric (e.g., number of clicks). The data in `events` includes fields such as `event_id`, `user_id`, and `event_value`, while `impressions` includes fields like `impression_id`, `event_id`, and `clicks`. The final output should include the `event_id`, the total number of clicks from the `impressions`, and the percentile rank indicating how each event's clicks compare to the overall distribution of clicks.

1. **Join the Tables**: Perform an INNER JOIN between `events` and `impressions` on `event_id` to combine relevant data. 
2. **Aggregate Clicks**: Use a GROUP BY clause to aggregate clicks from `impressions` for each `event_id`. 
3. **Calculate Percentile Rank**: Utilize a window function (e.g., `PERCENT_RANK()`) to calculate the percentile rank of each event based on the total clicks computed in the previous step. 
4. **Select Required Fields**: Finally, select the `event_id`, total clicks, and the computed percentile rank in the result set.

```sql
WITH Clicks AS (
    SELECT e.event_id, COALESCE(SUM(i.clicks), 0) AS total_clicks
    FROM events e
    LEFT JOIN impressions i ON e.event_id = i.event_id
    GROUP BY e.event_id
), RankedClicks AS (
    SELECT event_id, total_clicks,
           PERCENT_RANK() OVER (ORDER BY total_clicks) AS percentile_rank
    FROM Clicks
)
SELECT event_id, total_clicks, percentile_rank
FROM RankedClicks;
```

To optimize this query for large datasets, consider the following strategies: 
1. **Indexing**: Create indexes on `event_id` in both `events` and `impressions` tables to speed up the JOIN operation. 
2. **Partitioning**: If `impressions` is particularly large, consider partitioning it by date or another relevant dimension to reduce the amount of data scanned during the query. 
3. **Materialized Views**: If this query is run frequently, consider creating a materialized view for the aggregated clicks to avoid recalculating totals every time.

1. **NULL Values**: Handle NULL clicks in the `impressions` table using `COALESCE()` to ensure that they default to zero when calculating totals. 
2. **Duplicates**: Ensure that the `impressions` table does not have duplicate entries for clicks on the same `event_id`. If duplicates exist, apply DISTINCT in the aggregation step. 
3. **Outliers**: Consider how outliers in click data may affect the percentile rank. If necessary, apply a filter to exclude extreme values before calculating the rank.

Join events and clicks to find percentile rank

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