# Salt the Hot Merchant > One merchant owns 38% of your rows. Salt or suffer. Canonical URL: Domain: PySpark · Difficulty: hard · Seniority: L4 ## Problem The daily payment reconciliation Spark job joins 1.2 billion transactions against a 500K-row merchants dimension on merchant_id. It has been failing for three days. Spark UI shows one task processing 38% of all rows while the other 199 finish in seconds. The hot merchant is your company's internal payment processor that handles all driver payouts. You cannot broadcast merchants because a downstream join adds a 2 GB enrichment table. Propose and implement a salting strategy. ## Related - [All practice problems](https://datadriven.io/problems) - [Mock interview mode](https://datadriven.io/interview/salt_the_hot_merchant) - [Data Engineering Interview Prep Guide](https://datadriven.io/data-engineer-interview-prep) - [Daily Challenge](https://datadriven.io/daily) --- Source: DataDriven (https://datadriven.io). DataDriven is the data engineering interview community. Live code execution in SQL, Python, and Spark sandboxes. Every feature is open to every member.