Essential Data Lake Interview Questions: Flink, Hudi, Row_Number, and Best Practices

Hello everyone, today we share a recap of some interview questions (personal information anonymized).

The interview focused on data lake related skills, which some companies require, though not all business scenarios need a data lake.

1. What problems does a data lake project aim to solve?
2. Why must we use row_number deduplication when using Flink to sync MySQL to Hudi? Can we write directly to Hudi/Paimon primary key tables?
3. How to address the amplification issue caused by row_number and what is a retract stream?
4. What architectural optimizations can be applied to the data lake pipeline to reduce maintenance complexity?
5. What are the best practices for reading and writing in a data lake pipeline?

Question 1: A data lake component is introduced to solve concrete business problems, not for its own sake. For example, Paimon offers stream‑batch read/write, minute‑level freshness, primary‑key and non‑primary‑key tables, dimensional tables, and finer‑grained columns. The business scenario should leverage these features to address specific issues.

Question 2: During Flink ingestion, it is strongly recommended to handle out‑of‑order data at the ODS layer using row_number deduplication. This prevents ordering problems, especially when downstream tables have primary keys; without it, results can be incorrect. Choose primary‑key or non‑primary‑key tables based on whether you need to track intermediate data changes.

Question 3: A "retract stream" represents data changes with "+" (insert) and "-" (delete) symbols, a core mechanism for incremental updates in streaming. Retract streams can cause bandwidth, resource, and state‑size challenges. Solutions include aggregation‑operator retracts for state updates and sink‑operator retracts for CDC scenarios. At the development level, proper PARTITION BY and pre‑aggregation can reduce retract volume, while engine‑side optimizations (e.g., batching updates, caching between AGG and sink nodes) further mitigate pressure.

Question 4: Architectural optimizations for the data lake pipeline may include unified ingestion, strict data format validation, centralized metadata management, and standardized storage layers. Tailor these ideas to your business context to simplify maintenance.

Question 5: Best practices for read/write include following proven patterns from production articles, adapting them to your real scenarios, and avoiding vague, generic answers.

Paimon production practice and optimization summary

Paimon production issue brief