Interview Insights on Spark Optimization, Flink Exactly-Once Semantics, and Paimon Asynchronous Merging

Hello everyone, the recent JD vs. Meituan delivery war has been entertaining, and many are discussing it. A competitive market benefits job seekers as both companies are hiring, bringing vitality to the employment market.

Today we share three interview questions asked to a candidate who secured an offer at JD's big‑data team, aiming to help others.

Spark tuning experience, with examples

This is a classic question; you can refer to historical documents such as "Spark Performance Optimization Summary" and, if familiar with Spark 3.0/4.0, also discuss adaptive query execution (AQE) and efficient join optimizations.

However, merely reciting these "old‑fashioned" points is insufficient. You should start from a concrete business scenario, describe the problem you faced, how you diagnosed it, the technical solution you chose, and any additional optimization strategies to earn full marks.

2. How does Flink guarantee exactly‑once consumption? How is it used in production?

This is also a common question; see references like "What does Flink's Exactly‑Once semantics really mean?" and interview anecdotes.

In practice, very few production scenarios use true exactly‑once because the cost is high and relies on external storage; most adopt at‑least‑once with downstream deduplication.

3. How does Paimon implement asynchronous merging, why use it, and how is it applied in production?

Paimon's asynchronous merge avoids blocking normal data writes by writing new data to temporary files and merging them later based on time, file count, or size thresholds.

The three main benefits are:

Improved write performance: merge runs in the background, allowing continuous fast writes.

Resource savings: merges run in a thread pool, utilizing idle resources and throttling when resources are scarce.

Data consistency: writes and merges are decoupled, and atomic metadata updates ensure the table reflects the latest state.

In production we use asynchronous merging; when Paimon integrates with Flink, Flink handles read/write while Paimon manages the merge without extra Flink jobs, unlike other lake frameworks such as Hudi that require a separate Flink task.

That concludes the sharing; now we can prepare to "charge" JD delivery! 😄