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What are the performance considerations when using Auto Loader?

Spark/Big Dataeasy0.5 min read

**File Discovery**: Directory listing on S3/ADLS has latency and cost (LIST requests). Use **cloud file notifications** (S3 Events, EventGrid) when available—faster and cheaper at scale. **Schema Inference**: Inferring schema from files adds overhead. Provide explicit schema...

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Frequency
Low
Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
TCS

Why This Question Matters

This easy-level Spark/Big Data question appears frequently in data engineering interviews at companies like TCS. While less common, it tests deeper understanding that distinguishes strong candidates.

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Expert Answer
103 words

File Discovery: Directory listing on S3/ADLS has latency and cost (LIST requests). Use cloud file notifications (S3 Events, EventGrid) when available—faster and cheaper at scale.

Schema Inference: Inferring schema from files adds overhead. Provide explicit schema for static schemas.

Checkpoint: Durable storage (S3, DBFS); avoid small-object problem. Checkpoint growth with many files.

Backlog: Initial load of millions of files—use trigger(availableNow) in batches; avoid single run.

Parallelism: Limited by new file arrival; more files = more parallelism.

Scalability Trade-offs: Notification-based scales to millions of files; listing scales poorly.

Cost Implications: S3 LIST costs at scale; notifications are cheaper. Checkpoint in same region as source.

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