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Home/Questions/Spark/Big Data/A JSON file with evolving schema needs to be ingested into a DataFrame. How would you handle new fields dynamically in PySpark without breaking the job for previous structures?

A JSON file with evolving schema needs to be ingested into a DataFrame. How would you handle new fields dynamically in PySpark without breaking the job for previous structures?

Spark/Big Dataeasy0.3 min read

Use schema-on-read with mergeSchema: df = spark.read.option("mergeSchema", "true").json("s3://bucket/events/"). **Why**: New fields appear as null in old records; no job failure. **Production**: Schema registry or versioned schemas; explicit schema with allowNull for new...

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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
Dunnhumby
Key Concepts Tested
spark

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Expert Answer
52 words

Use schema-on-read with mergeSchema: df = spark.read.option("mergeSchema", "true").json("s3://bucket/events/"). Why: New fields appear as null in old records; no job failure. Production: Schema registry or versioned schemas; explicit schema with allowNull for new columns; from_json with optional fields. Scalability: mergeSchema has overhead; use for bounded evolution. Cost: Avoid full re-read; incremental where possible.

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