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How would you read data from a web API using PySpark?

Spark/Big Datamedium0.7 min readPremium
Frequency
Low
Asked at 2 companies
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
AltimetrikInfosys
Key Concepts Tested
airflowpartitionspark
Expert AnswerPremium
142 wordsInterview-ready
PySpark has no native API source; the pattern is driver fetch or executor fetch. **Approaches**: (1) **Driver + parallelize**: data = requests.get(url).json(); df = spark.createDataFrame(data). Scales to API response size (typically MBs); driver is bottleneck. (2) **mapPartitions on executor**: Pass partition of IDs to each task; each calls API. Scales to many IDs but risks rate limiting and API abuse. (3) **Orchestrator + landing**: Airflow/Prefect fetches API → lands to S3/GCS → Spark reads....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like Altimetrik, Infosys. The answer also includes follow-up discussion points that interviewers commonly explore.

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