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Home/Questions/Spark/Big Data/Code a simple PySpark job to read a JSON file, filter records, and write output in Parquet format.

Code a simple PySpark job to read a JSON file, filter records, and write output in Parquet format.

Spark/Big Datamedium0.5 min readPremium

**Production-grade example** (with schema, error handling): ```python from pyspark.sql import SparkSession from pyspark.sql.functions import col spark = SparkSession.builder.appName("json_to_parquet").getOrCreate() # Provide schema to avoid inference cost on large reads df =...

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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
American Express
Key Concepts Tested
partitionpythonsparksql

Why This Question Matters

This medium-level Spark/Big Data question appears frequently in data engineering interviews at companies like American Express. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (partition, python, spark) will help you answer variations of this question confidently.

How to Approach This

Break this problem into components. Identify the core trade-offs involved, then walk the interviewer through your reasoning step by step. Demonstrate awareness of edge cases and production considerations - this is what separates good answers from great ones. The expert answer includes a code example that demonstrates the implementation pattern.

Expert Answer
97 wordsIncludes code

Production-grade example (with schema, error handling):

```python
from pyspark.sql import SparkSession
from pyspark.sql.functions import col

spark = SparkSession.builder.appName("json_to_parquet").getOrCreate()

# Provide schema to avoid inference cost on large reads
df = spark.read.schema("id INT, status STRING, amount DOUBLE") \
.json("s3://bucket/input/*.json")

filtered = df.filter((col("status") == "active") & (col("amount") > 0))

filtered.write.mode("overwrite") \
.parquet("s3://bucket/output/")
``

Why schema: Inference scans data; explicit schema avoids that on large files. Scalability trade-offs: *.json` = many small files = many partitions; coalesce before write to avoid small-file problem. Cost implications: Filter early reduces bytes processed; partition output by date if downstream queries filter by date.

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