How do you read and write Parquet files in PySpark?

Spark provides first-class Parquet support via the DataFrameReader and DataFrameWriter APIs.

Read:

# Read a single file or directory of Parquet files
df = spark.read.parquet("s3://my-bucket/events/")

# With options
df = (spark.read
      .option("mergeSchema", "true")
      .parquet("hdfs:///datalake/transactions/"))

Write:

# Overwrite with Snappy compression (default)
df.write.mode("overwrite").parquet("s3://my-bucket/output/")

# Partition by date and region, use ZSTD
(df.write
   .partitionBy("date", "region")
   .option("compression", "zstd")
   .mode("append")
   .parquet("s3://my-bucket/partitioned/"))

Register as temp view for SQL:

df.createOrReplaceTempView("events")
spark.sql("SELECT date, SUM(revenue) FROM events GROUP BY date").show()

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