Working with Parquet Files in PySpark 

Working with Parquet files in PySpark involves using the spark.read.parquet() method to read a Parquet file and convert it into a PySpark DataFrame. The DataFrame can then be manipulated using various PySpark DataFrame operations. Once the desired operations have been performed, the DataFrame can be written to a Parquet file using the df.write.parquet() method. It's also possible to specify various options like compression codec, partitioning and save mode when reading or writing Parquet files. Additionally, you can use the save method and format option to write a dataframe in parquet format. It is important to note that the path of the Parquet file can be a local file system path or a HDFS, S3, GCS, etc. path.

How to read Parquet Files in PySpark

In PySpark, you can read a Parquet file using the spark.read.parquet() method. This method takes in the path of the Parquet file as an argument and returns a DataFrame.

For example, to read a Parquet file located at '/path/to/file.parquet', you can use the following code:

Copydf = spark.read.parquet("/path/to/file.parquet") 

It's also possible to specify some options when reading the parquet file like :

• schema : to specify the schema to use when reading the parquet file
• mergeSchema : to specify if the schema should be merged when reading the parquet file
• columns : to specify which columns to read
• filter : to specify filter predicates to filter rows while reading
• timestampFormat : to specify the timestamp format to use

For example :

Copydf = spark.read.parquet("/path/to/file.parquet", columns=["col1","col2"], filter="col1='value'") 

It is important to note that the path of the Parquet file can be a local file system path or a HDFS, S3, GCS, etc. path.

Once you have read the parquet file, you can perform various operations on it like filtering, aggregation, join, etc.

How to write Parquet Files in PySpark

In PySpark, you can write a DataFrame to a Parquet file using the df.write.parquet() method. This method takes in the path of the destination file as an argument.

For example, to write the DataFrame 'df' to a file located at '/path/to/destination.parquet', you can use the following code:

Copydf.write.parquet("/path/to/destination.parquet") 

It's also possible to specify some options when writing the parquet file like :

• mode : specify the save mode (overwrite, append, etc.)
• compression : specify the compression codec to use (snappy, gzip, etc.)
• partitionBy : specify the column(s) to use as partitioning when saving the dataframe
• saveMode : specify the save mode (overwrite, append, etc.)

For example :

Copydf.write.mode("overwrite").compression("gzip").partitionBy("year","month").parquet("/path/to/destination.parquet") 

It is important to note that the path of the destination file can be a local file system path or a HDFS, S3, GCS, etc. path.

It's worth noting that the performance of writing Parquet files in PySpark can be improved by using the snappy compression codec, as it is optimized for use with columnar storage formats like Parquet.

Also, you can use the save method to write a dataframe in different format including parquet :

Copydf.write.save("/path/to/destination.parquet", format="parquet") 

or

Copydf.write.format("parquet").save("/path/to/destination.parquet") 

This will have the same behavior as the previous example.