Pyspark serialization

[yusufd]

Hi,

I was looking for comprehensive documentation on implementing serialization in pyspark, most of the places I have seen is all about serialization with scala. Could you point out where I can get a detailed explanation on it?

[Kaniz_Fatma · Accepted solution]

Hi @yusufd, PySpark supports custom serializers for transferring data, which can significantly impact performance. Let me guide you through the available serializers and how to choose the right one for your use case.

1. PickleSerializer:

   • By default, PySpark uses PickleSerializer to serialize objects using Python’s cPickle serializer. This serializer can handle nearly any Python object.
   • It’s a good choice when you need flexibility and compatibility with various data types.
   • Example:

     from pyspark.context import SparkContext
     from pyspark.serializers import PickleSerializer
     
     sc = SparkContext('local', 'test', serializer=PickleSerializer())
     rdd = sc.parallelize(list(range(1000))).map(lambda x: 2 * x).take(10)
     

2. MarshalSerializer:

   • If you’re looking for faster serialization, consider using MarshalSerializer.
   • It supports fewer data types but can be more efficient.
   • Example:

     from pyspark.context import SparkContext
     from pyspark.serializers import MarshalSerializer
     
     sc = SparkContext('local', 'test', serializer=MarshalSerializer())
     rdd = sc.parallelize(list(range(1000))).map(lambda x: 2 * x).take(10)
     

3. Batch Serialization:

   • PySpark serializes objects in batches. The default batch size is chosen based on object size but can be configured using SparkContext’s batchSize parameter.
   • Example:

     sc = SparkContext('local', 'test', batchSize=2)
     rdd = sc.parallelize(range(16), 4).map(lambda x: x)
     # Behind the scenes, this creates a JavaRDD with four partitions, each containing two batches of two objects.
     

For more details, you can refer to the PySpark 3.0.1 documentation. It covers these serializers and their usage in depth. Happy coding! 😊🚀¹. If you’re interested in Pandas API on Spark, you can explore the Databricks documentation as well².

[yusufd]

This is awesome. Thank you for replying. 

I want to ask one more thing before we close this, in Scala-spark java serialization is default and additionally we have kryo serialization as well which is better. So these are not applicable in pyspark if i get correctly. Kindly confirm.

[Kaniz_Fatma · Accepted solution]

Hi @yusufd, You’re correct! In PySpark, the serialization mechanism is different from Scala-Spark. While Scala-Spark uses Java serialization by default and also provides Kryo serialization as an option, PySpark uses a different approach.

In PySpark, the default serialization library is Pyrolite, which is a Python library for efficient serialization. Pyrolite is specifically designed to work well with Python objects and integrates seamlessly with PySpark. It’s optimized for performance and compatibility with Python data types.

So, you don’t need to worry about explicitly choosing between Java serialization and Kryo serialization in PySpark. Pyrolite takes care of serialization for you, allowing you to focus on your data-processing tasks.

If you have any more questions or need further clarification, feel free to ask! 😊

[yusufd]

This is great to know!

Thank you for the explanation.

[yusufd]

This is awesome. Thank you for replying. 

I want to ask one more thing before we close this, in Scala-spark java serialization is default and additionally we have kryo serialization as well which is better. So, can we use them in pyspark as well?

Another important thing, the code below creates a sparkcontext on local, that doesnt work on databricks. When I try to change the sparkcontext arguments, i get an error , attached screenshot, how can we resolve this, ultimately i dont want to run spark locally, but on databricks. Would appreciate if you answer this.

Thanks for the support.

[yusufd]

@Kaniz_Fatma Could you clarify on my query? Eagerly awaiting response.

[Kaniz_Fatma · Accepted solution]

Hi @yusufd, Let’s address both of your questions:

1. Serialization in PySpark:

   • PySpark supports custom serializers for transferring data, which can significantly improve performance.
   • By default, PySpark uses the PickleSerializer, which leverages Python’s cPickle serializer to serialize almost any Python object.
   • Additionally, you can use the MarshalSerializer, which supports fewer data types but can be faster¹.
   • To switch to Kryo serialization (which is faster and more compact than Java serialization), initiali...².
   • Register your custom classes with Kryo using conf.registerKryoClasses(Array(classOf[MyClass1], classOf[MyClass2])).

2. Databricks and SparkContext:

   • When working with Databricks, you don’t need to explicitly create a SparkContext. Databricks automatically provides a pre-configured Spark session.
   • To access the current Spark context settings in PySpark, you can use:

     spark.sparkContext.getConf().getAll()
     

   • This will give you a dictionary with all configured settings³.
   • Avoid creating a new SparkContext in Databricks; instead, use the existing one provided by the platform.

Feel free to explore Kryo serialization and leverage the existing Spark session in Databricks! 😊

[Kaniz_Fatma]

Hi @yusufd, PySpark provides custom serializers for transferring data, which can significantly improve performance. By default, PySpark uses the PickleSerializer, which leverages Python’s cPickle serializer to serialize almost any Python object. However, there are other serializers available, such as the MarshalSerializer, which supports fewer ...¹.

If you’re interested in exploring these serializers further, you can refer to the PySpark 3.0.1 documentation. It covers these serializers and their usage in depth.

Feel free to explore and experiment with different serializers to find the one that best suits your specific use case! 😊

[yusufd]

Thank you @Kaniz_Fatma  for the prompt reply. This clears the things and also distinguishes between spark-scala and pyspark. Appreciate your explanation. Will apply this and also share any findings based on this which will help the community!