06-21-2024 04:50 AM
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?
06-25-2024 11:42 AM
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.
PickleSerializer:
PickleSerializer
to serialize objects using Python’s cPickle
serializer. This serializer can handle nearly any Python object.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)
MarshalSerializer:
MarshalSerializer
.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)
Batch Serialization:
SparkContext
’s batchSize
parameter.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.
07-01-2024 05:36 AM
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! 😊
07-01-2024 05:45 AM
Hi @yusufd, Let’s address both of your questions:
Serialization in PySpark:
PickleSerializer
, which leverages Python’s cPickle
serializer to serialize almost any Python object.MarshalSerializer
, which supports fewer data types but can be faster1.
2.conf.registerKryoClasses(Array(classOf[MyClass1], classOf[MyClass2]))
.Databricks and SparkContext:
SparkContext
. Databricks automatically provides a pre-configured Spark session.spark.sparkContext.getConf().getAll()
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! 😊
06-25-2024 11:42 AM
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.
PickleSerializer:
PickleSerializer
to serialize objects using Python’s cPickle
serializer. This serializer can handle nearly any Python object.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)
MarshalSerializer:
MarshalSerializer
.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)
Batch Serialization:
SparkContext
’s batchSize
parameter.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.
06-26-2024 12:37 AM
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.
07-01-2024 05:36 AM
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! 😊
07-01-2024 06:06 AM
This is great to know!
Thank you for the explanation.
06-26-2024 02:50 AM
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.
07-01-2024 04:27 AM
@Kaniz_Fatma Could you clarify on my query? Eagerly awaiting response.
07-01-2024 05:45 AM
Hi @yusufd, Let’s address both of your questions:
Serialization in PySpark:
PickleSerializer
, which leverages Python’s cPickle
serializer to serialize almost any Python object.MarshalSerializer
, which supports fewer data types but can be faster1.
2.conf.registerKryoClasses(Array(classOf[MyClass1], classOf[MyClass2]))
.Databricks and SparkContext:
SparkContext
. Databricks automatically provides a pre-configured Spark session.spark.sparkContext.getConf().getAll()
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! 😊
07-01-2024 05:42 AM
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! 😊
07-01-2024 06:05 AM
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!
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