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Kryo Serializer in PySpark: How to Get the Most Out of Your Data

The Kryo serializer in PySpark is a powerful tool that can help you get the most out of your data. By using the Kryo serializer, you can reduce the overhead associated with serializing and deserializing objects in PySpark, allowing you to maximize performance and scalability. In this blog post, we will discuss the benefits of the Kryo serializer in PySpark and how to use it to get the most out of your data. We will also explore the differences between the Kryo serializer and other serializers, such as Java’s built-in serializers.

What is serialization?

Serialization is the process of converting data structures or objects into a format that can be stored and transmitted across different platforms. It is used to efficiently exchange data between systems, applications, and other components. It is also used to store data persistently in a database, file system, or other storage medium.

Serialization involves translating data into a compact, structured format that can be easily exchanged. This allows data to be sent over networks such as the internet, as well as stored in databases and file systems. The data can then be reconstructed into its original format when needed. Serialization provides an efficient way of exchanging data, allowing it to be transmitted quickly and securely between two systems.

Serialization is essential for distributed systems, enabling the quick and secure transmission of data over networks. In addition, serialization can provide better security since the data is encoded in a structured format that cannot be easily manipulated. Serialization also helps to reduce memory usage by only storing data that is necessary and reducing the amount of duplicate data stored on the system.

What is Kryo?

Kryo is an advanced serialization library used to boost the speed and efficiency of data processing in Java-based applications. It was designed to efficiently serialize (convert data into a format that can be easily stored and transmitted) large objects, collections and graphs of objects, making it especially useful for applications that need to move large amounts of data quickly. With Kryo, you can quickly serialize and deserialize large datasets without sacrificing performance or consuming large amounts of memory.

Kryo is an open-source library developed by Esoteric Software and is part of the Apache Spark project. It was built as a lightweight alternative to traditional Java serialization libraries such as Java Serialization and XStream. Kryo has been praised for its performance, scalability, and ease of use. Its use of bytecode enhancement and caching of class information makes it up to 10 times faster than traditional Java serialization libraries.

Kryo is an ideal solution for applications that require the rapid exchange of complex data structures between different components or systems. It is also well suited for scenarios where data needs to be rapidly compressed and uncompressed while still maintaining a high level of performance. This makes it the perfect choice for distributed systems that rely on high-performance data movement.

When should you serialize?

Serialization is a process of converting an object’s state into a byte stream, allowing for more efficient storage and retrieval of data. This is especially useful in distributed computing environments such as Apache Spark, where data is stored in memory across multiple nodes. Serialization can help reduce the size of the objects and improve performance by reducing the amount of time needed to transfer data between nodes.

For example, if you are using Apache Spark for data processing and analytics, you may need to serialize the data before sending it across the network. Serialization ensures that all the data is in the same format and can be easily read and processed by the receiving end. It also makes sure that the data is protected from any malicious changes that could occur during transmission.

In addition, serialization can also help to optimize data storage by reducing the amount of space required to store the data. By compressing the data and making it easier to transport, it takes less time and space to move it between nodes.

Kryo is one of the most popular and reliable serialization libraries available for Apache Spark. It offers fast, compact serialization and deserialization of objects, as well as various configuration options for customizing your data serialization needs. It is highly scalable and is used extensively in production systems.

What are the benefits of using Kryo?

Kryo is a high-performance serialization framework for Apache Spark applications. It provides a number of advantages over other serialization frameworks, such as improved performance, memory management and data compression.

The main benefit of Kryo is improved performance. Kryo serializes data faster than traditional Java serialization, leading to increased speed in executing Spark jobs. Additionally, it requires less memory when reading or writing objects, allowing for more efficient memory utilization.

Kryo also allows for better data compression than traditional Java serialization. This can be particularly useful in reducing network latency, as it allows larger amounts of data to be transferred over the network without increasing transmission time.

Finally, Kryo allows for more advanced memory management than other serialization frameworks. Kryo can be used to clean up unused objects during serialization, freeing up memory resources and improving overall system performance.

In summary, Kryo offers a number of benefits over traditional Java serialization in Apache Spark applications. It improves performance by reducing the amount of time needed to serialize objects, reduces network latency by compressing data, and provides better memory management for cleaner execution. For these reasons, it is a popular choice for developers looking to get the most out of their Apache Spark applications.

How to use Kryo with PySpark

By default in Spark, your data is already serialised using the Java serialiser (using the ObjectOutputStream framework). But it’s slow. Making a couple of tweaks to your can make a big improvement.

We can use the Kryo serialiser, which claims to be up to 10x faster than the Java alternative; which may well be true as my overall application executed 20% faster when switching to Kryo. That bought execution time down to 33 minutes.

The way I think of it is this – there is no harm in trying Kryo out – if it works out that it’s faster, great! If not, remove the two lines of code & revert back to Java.

It’s pretty easy to set this up. As below, when we configure out Spark session, we simply set the config to choose Kryo serializer. Here, I have also set registrationRequired to False as otherwise we will have to declare the classes that we want Kryo to serialize. I want it to serialize everything, so I don’t want to register specific classes.

def create_session(appname):
spark_session = SparkSession\
.config(“spark.serializer”, “org.apache.spark.serializer.KryoSerializer”)\
.config(“spark.kryo.registrationRequired”, “false”)\
return spark_session

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