I know of object serialized … Serialization is an important tuning for performance improvement and optimization in any distributed computing environment. In Spark,if you want to use unsafeshufflewriter,the records must support "serialized relocaton". However, all that data which is sent over the network or written to the disk or also which is persisted in the memory must be serialized. Spark provides two types of serialization libraries: Java serialization and (default) Kryo serialization. Spark jobs are distributed, so appropriate data serialization is important for the best performance. When our program starts up, our compiled code is loaded by all of these nodes. Spark is a distributed processing system consisting of a driver node and worker nodes. Spark supports two serialization libraries, as follows: Java Serialization; Kryo Serialization; What is Memory Tuning? In order for Spark to distribute a given operation, the function used in the operation needs to be serialized. tinydf = df.sample(False, 0.00001): It provides two serialization libraries: Java serialization: By default, Spark serializes objects using Java’s ObjectOutputStream framework, and can work with any class you create that implements java.io.Serializable. Why serialization? I have learned about shuffle in Spark. Data serialization. For faster serialization and deserialization spark itself recommends to use Kryo serialization in any network-intensive application. Spark aims to strike a balance between convenience (allowing you to work with any Java type in your operations) and performance. Running the above code with spark-submit on a single node repeatedly throws the following error, even if the size of the DataFrame is reduced prior to fitting the model (e.g. Kryo serialization is a newer format and can result in faster and more compact serialization than Java. In this Spark DataFrame tutorial, learn about creating DataFrames, its features, and uses. While tuning memory usage, there are three aspects that stand out: The entire dataset has to fit in memory, consideration of memory used by your objects is the must. Therefore, if your data objects are not in a good format, then you first need to convert them into serialized data objects. Optimize data serialization. In addition, we can say, in costly operations, serialization plays an important role. There are two serialization options for Spark: Java serialization is the default. Basically, for performance tuning on Apache Spark, Serialization is used. Spark DataFrame is a distributed collection of data, formed into rows and columns. Spark is not an exception, but Spark jobs are often data and computing extensive. Following on from the introductory post on serialization with spark, this post gets right into the thick of it with a tricky example of serialization with Spark. 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