The size of serialized results in Spark can be comprehended by monitoring the amount of data transferred between different components of Spark. This can be done using Spark UI or by monitoring the network traffic on the running Spark cluster.
Some of the factors that can affect the size of serialized results in Spark include the size of the data being processed, the number of tasks involved in the computation, the amount of data transferred between tasks, the serialization format used, and the compression settings.
To optimize the size of serialized results in Spark, it is important to minimize the amount of data transferred between tasks by using efficient data partitioning and caching strategies. It is also important to choose an appropriate serialization format and compression settings based on the nature of the data being processed and the resources available on the cluster.
Please start posting anonymously - your entry will be published after you log in or create a new account. This space is reserved only for answers. If you would like to engage in a discussion, please instead post a comment under the question or an answer that you would like to discuss
Asked: 2022-10-13 11:00:00 +0000
Seen: 12 times
Last updated: Dec 17 '22
How can one ensure that sub-classes have uniform method parameters in TypeScript?
How can the calculation of matrix determinant be performed using CUDA?
How can code repetition be prevented when using (box)plot functions?
What steps can I take to prevent my webpage from slowing down when all parts of a div are displayed?
How can circles be detected in openCV?
What is the method to determine the most precise categorization of data using Self Organizing Map?