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Previous | Next | Audio Book for Spark Interview Questions is available here | Top 150 Latest Spark Interview Questions | Quickly go through Spark Training Python & Scala
Question: When you need functionality from multiple files, then how do you provide?
Answer: If you use functionality from multiple python files, then you can create an egg/zip for the package, because --py-files flag also accepts a path to an egg file.
Question: What can be a problem of distributing egg files?
Answer: sending egg files is problematic because packages that contain native code must be compiled for the specific host on which it will run. When doing distributed computing with industry-standard hardware, you must assume is that the hardware is heterogeneous. However, because of the required C compilation, a Python egg built on a client host is specific to the client CPU architecture. Therefore, distributing an egg for complex, compiled packages like NumPy, SciPy, and pandas often fails. Instead of distributing egg files you should install the required Python packages on each host of the cluster and specify the path to the Python binaries for the worker hosts to use.
Question: You have been given below code snippet, do you think that data shuffling will be applied on partitions across the nodes?
Answer: When data in one partition is not depend on partition, as well as transformation, which does not require another partition from different node. Then shuffling will not be applied. Out of all three transformation map, flatMap and filter does not require shuffling of data across partitions.
Question: Why does having more stage, will impact on the performance of Spark Application?
Answer: If you have written a Spark code, which has more stages then it suffer from performance. Because on each stage data will be persisted (either in cache or disk), also possibility of shuffling data across the partitions. As you know, wherever these two I/O (network and disk), comers into the picture there will be huge impact on performance. And stage boundary in a Spark job will cause this.
Question: What is the impact of having/changing numberOfPartitions during transformation, will cause the performance?
Answer: Transformations that can trigger a stage boundary typically accept a numPartitions argument, which specifies into how many partitions to split the data in the child stage. Just as the number of reducers is an important parameter in MapReduce jobs, the number of partitions at stage boundaries can determine an application's performance.
Previous | Next | Audio Book for Spark Interview Questions is available here | Top 150 Latest Spark Interview Questions | Quickly go through Spark Training Python & Scala
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