Module Lead Software Engineer

Module Lead Software Engineer Interview Questions and Answers

Updated 19 Jan 2021

Q1. Mutithreading nd hhow to use future completable future

Ans.

Multithreading allows concurrent execution of multiple threads. Future and CompletableFuture are used for asynchronous programming.

  • Multithreading enables parallel processing of tasks

  • Future is used to represent a result that may not be available yet

  • CompletableFuture is a subclass of Future that provides more flexibility and functionality

  • CompletableFuture can be used to chain multiple asynchronous tasks

  • CompletableFuture can also be used to handle exceptions and timeouts

Q2. Spark wordcount and sort the word in descending order with their number occurrence in dataframe

Ans.

Spark wordcount and sort in descending order with their number occurrence in dataframe

  • Read text file into dataframe using spark.read.text()

  • Split each line into words using split() function

  • Use groupBy() and count() functions to count the occurrence of each word

  • Sort the resulting dataframe in descending order using orderBy() function

  • Show the resulting dataframe using show() function

Q3. Find the intersection point of an 2array

Ans.

Find the intersection point of two arrays of strings.

  • Convert arrays to sets to find common elements

  • Iterate through one array and check if element exists in the other array

  • Use built-in intersection method in Python

  • Sort arrays and use two pointers to find common elements

Q4. 1create stack from two queue

Ans.

To create a stack from two queues, we need to use one queue for storing elements and the other for temporary storage.

  • Push elements into the first queue

  • When popping, move all elements except the last one to the second queue

  • Pop the last element from the first queue and return it

  • Swap the names of the two queues to make the second queue the first one

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Q5. Performance optimization of spark

Ans.

Performance optimization of Spark involves tuning various parameters and optimizing code.

  • Tune memory allocation and garbage collection settings

  • Optimize data serialization and compression

  • Use efficient data structures and algorithms

  • Partition data appropriately

  • Use caching and persistence wisely

  • Avoid shuffling data unnecessarily

  • Monitor and analyze performance using Spark UI and other tools

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