How can you optimize Hadoop performance when dealing with large-scale data joins?
A. Use the MapReduce framework for all joins
B. Use Hadoop Streaming for efficient joins
C. Optimize join keys and consider using Map-side joins
D. Increase the number of reducers
Answer: Option C
This Question Belongs to Hadoop >> Best Practices And Optimization Of Hadoop
What is a common optimization technique to improve Hadoop MapReduce performance?
A. Increase block size
B. Decrease block size
C. Maintain the default block size
D. Use fewer mappers
Which compression codec is commonly used for optimizing storage in Hadoop?
A. Gzip
B. Snappy
C. Bzip2
D. LZO
What is the purpose of Hadoop speculative execution?
A. To handle speculative workloads
B. To minimize resource usage
C. To mitigate the impact of slow-running tasks
D. To speed up task completion
How can data skew in a Hadoop job be addressed for optimization?
A. Increase the number of reducers
B. Decrease the number of reducers
C. Use a combiner function
D. Use a custom partitioner
Join The Discussion