Why tuning prevents slow and failed jobs in Hadoop - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to set the number of reducers in a Hadoop job.

Hadoop

job.setNumReduceTasks([1])

Drag options to blanks, or click blank then click option'

A0

B10

C5

D100

Attempts:

3 left

💡 Hint

Common Mistakes

Setting reducers to 0 disables reduce phase, causing job failure.

Setting the number of reducers to 5 helps balance load and speed up the job.

2fill in blank

medium

Complete the code to set the memory allocation for map tasks.

Hadoop

conf.set("mapreduce.map.memory.mb", "[1]")

Drag options to blanks, or click blank then click option'

A1024

B2048

C512

D4096

Attempts:

3 left

💡 Hint

Common Mistakes

Setting memory too low causes task failures.

Allocating 2048 MB memory to map tasks helps prevent out-of-memory errors and slow processing.

3fill in blank

hard

Fix the error in setting the input split size to optimize job performance.

Hadoop

conf.setLong("mapreduce.input.fileinputformat.split.maxsize", [1])

Drag options to blanks, or click blank then click option'

A"268435456"

B268435456L

C26843545

D268435456

Attempts:

3 left

💡 Hint

Common Mistakes

Using quotes causes type error.

Using too small split size causes many small tasks.

The value must be a long integer without quotes or suffixes to set max split size correctly.

4fill in blank

hard

Fill both blanks to configure speculative execution to prevent slow tasks.

Hadoop

conf.setBoolean("mapreduce.map.speculative", [1])
conf.setBoolean("mapreduce.reduce.speculative", [2])

Drag options to blanks, or click blank then click option'

Atrue

Bfalse

C"true"

D"false"

Attempts:

3 left

💡 Hint

Common Mistakes

Using string values instead of boolean causes errors.

Disabling speculative execution can cause slow jobs.

Enabling speculative execution for both map and reduce tasks helps avoid slow tasks delaying the job.

5fill in blank

hard

Fill both blanks to create a dictionary comprehension that filters and maps data sizes.

Hadoop

sizes = {file:file_size[file]**2 for file in files if file_size[file] [1] 1000 and file_size[file] [2] 100000}

Drag options to blanks, or click blank then click option'

A:

B>

C<

D=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '=' instead of ':' in dict comprehension.

Using wrong comparison operators causing empty results.

The dictionary comprehension uses ':' to map file to size squared, and filters sizes between 1000 and 100000.