MapReduce job tuning parameters in Hadoop - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to set the number of reduce tasks in a MapReduce job.

Hadoop

job.setNumReduceTasks([1]);

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

AreduceTasks

B5

C10

Dmapreduce.job.reduces

Attempts:

3 left

💡 Hint

Common Mistakes

Using a string property name instead of an integer.

Confusing map tasks with reduce tasks.

The method setNumReduceTasks takes an integer to specify how many reduce tasks to run.

2fill in blank

medium

Complete the code to set the memory allocated for map tasks in megabytes.

Hadoop

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

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

A"2048"

B2GB

C2048

DmemoryMap

Attempts:

3 left

💡 Hint

Common Mistakes

Passing memory size as a string instead of integer.

Using units like '2GB' instead of megabytes.

The setInt method expects an integer value for memory in MB, so 2048 is correct.

3fill in blank

hard

Fix the error in setting the speculative execution for reduce tasks.

Hadoop

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

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

Atrue

B"true"

C1

Denabled

Attempts:

3 left

💡 Hint

Common Mistakes

Passing 'true' as a string instead of boolean.

Using 1 or 0 instead of true or false.

The setBoolean method requires a boolean value true or false, not a string or number.

4fill in blank

hard

Fill both blanks to configure the input split size and the maximum split size.

Hadoop

conf.setLong("mapreduce.input.fileinputformat.split.minsize", [1]);
conf.setLong("mapreduce.input.fileinputformat.split.maxsize", [2]);

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

A134217728

B268435456

CsplitSize

DmaxSplit

Attempts:

3 left

💡 Hint

Common Mistakes

Using variable names instead of numeric byte values.

Confusing min and max split sizes.

The minimum split size is set to 128MB (134217728 bytes) and the maximum split size to 256MB (268435456 bytes).

5fill in blank

hard

Fill all three blanks to create a map output compression configuration.

Hadoop

conf.setBoolean("mapreduce.map.output.compress", [1]);
conf.set("mapreduce.map.output.compress.codec", [2]);
conf.setInt("mapreduce.task.io.sort.mb", [3]);

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

Atrue

B"org.apache.hadoop.io.compress.SnappyCodec"

C256

Dfalse

Attempts:

3 left

💡 Hint

Common Mistakes

Disabling compression by setting false.

Using incorrect codec class names.

Passing sort buffer size as string instead of integer.

Enable compression with true, set codec to Snappy, and allocate 256MB for sort buffer.

Practice

1. Which MapReduce parameter controls the number of reduce tasks in a job?

easy

A. mapreduce.job.reduces

B. mapreduce.job.maps

C. mapreduce.task.io.sort.mb

D. mapreduce.reduce.shuffle.parallelcopies

Solution

1. Step 1: Understand the role of reduce tasks

   Reduce tasks process the output from map tasks and aggregate results.

2. Step 2: Identify the parameter controlling reduce tasks

   mapreduce.job.reduces sets the number of reduce tasks in a MapReduce job.

3. Final Answer:

   mapreduce.job.reduces -> Option A

4. Quick Check:

   Reduce tasks count = mapreduce.job.reduces [OK]

Hint: Reducers controlled by mapreduce.job.reduces parameter [OK]

Common Mistakes:

• Confusing mapreduce.job.maps with reducers
• Thinking shuffle parameters control reduce count
• Mixing memory parameters with task counts

2. Which of the following is the correct syntax to set the memory allocated to each map task in a Hadoop job configuration?

easy

A. mapreduce.map.memory.mb=2048

B. mapreduce.task.memory.map=2048

C. mapreduce.memory.map.task=2048

D. mapreduce.mapreduce.memory.mb=2048

Solution

1. Step 1: Recall the standard memory parameter for map tasks

   The correct parameter to set map task memory is mapreduce.map.memory.mb.

2. Step 2: Verify syntax correctness

   mapreduce.map.memory.mb=2048 matches the exact syntax used in Hadoop configurations.

3. Final Answer:

   mapreduce.map.memory.mb=2048 -> Option A

4. Quick Check:

   Map task memory = mapreduce.map.memory.mb [OK]

Hint: Map memory uses mapreduce.map.memory.mb [OK]

Common Mistakes:

• Swapping map and reduce memory parameters
• Using incorrect parameter names
• Adding extra words like 'task' incorrectly

3. Given the following configuration snippet in a MapReduce job:

mapreduce.job.reduces=3
mapreduce.map.memory.mb=1024
mapreduce.reduce.memory.mb=2048

What will be the number of reduce tasks and memory allocated to each reduce task?

medium

A. 1 reduce task, 2048 MB memory each

B. 3 reduce tasks, 1024 MB memory each

C. 3 reduce tasks, 2048 MB memory each

D. 1 reduce task, 1024 MB memory each

Solution

1. Step 1: Identify number of reduce tasks

   The parameter mapreduce.job.reduces=3 sets 3 reduce tasks.

2. Step 2: Identify memory per reduce task

   mapreduce.reduce.memory.mb=2048 sets 2048 MB memory for each reduce task.

3. Final Answer:

   3 reduce tasks, 2048 MB memory each -> Option C

4. Quick Check:

   Reducers=3, Reduce memory=2048 MB [OK]

Hint: Reducers = mapreduce.job.reduces; reduce memory = mapreduce.reduce.memory.mb [OK]

Common Mistakes:

• Confusing map memory with reduce memory
• Assuming default reduce task count is 1 always
• Ignoring memory parameters

4. You have set mapreduce.job.reduces=0 in your job configuration, but your job still runs reduce tasks. What is the likely cause?

medium

A. The job uses a custom partitioner forcing reduce tasks

B. The parameter is overridden by command line or code

C. Reduce tasks run by default regardless of setting

D. Map tasks are mistakenly counted as reduce tasks

Solution

1. Step 1: Understand effect of mapreduce.job.reduces=0

   This setting means no reduce tasks should run.

2. Step 2: Identify why reduce tasks still run

   If reduce tasks run, likely the setting is overridden by command line options or programmatic code.

3. Final Answer:

   The parameter is overridden by command line or code -> Option B

4. Quick Check:

   Overrides cause unexpected reduce tasks [OK]

Hint: Check for overrides if reduce count ignored [OK]

Common Mistakes:

• Assuming reduce tasks always run regardless
• Confusing map tasks with reduce tasks
• Ignoring programmatic overrides

5. You want to optimize a MapReduce job that processes large input data with many small files. Which tuning parameter adjustment can help reduce overhead and improve performance?

hard

A. Increase mapreduce.reduce.shuffle.parallelcopies to 1 to limit network usage

B. Decrease mapreduce.job.reduces to zero to skip reduce phase

C. Set mapreduce.task.io.sort.mb to a very low value to reduce memory use

D. Increase mapreduce.input.fileinputformat.split.maxsize to combine small files into fewer splits

Solution

1. Step 1: Understand problem with many small files

   Many small files cause many small input splits, increasing overhead.

2. Step 2: Identify tuning to reduce splits

   Increasing mapreduce.input.fileinputformat.split.maxsize combines small files into fewer splits, reducing overhead.

3. Step 3: Evaluate other options

   Setting reducers to zero skips reduce phase but may lose aggregation. Low sort memory hurts performance. Limiting shuffle copies to 1 reduces parallelism.

4. Final Answer:

   Increase mapreduce.input.fileinputformat.split.maxsize to combine small files into fewer splits -> Option D

5. Quick Check:

   Combine small files by increasing split max size [OK]

Hint: Combine small files by increasing split max size [OK]

Common Mistakes:

• Setting reducers to zero losing reduce benefits
• Reducing sort memory hurting map output sorting
• Limiting shuffle copies reducing network throughput