Memory and container sizing in Hadoop - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to set the container memory size to 4096 MB.

Hadoop

yarn.scheduler.maximum-allocation-mb=[1]

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

A2048

B4096

C8192

D1024

Attempts:

3 left

💡 Hint

Common Mistakes

Setting the memory too low causing container failures.

Confusing memory size units.

The maximum allocation memory for containers is set to 4096 MB to allow larger container sizes.

2fill in blank

medium

Complete the code to configure the minimum container memory size to 1024 MB.

Hadoop

yarn.scheduler.minimum-allocation-mb=[1]

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

A512

B2048

C1024

D4096

Attempts:

3 left

💡 Hint

Common Mistakes

Setting minimum memory too high, wasting resources.

Setting minimum memory too low, causing task failures.

The minimum allocation memory is set to 1024 MB to ensure containers have enough memory to run basic tasks.

3fill in blank

hard

Fix the error in the code to correctly set the container memory size to 3072 MB.

Hadoop

yarn.nodemanager.resource.memory-mb=[1]

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

A3072

B2048

C4096

D1024

Attempts:

3 left

💡 Hint

Common Mistakes

Setting memory less than minimum container size.

Using incorrect configuration property name.

The node manager's resource memory must be set to 3072 MB to allocate enough memory for containers.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each container size in MB to its size in GB, only for sizes greater than 2 GB.

Hadoop

container_sizes_gb = {size: size [1] 1024 for size in sizes if size [2] 2048}

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

A//

B>

C<

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using '*' instead of '//' for conversion.

Using '<' instead of '>' in the condition.

The floor division operator '//' converts MB to GB by dividing by 1024. The condition '> 2048' filters sizes greater than 2 GB.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps node names in uppercase to their memory in GB, only for nodes with memory greater than 4096 MB.

Hadoop

node_memory_gb = { [1]: [2] [3] 1024 for node, mem in nodes.items() if mem > 4096 }

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

Anode.upper()

Bmem

C//

Dnode.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using multiplication instead of floor division.

Using node.lower() instead of uppercase.

Not converting memory units.

We convert node names to uppercase with node.upper(), keep memory as mem, and convert MB to GB using floor division //.

Practice

1. What is the main purpose of setting container memory size in Hadoop?

easy

A. To set the network bandwidth for data transfer

B. To increase the disk space available for Hadoop jobs

C. To control the number of CPU cores assigned to a task

D. To limit the amount of memory a task can use during execution

Solution

1. Step 1: Understand container memory role and compare options

   Container memory size limits how much RAM a Hadoop task can use while running. Only To limit the amount of memory a task can use during execution correctly describes this purpose; others relate to disk, CPU, or network, which are unrelated.

2. Final Answer:

   To limit the amount of memory a task can use during execution -> Option D

3. Quick Check:

   Container memory = task RAM limit [OK]

Hint: Container memory controls RAM usage per task [OK]

Common Mistakes:

• Confusing memory with disk space
• Thinking container memory controls CPU cores
• Assuming it sets network bandwidth

2. Which of the following is the correct way to set the Java heap size for a Hadoop container in the configuration?

easy

A. mapreduce.map.memory.mb=512\nmapreduce.map.java.opts=-Xmx5120m

B. mapreduce.map.memory.mb=2048\nmapreduce.map.java.opts=-Xmx1536m

C. mapreduce.map.memory.mb=1024\nmapreduce.map.java.opts=-Xmx2048m

D. mapreduce.map.memory.mb=4096\nmapreduce.map.java.opts=-Xmx8192m

Solution

1. Step 1: Understand heap size vs container memory and check options

   Java heap size (-Xmx) must be less than container memory to avoid errors. mapreduce.map.memory.mb=2048\nmapreduce.map.java.opts=-Xmx1536m sets container memory to 2048 MB and heap to 1536 MB, which is valid. Others have heap larger than container memory, causing errors.

2. Final Answer:

   mapreduce.map.memory.mb=2048\nmapreduce.map.java.opts=-Xmx1536m -> Option B

3. Quick Check:

   Heap size < container memory [OK]

Hint: Heap size must be smaller than container memory [OK]

Common Mistakes:

• Setting heap size larger than container memory
• Using inconsistent units (MB vs GB)
• Forgetting to set Java opts after memory

3. Given the following configuration:

mapreduce.reduce.memory.mb=4096
mapreduce.reduce.java.opts=-Xmx3072m

What will happen if a reduce task tries to use 3500 MB heap memory?

medium

A. The task will run successfully using 3500 MB heap

B. The task will automatically increase container memory

C. The task will fail due to exceeding Java heap size limit

D. The task will fail due to exceeding container memory limit

Solution

1. Step 1: Compare requested heap with limits

   Heap limit is 3072 MB, but task tries 3500 MB, which exceeds Java heap limit (-Xmx). Container memory is 4096 MB, so 3500 MB fits container but exceeds heap limit, causing failure.

2. Final Answer:

   The task will fail due to exceeding Java heap size limit -> Option C

3. Quick Check:

   Heap > Java heap limit = failure [OK]

Hint: Heap usage must not exceed Java heap size (-Xmx) [OK]

Common Mistakes:

• Confusing container memory with Java heap size
• Assuming container memory limits heap directly
• Thinking container memory auto-adjusts

4. Identify the error in this Hadoop container memory configuration:

mapreduce.map.memory.mb=1024
mapreduce.map.java.opts=-Xmx1536m

medium

A. Java heap size is larger than container memory size

B. Container memory size is too large for the job

C. Java opts syntax is incorrect

D. No error, configuration is valid

Solution

1. Step 1: Compare Java heap size and container memory

   Heap size (-Xmx1536m) is 1536 MB, which is larger than container memory (1024 MB). This causes the task to fail because heap cannot exceed container memory.

2. Final Answer:

   Java heap size is larger than container memory size -> Option A

3. Quick Check:

   Heap > container memory = error [OK]

Hint: Heap size must never exceed container memory [OK]

Common Mistakes:

• Ignoring heap size limits
• Assuming syntax error instead of sizing error
• Thinking container memory can auto-expand

5. You have a Hadoop job with 10 map tasks. Each map task requires 3 GB of Java heap memory. To avoid container failures, what is the minimum container memory size you should set per map task?

hard

A. 4 GB

B. 3 GB

C. 6 GB

D. 10 GB

Solution

1. Step 1: Calculate container memory with overhead

   Container memory must be larger than Java heap size to allow overhead (usually 25-30%). Heap = 3 GB, so container memory should be at least 3 GB + 25% = 3.75 GB, rounded to 4 GB.

2. Final Answer:

   4 GB -> Option A

3. Quick Check:

   Container memory > heap size + overhead [OK]

Hint: Add 25% overhead to heap for container memory [OK]

Common Mistakes:

• Setting container memory equal to heap size
• Ignoring overhead memory needs
• Confusing total job memory with per-task memory