Caching and persistence in Apache Spark - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output count after caching?

Consider the following Apache Spark code snippet:

data = spark.range(1000)
cached_data = data.cache()
count1 = cached_data.count()
count2 = cached_data.count()

What is the value of count2?

Apache Spark

data = spark.range(1000)
cached_data = data.cache()
count1 = cached_data.count()
count2 = cached_data.count()
print(count2)

A Raises an error

B 0

C None

D 1000

💡 Hint

Caching stores the data in memory after the first action.

The first count() triggers computation and caches the data. The second count() uses the cached data, so it returns 1000 immediately.

❓ data_output

intermediate

What is the storage level after persisting with MEMORY_AND_DISK?

Given this Spark code:

df = spark.range(10)
df.persist(StorageLevel.MEMORY_AND_DISK)
print(df.storageLevel)

What will be printed?

Apache Spark

from pyspark import StorageLevel
df = spark.range(10)
df.persist(StorageLevel.MEMORY_AND_DISK)
print(df.storageLevel)

A StorageLevel(memory=True, disk=True, offHeap=False, deserialized=True, replication=1)

B StorageLevel(memory=False, disk=True, offHeap=False, deserialized=True, replication=1)

C StorageLevel(memory=True, disk=False, offHeap=False, deserialized=True, replication=1)

D StorageLevel(memory=False, disk=False, offHeap=False, deserialized=False, replication=1)

💡 Hint

MEMORY_AND_DISK means data is stored in memory and spilled to disk if needed.

The persist(StorageLevel.MEMORY_AND_DISK) sets the storage level to keep data in memory and disk with deserialized format.

🔧 Debug

advanced

Why does unpersisting not free memory immediately?

In Spark, after calling df.unpersist(), the memory is not freed immediately. Why?

A Because unpersist() is asynchronous and frees memory lazily

B Because unpersist() deletes the original data source

C Because unpersist() caches data again automatically

D Because unpersist() triggers a job that blocks memory release

💡 Hint

Think about how Spark manages memory and tasks asynchronously.

Calling unpersist() marks the data for removal but frees memory lazily after current tasks finish.

🚀 Application

advanced

Choosing the right persistence level for iterative algorithms

You have a large DataFrame used in multiple iterations of a machine learning algorithm. Which persistence level is best to optimize performance and resource usage?

A StorageLevel.DISK_ONLY

B StorageLevel.MEMORY_AND_DISK_SER

C StorageLevel.MEMORY_ONLY

D StorageLevel.OFF_HEAP

💡 Hint

Consider serialization and fallback when memory is insufficient.

MEMORY_AND_DISK_SER stores serialized data in memory and spills to disk if needed, saving memory and improving performance for iterative tasks.

🧠 Conceptual

expert

What happens if you cache a DataFrame but the cluster runs out of memory?

When you cache a DataFrame in Spark and the cluster memory is full, what is the expected behavior?

A Spark will automatically increase cluster memory to fit the cache

B Spark will crash immediately due to out-of-memory error

C Spark will evict cached partitions using LRU policy and recompute them when needed

D Spark will convert the DataFrame to disk-only storage without eviction

💡 Hint

Think about Spark's memory management and eviction policies.

Spark uses a Least Recently Used (LRU) policy to evict cached partitions when memory is full, allowing recomputation later.

Practice

1. What is the main purpose of using cache() in Apache Spark?

easy

A. To delete data from memory

B. To permanently save data to disk

C. To store data in memory for faster repeated access

D. To convert data into a different format

Solution

1. Step 1: Understand what caching means in Spark

   Caching stores data in memory to speed up repeated operations on the same data.

2. Step 2: Identify the role of cache()

   The cache() method specifically stores the DataFrame or RDD in memory for quick access.

3. Final Answer:

   To store data in memory for faster repeated access -> Option C

4. Quick Check:

   Caching = store in memory [OK]

Hint: Cache means keep data in memory for speed [OK]

Common Mistakes:

• Thinking cache saves data permanently on disk
• Confusing cache with deleting data
• Assuming cache changes data format

2. Which of the following is the correct syntax to persist a DataFrame df with disk storage level in Spark?

easy

A. df.persist(StorageLevel.DISK_ONLY)

B. df.cache(StorageLevel.DISK_ONLY)

C. df.persist('disk_only')

D. df.cache('disk_only')

Solution

1. Step 1: Recall the correct method to specify storage level

   To persist with a specific storage level, use persist() with a StorageLevel object.

2. Step 2: Check the syntax for disk-only persistence

   The correct syntax is df.persist(StorageLevel.DISK_ONLY). The cache() method does not accept parameters.

3. Final Answer:

   df.persist(StorageLevel.DISK_ONLY) -> Option A

4. Quick Check:

   persist + StorageLevel object = correct syntax [OK]

Hint: Use persist() with StorageLevel, cache() has no params [OK]

Common Mistakes:

• Using cache() with parameters
• Passing storage level as string instead of StorageLevel object
• Using persist() without StorageLevel

3. Consider the following Spark code snippet:

val df = spark.read.csv("data.csv")
df.cache()
df.count()
df.count()

What is the effect of calling df.count() twice after caching?

medium

A. Both counts reload data from disk without caching

B. The first count triggers data loading and caching; the second count uses cached data for faster result

C. The second count triggers caching; the first count uses cached data

D. Both counts use cached data without loading from disk

Solution

1. Step 1: Understand caching behavior on first action

   The first action (count()) triggers loading data and caches it in memory.

2. Step 2: Effect on second action

   The second count() uses the cached data, so it runs faster without reloading from disk.

3. Final Answer:

   The first count triggers data loading and caching; the second count uses cached data for faster result -> Option B

4. Quick Check:

   First action caches, second uses cache [OK]

Hint: First action caches, second action reads cache [OK]

Common Mistakes:

• Assuming cache happens before first action
• Thinking both counts reload from disk
• Believing second count triggers caching

4. Given this Spark code:

val df = spark.read.json("data.json")
df.persist(StorageLevel.MEMORY_AND_DISK)
df.unpersist()

What is the main issue with this code?

medium

A. Calling unpersist() immediately removes cached data, so persistence is ineffective

B. The persist() method requires a string, not StorageLevel object

C. You must call cache() before persist()

D. The code will cause a syntax error due to missing parentheses

Solution

1. Step 1: Understand the effect of persist()

   The persist() call stores the DataFrame in memory and disk as specified.

2. Step 2: Effect of immediate unpersist()

   Calling unpersist() right after removes the cached data, negating the persistence benefit.

3. Final Answer:

   Calling unpersist() immediately removes cached data, so persistence is ineffective -> Option A

4. Quick Check:

   Immediate unpersist clears cache [OK]

Hint: Unpersist right after persist clears cache [OK]

Common Mistakes:

• Thinking persist needs string argument
• Believing cache must be called before persist
• Assuming syntax error due to parentheses

5. You have a large DataFrame df used multiple times in your Spark job. You want to cache it but ensure it uses memory first and spills to disk if memory is full. Which persistence level should you use and why?

hard

A. Use df.persist(StorageLevel.DISK_ONLY) to avoid memory usage

B. Use df.cache() because it automatically spills to disk

C. Use df.unpersist() to keep data only in memory

D. Use df.persist(StorageLevel.MEMORY_AND_DISK) to store in memory and spill to disk if needed

Solution

1. Step 1: Identify persistence levels for memory and disk

   StorageLevel.MEMORY_AND_DISK stores data in memory and spills to disk if memory is insufficient.

2. Step 2: Compare with other options

   cache() only stores in memory and may fail if memory is full; DISK_ONLY avoids memory; unpersist() removes cached data.

3. Final Answer:

   Use df.persist(StorageLevel.MEMORY_AND_DISK) to store in memory and spill to disk if needed -> Option D

4. Quick Check:

   MEMORY_AND_DISK = memory first, then disk spill [OK]

Hint: MEMORY_AND_DISK spills to disk if memory full [OK]

Common Mistakes:

• Assuming cache spills to disk automatically
• Choosing DISK_ONLY to save memory but slower
• Using unpersist to keep data cached