The Top 20 Spark Interview Questions

Apache Spark is a unified analytics engine for data engineering, data science, and machine learning at scale. It can be used with Python, SQL, R, Java, or Scala. Spark was originally started at the University of California, Berkeley, in 2009 and later was donated to the Apache Software Foundation in 2013. It is now “the most widely-used engine for scalable computing,” with thousands of postings of jobs that utilize the technology. Being such a highly valued skill in the data engineering world, here are interview questions to assist either your job search or your search for talent that has experience with Spark. The coding answers will be provided in Python.

Basic Spark Interview Questions

These questions cover some of the fundamentals of Spark and are appropriate for those who have only basic experience using it. If you need a refresher, our Introduction to Spark SQL in Python course is the ideal place to start. 

1. What is Apache Spark, and why is it used in data processing?

This question assesses the candidate's general understanding of Apache Spark and its role in the big data ecosystem.

Answer:

Apache Spark is an open-source, distributed computing system that provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. It's used for large-scale data processing due to its speed and ease of use compared to traditional MapReduce.

Key Features:

• In-Memory Computing: Stores data in memory for faster processing.
• Scalability: Can handle petabytes of data using a cluster of machines.
• Ease of Use: Provides APIs in Java, Scala, Python, and R.
• Unified Analytics Engine: Supports SQL, streaming data, machine learning, and graph processing.

2. Explain the concept of Resilient Distributed Datasets (RDDs)

This questions tests you on the fundamental concepts of Apache Spark. Make sure you understand one of the critical components which make Spark so powerful.

Resilient Distributed Datasets (RDDs) are the fundamental building blocks of Apache Spark. They represent an immutable, distributed collection of objects that can be operated on in parallel across a cluster. Here's an explanation of the key characteristics and concepts associated with RDDs:

1. Immutable: RDDs are immutable, meaning once created, their content cannot be modified. You can only transform RDDs by applying transformations to create new RDDs. This immutability simplifies fault tolerance and enables Spark's lazy evaluation model.
2. Distributed: RDDs are distributed across multiple nodes in a cluster, allowing Spark to perform parallel operations on them. Each RDD is divided into multiple partitions, and these partitions can be processed independently on different nodes.
3. Resilient: The "Resilient" in RDD stands for fault tolerance. Spark ensures resilience by keeping track of the lineage of each RDD. If a partition of an RDD is lost due to a node failure, Spark can recompute that partition using the lineage information and the transformations applied to the original data.
4. Dataset: RDDs are a distributed representation of data, which means they can hold any type of data, including structured or unstructured data. Spark provides APIs in multiple languages (like Scala, Java, Python, and R) to work with RDDs, making it versatile for various use cases and data types.
5. Lazy Evaluation: RDDs support lazy evaluation, meaning transformations on RDDs are not executed immediately. Instead, Spark builds up a directed acyclic graph (DAG) of transformations that define the computation but delays execution until an action is triggered. This optimization allows Spark to optimize the execution plan and improve performance.

3. What is YARN?

YARN is a distributed container manager that manages resources in Hadoop. Spark can utilize YARN when running on Hadoop clusters for more effective and efficient resource management. One of the critical components of YARN is its ability to efficiently allocate resources across the cluster, schedule jobs efficiently, and be fault tolerant in the event of node failures. It is one of the many components which make Spark a powerful tool.

4. What is the difference between map and flatMap transformations in Spark RDDs?

This question helps determine if you understand different types of transformations in Spark RDDs (Resilient Distributed Datasets).

Answer:

• .map(): Transforms each element of the RDD into exactly one new element. The result is an RDD with the same number of elements as the input RDD.
• .flatMap(): Transforms each element of the RDD into zero or more new elements. The result is an RDD with potentially different numbers of elements than the input RDD.

# Example of map
rdd = spark.sparkContext.parallelize([1, 2, 3])
mapped_rdd = rdd.map(lambda x: x * 2)
print(mapped_rdd.collect())  # Output: [2, 4, 6]
# Example of flatMap
flat_mapped_rdd = rdd.flatMap(lambda x: [x, x * 2])
print(flat_mapped_rdd.collect())  # Output: [1, 2, 2, 4, 3, 6]

This code illustrates the difference between map and flatMap by transforming an RDD of integers.

5. How do you use Spark SQL to query data from a DataFrame?

This question checks the candidate's ability to use Spark SQL for querying data, which is essential for data analysis tasks.

Answer:

# Register the DataFrame as a SQL temporary view
df.createOrReplaceTempView("table")
# Execute SQL query
result = spark.sql("SELECT column1, SUM(column2) FROM table GROUP BY column1")
# Show the results
result.show()

This snippet demonstrates creating a temporary view from a DataFrame and using Spark SQL to perform a group-by query.

Intermediate Spark Interview Questions

For those who have mastered the basics and applied them in their professional roles, these questions might be more common: 

6. Explain the concept of lazy evaluation in Spark and why it is important

This question assesses the candidate's understanding of one of Spark's core principles, which is crucial for optimizing performance.

Answer:

Lazy evaluation means that Spark does not immediately execute transformations as they are called. Instead, it builds a logical execution plan. The transformations are only executed when an action (like collect or count) is called, which triggers the actual computation.

Lazy evaluation is important for two reasons:

1. It allows Spark to optimize the entire data processing workflow before executing it, combining operations to minimize data shuffling.
2. It reduces the number of passes through the data, improving performance.

7. How do you persist data in Spark, and what are the different storage levels available?

This question checks the candidate's knowledge of data persistence in Spark, which is important for performance tuning and iterative algorithms.

Answer:

Data can be persisted in Spark using the .persist() or .cache() methods. .cache() is a shorthand for .persist() with the default storage level.

Storage Levels:

• MEMORY_ONLY: Stores RDD as deserialized Java objects in the JVM. If the RDD does not fit in memory, some partitions will not be cached.
• MEMORY_AND_DISK: Stores RDD as deserialized Java objects in memory. If the RDD does not fit in memory, partitions are stored on disk.
• MEMORY_ONLY_SER: Stores RDD as serialized Java objects in the JVM. This reduces the memory usage but increases CPU overhead for serialization/deserialization.
• MEMORY_AND_DISK_SER: Similar to MEMORY_AND_DISK but stores serialized objects.
• DISK_ONLY: Stores RDD partitions only on disk.

rdd = spark.sparkContext.parallelize([1, 2, 3, 4, 5])
rdd.persist(storageLevel=StorageLevel.MEMORY_AND_DISK)

8. How do you handle skewed data in Spark?

This question evaluates the candidate's understanding of data skew and how to manage it, which is critical for ensuring efficient data processing.

Answer:

Data skew occurs when some partitions have significantly more data than others, leading to performance bottlenecks. Strategies to handle skewed data include:

• Salting: Adding a random key to the data to distribute it more evenly across partitions.
• Repartitioning: Increasing the number of partitions to distribute the data more evenly.
• Broadcast Variables: Broadcasting a small dataset to all nodes to avoid shuffling large datasets.

from pyspark.sql.functions import monotonically_increasing_id, col
# Example of salting
df = df.withColumn("salt", monotonically_increasing_id() % 10)
df = df.withColumn("new_key", col("original_key") + col("salt"))

9. Explain the difference between narrow and wide transformations in Spark

This question tests the candidate's understanding of Spark's execution model and the impact of different types of transformations on performance.

Answer:

• Narrow Transformations: Operations where each input partition contributes to exactly one output partition. Examples include .map(), .filter(), and .union(). They are generally faster because they do not require data shuffling.
• Wide Transformations: Operations where each input partition contributes to multiple output partitions. Examples include .groupByKey(), .reduceByKey(), and .join(). They require data shuffling across the network, which can be time-consuming.

# Narrow transformation example
rdd1 = rdd.map(lambda x: x * 2)
# Wide transformation example
rdd2 = rdd.groupByKey()

10. Spark Streaming in Real-Time Data Processing

Spark excels at streaming real-time data from sources such as Apache Kafka or Amazon Kinesis because it is scalable and fault-tolerant. It does so through the extension Spark Streaming. It interacts with external data sources using input DStreams, which represent a continuous stream of data from these sources. 

Spark Streaming ensures fault tolerance and data consistency through techniques like checkpointing and write-ahead logs. Checkpointing periodically saves the state of the streaming application to durable storage (e.g., HDFS) to recover from failures, while write-ahead logs provide fault tolerance for data received from external sources.

Advanced Spark Interview Questions

These questions are for users with more hands-on experience with Spark, particularly with more sophisticated topics. If you need a refresher, check out our Spark Machine Learning tutorial. 

11. Discuss how Spark may be utilized for machine learning

This question tests the interviewee’s understanding of Spark’s environment and the MLib library. 

Spark's MLlib library provides a rich set of tools and algorithms for performing machine learning tasks at scale. When it comes to feature engineering and preprocessing for large-scale datasets, MLlib offers several advanced techniques and optimizations:

1. Feature Transformation and Selection: MLlib provides a range of feature transformation techniques, such as scaling, normalization, binarization, and vectorization (e.g., one-hot encoding). Additionally, it offers methods for feature selection, including filtering based on correlation, information gain, or statistical tests, as well as more advanced techniques like Principal Component Analysis (PCA) for dimensionality reduction.

2. Handling Categorical Features: MLlib includes tools for efficiently handling categorical features, such as StringIndexer for converting categorical variables into numerical representations and OneHotEncoder for converting them into binary vectors. These transformations are optimized for parallel execution across distributed Spark clusters.

3. Pipeline API: Spark's Pipeline API allows users to chain together multiple stages of feature engineering and modeling into a single workflow. This facilitates the creation of complex feature transformation pipelines while ensuring consistency and reproducibility across different datasets and machine learning tasks.

4. Custom Transformers and Estimators: MLlib allows users to define custom feature transformers and estimators using Spark's DataFrame API. This enables the integration of domain-specific feature engineering techniques or third-party libraries into the Spark ML pipeline, extending its functionality and flexibility.

12. Explain how Spark integrates with external storage systems like Apache Hadoop HDFS and Apache Cassandra. What are the advantages of leveraging these integrations in a Spark-based data pipeline?

This tests whether users understand the underpinning functionality of Spark-based systems and how Spark works with HDFS and Apache Cassandra. It is important to understand both how to retrieve data through coding and how that data moves throughout the system.

1. Hadoop HDFS Connection: Spark integrates with external storage systems like Apache Hadoop HDFS and Apache Cassandra through connectors or libraries specifically designed for each system. For example, HDFS integration is native to Spark, allowing Spark to read and write data directly from/to HDFS using Hadoop InputFormat and OutputFormat APIs.
2. Apache Cassandra Connection: The advantages of leveraging these integrations include improved performance due to data locality (in the case of HDFS), simplified data access and manipulation, and compatibility with existing data infrastructure. Additionally, Spark can exploit the distributed nature of these storage systems for parallel processing, enabling scalable data processing.

13. Explain the concept of broadcast variables in Spark

Broadcast variables in Spark are read-only variables that are cached and made available to all worker nodes in a distributed Spark application. They are used to efficiently distribute large, read-only datasets or values to worker nodes, thereby reducing network overhead and improving task performance. 

Broadcast variables are serialized and sent to each worker node only once, where they are cached in memory and reused across multiple tasks. This eliminates the need to send the variable with each task, reducing data transfer overhead, especially for large datasets.

• Utilization: Broadcast variables are commonly used in scenarios where a large dataset or value needs to be shared across multiple tasks or stages of computation. For example, in join operations where one DataFrame or RDD is significantly smaller than the other, broadcasting the smaller DataFrame/RDD can significantly reduce the amount of data shuffled across the network during the join operation.
• Beneficial Scenarios:
• Join Operations: Broadcasting smaller datasets for join operations can greatly improve performance by reducing network traffic and speeding up task execution.
• Lookup Tables: Broadcasting small lookup tables or dictionaries that are used for enrichment or filtering operations can enhance performance by avoiding repeated data transfers.
• Machine Learning: Broadcasting feature vectors or model parameters to worker nodes during distributed training can expedite the training process, especially when the feature vectors or parameters are relatively small compared to the dataset.
• Challenges:
• Memory Overhead: Broadcasting large variables can consume significant memory on worker nodes, potentially leading to out-of-memory errors if not managed carefully.
• Network Congestion: Broadcasting large variables can also introduce network congestion during the initial broadcast phase, especially in large clusters with limited network bandwidth.
• Dynamic Data: Broadcast variables are immutable once broadcasted, so they are not suitable for scenarios where the broadcasted data needs to be updated dynamically during the execution of the Spark job.

14. How do you optimize a Spark job using partitioning and coalescing? 

This question assesses the candidate's ability to optimize Spark jobs, a key skill for improving performance and efficiency. Through Spark performance tuning, we can leverage Spark’s distributed framework by using partitioning and coalescing, which manages the workload distribution across the cluster to perform data operations more quickly. 

Answer:

• Partitioning: Controls the number of partitions in an RDD or DataFrame. Use .repartition() to increase or evenly distribute partitions. This is more computationally expensive and should only be used when we require the data be divided up evenly for balanced processing.
• Coalescing: Reduces the number of partitions without performing a full shuffle, which is more efficient than repartition when reducing the number of partitions. We do this by using .coalesce().

# Increasing partitions (full shuffle)
df_repartitioned = df.repartition(10)
# Reducing partitions (no full shuffle)
df_coalesced = df.coalesce(2)

Note that a follow-up question may mention when these operations are most useful. Make sure to mention that these are more effective when working on large datasets, and computing power should not be wasted on smaller datasets.

15. Explains Sparks’ interoperability with data serialization formats

Data professionals will interact with a wide variety of different data formats. Each of these have different trade-offs. Make sure you can explain how Spark generally interacts with these formats and offer high-level performance as well as considerations that need to make for the larger ecosystem.

1. Data Serialization Format Support: Spark interoperates with data serialization formats like Avro, Parquet, or ORC through built-in support or third-party libraries. These formats offer advantages such as efficient compression, columnar storage, and schema evolution, making them suitable for data processing and storage in Spark-based pipelines.
2. Data Reading Optimization: Spark optimizes data reading and writing operations with these formats by utilizing specialized readers and writers that exploit their internal structure and compression techniques. For example, Parquet and ORC leverage columnar storage to minimize I/O overhead and improve query performance.
3. Data Format Tradeoffs: Trade-offs include storage efficiency (e.g., compression ratio), performance (e.g., read/write throughput), and compatibility with other data processing tools. Choosing the right serialization format depends on factors such as data characteristics, query patterns, and integration requirements within the data pipeline.

Spark Coding Interview Questions

These coding questions will focus on the usage of PySpark in order to interact with a spark environment. 

16. Find the top N most frequent words in a large text file

This question checks for your ability to interact with Spark and understand the utilization of mapping in Spark itself.

from pyspark import SparkContext
# create your spark context
sc = SparkContext("local", "WordCount")
# import a text file from a local path
lines = sc.textFile("path/to/your/text/file.txt")
# split and map the words
# then reduce by using the words as keys and add to the count
word_counts = lines.flatMap(lambda line: line.split(" ")) \
                  .map(lambda word: (word, 1)) \
                  .reduceByKey(lambda a, b: a + b)
# order the words and take only the top N frequent words
top_n_words = word_counts.takeOrdered(N, key=lambda x: -x[1])
print(top_n_words)

17. Find the average of values in a given RDD

This question is a great way to showcase whether someone knows how to create a simple RDD and manipulate it. Finding the average of values is a very common task given to data professionals and it’s key you understand how to take data and form it within a Spark context.

from pyspark import SparkContext
# Create sparkContext and name it “Average”
sc = SparkContext("local", "Average")
# Generate Spark RDD
data = sc.parallelize([1, 2, 3, 4, 5])
# Sum the RDD, count the number of values in RDD
total_sum = data.sum()
count = data.count()
# divide sum by count to get average 
average = total_sum / count
print("Average:", average)
	

18. Perform a left outer join between two RDDs

Performing data manipulation and transformation tasks such as joins is a key component of SparkSQL. This allows the data to be combined across different sources for data analysis. 

from pyspark import SparkContext
# Create SparkContext
sc = SparkContext("local", "LeftOuterJoin")
# Create two RDDs with tuples sharing keys
rdd1 = sc.parallelize([(1, 'a'), (2, 'b'), (3, 'c')])
rdd2 = sc.parallelize([(1, 'x'), (2, 'y')])
# Use the .leftOuterJoin() method to join the first rdd to the second rdd
joined_rdd = rdd1.leftOuterJoin(rdd2)
# Use the .collect() method to show the rdd
print(joined_rdd.collect())
	

19. Read data from Kafka, perform transformations, and then write the results to HDFS

This tests your ability to bring data in from external data sources and understanding of how Spark can connect to external data sources. Focus here on the general concepts, such as the need to import extensions/utilities for a particular data stream as opposed to memorizing the exact coding. Note that for the SparkContext, we have chosen to have an appname (KafkaWordCount) as an optional parameter, but it is nice to have as it keeps processes clear.

# Import the sparkcontext, additionally import streaming context and Kafka
from pyspark import SparkContext
from pyspark.streaming import StreamingContext
from pyspark.streaming.kafka import KafkaUtils
# Create context 
sc = SparkContext("local", "KafkaWordCount")
# Use streaming context to bring in data at 10 second intervals
ssc = StreamingContext(sc, 10)  # 10-second batch interval
# Use Kafka param dictionary in order to connect to the stream using the streaming context, the topic of interest, and the parameters
kafka_params = {"metadata.broker.list": "broker1:9092,broker2:9092"}
kafka_stream = KafkaUtils.createDirectStream(ssc, ["topic"], kafka_params)
# save the results of this stream to lines
# perform MapReduce in order to generate dictionary and count by keys
lines = kafka_stream.map(lambda x: x[1])
word_counts = lines.flatMap(lambda line: line.split(" ")) \
                  .map(lambda word: (word, 1)) \
                  .reduceByKey(lambda a, b: a + b)
# save to external file
word_counts.saveAsTextFiles("hdfs://path/to/save")
# start context until you terminate
ssc.start()
ssc.awaitTermination()
	

20. How do you perform basic transformations and actions on a Spark DataFrame?

This question evaluates the candidate's understanding of DataFrame operations in Spark.

Transformations are operations on DataFrames that return a new DataFrame, such as select, filter, and groupBy. Actions are operations that trigger computation and return results, like show, count, and collect.

This snippet shows selecting columns, filtering rows, and performing a group-by aggregation.

# Select specific columns
selected_df = df.select("column1", "column2")
# Filter rows based on a condition
filtered_df = df.filter(df["column1"] > 100)
# Group by a column and perform aggregation
grouped_df = df.groupBy("column2").agg({"column1": "sum"})
# Show the results
selected_df.show()
filtered_df.show()
grouped_df.show()

Final Thoughts

Mastering these interview questions is a great first step toward becoming a data professional. Spark is a common infrastructure utilized by many organizations to handle their big data pipelines. Understanding the benefits and challenges of Spark will help you stand out as a knowledgeable data professional. This is just the beginning! Getting hands-on experience with Spark is the best way to learn. 

You can get started with the following PySpark courses and tutorials on DataCamp:

• Introduction to PySpark
• Introduction to Spark SQL in Python
• Big Data with PySpark
• Apache Spark Tutorial: ML with PySpark