1. Introduction to Spark
Contents
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- What is Apache Spark, and how does it differ from Hadoop MapReduce?
- List some key features of Apache Spark that contribute to its performance.
- Explain the concept of in-memory computation in Spark.
- How does Spark achieve fault tolerance?
2. Spark Architecture
- Describe the main components of Spark’s architecture.
- What is the role of the Spark Driver?
- Explain the function of Executors in Spark.
- How does the Cluster Manager fit into Spark’s architecture?
- Differentiate between Standalone, YARN, and Mesos cluster managers.
3. Resilient Distributed Datasets (RDDs)
- What is an RDD in Spark, and why is it fundamental?
- Explain the difference between transformations and actions in RDDs.
- How do you create an RDD in Spark?
- What are some common transformations and actions on RDDs?
- Discuss the lineage graph and its importance in Spark.
4. DataFrames and Datasets
- What are DataFrames in Spark, and how do they differ from RDDs?
- Explain the concept of Datasets and how they unify DataFrames and RDDs.
- When would you choose to use a DataFrame over an RDD?
- Discuss the advantages of using Datasets in Spark 2.0 and later.
- How do you convert an RDD to a DataFrame or Dataset?
5. Spark SQL
- What is Spark SQL, and how does it integrate with Spark’s core API?
- How can you run SQL queries on Spark DataFrames?
- Explain the role of the Catalyst optimizer in Spark SQL.
- What are the advantages of using Spark SQL for data processing?
- How do you read and write data from various data sources using Spark SQL?
6. Spark Streaming
- What is Spark Streaming, and how does it handle real-time data?
- Explain the concept of DStreams in Spark Streaming.
- How does Spark Streaming achieve fault tolerance and load balancing?
- What is the difference between Spark Streaming and Structured Streaming?
- Discuss window operations in Spark Streaming.
7. Structured Streaming
- What is Structured Streaming, and how does it improve upon Spark Streaming?
- Explain the concept of the continuous processing model in Structured Streaming.
- How does watermarking work in Structured Streaming?
- What are the fault tolerance mechanisms in Structured Streaming?
- How do you handle late data in Structured Streaming applications?
8. PySpark
- What is PySpark, and how does it relate to Apache Spark?
- How do you create a SparkSession in PySpark?
- What are some common data types in PySpark DataFrames?
- Explain how to use User-Defined Functions (UDFs) in PySpark.
- How do you optimize PySpark code for better performance?
9. Machine Learning with MLlib
- What is MLlib in Spark, and what are its main components?
- Explain how to create a machine learning pipeline in Spark MLlib.
- Discuss the difference between the original MLlib API and the DataFrame-based API.
- How do you handle categorical features in Spark MLlib?
- Provide an example of implementing a logistic regression model using MLlib.
10. Graph Processing with GraphX
- What is GraphX in Spark, and what problems does it solve?
- Explain the Pregel API in GraphX.
- How do you represent graphs in GraphX?
- Discuss some common graph algorithms available in GraphX.
- What are the benefits of using GraphX for graph processing?
11. Deployment and Configuration
- What are the different ways to deploy a Spark application?
- How do you submit a Spark job using
spark-submit? - Explain the purpose of driver and executor memory settings.
- How do you configure Spark for optimal performance in a cluster environment?
- What are some best practices for deploying Spark applications on YARN?
12. Performance Tuning
- What are some common performance bottlenecks in Spark applications?
- Explain how caching and persistence work in Spark.
- How does Spark’s memory management affect performance?
- Discuss the importance of data partitioning and how to optimize it.
- What tools or methods can you use for monitoring and debugging Spark applications?
13. Advanced Topics
- What are Broadcast Variables and Accumulators in Spark?
- Explain the concept of lazy evaluation in Spark and its benefits.
- How does Spark handle data skew, and what strategies can mitigate it?
- Discuss the difference between narrow and wide transformations.
- What is the Tungsten project in Spark, and how does it improve performance?
14. Spark Internals
- How does Spark’s DAG (Directed Acyclic Graph) scheduler work?
- Explain task serialization and deserialization in Spark.
- What is Speculative Execution in Spark, and when is it useful?
- How does Spark ensure data consistency and fault tolerance?
- Discuss the role of shuffle operations and how they impact performance.
15. Integration and Ecosystem
- How does Spark integrate with Hadoop and other Big Data tools?
- Explain how you can use Spark with AWS S3 or other cloud storage systems.
- What is the role of Apache Hive in relation to Spark SQL?
- How can you use Spark with NoSQL databases like Cassandra or HBase?
- Discuss how Kafka can be used with Spark Streaming for real-time data processing.