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PySpark Interview Questions
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- What is PySpark?
- PySpark is the Python API for Apache Spark, an open-source, distributed computing system that provides an interface for programming entire clusters with implicit data parallelism and fault tolerance.
- Can you explain what RDDs are in PySpark?
- RDD stands for Resilient Distributed Dataset, which is a fundamental data structure of PySpark. It is an immutable distributed collection of objects that can be processed in parallel.
- How does PySpark handle data serialization?
- PySpark uses Py4J for data serialization and deserialization. Py4J allows Python to dynamically interface with JVM objects (RDDs).
- What are the main features of PySpark SQL?
- PySpark SQL provides a programming abstraction called DataFrames and can also act as a distributed SQL query engine.
- What is the difference between transformations and actions in PySpark?
- Transformations create a new RDD from an existing one, while actions compute a result based on an RDD and either return it to the driver program or save it to an external storage system.
- How does PySpark utilize the concept of lazy evaluation?
- PySpark operations are lazily evaluated, meaning that they are not executed until an action is performed. This allows PySpark to optimize the overall data processing workflow.
- What is the role of the SparkContext in PySpark?
- The SparkContext is the entry point for any PySpark application and allows the application to connect to the Spark cluster.
- Can you describe the PySpark Streaming feature?
- PySpark Streaming is a scalable and fault-tolerant stream processing system that supports high-throughput and fault-tolerant stream processing of live data streams.
- What are DataFrames in PySpark?
- DataFrames are a distributed collection of data organized into named columns, similar to a table in a relational database but with richer optimizations under the hood.
- How can you improve the performance of a PySpark application?
- Performance can be improved by optimizing transformations, using broadcast variables, accumulator variables, and tuning the Spark configuration parameters.
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