Apache Spark & Advanced DataFrames

This session covers Apache Spark, its architecture, and various components, providing a comprehensive overview of the unified analytics engine.

We discuss how Apache Spark offers in-memory processing and supports a wide range of data sources, enabling it to handle large-scale datasets by distributing computations across multiple nodes in the cluster. We also explore its key features, including high-level APIs, streaming capabilities, and advanced data processing techniques.

We compare Spark to Pandas, highlighting their respective strengths and use cases. We show how Spark is particularly suited for big data analytics and distributed computing, whereas Pandas excels at in-memory computing with smaller datasets. The choice between the two ultimately depends on the specific requirements of a project.

In this session, we introduce Spark DataFrames, covering various aspects such as schema, loading data, and inspecting DataFrames. We demonstrate how to create a DataFrame from different sources like CSV files, JSON, or databases, and explain how to manipulate DataFrames using methods provided by the API.

We also cover advanced operations on DataFrames, including column and row manipulations, filtering and conditional logic, aggregations, joins, window functions, handling missing data, and integrating with SQL queries. Throughout this session, we provide various techniques for optimizing performance and ensuring efficient computation.

Required Reading and Listening

Listen to the podcast:

Read the following:

  1. Summary Page: Big Data Processing Evolution
  2. Textbook: Part I in Jonathan Rioux, Data Analysis with Python and PySpark O’Reilly Media Inc., 2022.

Study Guide - Questions

1. Apache Spark: Architecture & Components

  1. What is Apache Spark, and what problem does it solve?
  2. Describe the core components of the Spark architecture. How do the Driver, Executor, and Cluster Manager interact?
  3. What are RDDs? What are their key characteristics, and what are some limitations?
  4. What are DataFrames? How do they differ from RDDs, and what advantages do they offer?
  5. Explain Spark SQL and how it relates to DataFrames. What are the benefits of using Spark SQL?
  6. Compare and contrast RDDs, DataFrames, and Datasets. When might you choose one over the others?

2. Spark vs Pandas: Why and When to Use Spark

  1. What is Pandas, and what is it commonly used for?
  2. What are the key differences between Spark and Pandas in terms of data storage and processing?
  3. Describe scenarios where Spark would be a better choice than Pandas. Consider data size and processing requirements.
  4. Describe scenarios where Pandas would be a better choice than Spark.
  5. Explain the concept of “lazy evaluation” in Spark and how it differs from Pandas. How does this impact performance?

3. Introduction to Spark DataFrames: Schema, Loading Data, Inspecting DataFrames

  1. What is a DataFrame schema? Why is defining a schema important when working with DataFrames?
  2. Describe the common methods for loading data into a Spark DataFrame (e.g., from CSV, JSON, Parquet).
  3. What are some common methods for inspecting a DataFrame (e.g., show(), printSchema(), count())? What information does each provide?
  4. You have a CSV file with inconsistent data types in some columns. How would you handle this when loading it into a Spark DataFrame? Explain your approach.

4. Distributed Computing Basics in Spark: Partitions and Transformations

  1. What is a partition in Spark? Why are partitions important for distributed computing?
  2. Explain the difference between transformations and actions in Spark. Give examples of each.
  3. What is “lazy evaluation” in the context of Spark transformations? How does it affect performance?
  4. You have a large DataFrame and want to optimize its performance. How can you control the number of partitions to achieve better parallelism? Explain the trade-offs.

5. Advanced DataFrame Operations

  1. Column and Row Manipulations

    1. How do you add a new column to a DataFrame? How do you rename an existing column?
    2. How do you select specific columns from a DataFrame?
    3. You need to create a new column based on a complex calculation involving multiple existing columns. How would you achieve this using Spark DataFrame operations?

  2. Complex Filtering and Conditional Logic

    1. How do you filter a DataFrame based on multiple conditions?
    2. You need to apply different transformations to rows in a DataFrame based on the values in a specific column. How would you achieve this using conditional logic?

  3. GroupBy, Aggregations

    1. Explain how to use groupBy() to group rows in a DataFrame.
    2. What are some common aggregation functions available in Spark (e.g., sum(), avg(), count(), min(), max())?
    3. You need to calculate the average value of a column for each group, but only include rows where a specific condition is met. How would you achieve this using groupBy() and aggregation?

  4. Joins and Window Functions

    1. Describe the different types of joins available in Spark (e.g., inner join, left join, right join, outer join).
    2. When would you use a window function? Give an example of a common window function and its purpose.
    3. You need to calculate a running total for each group in a DataFrame. How would you achieve this using window functions?

6.Handling Missing Data and Data Types 1. How can you identify missing values in a DataFrame? 1. Describe different strategies for handling missing data (e.g., dropping rows, filling with a default value, imputation). 1. How do you change the data type of a column in a DataFrame? 1. You have a DataFrame with a column containing mixed data types. How would you clean and transform this column to ensure consistent data types?

7. Integrating with SQL: Using Spark SQL Queries with Python 1. How can you register a DataFrame as a temporary view in Spark SQL? 1. Write a Spark SQL query to select specific columns from a DataFrame. 1. You have a complex SQL query that you want to execute on a Spark DataFrame. How would you integrate this query with your Python code?

Additional Resources