Transformations Narrow Vs Wide
Lesson objectives
In this lesson, we will explain the following topics:
- Learn about the two types of Spark transformations: narrow and wide.
- Understand the characteristics and benefits of narrow transformations.
- Explore the implications and performance considerations of wide transformations.
Narrow and Wide Transformations
Introduction to Spark Transformations
- Transformations create new RDDs from existing ones.
- Spark has two types of transformations: Narrow and Wide.
What are Narrow Transformations?
- Transformations that do not require data shuffling between partitions.
- Examples:
map(),filter(). - Data processing is limited to a single partition.
What are Narrow Transformations?
Benefits of Narrow Transformations
- Efficient with minimal data movement.
- Best for independent data processing tasks.
What are Wide Transformations?
- Transformations that involve shuffling data across partitions.
- Examples:
groupBy(),reduceByKey().
What are Wide Transformations?
Wide Transformations and Dependencies
- Wide Dependencies: Require data from multiple partitions, often involving shuffling.
- Examples:
groupBy(),orderBy()- data is combined across partitions, affecting performance. - Impact: These transformations are necessary for operations like counting occurrences across a dataset.
Implications of Wide Transformations
- Shuffling can be expensive in terms of time and network I/O.
- Essential for aggregation and grouping operations.
Narrow vs. Wide Dependencies
- Narrow Dependencies: A single output partition can be computed from a single input partition without data exchange.
- Examples:
filter(),contains()- operate independently on partitions.
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