Understand the advantages and limitations of parallel collections.
- [Instructor] Here are some things to keep in mind…when considering the use of parallel collections.…First of all, parallel collections should be considered…only when you have at least thousands,…possibly tens of thousands of elements.…For some types of collections, converting between…the sequential and parallel type requires copying data,…so keep that in mind.…Now you want to avoid side effects.…It's best to avoid applying procedures with side effects…in parallel collections.…Side effects can lead to nondeterminism.…
That means different times you execute the operation…you may get different ordering of results.…And side effects could take affect in different orders…each time the operation is executed.…Also you want to avoid nonassociative operations…when working with parallel collections.…In associative operations, the order…of operations doesn't matter.…Now if your computation depends on state information…as you go through the processing of a collection,…and the order of that operations matters,…then you should not use parallel collections.…
Dan also focuses on using Scala with Spark, a distributed processing platform. He first describes how to work with Resilient Distributed Datasets (RDDs)—a fundamental Spark data structure—and then explains how to use Scala with Spark DataFrames, a new class of data structure specially designed for analytic processing. He wraps up the course by providing a summary of advantages of using Scala for data science.
- The advantages of Scala for data science
- Scala data types
- Scala arrays, vectors, and ranges
- Parallel processing in Scala
- Mapping functions over parallel collections
- When and when not to use parallel collections
- Using SQL in Scala
- Scala and Spark RDDs
- Scala and Spark DataFrames
- Creating DataFrames
Skill Level Intermediate
Java for Data Scientists Essential Trainingwith Charles Kelly2h 43m Intermediate
1. Introduction to Scala
2. Parallel Processing in Scala
3. Using SQL in Scala
4. Scala and Spark RDDs
5. Scala and Spark DataFrames
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