Scala examples for learning to use Spark
This project contains snippets of Scala code for illustrating various
Apache Spark concepts. It is
intended to help you get started with learning Apache Spark (as a Scala programmer) by providing a super easy on-ramp that doesn’t involve Unix, cluster configuration, building from sources or
installing Hadoop. Many of these activities will be necessary later in your
learning experience, after you’ve used these examples to achieve basic familiarity.
It is intended to accompany a number of posts on the blog
A River of Bytes.
The project was created with IntelliJ Idea 14 Community Edition,
currently using JDK 1.8, Scala 2.11.12 and Spark 2.3.0 on Ubuntu Linux.
Versions of these examples for other configurations (older versions of Scala and Spark) can be
found in various branches.
These are much less developed than the Scala examples below.
Note that they written to use Java 7 and Spark 2.0.0 only — if you go back to the before_spark2.0.0 branch
you won’t find any Java examples at all. I’m adding these partly out of curiosity (because I like Java
almost as much as Scala) and partly because of a realization that lots of Spark programmers use Java.
There are a number of things it’s important to realize I’m not promising to do:
Spark 2.2.0 note: Now that support for Java 7 has been dropped, these “old-fashioned” Java examples are of
dubious value, and I’ll probably delete them soon in favor of the separate Java/Maven project mentioned below.
I’ve completely stopped working on them, so I can focus on the Scala and Java 8 examples.
If you are using Java 8 or later, you may be interested in the new
learning-spark-with-java
project based completely on Java 8 and Maven.
Package | What’s Illustrated |
---|---|
rdd | The JavaRDD: core Spark data structure — see the local README.md in that directory for details. |
dataset | A range of Dataset examples (queryable collection that is statically typed) — see the local README.md in that directory for details. |
dataframe | A range of DataFrame/Dataset |
The examples can be found under src/main/scala. The best way to use them is to start by reading the code and its comments. Then, since each file contains an object definition with a main method, run it and consider the output. Relevant blog posts and StackOverflow answers are listed in the various package README.md files.
Package or File | What’s Illustrated |
---|---|
Ex1_SimpleRDD | How to execute your first, very simple, Spark Job. See also An easy way to start learning Spark. |
Ex2_Computations | How RDDs work in more complex computations. See also Spark computations. |
Ex3_CombiningRDDs | Operations on multiple RDDs |
Ex4_MoreOperationsOnRDDs | More complex operations on individual RDDs |
Ex5_Partitions | Explicit control of partitioning for performance and scalability. |
Ex6_Accumulators | How to use Spark accumulators to efficiently gather the results of distributed computations. |
hiveql | Using HiveQL features in a HiveContext. See the local README.md in that directory for details. |
special | Special/adbanced RDD examples — see the local README.md in that directory for details. |
dataset | A range of Dataset examples (queryable collection that is statically typed) — see the local README.md in that directory for details. |
dataframe | A range of DataFrame examples (queryable collection that is dynamically — and weakly — typed)— see the local README.md in that directory for details. |
sql | A range of SQL examples — see the local README.md in that directory for details. |
datasourcev2 | New experimental API for developing external data sources, as of Spark 2.3.0 — removed in favor of the new repository https://github.com/spirom/spark-data-sources, which explores the new API in some detail. |
streaming | Streaming examples — see the local README.md in that directory for details. |
streaming/structured | Structured streaming examples (Spark 2.0) — see the local README.md in that directory for details. |
graphx | A range of GraphX examples — see the local README.md in that directory for details. |
Additional Scala code is “work in progress”.