This repository contains hands-on labs and data files for a full-day Apache Spark workshop. This file is also the index for the hands-on labs.
Copyright (C) Sasha Goldshtein, 2016. All rights reserved.
Labs and data files for a full-day Spark workshop
License: MIT License
This repository contains hands-on labs and data files for a full-day Apache Spark workshop. This file is also the index for the hands-on labs.
Copyright (C) Sasha Goldshtein, 2016. All rights reserved.
There's no reason to suffer through all the aggregations when DataFrames are becoming more and more prevalent. At least point out that the DataFrame version is available, and have attendees experiment with it -- even if it's before we had a chance to teach SparkSQL.
Ideally a simple bash script that install dependencies, downloads and extracts Spark, Zeppelin, the course data files, and sets up everything so that workshop attendees can start being productive right away. If necessary, can assume Ubuntu 14.04 as the base image.
NOTE: Some Python labs depend on external modules that need to be brought in through easy_install
.
For each lab, add side-by-side Scala instructions. For the first couple of labs, attendees should be using spark-shell
directly, and there should be at least one example of taking a stand-alone .scala file and submitting it. The rest of the labs can use Zeppelin.
The SparkSQL examples can be adjusted to also cover the DataFrame API without writing SQL statements. The same applies to UDF. For example:
compensation = udf(lambda delay: 0 if delay < 15 else delay * 10)
df.select(..., compensation(df['ArrDelay'])).groupBy("carrier")...
Rework the course structure so that it puts an emphasis on the DataFrame / DataSet API first, and covers RDD as an implementation detail.
topFive = sorted(enumerate(similarities.collect()), key=lambda (k, v): -v)[0:5]
Near this particula line it shows pyspark 5063 error
Add an intro that highlights key concepts in ML and a few comments on some algorithms that Spark has to offer. Then we can do labs on:
Simply run pyspark --packages com.databricks:spark-csv_2.11:1.4.0
to have it detect and download the dependency automatically. Also update the slides to mention Spark Packages and this specific spark-csv package.
The docs for spark-csv are quite good as well. Should explain that the SQLContext.read
method has support for pluggable format, and maybe also mention that there's roundtripping support with SQLContext.write
.
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