Efficient Spark Dataframe Transforms

If you are working with Spark, you will most likely have to write transforms on dataframes. Dataframe exposes the obvious method df.withColumn(col_name,col_expression) for adding a column with a specified expression. Now, as we know that the dataframes are immutable in nature, so we are getting a newly …

in spark · Sun 18 March 2018

Writing Generic UDFs in Spark

Apache Spark offers the ability to write Generic UDFs. However, for an idiomatic implementation, there are a couple of things that one needs to keep in mind.

1. You should return a subtype of Option because Spark treats None subtype automatically as null and is able to extract value from Some …

in spark · Wed 24 January 2018

Testing Spark Dataframes

Testing Spark Dataframe transforms is essential and can be accomplished in a more reusable manner. The way, I generally accomplish that is to

• Read the expected and test Dataframe, and
• Invoke the desired transform, and
• Calculate the difference between dataframes. The only caveat in calculating the difference is that in …

in spark · Wed 17 January 2018

Parallel Orchestration of Spark ETL Processing

I have been working a lot on Spark and Scala. I have really like scala as a language, due to its numerous advantages over Java, the foremost being that for a simpler API having Type classes and Default Method Arguments does wonders. Also, idiomatic scala code uses higher order functions …

in spark · Sat 28 October 2017

Apache Zeppelin Notebooks

Apache Zeppelin provides a Web-UI where you can iteratively build spark scripts in Scala, Python, etc. (It also provides autocomplete support), run Sparkql queries against Hive or other store and visualize the results from the query or spark dataframes. This is somewhat akin to what Ipython notebooks do for python …

in Big Data · Sat 04 June 2016