Category: Spark

Landon Robinson shows how to override a Spark dependency located on the classpath:

This doesn’t draw the line exactly where the method changed from private to public, but generally speaking:
– gson-2.2.4.jar: the method is private, and therefore too old for use here
– gson-2.6.1: the method is public, and works fine.
– Somewhere between the two, the method’s status changed.

So, because I had some functionality that required the method be public and accessible, it was important I specify the right version in my dependency manager (SBT). “That’s easy,” I thought. “No problem.”

Spoilers: there was a problem.

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Achilleus explains one of the big announcements at Spark+AI Summit 2019:

Delta Lake is basically a compute layer that would sit on top of your existing On Prem HDFS cluster, your favourite Cloud storage or even run it locally on your laptop(Best part)! Data is stored on the above-mentioned storage as versioned Parquet files. Any data that is read using Spark can be used to read and write with Delta Lake. Delta lakes provides an unified platform to support both Batch Processing and Stream processing workloads on a single platform.

Read on to understand just how useful this is.

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Achilleus shows us how to create a user-defined function for Spark in Scala, as well as the performance drawbacks:

It is pretty straight forward and easy to create it in spark. Let’s say we have this customer data from Central Perk. If you look at the country data, it has a lot of discrepancies but we kinda know its the right country, it’s just that the way it is entered is not typical. Let’s say we need to normalize it to the USA that is similar with the help of a known dictionary.

The performance hit is often too much for me to accept, though that could just be that I write bad functions.

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Subhasish Guha shows how you can read a fixed-with file with Apache Spark:

A fixed width file is a very common flat file format when working with SAP, Mainframe, and Web Logs. Converting the data into a dataframe using metadata is always a challenge for Spark Developers. This particular article talks about all kinds of typical scenarios that a developer might face while working with a fixed witdth file. This solution is generic to any fixed width file and very easy to implement. This also takes care of the Tail Safe Stack as the RDD gets into the foldLeft operator.

It’s a little more complicated than with R, where stringr can handle fixed-width formats. But it’s not bad.

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Unmesha Sreeveni shows how we can pivot a DataFrame in Apache Spark using one line of code:

A pivot can be thought of as translating rows into columns while applying one or more aggregations.

Lets see how we can achieve the same using the above dataframe.

We will pivot the data based on “Item” column.

Click through for the code. This is an area where dropping back into Scala or Python is a lot more lines-of-code efficient than sticking to SQL.

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Karunanithi Shanmugam gives us some tips on memory management for Spark in Amazon’s ElasticMapReduce:

Amazon EMR provides high-level information on how it sets the default values for Spark parameters in the release guide. These values are automatically set in the spark-defaults settings based on the core and task instance types in the cluster.

To use all the resources available in a cluster, set the maximizeResourceAllocation parameter to true. This EMR-specific option calculates the maximum compute and memory resources available for an executor on an instance in the core instance group. It then sets these parameters in the spark-defaults settings. Even with this setting, generally the default numbers are low and the application doesn’t use the full strength of the cluster. For example, the default for spark.default.parallelism is only 2 x the number of virtual cores available, though parallelism can be higher for a large cluster.

Spark on YARN can dynamically scale the number of executors used for a Spark application based on the workloads. Using Amazon EMR release version 4.4.0 and later, dynamic allocation is enabled by default (as described in the Spark documentation).

There’s a lot in here, much of which applies to Spark in general and not just EMR.

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