Category: Spark

Prithviraj Bose explains accumulators in Spark:

However, the logs can be corrupted. For example, the second line is a blank line, the fourth line reports some network issues and finally the last line shows a sales value of zero (which cannot happen!).

We can use accumulators to analyse the transaction log to find out the number of blank logs (blank lines), number of times the network failed, any product that does not have a category or even number of times zero sales were recorded. The full sample log can be found here.
Accumulators are applicable to any operation which are,
1. Commutative -> f(x, y) = f(y, x), and
2. Associative -> f(f(x, y), z) = f(f(x, z), y) = f(f(y, z), x)
For example, sum and max functions satisfy the above conditions whereas average does not.

Accumulators are an important way of measuring just how messy your semi-structured data is.

Ian Hellstrom has scripts to simplify Apache Spark application rollout:

When creating Apache Spark applications the basic structure is pretty much the same: for sbt you need the same build.sbt, the same imports, and the skeleton application looks the same. All that really changes is the main entry point, that is the fully qualified class. Since that’s easy to automate, I present a couple of shell scripts that help you create the basic building blocks to kick-start Spark application development and allow you to easily upgrade versions in the configuration.

Check these out if you’re interested in Spark.