Category Archives: Spark Serializing and Encoding

Deployment, Distributed Systems, Feature Engineering, Java, Java Machine learning, Spark, Spark datasets and feature engineering, Spark Serializing and Encoding, State of the art distributed Machine learning tools

Java Spark Tips, Tricks and Basics 7 – How to accumulate a variable in Spark cluster? Why do we need to accumulate variables?

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Why do we need Spark accumulators

An accumulator is a shared variable across all the nodes and it is used to accumulate values of a type ( Long or Double).

It is necessary to use an accumulator, to implement a distributed counting variable which can be updated by multiple processes.

Nodes may not read the value of an accumulator, but the driver has full access to it.

Nodes can only accumulate values into the accumulator.

You will find the functionality for this in the accumulator Class of Spark. Keep in mind, that we are using the AccumulatorV2, older accumulators are deprecated for Spark version below 2.0

 

Don’t forget to register your accumulator to the Spark Context if you create it separately.

 

What did we learn?

In this short tutorial, you learned what Spark Accumulators are for,  what accumulators do  and how to use them in Java.

Distributed Systems, Exceptions and solutions, Feature Engineering, Java, Java Machine learning, Machine Learning Proejcts, Spark, Spark datasets and feature engineering, Spark Serializing and Encoding, State of the art distributed Machine learning tools, Uncategorized

Java Spark Tips, Tricks and Basics 6 – How to broadcast a variable to Spark cluster? Why do we need to broadcast variables?

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Why do we need Spark broadcasters?

Spark is all about cluster computing. In a cluster of nodes, each node of course has it’s personal private memory.

If we want all the nodes in the cluster to work towards a common goal,  having shared variables just seems necessary.

Let’s say we want to sum up all the rows in a CSV table with 1 million lines. It makes just sense, to let 1 node work with 1/2 million and the other work with the other 1/2 million rows. Both calculate their results and then the driver program will combine their results.

Broadcasting allows us to create a read-only cached copy of a variable on every node in our cluster. The distribution of those variables is handled by efficient broadcast algorithms implemented by Spark under the hood. This will also take the burden of thinking about serialization and deserialization since good old Spark takes care of that!

This great functionality for broadcasting is provided by the SparkContext class.  Alternatively, one can also consider to use the broadcast class right away, do your work

How to broadcast a variable in Spark Java

What did we learn?

In this short tutorial, you learned what Spark Broadcast is for,  what Broadcast does and how to use it in Java.

Distributed Systems, Machine Learning Proejcts, Spark, Spark Serializing and Encoding, State of the art distributed Machine learning tools

Spark Encoders, what are they there for and why do we need them?

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  • Encoders main purpose is the task of performing  serialization  and deserialization (SerDe)
  • Since Spark does not save data as JVM objects, but instead in it’s very own binary format.
  • Spark comes with a lot of build in encoders
  • An Encoder  priovides information about a tables schema, without  having to deserialize the whole object.
  • Encoders are nessecary when mapping datasets