It’s been 10 years that I develop in Java, and some years that I know Scala. Surely I do not consider myself a ninja, but I have some notions about programming.

Recently I started to use Apache Spark for a project I was involved to. In this project we have to process a huge amount of data (somebody calls such applications as Big-data, ;) ) and to accomplish this job we have to use some distributed programming on a cluster.

Apache Spark is a library that let you write code, that will be executed in a distributed fashion, as a simple single threaded program. Spark will make the dirty job for you: it will distribute the code and the execution, manage remote objects for you, optimize the algorithm for you. However, be aware: there is no kind of magic behind Spark. If you do not deeply understand the logic behind it, you will discover new way to loss your time searching for incomprehensible bugs.

## Spark distributed data structures

One of the core concepts of Apache Spark is the way it represents distributed data structures. Resilient Distributed Dataset, a.k.a. RDD, is the type that is used by Spark to work with data in a distributed way. RDD[T] is a invariant generic type and in its simplest form can be seen as a distributed implementation of a List.

In the book Learning Spark RDD is defined as follow:

An RDD in Spark is simply an immutable distributed collection of objects. Each RDD is split into multiple partitions, which may be computed on different nodes of the cluster. RDDs can contain any type of Python, Java, or Scala objects, including user defined classes.

So, Spark uses to partition data contained in an RDD to different nodes. The main node, where the program is started is called the driver node.

On a RDD you can do two type of operations, transformations and actions. Transformations construct a new RDD from a previous one. For example. map and flatMap operations are considered a transformations.

Actions compute a result based on an RDD, and either return it to the driver program or save it to an external storage system (such as HDFS, HBase, …).

Finally, when you work with RDD you have to remember that

although you can define new RDDs any time, Spark computes them only in a lazy fashion —that is, the first time they are used in an action.

## Spark and serialization

Using the information given above, it’s easy to imagine that Spark does a lot of work on your code. First of all, it has to partition the execution of transformations on RDD, and then it has to rewrite your code in a way that enables the laziness.

What we want to analyze in this post is the issue regarding the distribution of the execution of the transformation to the workers. As a proof of concepts, take the following code.

First of all we have to operate some distinction between the objects that are used inside the two transformations. Here we have two kind of objects, which are:

• The Strings that are wrapped inside the RDD
• All other objects that are passed in the closures of flatMap and filter

Have you ever heard about Java RMI Specification? The behaviour we are gonna to analyze is very similar to the behaviour described in the RMI specification. In RMI types are divided in two main subsets: the types that implement the java.rmi.Remote interface and the types that does not implement this interface.

The former are remote types, which means that their methods can be accessed remotely, i.e. they can be invoked from objects that live on nodes in computer network different from the node in which remote objects were instantiated. The state of these objects is shared, which means that all the changes made by a method invoked from a node are visible to all other nodes that owns a reference to the same remote object.

Distributed shared object, what an hell!!!

The latter are the object that are usually passed as parameters to methods of the remote objects. Let’s say that we have the following remote interface:

The Task type is not a remote object; it is just passed to a method of a remote object. Which is the destiny of such types? First of all, they must implement the java.io.Serializable. Why? Because they will be serialized and sent through the network, among server nodes.

Have I mentioned serialization? As you know, each object that is serialized is first of all copied and then processed. Returning to the above example, after a Task object was serialized and sent to another node, it would not have any relation with the original copy.

No shared state at all.

Spark adopt exactly the same behaviour: every object that is not enclosed inside an RDD and it is passed to a transformation will be strictly serialized and send to worker nodes. It will share no state with the original copy in the driver node.

## One line of code to ruin them all

Now that we presented the basic behaviour of Spark, we can return to the object of this article: the new kind of bugs you can nicely find using Spark. Let’s start from the code.

The above code is absolutely senseless, but it helps us to explain what’s going on. We would expect that if the file contains the string 42, the value of the attribute answer of the object packet after the execution of the action transformed.count shall be 42, wouldn’t we?

Wrong! The packet object is not enclosed inside an RDD. When it is passed to the map transformation, first of all it is serialized, and then sent to the worker nodes. Every update to the packet object made inside the transformation will be lost and not reflected to the copy of the object owned by the driver.

And things can get even worse. Imagine that you want to pass a collection to the closure , let say a List of objects. For every element of the RDD you select an item of the list, modify it and finally create a new RDD item that include that object.

Can you see the madness of the above code? How many different Packet object will we have in the RDD of couples? One? Two? One for every token? Probably the more correct answer is it depends.

It depends from how many worker are configured in the cluster and on how many of them have been used to partition the original RDD during the elaboration. Each worker has a different copy of the packets list, and it uses the local list to compute the final value. Every change to the packets objects is made on the local copy, but then shared with the driver through the resulting RDD.

And this thing has cost me more or less 4 hours of my life :(

Clearly, the problem is that the above code is not functional, which means that it uses object with mutable state and the transformation has some side effects on its input. Remove the mutable state, and you will remove (more or less) every code smell.

## On Spark distribution model

The parallelism that I’ve made between Java RMI and Apache Spark is merely related to the external behaviour seen by the developer, and not related to the implementation of Spark. As Roland Kuhn wrote in an email that kindly answer a question that I have previously made to him

None of the proper reactive solutions use RMI, that technique is obsolete (search for the difference between transparent remoting and location transparency). In particular Spark does not perform remote calls in the way you think, that would slow it down by orders of magnitude. Instead it serializes the code and sends that to the worker nodes.

And that’s all, folks!