Sharing variables across cluster nodes
When we're working on a distributed environment, sometimes it is required to share information across nodes so that all the nodes can operate using consistent variables. Spark handles this case by providing two kinds of variables: read-only and write-only variables. By not ensuring that a shared variable is both readable and writable anymore, it also drops the consistency requirement, letting the hard work of managing this situation fall on the developer's shoulders. Usually, a solution is quickly reached as Spark is really flexible and adaptive.
Broadcast read-only variables
Broadcast variables are variables shared by the driver node, that is, the node running the IPython Notebook in our configuration, with all the nodes in the cluster. It's a read-only variable as the variable is broadcast by one node and never read back if another node changes it.
Let's now see how it works on a simple example: we want to one-hot encode a dataset containing just gender...
