Spark is the de facto standard in clustering computing at this point in time.  At a high level Spark executes code that is distributed throughout a cluster so that the code being executed is as close as possible to where the data lives so as to minimize transferring of large amounts of data.  The code that needs to be executed are packaged up into units called Resilient Distributed Dataset (RDD).  RDDs are lazy evaluated and are essential graphs of the operations that need to be performed on the data.  They are capable of reading data from many types of sources, outputting to multiple types of sources, containing the code that needs to be executed, and are also responsible to caching or keeping results in memory for future RDDs that maybe executed.

If you write all your code in Java or Scala, its execution will be performed in JVMs distributed in the cluster.  On the other hand, Spark does not limit its use to only Java based languages so Python can be used.  In the case of Python the PySpark library is used.  When Python is used, the PySpark library can be used to define the RDDs that will be executed under the JVM.  In this scenario, only if required, the final results of the calculations will end up being passed to Python.  I say only if necessary as its possible the end results may just be left in memory or to create an output such as an hdfs file in hadoop and does not need to be transferred to Python. Under this scenario the code is written in Python but effectively all the "real" work is performed under the JVM.

Often someone writing Python is also going to want to perform some of the operations under Python.  This can be done as the RDDs that are created can contain both operations that get performed under the JVM as well as Python (and of course other languages are supported).  When Python is involved Spark will start up Python VMs on the required nodes so that the Python portions of the work can be performed.  The Python VMs can either be CPython, PyPy or even a mix of both CPython and PyPy.  The downside to using non Java languages is the overhead of passing data between the JVM and the Python VM as the memory is not shared between the processes but instead copied/serialized between them.

Because this data is copied between the 2 VMs, anyone who writes Python code for this environment always has to be conscious of the data being copied between the processes so as to not let the amount of the extra overhead become a large burden.  Quite often the goal will be to first perform the bulk of the operations under the JVM and then hopefully only a smaller subset of the data will have to be processed under Python.  If this can be done then the overhead can be minimized and then there is essential no down sides to using Python in the pipeline of operations.

If your unfortunate and need to perform some of the processing early in the pipline under Python and worse yet if there is a need to go back and forth many times between Python and Java the overhead of coping huge amounts of data can significantly slow things down which essentially puts Python at a disadvantage to Java.

If it was possible to change the model of execution such that it was possible to embed the Python VM in the JVM or vice versa and that the memory could be shared between the 2 VMs the downside of using Python in this environment would be eliminated or at the very least minimized to the point where it is no longer an issue.  Thus the need for a jffi library.

There is a strong desire by many to use dynamic languages in these clustered environments and Python is likely in the best position to become the language of choice due to its ability to work with C based libraries and of course its syntax.  The issues that hold Python back at this point is the serialization overhead, not so great state of packaging, and not having both the speed of the JIT and complete access to numpy/scipy ecosystem.

Luckily for Python at this point there is no other dynamic language that is a clear winner today.  But if too much time passes before these issues are solved I'm sure another language will step up to the plate.  At this point my expectations is that Node could likely make a move.  It already has the speed due to the Java Script JITs, it already has a great story for packaging and deployment, and its growth is exploding on the server side due to all the money being poured into it.  What it strongly lacks today is the connection to C/legacy code, numerical/scientific modules and of course it also does not have a solution to the data copying overhead it also has with the JVM.

Any way, this is just my 2 cents on what is currently holding Python back from taking off in this space.