A Python implementation of Spark's Python API, but working on a single machine.
Programming directly on Spark is a hard task:
- The behavior of Spark is hard to understand;
- The error message is hard to decipher;
- There is few debugging tools;
- The test is time consuming.
With this package, you can get rid of all these problems. Use this package to prototype or experiment on a single machine.
- Your code runs fast.
- The error message is stardard Python error message, and thus easy to understand.
- You have automatically all debugging tools, which you are used to using.
- Any further doubt? Just peek into this package's source code. There are no "magic" inside.
As soon as your program runs correctly, use Spark to deploy it on a cluster. Your code needs no or little modification.
import park as sc sc.parallelize()
If you ever use the
mapPartitions() function, you will need to do a mild modification when you pass to Spark.
For every function which you pass to Spark's
mapPartitions() function, you should add a
 around the returned value(s).