Streaming clustering in Spark
Up to this point, we have mainly demonstrated examples for ad hoc exploratory analysis. In building up analytical applications, we need to begin putting these into a more robust framework. As an example, we will demonstrate the use of a streaming clustering pipeline using PySpark. This application will potentially scale to very large datasets, and we will compose the pieces of the analysis in such a way that it is robust to failure in the case of malformed data.
As we will be using similar examples with PySpark in the following chapters, let's review the key ingredients we need in such application, some of which we already saw in Chapter 2, Exploratory Data Analysis and Visualization in Python. Most PySpark jobs we will create in this book consist of the following steps:
Construct a Spark context. The context contains information about the name of the application, and parameters such as memory and number of tasks.
The Spark context may be used to construct secondary...
