Enterprise Data Workflows with Cascading

Chapter 3. Test-Driven Development

Example 5: TF-IDF Implementation

In the previous example, we looked at extending pipe assemblies in Cascading workflows. Functionally, Example 4: Replicated Joins is only a few changes away from implementing an algorithm called term frequency–inverse document frequency (TF-IDF). This is the basis for many search indexing metrics, such as in the popular open source search engine Apache Lucene. See the Similarity class in Lucene for a great discussion of the algorithm and its use.

For this example, let’s show how to implement TF-IDF in Cascading—which is a useful subassembly to reuse in a variety of apps. Figure 3-1 shows a conceptual diagram for this. Based on having a more complex app to work with, we’ll begin to examine Cascading features for testing at scale.

Figure 3-1. Conceptual flow diagram for Example 5: TF-IDF Implementation

Starting from the source code directory that you cloned in Git, connect into the part5 subdirectory. First let’s add another sink tap to write the TF-IDF weights:

String tfidfPath = args[ 3 ];
Tap tfidfTap = new Hfs( new TextDelimited( true, "\t" ), tfidfPath );

Next we’ll modify the existing pipe assemblies for Word Count, beginning immediately after the join used as a “stop words” filter. We add the following line to retain only the doc_id and token fields in the output tuple stream, based on the fieldSelector parameter: ...