In this tutorial we'll cover most of the topics involved in building a batch processing job with Jet's Core API. We'll show you how to decide on processor parallelism, partitioning and forwarding patterns on edges, and how to optimally leverage the Core API to build the vertex logic with minimum boilerplate.
The full code is available at the
Our example, the inverted index, is a basic data structure in the domain of full-text search. The goal is to be able to quickly find the documents that contain a given set of search terms, and to sort them by relevance. To understand it we'll need to throw in some terminology...
- A document is treated as a list of words that has a unique ID. It is useful to define the notion of a document index which maps each document ID to the list of words it contains. We won't build this index; it's just for the sake of explanation.
- The inverted index is the inverse of the document index: it maps each word to the list of documents that contain it. This is the fundamental building block in our search algorithm: it will allow us to find in O(1) time all documents relevant to a search term.
- In the inverted index, each entry in the list is assigned a TF-IDF
score which quantifies how relevant the document is to the search
- Let DF (document frequency) be the length of the list: the number of documents that contain the word.
- Let D be the total number of documents that were indexed.
- IDF (inverse document frequency) is equal to
- TF (term frequency) is the number of occurrences of the word in the document.
- TF-IDF score is simply the product of
TF * IDF.
Note that IDF is a property of the word itself: it quantifies the relevance of each entered word to the search request as a whole. The list of entered words can be perceived as a list of filtering functions that we apply to the full set of documents. A more relevant word will apply a stronger filter. Specifically, common words like "the", "it", "on" act as pure "pass-through" filters and consequently have an IDF of zero, making them completely irrelevant to the search.
TF, on the other hand, is the property of the combination of word and document, and tells us how relevant the document is to the word, regardless of the relevance of the word itself.
When the user enters a search phrase:
- each individual term from the phrase is looked up in the inverted index;
- an intersection is found of all the lists, resulting in the list of documents that contain all the words;
- each document is scored by summing the TF-IDF contributions of each word;
- the result list is sorted by score (descending) and presented to the user.
Let's have a look at a specific search phrase:
the man in the black suit murdered the king
The list of documents that contain all the above words is quite long... how do we decide which are the most relevant? The TF-IDF logic will make those stand out that have an above-average occurrence of words that are generally rare across all documents. For example, "murdered" occurs in far fewer documents than "black"... so given two documents where one has the same number of "murdered" as the other one has of "black", the one with "murdered" wins because its word is more salient in general. On the other hand, "suit" and "king" might have a similar IDF, so the document that simply contains more of both wins.
Also note the limitation of this technique: a phrase is treated as just the sum of its parts; a document may contain the exact phrase and this will not affect its score.