MapReduce patterns

After having modified and run a job in the last post, we can now examine which are the most frequent patterns we encounter in MapReduce programming.
Although there are many of them, I think that the most important ones are:

• Summarization
• Filtering
• Structural

Let's examine them in detail.

Summarization
By summarization we mean all the jobs that perform numerical computation over a set of data, like:

• indexing
• mean (or other statistical functions) computation
• min/max computation
• count (we've seen the WordCount example)

Filtering
Filtering is the act of retrieving only a subset of a bigger dataset. Most used cases are retrieving all data belonging to a single user or the top-N elements (by some criteria) of the dataset. Another frequent use of filtering is for sampling a dataset: when we're dealing with a lot of data , is usually a good idea to subset the original data by choosing some elements randomly to verify the behaviour of our job.

Structural
When you need to operate on the structure of the data; most used case is a join on different data, like the ones we're used to on a RDBMS.

In the next posts, we'll see in more detail how to deal with these patterns.