Chapter 6. Advanced Spark Programming


This chapter introduces a variety of advanced Spark programming features that we didn’t get to cover in the previous chapters. We introduce two types of shared variables: accumulators to aggregate information and broadcast variables to efficiently distribute large values. Building on our existing transformations on RDDs, we introduce batch operations for tasks with high setup costs, like querying a database. To expand the range of tools accessible to us, we cover Spark’s methods for interacting with external programs, such as scripts written in R.

Throughout this chapter we build an example using ham radio operators’ call logs as the input. These logs, at the minimum, include the call signs of the stations contacted. Call signs are assigned by country, and each country has its own range of call signs so we can look up the countries involved. Some call logs also include the physical location of the operators, which we can use to determine the distance involved. We include a sample log entry in Example 6-1. The book’s sample repo includes a list of call signs to look up the call logs for and process the results.

Example 6-1. Sample call log entry in JSON, with some fields removed
{"address":"address here", "band":"40m","callsign":"KK6JLK","city":"SUNNYVALE",
"county":"Santa Clara","dxcc":"291","fullname":"MATTHEW McPherrin",
"id":57779,"mode":"FM","mylat":"37.751952821","mylong" ...

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