This chapter has used three case studies to highlight some more advanced aspects of Hadoop and its broader ecosystem. In particular, we covered the nature of join-type problems and where they are seen, how reduce-side joins can be implemented with relative ease but with an efficiency penalty, and how to use optimizations to avoid full joins in the map-side by pushing data into the Distributed Cache.
We then learned how full map-side joins can be implemented, but require significant input data processing; how other tools such as Hive and Pig should be investigated if joins are a frequently encountered use case; and how to think about complex types like graphs and how they can be represented in a way that can be used in MapReduce.
We also saw techniques for breaking graph algorithms into multistage MapReduce jobs, the importance of language-independent data types, how Avro can be used for both language independence as well as complex Java-consumed types, and the Avro extensions to the...