MapReduce C++ Library

The MapReduce C++ Library implements a single-machine platform for programming using the the Google MapReduce idiom. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. Many real world tasks are expressible in this model, as shown in the Google paper.

map (k1,v1) --> list(k2,v2)
reduce (k2,list(v2)) --> list(v2)

Synopsis

namespace mapreduce {

template<typename MapTask,
		 typename ReduceTask,
		 typename Datasource=datasource::directory_iterator<MapTask>,
		 typename Combiner=null_combiner,
		 typename IntermediateStore=intermediates::local_disk<MapTask> >
class job;

} // namespace mapreduce

The developer is required to write two classes; MapTask implements a mapping function to process key/value pairs generate a set of intermediate key/value pairs and ReduceTask that implements a reduce function to merges all intermediate values associated with the same intermediate key. In addition, there are three optional template parameters that can be used to modify the default implementation behavior; Datasource that implements a mechanism to feed data to the Map Tasks - on request of the MapReduce library, Combiner that can be used to partially consolidate results of the Map Task before they are passed to the Reduce Tasks, and IntermediateStore that handles storage, merging and sorting of intermediate results between the Map and Reduce phases. The MapTask class must define four data types; the key/value types for the inputs to the Map Tasks and the intermediate types.

class map_task
{
  public:
	typedef std::string   key_type;
	typedef std::ifstream value_type;
	typedef std::string   intermediate_key_type;
	typedef unsigned      intermediate_value_type;

	map_task(job::map_task_runner &runner);
	void operator()(key_type const &key, value_type const &value);
};

The ReduceTask must define the key/value types for the results of the Reduce phase.

class reduce_task
{
  public:
	typedef std::string  key_type;
	typedef size_t       value_type;

	reduce_task(job::reduce_task_runner &runner);

	template<typename It>
	void operator()(typename map_task::intermediate_key_type const &key, It it, It ite)
};

Extensibility

The library is designed to be extensible and configurable through a Policy-based mechanism. Default implementations are provided to enable the library user to run MapReduce simply by implementing the core Map and Reduce tasks, but can be replaced to provide specific features.

Datasource

This policy implements a data provider for Map Tasks. The default implementation iterates a given directory and feeds each Map Task with a Filename and std::ifstream to the open file as a key/value pair. Combiner

A Combiner is an optimization technique, originally designed to reduce network traffic by applying a local reduction of intermediate key/value pairs in the Map phase before being passed to the Reduce phase. The combiner is optional, and can actually degrade performance on a single machine implementation due to the additional file sorting that is required. The default is therefore a null_combiner which does nothing. IntermediateStore

The policy class implements the behavior for storing, sorting and merging intermediate results between the Map and Reduce phases. The default implementation uses temporary files on the local file system. SortFn

Used to sort external intermediate files. Current default implementation uses a system() call to shell out to the operating system SORT process. A Merge Sort implementation is currently in development. MergeFn

Used to merge external intermediate files. Current default implementation uses a system() call to shell out to the operating system COPY process (Win32 only). A platform independent in-process implementation is required. SchedulePolicy

This policy is the core of the scheduling algorithm and runs the Map and Reduce Tasks. Two schedule policies are supplied, cpu_parallel uses the maximum available CPU cores to run as many map simultaneous tasks as possible (within a limit given in the mapreduce::specification object). The sequential scheduler will run one map task followed by one reduce task, which is useful for debugging purposes.

See the MapReduce C++ Library page for more information, and a sample program.