MapReduce C++ Library for single-machine, multicore applications
转自http://cdmh.co.uk/papers/software_scalability_mapreduce/library
MapReduce C++ Library for single-machine, multicore applications
Distributed and scalable computing disciplines have recognized that immutable data, lock free access, and isolated data processing is not only inevitable across a number of machines, but has significant benefits for reliability and scalability. These benefit can be fostered in application design to improve reliability and take advantage of increasingly multi-core processor that are available on end-user devices as well as server machines.
MapReduce is an architecture framework designed for creating and processing large volumes of data using clusters of computers. For background information on MapReduce see Software Scalability with MapReduce.
An important development from Google's original paper is in applying MapReduce to parallel processing on multi-core environments such as multi-core and multi-processor machines and graphics processors (GPUs).
The scalability achieved using MapReduce to implement data processing across a large volume of machines with low implementation costs motivates the design of this library. By taking the principles that have been proven in a distributed MapReduce system and applying them to a single-machine, multicore implementation, reliability and execution efficiency can be attained in a reusable framework. In scaling down the architecture from multi-machine to multi-CPU or multi-core, threads of execution become analogous to machines in a distributed environment as a unit of process execution.
The MapReduce C++ Library implements a single-machine platform for programming using the theGoogle 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.
| Policy | Application | Supplied Implementation(s) | ||
Datasource |
|
datasource::directory_iterator<MapTask> |
||
Combiner |
|
null_combiner |
||
IntermediateStore |
|
local_disk<MapTask, SortFn, MergeFn> |
||
SortFn |
|
external_file_sort |
||
|
|
|
external_file_merge |
||
SchedulePolicy |
|
|
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 filesystem.
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.
Example - WordCount
Below is a simplified - but complete - WordCount example using the library. Error checking is removed and a simplified definition of a 'word' is used for brevity.
namespace wordcount {
class map_task;
class reduce_task;
typedef
mapreduce::job<
wordcount::map_task,
wordcount::reduce_task>
job;
class map_task : boost::noncopyable
{
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)
: runner_(runner)
{
}
// 'value_type' is not a reference to const to enable streams to be passed
// key: input filename
// value: ifstream of the open file
void operator()(key_type const &/*key*/, value_type &value)
{
while (!value.eof())
{
std::string word;
value >> word;
std::transform(word.begin(), word.end(), word.begin(),
std::bind1st(
std::mem_fun(&std::ctype<char>::tolower),
&std::use_facet<std::ctype<char> >(std::locale::classic())));
runner_.emit_intermediate(word, 1);
}
}
private:
job::map_task_runner &runner_;
};
class reduce_task : boost::noncopyable
{
public:
typedef std::string key_type;
typedef size_t value_type;
reduce_task(job::reduce_task_runner &runner)
: runner_(runner)
{
}
template<typename It>
void operator()(typename map_task::intermediate_key_type const &key, It it, It ite)
{
reduce_task::value_type result = 0;
for (; it!=ite; ++it)
result += *it;
runner_.emit(key, result);
}
private:
job::reduce_task_runner &runner_;
};
} // namespace wordcount
int main(int argc, char **argv)
{
wordcount::job::datasource_type datasource;
datasource.set_directory(argv[1]);
mapreduce::specification spec;
mapreduce::results result;
wordcount::job mr(datasource);
mr.run<mapreduce::schedule_policy::cpu_parallel>(spec, result);
// output the results
std::cout << std::endl << "\n" << "MapReduce statistics:";
std::cout << "\n " << "MapReduce job runtime : " << result.job_runtime << " seconds, of which...";
std::cout << "\n " << " Map phase runtime : " << result.map_runtime << " seconds";
std::cout << "\n " << " Reduce phase runtime : " << result.reduce_runtime << " seconds";
std::cout << "\n\n " << "Map:";
std::cout << "\n " << "Total Map keys : " << result.counters.map_tasks;
std::cout << "\n " << "Map keys processed : " << result.counters.map_tasks_completed;
std::cout << "\n " << "Map key processing errors : " << result.counters.map_tasks_error;
std::cout << "\n " << "Number of Map Tasks run (in parallel) : " << result.counters.actual_map_tasks;
std::cout << "\n " << "Fastest Map key processed in : " << *std::min_element(result.map_times.begin(), result.map_times.end()) << " seconds";
std::cout << "\n " << "Slowest Map key processed in : " << *std::max_element(result.map_times.begin(), result.map_times.end()) << " seconds";
std::cout << "\n " << "Average time to process Map keys : " << std::accumulate(result.map_times.begin(), result.map_times.end(), boost::int64_t()) / result.map_times.size() << " seconds";
std::cout << "\n\n " << "Reduce:";
std::cout << "\n " << "Number of Reduce Tasks run (in parallel): " << result.counters.actual_reduce_tasks;
std::cout << "\n " << "Number of Result Files : " << result.counters.num_result_files;
std::cout << "\n " << "Fastest Reduce key processed in : " << *std::min_element(result.reduce_times.begin(), result.reduce_times.end()) << " seconds";
std::cout << "\n " << "Slowest Reduce key processed in : " << *std::max_element(result.reduce_times.begin(), result.reduce_times.end()) << " seconds";
std::cout << "\n " << "Average time to process Reduce keys : " << std::accumulate(result.reduce_times.begin(), result.reduce_times.end(), boost::int64_t()) / result.map_times.size() << " seconds";
return 0;
}
Performance
Here are some results running the WordCount example on theWestbury Lab USENET corpus (2005)containing 9.92 GB (10,659,287,688 bytes) of data in 23 files.
Sequential MapReduce
The sequential schedulig algorithm gives a baseline timing for the WordCount implementation,running a single Map task followed by a single Reduce task.
MapReduce Wordcount Application
16 CPU cores
class mapreduce::job<class wordcount::map_task,class wordcount::reduce_task,struct mapreduce::null_c
ombiner,class mapreduce::datasource::directory_iterator<class wordcount::map_task>,class mapreduce::
intermediates::local_disk<class wordcount::map_task,struct win32::external_file_sort,struct win32::e
xternal_file_merge> >
Running Sequential MapReduce...
Finished.
MapReduce statistics:
MapReduce job runtime : 3105 seconds, of which...
Map phase runtime : 699 seconds
Reduce phase runtime : 2406 seconds
Map:
Total Map keys : 23
Map keys processed : 23
Map key processing errors : 0
Number of Map Tasks run (in parallel) : 1
Fastest Map key processed in : 0 seconds
Slowest Map key processed in : 96 seconds
Average time to process Map keys : 30 seconds
Reduce:
Number of Reduce Tasks run (in parallel): 1
Number of Result Files : 10
Fastest Reduce key processed in : 161 seconds
Slowest Reduce key processed in : 390 seconds
Average time to process Reduce keys : 104 seconds
CPU Parallel
Running on a 16 CPU-core Windows server, using all core for Map tasks, produces the following results:
MapReduce Wordcount Application
16 CPU cores
class mapreduce::job,class mapreduce::
intermediates::local_disk >
Running CPU Parallel MapReduce...
CPU Parallel MapReduce Finished.
MapReduce statistics:
MapReduce job runtime : 1608 seconds, of which...
Map phase runtime : 842 seconds
Reduce phase runtime : 766 seconds
Map:
Total Map keys : 23
Map keys processed : 23
Map key processing errors : 0
Number of Map Tasks run (in parallel) : 16
Fastest Map key processed in : 0 seconds
Slowest Map key processed in : 842 seconds
Average time to process Map keys : 433 seconds
Reduce:
Number of Reduce Tasks run (in parallel): 10
Number of Result Files : 10
Fastest Reduce key processed in : 384 seconds
Slowest Reduce key processed in : 766 seconds
Average time to process Reduce keys : 261 seconds
Running on the same server, restricting the number of Map task to 8 yields:
MapReduce Wordcount Application
16 CPU cores
class mapreduce::job<class wordcount::map_task,class wordcount::reduce_task,struct mapreduce::null_c
ombiner,class mapreduce::datasource::directory_iterator<class wordcount::map_task>,class mapreduce::
intermediates::local_disk<class wordcount::map_task,struct win32::external_file_sort,struct win32::e
xternal_file_merge> >
Running CPU Parallel MapReduce...
CPU Parallel MapReduce Finished.
MapReduce statistics:
MapReduce job runtime : 1743 seconds, of which...
Map phase runtime : 950 seconds
Reduce phase runtime : 793 seconds
Map:
Total Map keys : 23
Map keys processed : 23
Map key processing errors : 0
Number of Map Tasks run (in parallel) : 8
Fastest Map key processed in : 0 seconds
Slowest Map key processed in : 934 seconds
Average time to process Map keys : 303 seconds
Reduce:
Number of Reduce Tasks run (in parallel): 10
Number of Result Files : 10
Fastest Reduce key processed in : 396 seconds
Slowest Reduce key processed in : 793 seconds
Average time to process Reduce keys : 271 seconds
Get the source code on GitHub