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WeightedGraphMax_SavedPath

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资源说明：This Hadoop MapReduce program operates on a directed graph, in adjacency list format. The program computes the maximum total of node weights, from top to bottom of the directed graph, and records the path taken to get to the maximum total node weight, by performing a breadth-first graph search using an iterative map-reduce algorithm.

 Author: John Livingstone
  
 This Hadoop (Using Cloudera Hadoop version: 0.20.1+152) MapReduce program operates on a directed graph, in adjacency list format.
 The program computes the maximum total of node weights, from top to bottom of the directed graph, and records the path taken to
 get to the maximum total node weight, by performing a breadth-first graph search using an iterative map-reduce algorithm. 
 
 The directed graph is in the form of a triangle set of numbers.
 This program takes as input a 'triangle' set of numbers in an adjacency list format.
 The triangle of numbers represent a directed graph, where each node points to the 2 nodes directly below it on the next line.
 For example, for the triangle below: (note that I add an 'aggregation node' with zero weight to the triangle as the last node)
 
 21
 10 13
 45 21 32
 0
 
  Is represented by the following Adjacency List:
  Node position 	Weight	Points to Nodes (Edges)
  1				21		2,3
  2				10		4,5
  3				13		5,6
  4				45		7
  5				21		7
  6				32		7
  7				0		
  
  The input format is
  ID   WEIGHT|EDGES|DISTANCE|COLOR
  where
  ID = the unique identifier for a node (assumed to be an int here)
  WEIGHT = The value of the node - this integer value contributes to the 'distance' from the starting node
  EDGES = the list of edges emanating from the node (e.g. 3,8,9,12)
  DISTANCE = the Maximum 'to be determined' distance of the node from the source
  COLOR = a simple status tracking field to keep track of when we're finished with a node
  It assumes that the source node (the node from which to start the search) has
  been marked with distance of zero and color GRAY in the original input.  All other
  nodes will have input distance of zero and color WHITE.
  
  An example line of input format file:

1	5|2,3,|0|GRAY|
2	10|4,5,|0|WHITE|
3	12|5,6,|0|WHITE|
4	17|7,8,|0|WHITE|
5	14|8,9,|0|WHITE|
6	15|9,10,|0|WHITE|
7	9|11,12,|0|WHITE|
8	14|12,13,|0|WHITE|
9	10|13,14,|0|WHITE|
10	6|14,15,|0|WHITE|
11	5|16,17,|0|WHITE|
12	4|17,18,|0|WHITE|
13	3|18,19,|0|WHITE|
14	2|19,20,|0|WHITE|
15	7|20,21,|0|WHITE|
16	6|22,|0|WHITE|
17	8|22,|0|WHITE|
18	5|22,|0|WHITE|
19	4|22,|0|WHITE|
20	3|22,|0|WHITE|
21	2|22,|0|WHITE|
22	0|23,|0|WHITE|
 
 To run:   hadoop jar /home/training/workspace/Graph/bin/WeightedGraphMinSearch.jar -c IOFiles-HDFS-Config.xml
 
 The application takes one required parameter and a few optional parameters.
 The one required parameter is the "-c" input and output file configuration file that specified
 both the input and output file paths. The idea of locating the IO file information in a separate
 configuration file is that you move between separate sets of files based on where you are testing
 (local HDFS or Amazon S3) and what input data sets that you are operating upon.
 
 The usage syntax is displayed below:
Usage: WeightedGraphMinSearch -c "
Optional Parameters are:
 -i 
 -m 
 -r 
  where  is equal to the number of rows to be processed in the triangle 
  		or is the number of iterations to work through of the directed graph.  We can use this
  		parameter to set the upper limit of iterations that will run as we expand the frontier
  		of processed notes through the directed weighted graph.						
 

  
  The output format is
  ID   WEIGHT|EDGES|DISTANCE|COLOR|Path_taken_edges|
  where
  ID = the unique identifier for a node (assumed to be an int here)
  WEIGHT = The value of the node - this integer value contributes to the 'distance' from the starting node
  EDGES = the list of edges emanating from the node (e.g. 3,8,9,12)
  DISTANCE = the Maximum 'to be determined' distance of the node from the source starting node
  COLOR = a simple status tracking field to keep track of when we're finished with a node
  Path_taken_edges: the nodes edges taken from the starting node to get to this node.
  
  An example output file after the 6th iteration:
1	5|2,3,|0|BLACK||
2	10|4,5,|5|BLACK|1,|
3	12|5,6,|5|BLACK|1,|
4	17|7,8,|15|BLACK|1,2,|
5	14|8,9,|17|BLACK|1,3,|
6	15|9,10,|17|BLACK|1,3,|
7	9|11,12,|32|BLACK|1,2,4,|
8	14|12,13,|32|BLACK|1,2,4,|
9	10|13,14,|32|BLACK|1,3,6,|
10	6|14,15,|32|BLACK|1,3,6,|
11	5|16,17,|41|BLACK|1,2,4,7,|
12	4|17,18,|46|BLACK|1,2,4,8,|
13	3|18,19,|46|BLACK|1,2,4,8,|
14	2|19,20,|42|BLACK|1,3,6,9,|
15	7|20,21,|38|BLACK|1,3,6,10,|
16	6|22,|46|BLACK|1,2,4,7,11,|
17	8|22,|50|BLACK|1,2,4,8,12,|
18	5|22,|50|BLACK|1,2,4,8,12,|
19	4|22,|49|BLACK|1,2,4,8,13,|
20	3|22,|45|BLACK|1,3,6,10,15,|
21	2|22,|45|BLACK|1,3,6,10,15,|
22	0||58|BLACK|1,2,4,8,12,17,|



Some features of this program are the following:
1. The program performs multiple MapReduce (MR) iterations, using the output of the prior iteration as the input to the next iteration of the MR pass.
2. The program executes until one of the two conditions exist (a) either there are no more 'Gray' node to be processed or 
   (b) we have reached the maximum number of iteration that was specified on the command line.  We use Hadoop counters - specifically
   the 'NUMBER_OF_GRAY_NODES_TOBE_PROCESSED' counter - to keep track if we have any more gray nodes to process.
3. We also use the 'NUMBER_OF_GRAY_NODES_PROCESSED' counter, in combination with the 'NUMBER_OF_GRAY_NODES_TOBE_PROCESSED' counter to know that we are processing the last set of 
   gray nodes, and that we should make sure that we have only one Reducer task so that all results are aggregated into one output (Part00000) file.
4. We use job configuration files to specify and easily switch between differing input and output files.
5. To run the application on AWS, the jar location will be something like: /jhl-mapreduce/WeightedGraphMaxSearch.jar and the jar arguments will
   be something like: -c IOFiles-AWS-Config.xml

Still To Do:
1. Write a Combiner (just a varient of the Reducer).

The initial 'seed' source was taken from "John & Cailin breadth-first graph search using an iterative map-reduce algorithm" 
at http://www.johnandcailin.com/blog/cailin/breadth-first-graph-search-using-iterative-map-reduce-algorithm 

The idea to use the Hadoop counter to control the maximum number on iterations was taken from "cchandler / codekata19-mapreduce"
at http://github.com/cchandler/codekata19-mapreduce 

All are free to use the code in this MR application.
    
If you like this application and find it useful in your work or have any questions on its use - please send me a note at john.livingst@gmail.com

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