def __init__(self, vertex_jrdd, edge_jrdd,
partition_strategy=PartitionStrategy.EdgePartition1D):
self._vertex_jrdd = VertexRDD(vertex_jrdd, vertex_jrdd.context,
BatchedSerializer(PickleSerializer()))
self._edge_jrdd = EdgeRDD(edge_jrdd, edge_jrdd.context,
BatchedSerializer(PickleSerializer()))
self._partition_strategy = partition_strategy
self._jsc = vertex_jrdd.context
class Graph(object):
def __init__(self, vertex_jrdd, edge_jrdd,
partition_strategy=PartitionStrategy.EdgePartition1D):
self._vertex_jrdd = VertexRDD(vertex_jrdd, vertex_jrdd.context,
BatchedSerializer(PickleSerializer()))
self._edge_jrdd = EdgeRDD(edge_jrdd, edge_jrdd.context,
BatchedSerializer(PickleSerializer()))
self._partition_strategy = partition_strategy
self._jsc = vertex_jrdd.context
def persist(self, storageLevel):
self._vertex_jrdd.persist(storageLevel)
self._edge_jrdd.persist(storageLevel)
return
def cache(self):
self._vertex_jrdd.cache()
self._edge_jrdd.cache()
return
def vertices(self):
return self._vertex_jrdd
def edges(self):
return self._edge_jrdd
def numEdges(self):
return self._edge_jrdd.count()
def numVertices(self):
return self._vertex_jrdd.count()
# TODO
def partitionBy(self, partitionStrategy):
return
# TODO
def inDegrees(self):
return
# TODO
def outDegrees(self):
return
# TODO
def degrees(self):
return
def triplets(self):
if (isinstance(self._jsc, SparkContext)):
pyGraph = self._jsc.jvm.org.apache.spark.PythonGraph()
return pyGraph.triplets()
# TODO
def unpersistVertices(self, blocking = True):
return
def mapVertices(self, f):
return self._vertex_jrdd.mapValues(f)
def mapEdges(self, f):
return self._vertex_jrdd.mapValues(f)
# TODO
def mapTriplets(self, f):
return
# TODO
def reverse(self):
return
# TODO
def subgraph(self, epred, pred):
return
# TODO
def groupEdges(self, mergeFunc):
return
# TODO
def joinVertices(self, mapFunc):
return
# TODO
def outerJoinVertices(self, mapFunc):
return
# TODO
def collectNeighborIds(self, edgeDirection):
return
# TODO
def collectNeighbors(self, edgeDirection):
return
# TODO
def mapReduceTriplets(self, mapFunc, reduceFunc):
return
def pagerank(self, num_iterations, reset_probability = 0.15):
"""
Pagerank on the graph depends on valid vertex and edge RDDs
Users can specify terminating conditions as number of
iterations or the Random reset probability or alpha
:param num_iterations: Number of iterations for the
algorithm to terminate
:param reset_probability: Random reset probability
:return:
"""
py_graph = self._sc._jvm.org.apache.PythonGraph.pagerank(num_iterations, reset_probability)
return py_graph.asJavaRDD()
def connected_components(self):
py_graph = self._sc._jvm.org.apache.PythonGraph.connectedComponents()
return py_graph.asJavaRDD()
def reverse(self):
py_graph = self._sc._jvm.org.apache.PythonGraph.reverse()
return py_graph.asJavaRDD()
def apply(self, f):
def func(iterator):
return itertools.imap(f, iterator)
py_graph = self._sc._jvm.org.apache.PythonGraph.apply(func)
return py_graph.asJavaRDD()
# TODO
def triangleCount(self):
return
# TODO
def stronglyConnectedComponents(self, iterations):
return
def Pregel(self, initial_message, vertex_program, send_message, combine_message):
return
