# print("\n strong connected component")
# result = g.stronglyConnectedComponents(maxIter=10)
# result.select("id", "component").orderBy("component").show()
# Page rank
print("\n Page rank")
# # run until convergence to tol
# results = g.pageRank(resetProbability=0.15, tol=0.01)
# results.vertices.select("id", "pagerank").show()
# results.edges.select("src", "dst", "weight").show()
## Run PageRank personalized for vertex ["a", "b", "c", "d"] in parallel
# results4 = g.parallelPersonalizedPageRank(resetProbability=0.15, sourceIds=["a", "b", "c", "d"], maxIter=10)\
print("\n shortest paths from each node to landmards node")
results = g.shortestPaths(landmarks=["a", "d"])
results.select("id", "distances").show()
# # Saving and Loading GraphFrames
# g.vertices.write.parquet("hdfs://myLocation/vertices")
# g.edges.write.parquet("hdfs://myLocation/edges")
#
# # Load the vertices and edges back.
# sameV = sqlContext.read.parquet("hdfs://myLocation/vertices")
# sameE = sqlContext.read.parquet("hdfs://myLocation/edges")
# message passing via AggregateMessages
# For each user, sum the ages of the adjacent users.
msgToSrc = AM.dst["age"]
msgToDst = AM.src["age"]
agg = g.aggregateMessages(sum(AM.msg).alias("summedAges"),
# | a| b| follow|
# | b| c| follow|
# | c| d| follow|
# | d| e| follow|
# | b| e| follow|
# | c| e| follow|
# | e| f| follow|
# +---+---+------------+
# Step-3: Create a GraphFrame. Using GraphFrames API, a graph
# is built as an instance of a GraphFrame, which is a pair of
# vertices (as `v`) and edges (as `e`):
graph = GraphFrame(v, e)
print("graph=", graph)
# GraphFrame(v:[id: string, name: string ... 1 more field],
# e:[src: string, dst: string ... 1 more field])
# Computes shortest paths for landmarks ["a", "f"]
#
results = graph.shortestPaths(landmarks=["a", "f"])
print("results=", results)
#
print("results.show()=")
results.show(truncate=False)
#
print('results.select("id", "distances").show()=')
results.select("id", "distances").show()
# done!
spark.stop()
