Execute a Pregel-like iterative vertex-parallel abstraction.
Execute a Pregel-like iterative vertex-parallel abstraction. The
vprog is executed in parallel on
each vertex receiving any inbound messages and computing a new
value for the vertex. The
sendMsg function is then invoked on
all out-edges and is used to compute an optional message to the
destination vertex. The
mergeMsg function is a commutative
associative function used to combine messages destined to the
On the first iteration all vertices receive the
on subsequent iterations if a vertex does not receive a message
then the vertex-program is not invoked.
This function iterates until there are no remaining messages, or
the vertex data type
the edge data type
the Pregel message type
the input graph.
the message each vertex will receive at the first iteration
the maximum number of iterations to run for
the direction of edges incident to a vertex that received a message in
the previous round on which to run
sendMsg. For example, if this is
out-edges of vertices that received a message in the previous round will run. The default is
EdgeDirection.Either, which will run
sendMsg on edges where either side received a message
in the previous round. If this is
sendMsg will only run on edges where
*both* vertices received a message.
the user-defined vertex program which runs on each vertex and receives the inbound message and computes a new vertex value. On the first iteration the vertex program is invoked on all vertices and is passed the default message. On subsequent iterations the vertex program is only invoked on those vertices that receive messages.
a user supplied function that is applied to out edges of vertices that received messages in the current iteration
a user supplied function that takes two incoming messages of type A and merges them into a single message of type A. This function must be commutative and associative and ideally the size of A should not increase.
the resulting graph at the end of the computation
Implements a Pregel-like bulk-synchronous message-passing API.
Unlike the original Pregel API, the GraphX Pregel API factors the sendMessage computation over edges, enables the message sending computation to read both vertex attributes, and constrains messages to the graph structure. These changes allow for substantially more efficient distributed execution while also exposing greater flexibility for graph-based computation.
We can use the Pregel abstraction to implement PageRank: