This repository contains an implementation of the PageRank algorithm in timely dataflow, implemented in Rust. By default, it runs 20 PageRank iterations and then prints some statistics.
To run, clone the repo, prepare the inputs and run. We assume that you have a working Rust installation, and that your input graph is in text-based edge list format.
The input format for our PageRank implementation is a binary-packed adjacency
list for a graph. You can use the parse
binary to transform an ASCII/UTF-8
edge list into this format:
$ cargo run --release --bin parse -- my-edgelist.txt my-graph
This will generate binary files my-graph.offsets
and my-graph.targets
,
which can be the used as inputs to the pagerank
binary.
parse
assumes that the inputs are in tab-seperated text, but you can change
to a different delimiter by editing src/bin/parse.rs
. For a small example
data set that works out of the box, use the
LiveJournal graph.
A single-threaded iteration on this graph takes about 0.67s on a 2013 MacBook
Air.
Our blog
posts have links
to the larger twitter_rv
and uk_2007_05
data sets.
To run on inputs my-graph.offsets
and my-graph.targets
, run:
$ cargo run --release --bin pagerank -- my-graph worker [options]
The worker
argument indicates you would like worker-level aggregation.
You can alternately use process
for process-level aggregation.
Without any options, the code runs single-threadedly. The -w
option can be
used to set the number of threads to use; -h
,-n
and -p
can be used to
run distributedly:
$ cat hosts.txt
hostname0:port
hostname1:port
hostname2:port
hostname3:port
hostname0$ cargo run --release --bin pagerank -- my-graph -h hosts.txt -n 4 -p 0
hostname1$ cargo run --release --bin pagerank -- my-graph -h hosts.txt -n 4 -p 1
hostname2$ cargo run --release --bin pagerank -- my-graph -h hosts.txt -n 4 -p 2
hostname3$ cargo run --release --bin pagerank -- my-graph -h hosts.txt -n 4 -p 3
The inputs must already be present in the working directory on all hosts.
We have written blog posts about the development of this implementation; there, we also compare it against the widely used GraphX system for Apache Spark.
To learn more about timely dataflow in Rust, you might be interested in the following blog posts, too: