prototyping in ruby was a great way to prove the concept but my main motivation for this project was to play some more with pig.
the main approach will be
the full version is on github. read on for a line by line walkthrough
pig demo from Mat Kelcey on Vimeo.
based on a talk i gave at work recently
for v3 lets aggregate by time of the day, should make for an interesting animation
browsing the data there are lots of other lat longs in data, not just iPhone: and ÜT: there are also one tagged with Coppó:, Pre:, etc perhaps should just try to take anything that looks like a lat long.
furthermore lets switch to a bigger dataset again, 4.7e6 tweets from Oct 13 07:00 thru Oct 19 17:00,
i’ve been streaming all my tweets ( as previously discussed ) and been storing them in a directory json_stream
here are the steps…
let’s rewrite v1 using hadoop tooling, code is on github
we’ll run hadoop in non distributed standalone mode. in this mode everything runs in a single jvm so it’s nice and simple to dev against.
in v1 it was
bzcat sample.bz2 | ./extract_locations.pl > locations
using the the awesome hadoop streaming interface it’s not too different. this interface allows you to specify any app as the mapper or reducer. the main difference is that it works on directories not just files.
for the mapper we’ll use exactly the same script as before; extract_locations.pl and since there is no reduce component of this job so we use an “identity” script, ie cat, as the reduce phase.
mkdir json_stream bzcat sample.bz2 | gzip - > json_stream/input.gz # hadoop supports gzip out of the bound but not bzip2 :( export HADOOP_STREAMING_JAR=$HADOOP_HOME/contrib/streaming/hadoop-*-streaming.jar hadoop jar $HADOOP_STREAMING_JAR \ -mapper ./extract_locations.pl -reducer /bin/cat \ -input json_stream -output locations
this gives us the locations in a single file locations/part-0000
so e9 sip is on hold for a bit while i kick off e10 tgraph. was looking for another problem to try hadoop with and came across a classic graph one, pagerank. a well understood algorithm like page rank will be a great chance to try pig, the query language that sits on top of hadoop mapreduce.
so we need a graph to work on. my first thoughts were using one of the wikipedia linkage dumps but it feels a bit sterile. instead it’s a good excuse to do a little crawl of the following graph of twitter.
this will also be a chance to try to document a project via a blog. skorks‘ incessant blog rambling has convinced me to give it a go.
