Thanks for your feedback. My comments below.
On Tue, Jun 14, 2011 at 10:41 AM, Daniel Dai wrote:
Curious, couple of questions:
1. Are you running in local mode or mapreduce mode?
Local mode (-x local) when I ran it on my laptop, and mapreduce mode when I
ran it on ec2 cluster.
2. If mapreduce mode, did you look into the hadoop log to see how much slow
down each mapreduce job does?
I'm looking into that.
3. What kind of query is it?
The input is gzipped json files which has one event per line. Then I do
some hourly aggregation on the raw events, then do bunch of groupping,
joining and some metrics computing (like median, variance) on some fields.
Someone mentioned it's EC2's I/O performance. But I'm sure there are
plenty of people using EC2/EMR running big MR jobs so more likely I have
some configuration issues? My jobs can be optimized a bit but the fact that
running on my laptop is faster tells me this is a separate issue.
On 06/13/2011 11:54 AM, Dexin Wang wrote:
This is probably not directly a Pig question.
Anyone running Pig on amazon EC2 instances? Something's not making sense
me. I ran a Pig script that has about 10 mapred jobs in it on a 16 node
cluster using m1.small. It took *13 minutes*. The job reads input from S3
and writes output to S3. But from the logs the reading and writing part
to/from S3 is pretty fast. And all the intermediate steps should happen on
Running the same job on my mbp laptop, it only took *3 minutes*.
Amazon is using pig0.6 while I'm using pig 0.8 on laptop. I'll try Pig 0.6
on my laptop. Some hadoop config is probably also not ideal. I tried
m1.large instead of m1.small, doesn't seem to make a huge difference.
Anything you would suggest to look for the slowness on EC2?