Update 2014-11-19: a few papercuts have been fixed, and data location has changed, so the instructions below have been updated since the original post from 2013.

The Telemetry Server has been deployed on AWS for just over a month now, so it’s time for an update.

The server code repository has been moved into the Mozilla github group, and the mreid-moz repo forwards there, so the change should be seamless.

The Telemetry dashboards have also moved! They are now located at telemetry.mozilla.org, nice and easy to remember.

Moving on to more interesting news, anyone with an @mozilla.com email can now run their own Telemetry analysis jobs in the cloud. The procedure is still very much in alpha/beta state, but if you’ve got a question that can be answered using Telemetry data, you’re in luck.

Jonas has built a mechanism for provisioning a ubuntu server as an Amazon EC2 instance. These machines ( c3.4xlarge in EC2 terms) have read-only permission and a fast connection to Telemetry data stored in S3. Each machine will be available for 24 hours, and will cost about $40USD to run. If you don’t need it for the full day, you can kill it early by following the instructions in the webapp below.

Here’s how it works:

Visit the analysis provisioning dashboard at telemetry-dash.mozilla.org and sign in using Persona (with an @mozilla.com email address as mentioned above).

email address as mentioned above). Enter some details. The Server Name field should be a short descriptive name, something like ‘mreid chromehangs analysis’ is good. Upload your SSH public key (this allows you to log in to the server once it’s started up)

field should be a short descriptive name, something like ‘mreid chromehangs analysis’ is good. Upload your SSH public key (this allows you to log in to the server once it’s started up) Click Submit .

. A Ubuntu machine will be started up on Amazon’s EC2 infrastructure. Once it has started up, you can SSH in and run your analysis job. Reload the webpage after a couple of minutes to get the full SSH command you’ll use to log in.

Make sure to download your results when you’re finished! Your analysis machine will automatically get killed after 24 hours.

Ok, that’s all well and good, but what is an analysis job?

The easiest (and probably most familiar to anyone who has worked with Telemetry data in the past) is to run a MapReduce job.

This requires a bit of setup on the machine you provisioned above:

Set up some working directories:

1 $ mkdir -p /mnt/telemetry/work

Create an input filter to match the data you want. Look at the examples at mapreduce/examples/*.json . Here is a reasonably selective one.

. Here is a reasonably selective one. Create a new analysis script, or run one of the example ones:

1 2 3 4 5 6 7 8 9 $ cd ~/telemetry-server $ python -m mapreduce.job mapreduce/examples/osdistribution.py \ --input-filter /path/to/filter.json \ --num-mappers 16 \ --num-reducers 4 \ --data-dir /mnt/telemetry/work \ --work-dir /mnt/telemetry/work \ --output /mnt/telemetry/my_mapreduce_results.out \ --bucket "telemetry-published-v2"

A few notes for successful jobs.

Try to keep the amount of data you’re crunching to a minimum while you’re developing your job. It’ll save time, and prevent the system from running out of memory. A good start is to process just a single day’s nightly data.

data. If you do run out of memory, try increasing the number of mappers and reducers.

After the first run, you can point the “—data-dir” argument at <work-dir>/cache and add the “—local-only” parameter to skip downloading files from S3 every time

and add the “—local-only” parameter to skip downloading files from S3 every time Give us a heads-up in #telemetry and we’ll tell you about any other caveats.

One final note – the Telemetry MapReduce framework is a simple way to download the set of records you are interested in, and do something for each record in that data set.

If you don’t want to do your analysis with this framework, you can just use it to download the data (or even skip it altogether and download data using the AWS command-line tools directly). Once the data has been downloaded to the machine, you’re free to analyze it using whatever other language / tools you’re comfortable with.

Happy Data Crunching!