Yesterday was our target for cutting over to the “rebooted” telemetry server. Despite some last-minute travel (I spent Monday en route from Nova Scotia to San Francisco), I’m happy to report that the switch went rather smoothly on Tuesday! More details on the required changes can be found in Bug 921161.
Since my last update, there have been a few last-minute code changes to get things in ship-shape for deployment. The bulk of those changes were to the scripts used to provision machines on Amazon’s EC2 infrastructure, but there was one more structural change of note.
The logic for processing incoming submissions (that’s the “validation, conversion, and compression” part) was previously controlled by a master process which would launch a worker node to do the actual processing. Without an easy way for masters to co-ordinate, it was difficult to launch extra workers in cases where the rate of processing was not keeping up, since each master expected its worker to process all available data.
The solution was to switch to using a queue to keep track of data available to be processed, and having the worker nodes claim data from the queue. This results in a nicely decoupled architecture, where starting up more workers (or killing off idle ones) is clean and easy.
Anyways, getting back to the main point… The cutover is complete, and the Telemetry submissions are now going to “The Cloud!”
It turns out that the node.js version of the web server is efficient enough to allow us to handle the entire volume of traffic using only a pair of “t1.micro” nodes in EC2 (behind a load balancer). Pretty slick.
So far, running on AWS has been pretty nice. The Elastic Load Balancers make it nearly-trivial to add or remove nodes from the pool, and include useful (if limited) monitoring. On the HTTP-serving nodes themselves, we have some more detailed and application-specific monitoring using Heka. The boto library makes it very easy to provision new nodes using python.
Now that the Telemetry Server is out in the wild, the next step is to get the new Dashboard playing nicely with the new data source. Jonas Finnemann Jensen is working on that.
There’s still more work to do once the dashboard integration is complete, including finishing up the C++ port of the “process incoming” code (which will hopefully provide a large speedup compared to the current python implementation), migrating the provisioning over to Amazon Cloud Formation, creating a frontend for managing/running Telemetry MapReduce jobs, and exporting the historic data from the previous Hadoop backend into the new S3 storage.