Thanks
Sean

We are using nimbus tool to monitor the URL Synthetic monitoring through e2e_appmon probe. It covers searching text,logo,button and this action will repeating every 5 min.
We heard that, Appneta launches the product for URL Synthetic monitoring , so i want to know the product name, documents and is it covers all the features which currently Nimbus is providing us.
And also can we see the script , so that we can customize the monitoring by alerting the script, sending alert to netcool for incident managemnt etc

Please provide us the full details, so that i can prepare document and inform about your product to our management.
And also give me the company name which are currently using your product for URL synthetic monitoring.

Regards,
DJ

I continue to be the lead developer and maintainer of the project, and we’ve added many features we use as part of our RUM offerring at LogNormal (now part of SOASTA).

I’d also like to welcome you to the space. It’s great to see so many new players.

Philip

That caching example definitely works. Personally, I prefer to initialize instance variables in instance methods, just to explicitly mark the scope, but that’s more of a style preference than anything else.

Like you mentioned, though, the real advantage to using an @reconstructor function is more complicated initialization. In one reconstructor method, we actually set up a logger with some predefined constants, based on the persistant values in the row. In that case, it made more sense to initialize early — in the reconstructor method.

Thanks for the comment! There’s always more than one way to skin a cat.

class Foo(object):

_bar = None

def bar(self):
if self._bar is None:
self._bar = …
return self._bar

This is how I’ve always done cached properties

Or is the reset of self._bar that you need @reconstructor for? To avoid re-using the a stale cache? In that case, maybe your way is better – an Attrribute not found exception ensures you won’t be using the cached value by accident if you forget @reconstructor. Interesting.,,

I’ve got a couple to add to the book list. They are somewhat outliers, but they are worth their weight in gold. Niether is a technical book, but both will help you understand how to better use technology.

1) The Four Steps to the Epiphany by Steven Blank – This masterpiece indirectly covers much of the “why?” behind DevOps. If you want to read the primary source material that much of Eric Reiss’s Lean Startup is based on or want to build the case for Continuous Deployment as sound business strategy and not just a developer fantasy, this is the book that lays it all out.
http://www.amazon.com/The-Four-Steps-Epiphany-Successful/dp/0976470705

2) The Principles of Product Development Flow by Donald Reinertsen – Don’t let the title fool you, this book is not for just product managers. This book lays out the theories and math behind why so many lean principles and best practices actually work. You’ll occasionally find yourself thinking that he’s writing about web operations and DevOps directly until you encounter a sentence that brings you back to the fact he is not… it’s just that all of our “new” ideas are just new incarnations of decades of learning and research that’s already been done in other fields.
http://www.amazon.com/The-Principles-Product-Development-Flow/dp/1935401009/

Updated!

Great article otherwise!

This example in particular shows how awesome your heatmap is in comparison with an average for the complete app/stack. With an average graph, it would have been much harder to find and isolate the the problem. Here, it is clearly visible.

Just a heads up!

We’re actually working on a Data API, to free up some of this data. I think v1 will have timeseries data and Top-N lists, like in the app. Is there anything information in particular you were looking for?