Browsing posts tagged with: python

travisci

PyPI deployment with Travis CI
February 18, 2016 by

Application Performance Management

The TraceView team has added the ability to delete applications to our Application Management API, so now users are enabled to programmatically delete and clean up applications in TraceView that are no longer in use. Along with this helpful addition, I’ve just released an updated version of the python-traceview client library, to support this new
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Faking the Funk: Mocking External Services in Python Tests
July 25, 2014 by

Application Performance Management

In this day and age, it’s difficult to build an application that does not rely on some type of external service. Whether the service is handling user identity, analyzing interesting data, or hurling RESTful insults, you have to accept the fact that you now have a dependency on something you do not control. One place
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traceview

python-traceview: A Library for the TraceView API
June 18, 2014 by

Application Performance Management, Performance Management Tech

Not too long ago, we introduced the TraceView Data API, which exposes high level metrics and performance data related to your TraceView account via a RESTful API. For the unfamiliar, this means you can access server latency timeseries, application error rates, and even browsers used by end users. In an effort to make accessing the
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DRY Mockery in Python Testing
May 29, 2014 by

Application Performance Management, Industry Insights, Performance Management Tech

Note: the code referenced in this post, along with some extra context, can be found on github. I have tried to link into the repo at each of the inline code samples, for convenience. It might be useful to refer to the repo as a whole, though, rather than just the swatches under discussion. When
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Writing Purposeful Unit Tests
April 11, 2014 by

Application Performance Management, Performance Management Tech

Several recent blogs have discussed unit testing, some of them in considerable depth. One of my favorites is Jeff Knupp’s entry, which is a comprehensive look at how to write and understand good unit tests. Jeff touches on the motivations for testing, but like most other writers, moves on quickly to the details of actually
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Snakes on a Road Trip: PyCon 2014!
April 9, 2014 by

Application Performance Management, Performance Management Tech

PyCon is always a hotly anticipated event, and this year I’m quite excited to be headed up to Montreal with my coworkers Kristin, Ari, and Dan. Better yet, this is the first conference that we’re attending via road trip! I’ve done the same journey by train before, and we’ll have some nice scenery to look
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How to Save 90% on Your S3 Bill
January 31, 2014 by

Application Performance Management, Performance Management Tech

AppNeta has used a lot of open source libraries and programs in building and running our architecture. One utility in general that’s provided us with an easy way to slice up and investigate our AWS spending is the awesome Ice. Instead of having to do manual tabulation based on the monthly billing email from Amazon,
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Being Nothingness: Comparing to None in Python
January 27, 2014 by

Application Performance Management, Performance Management Tech

The “is None” Idiom Consider these two snippets from PEP-0008: “Comparisons to singletons like None should always be done with is or is not, never the equality operators. Also, beware of writing if x when you really mean if x is not None — e.g. when testing whether a variable or argument that defaults to
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python

Hold the Line: Line Profiling in Python
January 6, 2014 by

Application Performance Management, Performance Management Tech

If you’ve ever profiled code in Python, you’ve probably used the cProfile module. While the cProfile module is quite powerful, I find it involves a lot of boilerplate code to get it setup and configured before you can get useful information out of it. Being a fan of the KISS principle, I want an easy
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Loading up some Big Queries
December 5, 2013 by

Application Performance Management, Performance Management Tech

Google’s BigQuery service has a lot to offer for organizations with massive datasets to query against. Rented massive parallelism is much more cost effective than trying to set up the infrastructure to do it yourself. BigQuery has some limits, though — flexible boundaries that your design needs to accomodate. At AppNeta, our naive approach to
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