Last time we showed how Apigee calculates API response time. This time we want to discuss how Apigee calculates API error rates.

Apigee provides charts and tables with API error rates by day, hour or minute with drill downs by API URL. 

The 'error rate' calculation itself is simply # of errors divided by # of requests. Errors include both any Apigee error (including those that you might configure) or any target error (such as a rate limit error from the Twitter API).

Let's show this with Brian's Twitter Growl mashup.  Twitter Growl provides alerts on topics using Mac's Growl alerts.   For example, Brian gets alerts for Tweets on Detroit and Michigan news.

Brian monitors Twitter Growl API activity using Apigee.  You can see he had some stretches in mid August and early September with high error rates.  

You can also drill into the days or hours where these spikes in errors occurred, such as this 3 day period in September - high error rates on a single IP that was being rate limited by the Twitter API.

What was happening over this time?   The Detroit Tigers - in a pennant race for the AL Central -  were swept in a 3 game series with the Royals, resulting in rate-limit busting volumes of angst-ridden tweets from the Detroit Twitterati.

So there you have it.  The Tigers are behind all the errors.   (not sure we needed Apigee to tell us that.) And they still have a 4 and 1/2 game lead, FWIW.

If you'd like Twitter Growl to keep you up to date about the Tigers or any other topics on Twitter, thanks to Brian - his version is a fork of a project by visnup on github