App Insights Analytics: Extracting data from traces

I wanna show two real-world examples (it really happened to me!) of extracting data from traces, and then using that data to get really great insights.

So a little context here – I have a service that reads and processes messages from an Azure Queue. This message processing can fail, causing the same message to be retried many times.

I We recently introduced a bug into the service (as usual.. ) which caused requests to fail on a null reference exception. I wanted to know exactly how many messages were affected by this bug, but it was kind of hard to tell because the retries cause a lot of my service metrics to be off.

Luckily I have a trace just as I am beginning to process a message that shows the message id :

Start handling message id: 0828ae20-ba09-4f83-bb46-69f4fe25b510, dequeue count: 1, message: …

So what I did is extract the message id from the trace using a simple regex, and was then able to count messages using dcount:

traces
 | where timestamp > ago(1d)
 | where message startswith "Start handling"
 | extend messageid = tostring(extract("Start handling message id: ([^:\\/\\s]+), ", 1, message))
 | summarize dcount(messageid)

And in order to count how many messages were affected by the exception, I did a double join – to the failed requests and to exceptions tables:

requests 
| where timestamp > ago(1d)
| where success == "False"
| join (exceptions
   | where timestamp > ago(1d)
   | where type contains "NullRef"
   ) on operation_Id
| join (traces
   | where timestamp > ago(1d)
   | where message startswith "Start handling"
   | extend messageid = tostring(extract("Start handling message id: ([^:\\/\\s]+), ", 1, message))
   ) on operation_Id
| summarize dcount(messageid)

Voila!

The second example is similar, but this time I extracted a measurement.

Again I started from a trace – I have a trace detailing exactly how late a message that came in the queue is. It looks like this:

Latency: 21 minutes.

I wanted to turn these traces into measurable data that I can slice and dice on. So I used the same extend+extract method as before + a todouble:

traces
| where timestamp > ago(1d)
| where message contains "Latency: "
| extend latency = todouble(extract("Latency: ([^:\\/\\s]+) minutes.", 1, message))
| summarize percentile(latency, 90)

AWESOME!

Cool AppInsights Analytics: Charting request failure rate

Here is a really cool App Analytics query over App Insights that shows the request failure ratio of your app over the last week.

I use “extend” with the “iff” features to create a a successes field I can count, and then use “extend” again to create a failure ratio.

requests
| where timestamp > ago(7d)
| extend isSuccesss=iff(success=="True" ,1, 0)
| summarize failures=sum(1-isSuccesss) , successes=sum(isSuccesss)
by timestamp bin=20m
| extend ratio=todouble(failures) / todouble(failures+successes)
| project timestamp, failure_Percent=ratio*100
| render timechart

FailPercent