Cross App Queries in Azure Log Analytics

I’ll keep it short and simple this time. Here’s a great way to debug your app across multiple App Insights instances.

So, I have two Azure Functions services running, with one serving as an API, and the other serving as BE processing engine. Both report telemetry to App Insights (different apps), and I am passing a context along from one to the other – so I can correlate exceptions and bugs.

Wouldn’t it be great to be able to see what happened in a single session across the 2 apps?

It’s possible – using ‘app‘ – just plugin the name of the app insights resource you want to query, and a simple ‘union‘.

Here you go:

let session="reReYiRu";
union app('FE-prod').traces, app('BE-prod').traces
| where session_Id == session 
| project timestamp, session_Id, appName, message
| order by timestamp asc 


Don’t forget –

  1. You can use the field ‘appName‘ to see which app this particular trace is coming from.
  2. Different machines have different times.. Don’t count on the timestamp ordering to always be correct.

Search in App Analytics

The questions I get most often about Analytics aren’t usually about super-complicated queries or magic ML functions.

It’s usually just about how to find specific logs in an investigation.

App Insights Analytics has a really simple way to do it – search. This will search for a keyword across all your tables, across all columns.

search "Error"

If you look at the results, the first column is called $table – it is the name of the table from which the results came from.

You can combine search with a summarize, or any other filter you need:

search "Error" 
| summarize count() by bin(timestamp, 1h)
| render timechart  

It’s also possible to search in specific tables:

search "fail" in (customEvents, dependencies)

Happy searching!

Cool uses for the top-nested operator

There’s a pretty nice operator in Kusto (or App Insights Analytics) called top-nested.

It basically allows you to do a hierarchical drill-down by dimensions. Sounds a bit much, but it’s much clearer when looking at an example!

So a simple use for it could be something like getting the top 5 result-codes, and then a drill down for each result code of top 3 request names for each RC.

| where timestamp > ago(1h)
| top-nested 5 of resultCode by count(),
  top-nested 3 of name by count()

So I can easily see which operation names are generating the most 404’s for instance.

This is pretty cute, and can be handy for faceting.

But I actually find it more helpful in a couple of other scenarios.

First one is getting a chart of only the top N values. For instance, if I chart my app usage by country, I get a gazillion series of all different countries. How can I easily filter the chart to show just my top 10 countries? Well one way is to do the queries separately, and add a bunch of where filters to the chart…

But top nested can save me all that work:

let top_countries = view()
  | where timestamp > ago(3d)
  | top-nested 5 of client_CountryOrRegion by count()
| join kind= inner
    | where timestamp >= ago(3d)
   ) on client_CountryOrRegion
| summarize count() by bin(timestamp, 1h), client_CountryOrRegion
| render timechart


A beautiful view of just my top 5 countries…

I’ve actually used the same technique for a host of different dimensions (top countries, top pages, top errors etc.), and it can also be useful to filter OUT top values (such as top users skewing the numbers), by changing the join to anti-join.

The second neat scenario is calculating percentages of a whole. For instance – how do you calculate the percentage of traffic per RC daily?

Yeah, you can do this using a summarize and the (newly-added) areachart stacked100 chart kind:

| where timestamp >= ago(3d)
| where isnotempty(resultCode)
| summarize count() by bin(timestamp, 1h), resultCode
| render areachart kind=stacked100


But this only partially solves my problem.

Because ideally, I don’t want to look at all these 200’s crowding my chart. I would like to look at only the 40X’s and 500’s, but still as a percentage of ALL my traffic.

I could do this by adding a bunch of countif(rc=403)/count(), countif(rc=404)/count()… ad nauseum, but this is tiresome + you don’t always know all possible values when creating a query.

Here’s where top-nested comes in. Because it shows the aggregated value for each level, creating the percentages becomes super-easy. The trick is simply doing the first top-nested by timestamp:

| where timestamp > ago(14d)
| top-nested 14 of bin(timestamp, 1d) by count() ,
  top-nested 20 of resultCode by count()
| where resultCode !startswith("20")
| where resultCode !startswith("30")
| project pct=aggregated_resultCode * 1.0 / aggregated_timestamp, 
          timestamp, resultCode 
| render timechart


Pretty nice, no?

Cool AppInsights Analytics: Counting sampled data

If you’re doing stuff you’re supposed to be doing in Analytics – like slicing and dicing request, counting page views, etc. – then you should probably make sure you’re counting correctly.

2 big pitfalls here are:

  1. If you’re sampling your data with App Insights 2.0 sdk, then you should obviously reflect that when counting.
  2. If you’ve got a bunch of tests set up, then you probably don’t want to count those as page views.

For #1, you need to make sure you are always summing items – do sum(itemCount) instead of a simple count().

For #2, remember to add a where clause on the synthetic source field.

Here’s an example:

| where timestamp > ago(1d)
| where operation_SyntheticSource == ""
| summarize sum(itemCount) by performanceBucket