Monitor Power BI Queries And Refreshes With DirectQuery On Log Analytics, Part 4: Query Sessions

In my last post I showed how to use Log Analytics data to analyse Power BI query activity. The problem with looking at a long list of queries, though, is that it can be overwhelming and it can be hard to get a sense of when users were and weren’t actively interacting with a report. In this post I’ll show you how you can write a KQL query that gives you a summary view that solves this problem by grouping queries into sessions.

What is a session? I’m going to define it as a group of DAX queries – sessions – run by the same user that occur within 90 seconds of each other. If a user opens a Power BI report, clicks on a slicer or filter, changes a page or whatever, then so long as each of the DAX queries that are generated in the background when they do this have end times that are no more than 90 seconds apart, then that will count as a single session.

The key to solving this problem is the KQL scan operator. If, like me, you’re the kind of geek that likes data analysis you’re going to love the scan operator! There’s a great blog post explaining what it does here; in summary it allows you to do process mining, ie find sequences of events that match a given pattern. I can think of a few cool things I could do with it but grouping DAX queries into sessions is fairly straightforward. Here’s my KQL query:

| where TimeGenerated > ago(4h)
| where isempty(ExecutingUser)==false 
and ExecutingUser<>"Power BI Service"
and OperationName=="QueryEnd"
| project OperationName, OperationDetailName, TimeGenerated, 
EventText, DurationMs, ExecutingUser
| partition by ExecutingUser
sort by TimeGenerated asc
| scan declare (SessionStartTime:datetime) with
    step x: true => 
        SessionStartTime = 
| summarize 
    by ExecutingUser, SessionStartTime
| extend SessionLength = SessionEndTime - SessionStartTime
| sort by SessionStartTime asc

This is how the query works:

  • Filters the Log Analytics data down to the queries run in the last four hours which were run by a real user and not the Power BI Service
  • Splits this data into separate tables (using the KQL partition operator) for each user
  • Sorts these tables by the TimeGenerated column
  • For each of these tables, add a new column called SessionStartTime that contains the value from the TimeGenerated column and copies it down for each subsequent query if its start time is less than 90 seconds; if a query starts more than 90 seconds after the previous one then SessionStartTime contains that query’s TimeGenerated value again
  • Uses the summarize operator to group the data by the values in SessionStartTime and ExecutingUser

Here’s the output:

This query returns one row per user session where:

  • SessionStartTime is the end time of the first DAX query run in the session
  • SessionEnd is the end time of the last DAX query run in the session
  • QueryCount is the number of DAX queries run in the session
  • SessionLength is the duration of the session

Let me know if you have any ideas for modifying or improving this…

Monitor Power BI Queries And Refreshes With DirectQuery On Log Analytics, Part 3: Queries

In the first part of this series I showed how to create a Power BI DirectQuery dataset connected to Log Analytics to do near real-time monitoring of Analysis Services activity; in the second part I showed how to use this technique to monitor dataset refreshes in a workspace. In this blog post I’ll discuss how you can use this technique to monitor queries on your datasets.

As with the post on refreshes, the first and most important question to ask is: why do you need a DirectQuery dataset to monitor query activity? In all Power BI projects Import mode should be used where possible because it gives you better performance and more functionality. If you want to do long-term analysis of query activity then you’re better off importing the data from Log Analytics rather than using DirectQuery. So when and why would you use DirectQuery to monitory query activity in Power BI? The answer has to be when you need to be able to see what queries have been run recently, in the last hour or two, so when someone calls up to complain that their queries are slow you can see what queries they have just run and try and work out why they are slow.

It’s actually very easy to build a simple KQL query to look at query activity on your datasets: you just need to look at the QueryEnd event (or operation, as its called in Log Analytics), which is fired when a query finishes running. This event gives you all the information you need: the type of query (DAX or MDX), the duration, the CPU time, the query text and so on. The main challenge is that while you also get the IDs of the report and visual that generated the query, you don’t get the names of the report or visual. I wrote about how to get a list of visual and report IDs here and here, but how can you use that information?

Here’s an example KQL query that does just that:

let Reports = 
datatable (ReportId:string, ReportName:string)
    "Insert Report ID here", 
    "My Test Report"
let Visuals=
externaldata(VisualId: string,VisualTitle: string,VisualType: string)
    "Insert Shared Access Signature To CSV File Here"
with (format="csv", ignoreFirstRecord=true);
PowerBIDatasetsWorkspace | 
where TimeGenerated > ago(2h) |
where OperationName == 'QueryEnd' |
extend a = todynamic(ApplicationContext)|
extend VisualId = tostring(a.Sources[0].VisualId), 
ReportId = tostring(a.Sources[0].ReportId), 
Operation = coalesce(tostring(a.Sources[0].Operation), "Query"),
DatasetName = ArtifactName, Query = EventText |
lookup kind=leftouter Visuals on VisualId |
lookup kind=leftouter Reports on ReportId |
ExecutingUser, TimeGenerated, WorkspaceName, DatasetName, 
Operation, OperationDetailName, 
VisualTitle, VisualType,
Status, DurationMs, CpuTimeMs, Query |
order by TimeGenerated desc 

To extract the report and visual IDs from the JSON in the ApplicationContext column I’ve used the todynamic() function and then split them out into separate columns. Then, in order to have tables to do the lookups, I used two different techniques. I used the datatable operator to create a hard-coded table with the report IDs and names; in this case I’ve only got one report, but even if you were doing this in the real world at the workspace level I think it’s feasible to create hard-coded tables with report IDs and names in because there wouldn’t be that many of them. For the visuals I saved a table with the visual IDs, names and types in a CSV file and saved it to ADLSgen2 storage, then used the externaldata() (with a shared access signature in this case just to make authentication simple) operator to read this data into a table used for the lookup.

Here’s the output:

It’s then easy to use this table in a Power BI DirectQuery dataset to build a report that shows you what queries have been run recently. This report looks a bit rubbish and I’m sure you can build something that looks a lot nicer:

Getting The IDs Of All Visuals In A Power BI Report Page Using The Power BI Embedded Analytics Playground

Log Analytics contains information on the dataset, report and visual that are associated with a DAX query but that information is in the form of IDs rather than names. Getting the IDs for specific datasets and reports is fairly straightforward – you can get them from urls in the Power BI Portal – and as I wrote here, it’s possible to get a list of IDs and names for the visuals in a report from the JSON file you get when you export from Performance Analyzer in Power BI Desktop. However, my colleague Rui Romano recently showed me a different way to get the same information using the Power BI Embedded Analytics Playgound, which may be an easier option to use in some cases.

The Power BI Embedded Analytics Playground (more details here and here) is a site where developers can learn how to use Power BI’s APIs for embedding reports and dashboards in their own applications. “But Chris!”, I hear you cry, “I’m not using Power BI Embedded!” – don’t worry, this is all about embedding not Power BI Embedded (yes, there’s a difference) so it works with the regular Power BI Service. “I’m not a developer though!”, you add – don’t worry, neither am I and you don’t need to understand any code to do what I’m going to show you.

The first thing you need to do is go to:

When you go there you’ll see the following prompt:

Choose “Select report” under “Use my own Power BI report” and select the report whose visuals you want to get the IDs for.

At this point a page will open with a code editor at the top and your report shown at the bottom. Before you continue you will also need to open the Console pane in your browser’s developer tools. If you’re using Microsoft Edge you can learn how to do this here; if you’re using Chrome you can learn how to do this here.

At this point you should see something like this:

At this point you can start to generate some code by dragging and dropping from the left-hand pane to the code pane in the top-centre of the screen. There are two things you will need to do here: first, generate code to get the report to display the right page and second generate code to get all the visual IDs.

The easiest way to set the page is to generate code to set the page is to expand the Navigation node on the left hand pane and drag the “Page – Set active” item onto the bottom of the code in the code pane. You should then change the page index in the code to select the page; it’s zero-based, so to get the first page you set the index to 0, for the second page set it to 1 and so on.

Next, underneath that code, drag the “Get visuals” item and then click the Run button:

Finally, in the Console pane on the right-hand side of the screen, you’ll see a line was added that you can expand and when you do so, you’ll see the ID, visual type and title of all the visuals on the page:

There’s still a lot of manual work to do but it’s still a fairly easy process. I’m also sure there’s a developer out there who can write a script that can be pasted into the code window that a) loops through all the pages in the report and b) returns the IDs, name and titles in a more friendly format. I’m very impressed with how easy the Embedded Analytics Playground makes all this, though, even for a non-developer like me.

Monitor Power BI Queries And Refreshes With DirectQuery On Log Analytics, Part 2: Dataset Refreshes

In the first post in this series I showed how it was possible to create a DirectQuery dataset connected to Log Analytics so you could analyse Power BI query and refresh activity in near real-time. In this post I’ll take a closer look into how you can use this data to monitor refreshes.

The focus of this series is using DirectQuery on Log Analytics to get up-to-date information (remember there’s already an Import-mode report you can use for long-term analysis of this data), and this influences the design of the dataset and report. If you’re an administrator these are the refresh-related questions you will need answers to:

  1. Which datasets are refreshing right now?
  2. Which recent datasets refreshes succeeded and which ones failed?
  3. If a dataset refresh failed, why did it fail?
  4. How long did these refreshes take?

To answer these questions I built a simple dataset with two tables in. First of all, I built a KQL query that answers these four questions and used it as the source for a DirectQuery table in my dataset. Here’s the query:

//get all refresh events
RefreshesInLast24Hours = 
//this table holds all the Power BI log data for a workspace
//assume that no currently running refresh has run for more than 24 hours
| where TimeGenerated > ago(24h)
//only return the events fired when refreshes begin and end
| where OperationName in ('CommandBegin', 'CommandEnd')  
and EventText has_any ('<Refresh xmlns', '\"refresh\"');
//gets just the events fired when the refreshes begin
CommandBegins = 
| where OperationName == 'CommandBegin' ;
//gets just the events fired when the refreshes end
CommandEnds = 
| where OperationName == 'CommandEnd' ;
//do a full outer join between the previous two tables
| join kind=fullouter CommandEnds on XmlaRequestId, EventText
//select just the columns relevant to refreshes
| project ArtifactName, XmlaRequestId,
StartTime = TimeGenerated, EndTime = TimeGenerated1, 
DurationMs = DurationMs1, CpuTimeMs = CpuTimeMs1, 
Outcome = coalesce(Status1, 'In Progress'), 
ErrorCode = StatusCode1, 
WorkspaceId, WorkspaceName
| summarize StartTime=min(StartTime), EndTime=max(EndTime), DurationMs=max(DurationMs), 
CpuTimeMs=max(CpuTimeMs), ErrorCode=min(ErrorCode) 
by XmlaRequestId, ArtifactName, Outcome, WorkspaceId, WorkspaceName

Here’s an example of the output, a table with one row for each refresh in the last 24 hours:

Here’s how the query works:

  • Gets all the CommandBegin and CommandEnd events – the events that are fired when a refresh begins and ends – that occurred in the last 24 hours and stores them in two tables.
    • Only some of the CommandBegin and and CommandEnd events are related to refreshes, so the query filters on the EventText column to find them.
    • I chose to limit the data to the last 24 hours to keep the amount of data displayed to a minimum and because it’s unlikely that any refresh will take more than 24 hours. If I limited the data to the last hour, for example, then the query would not return the CommandBegin events or the CommandEnd events for any refreshes that started more than an hour ago and were still in progress, so these refreshes would not be visible in the report.
  • Does a full outer join between the table with the CommandBegin events and the table with the CommandEnd events, so the query returns one row for every that either started or ended in the last 24 hours.
  • Limits the columns in the results to show:
    • XmlaRequestId – the unique identifier that links all the events for a specific refresh operation
    • ArtifactName – the dataset name
    • WorkspaceId, WorkspaceName – the ID and name of the Power BI workspace where the dataset is stored
    • StartTime and EndTime – the refresh start time and end time,
    • DurationMs – the duration of the refresh in milliseconds
    • CpuTimeMs – the CPU Time consumed by the refresh in milliseconds,
    • Outcome – whether the refresh succeeded, failed or is still in progress
    • Errorcode – the error code that tells you why the refresh failed

Next I created a second table to return more detailed information about a specific refresh. Here’s an example of the KQL:

| where TimeGenerated > ago(24h)
| where OperationName in 
('CommandBegin', 'CommandEnd', 
| where XmlaRequestId in 
|project OperationName, OperationDetailName, TimeGenerated, 
EventText, XmlaRequestId, DurationMs, CpuTimeMs

This query returns the CommandBegin, CommandEnd, ProgressReportEnd and Error events for a specific refresh (so all these events have the same XmlaRequestId value).

One last complication was that, in my Power BI dataset, I was not able to create a relationship between these two table (I got some kind of query folding error although I don’t know why), so in order to be able to drill from the summary table to the detail table I had to use a dynamic M parameter for the XmlaRequestId used to filter the second table (see this post for more details on how to implement dynamic M parameters when working with KQL queries).

Here’s the report I built on this dataset showing how you can drill from the summary page to the details page for a particular refresh:

Not exactly pretty, but I’m not much of a data visualisation person and I think tables are fine for this type data. You can download a sample .pbix file (I couldn’t make a .pbit file work for some reason…) for this report here and prettify it yourself if you want. All you need to do is change the Cluster and Database parameters in the Power Query Editor to match the details of your Log Analytics workspace and database (see my last post for more details).

[Thanks to Chris Jones at Microsoft for his ideas and suggestions on this topic]

[Update 10th January 2022 – I’ve modified the summary KQL query based on feedback from another Microsoft colleague, Xiaodong Zhang, who pointed out that it didn’t work for TMSL scripts or datasets that use incremental refresh]

Monitor Power BI Queries And Refreshes With DirectQuery On Log Analytics, Part 1: Creating A Dataset

As a Power BI administrator you want to see what’s happening in your tenant right now: who’s running queries, which datasets are refreshing and so on. That way if a user calls you to complain that their report is slow or their dataset hasn’t refreshed yet you can start troubleshooting immediately. Power BI’s integration with Log Analytics (currently in preview with some limitations) is a great source of information for this kind of troubleshooting: it gives you the ability to send various useful Analysis Services engine events, events that give you detailed information about queries and refreshes among other things, to Log Analytics with a latency of only a few minutes. Once you’ve done that you can write KQL queries to understand what’s going on, but writing queries is time consuming – what you want, of course, is a Power BI report.

The dataset behind that Power BI report will need to be in DirectQuery mode if you want to see the most up-to-date data; unfortunately the current Log Analytics for Power BI Datasets template app is Import mode, which is only good if you want to look at long-term trends. It’s not obvious how to connect Power BI to Log Analytics in DirectQuery mode but it is possible: some time ago John White wrote a detailed blog post showing how you could use the Azure Data Explorer connector to allow Power BI to connect to Log Analytics in DirectQuery mode. If you read the comments on that post you’ll see that this method stopped working soon afterwards, although there was a workaround using an older version of the connector. The good news is that as of the December 2021 release of Power BI Desktop the bug was fixed and the details in John’s post are correct again. There is one other issue that needs mentioning: at the time of writing it only seems to be possible to create a DirectQuery dataset on Log Analytics if you specify a native KQL query, but since that can be simply the name of the table you want to query in Log Analytics that’s not much of a blocker.

Putting this all together, to be able to build a Power BI DirectQuery dataset and report to analyse query and refresh activity, you need to:

  1. Set up a Log Analytics workspace in the Azure Portal – see here for instructions.
  2. Connect the Power BI Service to this new Log Analytics workspace – see here for instructions – so that query and refresh events are logged to it.
  3. Open up Power BI Desktop and connect to the Log Analytics workspace using the Azure Data Explorer connector. In the connection dialog:
    • Enter a url pointing to your Log Analytics workspace in the following format in the Cluster box: Azure subscription id here/resourcegroups/enter Azure resource group name here/providers/microsoft.operationalinsights/workspaces/enter Log Analytics workspace name here
    • Enter your Log Analytics workspace name in the Database box
    • In the “Table name or Azure Data Explorer query (optional)” box you need to enter a custom KQL query, which can simply be the following table name:
    • Scroll down to the end of the dialog (which may be off the screen) and select DirectQuery for the Data Connectivity mode.

At this point you’ll have a dataset with a single table in DirectQuery mode that you can build your reports from; you might also want to use automatic page refresh to make sure the report page always shows the latest data. For example, here’s a very simple report showing query durations:

How do you interpret the data in the table in the dataset? Are there any interesting analyses you can do with more complex KQL queries or different ways of modelling the data? All good questions and ones that are out of scope for now. I’ll try to answer these questions in my next few posts though.

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