Monitoring Power Query Online Memory And CPU Usage

Power Query Online is, as the name suggests, the online version of Power Query – it’s what you use when you’re developing Power BI Dataflows for example. Sometimes when you’re building a complex, slow query in the Query Editor you’ll notice a message in the status bar at the bottom of the page telling you how long the query has been running for and how much memory and CPU it’s using:

The duration and CPU values are straightforward, but what does the memory value actually represent? It turns out it’s the “Commit (Bytes)” value documented here for Query Diagnostics, that’s to say the amount of virtual memory being used by the query. That’s different to the “Working Set (Bytes)” value which is the amount of physical memory used by the query, and which is not visible anywhere. For a more detailed discussion of these values in Power Query in Power BI Desktop see this post. The maximum commit or working set for a query evalation in Power Query Online isn’t officially documented anywhere (and may change) but I can say three things:

  1. The maximum commit is larger than the maximimum working set.
  2. If Power Query Online uses more than the maximum working set then query evaluation will get slow, so if your query uses a lot of memory (say, over 1GB – I suspect you’ll only see this message if it is using a lot of memory…) then you need to do some tuning to reduce it. Probably the best way to do this is to look at the query plan for your dataflow and try to avoid any operations marked as “Full Scan”, as documented here.
  3. If your query uses more than the maximum commit then it may get cancelled and you’ll see an error (note that the maximum time a query evaluation can run for in Power Query Online anyway is 10 minutes, which is documented here).

[Thanks to Jorge Gomez Basanta for this information]

Cancelling Power BI Dataset Refreshes With The Enhanced Refresh API

The most exciting (at least for me) feature in the new Enhanced Refresh API (blog announcement | docs) is the ability to cancel a dataset refresh that’s currently in progress. Up until now, as this blog post by my colleague Michael Kovalsky shows, this has been quite difficult to do: not only do you need to use the XMLA Endpoint but you also need to take into account that in many cases the Power BI Service will automatically restart the refresh even after you’ve cancelled it. Now, though, if (and only if) you start the refresh using the Enhanced Refresh API you can also cancel it via the Enhanced Refresh API too. This is important because I’ve seen a few cases where rogue refreshes have consumed a lot of CPU on a Premium capacity and caused throttling, even after all CPU smoothing has taken place, and Power BI admins have struggled to cancel the refreshes.

This and all the other great functionality the new API includes (the ability to refresh individual tables or partitions! control over parallelism!) means that it can handle many of the advanced scenarios that, in the past, you’d have had to write some complex TMSL commands for; in my opinion anyone working on an enterprise-level dataset in Power BI Premium should be using it for their refreshes.

But Chris, I hear you say, I’m a data person and find working with APIs confusing and difficult! Yeah, me too – which is why, when I saw this tweet by Stephen Maguire about .NET interactive notebook for Visual Studio Code he’s built for the Enhanced Refresh API, I was interested:

It’s a really great set of examples for learning how to use the Enhanced Refresh API through PowerShell and the notebook format makes it a lot more user-friendly than just another bunch of scripts. I highly recommend that you check it out.

How The “Maximum Connections Per Data Source” Property On Power BI DirectQuery Datasets Can Affect Report Performance

If you’re working with DirectQuery in Power BI then one of the most important properties you can set on your dataset is the “Maximum connections per data source” property. You can find it on the Published Dataset Settings tab in the Options dialog in Power BI Desktop:

The description of what it does in the guidance documentation is pretty comprehensive:

You can set the maximum number of connections DirectQuery opens for each underlying data source. It controls the number of queries concurrently sent to the data source.

The setting is only enabled when there’s at least one DirectQuery source in the model. The value applies to all DirectQuery sources, and to any new DirectQuery sources added to the model.

Increasing the Maximum Connections per Data Source value ensures more queries (up to the maximum number specified) can be sent to the underlying data source, which is useful when numerous visuals are on a single page, or many users access a report at the same time. Once the maximum number of connections is reached, further queries are queued until a connection becomes available. Increasing this limit does result in more load on the underlying data source, so the setting isn’t guaranteed to improve overall performance.

When the model is published to Power BI, the maximum number of concurrent queries sent to the underlying data source also depends on the environment. Different environments (such as Power BI, Power BI Premium, or Power BI Report Server) each can impose different throughput constraints.

I thought it would be interesting to do some experiments to see how this property behaves, what you see in Profiler (or Log Analytics) when connections are queued up, and how you can find an optimal value for your dataset.

The first thing to mention – and this is something I only realised relatively recently – is that this property applies to DirectQuery on Power BI datasets and Analysis Services as well as traditional DirectQuery to external databases. I’m a lot more comfortable with Power BI than any relational database so I decided to do my testing with a DirectQuery dataset connected back to another Power BI dataset; the behaviour of the feature is the same as with DirectQuery to a relational database.

For my tests I created a simple dataset – let’s call it Dataset A – with not much data but a really inefficient DAX measure on it. I then created a composite model dataset – let’s call this Dataset B – with a DirectQuery connection to Dataset A. Finally I created a report with a Live connection to Dataset B with 25 card visuals on, each of which used the inefficient measure with a different filter. The DAX query for each of these cards, when run on its own through DAX Studio, took around 28 seconds, with almost all that time spent in the Formula Engine. The datasets and reports were published to a PPU workspace and all tests were run in the Power BI Service and not in Power BI Desktop (at the time of writing, things work differently in Desktop – which means you should always test performance of DirectQuery reports in the Service and not in Desktop). I ran Profiler traces using the Query Begin and Query End events on both Dataset A and Dataset B during my tests.

First of all, let’s see what happened when the Maximum Number of Connections property on Dataset B was set to 1. This means that Dataset B is only allowed to have one connection open to Dataset A to run its DirectQuery queries. When the report was run, right at the start the Profiler trace on Dataset B showed 25 Query Begin events indicating all 25 queries for the 25 card visuals were being run in parallel; the Profiler trace on Dataset A showed just 1 Query Begin event:

This is what you would expect: since Dataset B can only use one connection to Dataset A it can only run one query at a time and the other 24 queries have to queue up to wait for the connection. When the first query against Dataset A completed, another one started and so on. Since the maximum length of time that a DAX query can run in the Power BI Service is 225 seconds, after 225 seconds any remaining queries timed out. At that point 8 queries had completed, so 8 cards were rendered, and all the remaining cards showed the timeout error I blogged about here:

At the end, the Profiler trace against Dataset A showed 8 completed Query Begin/End pairs:

While the Profiler Trace against Dataset B showed, after the 25 Query Begin events, 8 Query End events for the successful queries and then 17 Query End events with timeout errors for the unsuccessful queries.

One interesting thing to notice is the durations of the queries. As I said, when run on their own each of these queries took around 28 seconds, and the Profiler trace on Dataset A shows each query taking around 28 seconds. If you look at the successful queries on Dataset B you’ll see that their duration goes up in increments of around 28 seconds: the first takes 29984ms, the second takes 58191ms, the third takes 87236ms and so on until you hit the 225 second timeout limit. This shows that the duration of the queries against Dataset B, the composite model, includes the time waiting to acquire a connection. Notice also that the CPU Time of the queries against Dataset B is minimal because it only includes the CPU used by the query for Dataset B; you have to add the CPU Time to the related queries on Dataset A to get the total CPU Time used by these queries.

The important question is, though, what is the effect of increasing the Maximum Connections Per Data Source property? Increasing it will increase the number of queries run in parallel, but is more paralellism always better? I reran my tests with the property set to 5, 10 (which is the default value and the maximum that can be used for datasets not in Premium capacity) and 30 (which is the maximum value that can be used for datasets in any form of Premium capacity). Here are the results:

Maximum Connections Per Data SourceNumber of Visuals That Render Successfully In 225 Seconds

As you can see increasing the parallelism a little bit helps more than increasing the parallelism a lot, and in this case reducing the value from the default was better than increasing it: overloading your source with a lot of expensive parallel queries is often a bad thing. This test isn’t representative of most real-world reports – you shouldn’t have one visual, let alone 30, with queries that run for as long as 30 seconds and the best way to optimise a report like this would be to display the same data in a smaller number of visuals – but I think it’s a useful illustration of how this property works and how it can affect report performance.

Build Web Sites And Embed Power BI Reports In Them Using Power Pages

In amongst all the announcements at Build recently, you may have heard about a new member of the Power Platform being launched: Power Pages. You can read the docs here, and there’s a good, detailed video overview here, but here’s a quick summary of what it is:

Microsoft Power Pages is a secure, enterprise-grade, low-code software as a service (SaaS) platform for creating, hosting, and administering modern external-facing business websites. Whether you’re a low-code maker or a professional developer, Power Pages enables you to rapidly design, configure, and publish websites that seamlessly work across web browsers and devices.

So what? I’m not a web designer and I’m pretty sure most of you aren’t either, so why blog about it here? Most data and BI people don’t need to build web sites… do they?

Well I was playing around with it and noticed one important detail:

It has built-in support for embedding Power BI reports into the web sites you build! You can read more about this here and here. What’s more, it supports all forms of Power BI embedding (which can be an extremely confusing subject): as well as the use of Power BI Embedded, for sharing reports with external users, you can use regular Power BI Premium, Secure Embedding (which doesn’t need Premium), and Publish to Web for sharing with the general public. It also supports embedding of reports and dashboards (though not paginated reports) and also more complex security scenarios if you have the relevant web development skills.

As someone with no web development skills whatsoever it was very easy for me to build a web site with a Power BI report embedded into it:

When would this be useful? I talk to a lot of people who want to share Power BI reports with external users. You can use Azure B2B for this, although it doesn’t give you the smooth experience a custom-built web site does – but using a custom-built web site of course requires you to actually build that web site, and not everyone has a web developer available to do this work. This is where I see Power Pages being extremely useful: self-service web development for self-service data people, letting you share data securely outside your organisation quickly and easily.

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