Question: When an activity in a Data Factory pipeline fails, does the entire pipeline fail? Answer: It depends
In Azure Data Factory, a pipeline is a logical grouping of activities that together perform a task. It is the unit of execution – you schedule and execute a pipeline. Activities in a pipeline define actions to perform on your data. Activities can be categorized as data movement, data transformation, or control activities.
In many instances, when an activity fails during a pipeline run, the pipeline run will report failure as well. But this is not always the case.
There are two main scenarios where an activity would report failure, but the pipeline would report success:
The maximum number of retry attempts is greater than 0, and the initial activity execution fails but the second attempt succeeds
The failed activity has a failure path or a completion path to a subsequent activity and no success path
In the General settings of any activity is a property called Retry. This is the number of times Data Factory can try to execute the activity again if the initial execution fails. The default number of retries is 0. If we execute a pipeline containing one activity with the default Retry setting, the failure of the activity would cause the pipeline to fail.
I often set retries to a non-zero number in copy activities, lookups, and data flows in case there are transient issues that would cause a failure that might not be present if we waited 30 seconds and tried the activity again.
Dependency with a Failure Condition
Activities are linked together via dependencies. A dependency has a condition of one of the following: Succeeded, Failed, Skipped, or Completed. If we have a pipeline containing Activity1 and Activity2, and Activity2 has a success dependency on Activity1, it will only execute if Activity1 is successful. In this scenario, if Activity1 fails, the pipeline will fail.
But if we have a pipeline with two activities where Activity2 has a failure dependency on Activity1, the pipeline will not fail just because Activity1 failed. If Activity1 fails and Activity2 succeeds, the pipeline will succeed. This scenario is treated as a try-catch block by Data Factory.
Now let’s say we have a pipeline with 3 activities, where Activity1 has a success path to Activity2 and a failure path to Activity3. If Activity1 fails and Activity2 succeeds, the pipeline will fail. The presence of the success path alongside the failure path changes the outcome reported by the pipeline, even though the activity executions from the pipeline are the same as the previous scenario.
What This Means for Monitoring
This difference between pipeline and activity status has a few implications of which we should be aware as we monitor our data factories.
If we are using Azure Monitor alerts, we need to understand that setting an alert for pipeline failures doesn’t catch all activity failures. If there is a retry of an activity and the second attempt is successful, there would be an activity failure but no pipeline failure.
Conversely, if we set an alert to notify us of activity failures, and we have a pipeline designed with the try-catch pattern, we might get an alert about an activity failure, but the pipeline would still show success. You would need to look at the status of the activities within the pipeline execution to see the failure of which you were alerted.
For many of my implementations, just setting an alert to notify me when any activity failure occurs is fine. For others, I really only care if the pipeline fails. Sometimes I need to set more specific alerts where I choose only certain activities to monitor for failure.
You could also use the Data Factory SDK to roll your own monitoring solution. If you write PowerShell, C#, or Python, you can retrieve the status of any pipeline or activity run and take subsequent actions based upon the results.
What This Means for Pipeline Design
You may need to add activities to your pipelines to support your monitoring scenarios if you need something more customized than what is offered from Azure Monitor and don’t want to use the SDK.
If you have notification needs that Azure Monitor can’t accommodate, you could add an activity in your pipelines to send an email based upon your desired activity outcomes. You can cause that activity to execute using an activity dependency alone, or by combining it with a variable and an If Condition activity.
There are times where we may need a pipeline to fail even though we are using the try-catch pattern that results in pipeline success. In that case, I add an additional web activity to the end of my pipeline failure path that hits an invalid url like http://throwanerror. The failure of this activity will cause the pipeline to fail. Keep monitoring and notifications in mind as you design your pipelines so you are alerted as appropriate.
Azure Data Factory Activity and Pipeline Outcomes
To help clarify these concepts I made the below guide to Data Factory activity and pipeline outcomes. Feel free to share it with others. You can download it directly from this link.
Have you ever tried to use your browser to zoom in on a visual in a Power BI report? If you simply published your report and then zoomed in, you might have experienced something like the video below.
With the default settings of the report, when you zoom in, only the menus around the report change. This is because of report responsiveness and the View setting. By default, reports are set to Fit to page. Power BI is refitting the report to the page every time you zoom.
Why would we need to zoom in?
There might be accessibility or compliance reasons to allow people to zoom in. For instance, WCAG 2.1 Success Criterion 1.4.4 states “Except for captions and images of text, text can be resized without assistive technology up to 200 percent without loss of content or functionality.” People with low vision or other vision impairments might benefit from the ability to zoom within a report page.
Another reason might be that a user simply wants to focus on one chart at a time. Power BI does have a Focus mode. Unfortunately, it currently does a poor job of increasing the font sizes on the visual that is in focus, often rendering it unhelpful.
What Are Our Other Options?
There are a couple of workarounds for users who need to zoom in on visuals.
We can set the report view — or teach users to set the report view — to Actual size. This then allows the browser zoom to work as anticipated. We probably don’t want to set all our reports to actual size because we would lose valuable screen real estate and diminish the experience for some users who don’t need to zoom. Having the report automatically fit to the user’s screen is usually helpful. But if users can change that setting as they need too, that might be ok. Here’s an example of how that works.
2. We can use assistive technology to zoom. Both Windows and MacOS have built-in magnifier functionality. The downside to this is that using it would not satisfy WCAG 2.1 Success Criterion 1.4.4. I think there is still some gray area/lack of expertise as far as how people are making data visualizations WCAG compliant because it’s part text and part image/shape (although it’s not rendered on the page as an image in Power BI). I’m usually more concerned that users get the information they need an have a good experience. But I want to note this in case you are trying to be WCAG compliant and might run into this issue. Here’s an example of using the magnifier in Windows. You can still use the interactivity in the report. And you can change the size of the magnification window and the level of magnification.
3. Zooming in on the report page with a touch screen works fine. If users have tablets or laptops with a touch screen, they can use their fingers to zoom and it will behave as expected. Here’s a video that shows that experience.
Those are all the workarounds I’m aware of, but I’m interested to hear how you have worked around this issue. If you have other suggestions please leave them in the comments.
It’s common that users only have access to certain folders in an Azure Data Lake Storage container. These permissions are provided not through Azure RBAC (role-based access control) roles but through POSIX-like ACLs (access control lists).
You can manage ACLs through the Azure Storage Explorer application or in the Storage Explorer preview in the Azure Portal. As an example, I have a storage account with the hierarchical namespace enabled. In the container named filesystem1 is a folder called Test. Test contains 3 files, and I want a user to import Categories.csv into Power BI.
If I select the Test folder and then select Manage Access, I can see that an AAD user named Data Lake User has been granted access and default ACLs. Note that the user needs at least Read and Execute. Write isn’t necessary if they don’t need to change the file.
But with those permissions on the Test folder, I’m not able to connect to it from Power BI Desktop. If I try, I’ll get an error that says “Access to the resource is forbidden.”
This is because the user is missing some permissions. We need to grant Execute permissions on all parent folders up to the root (the container).
In this case, there is only one level above my Test folder. So I select the filesystem1 container, go to Manage Access, and grant it Execute permissions.
Note that changing the Default ACL on a parent does not affect the access ACL or default ACL of child items that already exist. So if you have existing subfolders and files to which users need access, you will need to grant access at each parent level because the default ACLs won’t apply.
Thanks to Gerhard Brueckl for noting that I needed Execute permissions on parent folders when I got stuck in testing.
If you find yourself hitting that access forbidden message in Power BI when accessing a file in ADLS Gen2, double check the user’s Execute permissions on the parent folders.
Jon Schwabish over at PolicyViz has created great initiative called the One Chart at a Time Video Series. It’s an effort to expand readers’ graphic literacy through short videos explaining how to read and use different charts. Each video is from a different person in the data visualization industry. Participants include people I admire such as Andy Kirk, Ben Jones, and Cole Nussbaumer Knaflic. Jon releases a new video each weekday. The initiative started January 11 and will continue through mid-March.
In each video, the presenter answers 3 questions:
Can you please describe the graph?
Can you please describe any considerations chart makers need to take into account when creating this type of chart?
Can you please share an example of this chart you really like?
I was honored to be asked to participate and talk about waterfall charts. My video (episode 13) has been published! You can check it out below, but I highly recommend you check out all the previous episodes and stay tuned for future episodes.
Links to examples
My video contains a few examples of waterfall charts, and I want to make sure to give credit to these designers for their work here, in addition to noting them in my video.
As Microsoft MVP’s and Partners as well as VMware experts, we are summoned by companies all over the world to fine-tune and problem-solve the most difficult architecture, infrastructure and network challenges.
And sometimes we’re asked to share what we did, at events like Microsoft’s PASS Summit 2015.
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