I have main adf pipeline which has several child pipelines. Those pipelines are getting data from different sources from azure blob and loading data into different snowflake tables.
Individually each child pipeline run for average of 4 mins. However they are running parallelly
under main pipeline where main pipeline runs for around 8 mins. If I sum each child pipeline execution time it will total up to 40 mins.
So will I be charged for 8 mins of parallel execution or 40 mins of pipeline run based on total of all child pipeline runs.
I have already checked in cost-analysis and it does not give costing based on individual pipeline
I have validated the scenario asked in the question . It seems adf charges for each pipeline run . I recreated the scenario as below
Created one main pipeline and ran 3 child pipelines parallelly within main pipeline for couple of days. Main pipeline ran for 17 mins
Removed 2 child pipelines and ran Main pipeline with 1 child pipeline for couple of days. Main pipeline ran for 17 mins.
In 2nd scenario it cost around 1.490376279 INR per day while it cost 8.141834833 INR in scenario 1. This validates that ADF costing happens for each pipeline irrespective of how it runs
Related
In gitlb pipeline,we had a downstream job.This downstream jobs takes little time and I would like to know the total time taken by a pipeline including the downstream job
I am able to get execution time in gitlab pipeline screen with the component on status,but it does not contain downstream job execution time.I deally what i am expecting is the total time taken including thedownstream job execution
I need to run a GitLab pipeline at four specific times each day, which I have solved by setting up four schedules, one for each desired point in time. All pipelines run on the same branch, master.
In the list of pipelines, I get the following information for each pipeline:
status (success or not)
pipeline ID, a label indicating the pipeline was triggered by a schedule, and a label indicating the pipeline was run on the latest commit on that branch
the user that triggered the pipeline
branch and commit on which the pipeline was run
status (success, warning, failure) for each stage
duration and time at which the pipeline was run (X hours/days/... ago)
This seems optimized to pipelines which typically run no more than once after each commit: in such a scenario, it is relatively easy to identify a particular pipeline.
In my case, however, the code itself has relatively little changes (the main purpose of the pipeline is to verify against external data which changes several times a day). As a result, I end up with a list of near-identical entries. In fact, the only difference is the time at which the pipeline was run, though for anything older than 24 hours I will get 4 pipelines that ran “2 days ago”.
Is there any way I can customize these entries? For a scheduled pipeline, I would like to have an indicator of the schedule which triggered the pipeline or the time of day (even for pipelines older than 24 hours), optionally a date (e.g. “August 16” rather than “5 days ago”).
To enable the use of absolute times in GitLab:
Click your Avatar in the top right corner.
Click Preferences.
Scroll to Time preferences and uncheck the box next to Use relative times.
Your pipelines will now show the actual date and time at which they were triggered rather than a relative time.
More info here: https://gitlab.com/help/user/profile/preferences#time-preferences
Lets assume a scenario where pipeline A runs every day and pipeline B runs once in every month and it is dependent on pipeline A (pipeline B should trigger after successful completion of pipeline A).
Using scheduled trigger, we cannot have hard dependencies between 2 pipelines, where as with tumbling window, we cannot exactly specify the day which the pipeline B should run(it has only two options, minutes and hours where as scheduled trigger has months and weeks also)
Both the triggers has its disadvantages with respect to this scenario.
What could be the best possible solution for this scenario?
You can Run Pipeline A everyday, and have an IF check that checks if its a specific date today, then run Pipeline B if TRUE and nothing if FALSE.
For the settings of If Condition, you can use this as variable, if you want to run it every 1st of every month:
#Contains('01',Substring(formatDateTime(utcnow()),8,2))
With an Azure Data Factory "Tumbling Window" trigger, is it possible to limit the hours of each day that it triggers during (adding a window you might say)?
For example I have a Tumbling Window trigger that runs a pipeline every 15 minutes. This is currently running 24/7 but I'd like it to only run during business hours (0700-1900) to reduce costs.
Edit:
I played around with this, and found another option which isn't ideal from a monitoring perspective, but it appears to work:
Create a new pipeline with a single "If Condition" step with a dynamic Expression like this:
#and(greater(int(formatDateTime(utcnow(),'HH')),6),less(int(formatDateTime(utcnow(),'HH')),20))
In the true case activity, add an Execute Pipeline step executing your original pipeline (with "Wait on completion" ticked)
In the false case activity, add a wait step which sleeps for X minutes
The longer you sleep for, the longer you can possibly encroach on your window, so adjust that to match.
I need to give it a couple of days before I check the billing on the portal to see if it has reduced costs. At the moment I'm assuming a job which just sleeps for 15 minutes won't incur the costs that one running and processing data would.
there is no easy way but you can create two deployment pipelines for the same job in Azure devops and as soon as your winodw 0700 to 1900 expires you replace that job with a dummy job using azure dev ops pipeline.
Doing some tests, I could see that having an Azure Integration Runtime (AIR) allowed us to reduce considerably the amount of time required to finish a pipeline.
To fully understand the use of this configuration and its billing as well, I've got these questions. Let's assume I've got two independent pipelines, all of their Data Flow activities use the same AIR with a TTL = 10 minutes.
The first pipeline takes 7 minutes to finish. The billing will be (if I understand well):
billing: time to acquire cluster + job execution time + TTL (7 + 10)
Five minutes later, I trigger the second pipeline. It will take only 3 minutes to finish (I understand it will also use the same pool as the first one). After it concludes, the TTL is setting up to 10 minutes again or is equal to 2 minutes
10 - 5 -3 (original TTL - start time second pipe - runtime second pipe), in this case, what will happen if I trigger a third pipeline that could take more than 2 minutes?
What about the billing, how is it going to be calculated?
Regards.
Look at the ADF pipeline monitoring view and find all of your data flow activity executions.
Add up that total data flow activity execution time.
Now add the TTL value for that Azure IR you were using to that total.
That is the total time you will be billed.