I have a folder of pipelines, and I want to execute the pipelines inside the folder using a single pipeline. There will be times when there will be another pipeline added to the folder, so creating a pipeline filled with Execute Pipelines is not an option (well, it is the current method, but it's not very "automate-y" and adding another Execute Pipeline whenever a new pipeline is added is, as you can imagine, a pain). I thought of the ForEach Activity, but I don't know what the approach is.
I have not tried this approach but I think we can use the
ADF RestAPI to get all the details of the pipelines which needs to be executed. Since the response is in JSON you can write it back to temp blob and add filter and focus on what you need .
https://learn.microsoft.com/en-us/rest/api/datafactory/pipelines/list-by-factory?tabs=HTTP
You can use the Create RUN API to trigger the pipeline .
https://learn.microsoft.com/en-us/rest/api/datafactory/pipelines/create-run?tabs=HTTP
As Joel called out , if different pipeline has different count of paramter , it will be little messy to maintain .
Folders are really just organizational structures for the code assets that describe pipelines (same for Datasets and Data Flows), they have no real substance or purpose inside the executing environment. This is why pipeline names have to be globally unique rather than unique to their containing folder.
Another problem you are going to face is that the "Execute Pipeline" activity is not very dynamic. The pipeline name has to be known as design time, and while parameter values are dynamic, the parameter names are not. For these reasons, you can't have a foreach loop that dynamically executes child pipelines.
If I were tackling this problem, it would be through an external pipeline management system that you would have to build yourself. This is not trivial, and in your case would have additional challenges because of the folder level focus.
Related
I have a drone file containing multiple pipelines that run in a sequence via dependancies.
In the first pipeline a value is generated that I would like to store as a variable and use in one of the other pipelines.
How would I go about doing this? I’ve seen that variables can be passed between steps via a file but this isn’t possible with pipelines from what i’ve seen and tried.
Thanks
The way my ADF setup currently works, is that I have multiple pipelines, each containing atleast one activity. Then I have one big pipeline that sort of chains these pipelines together.
However, now in the big "master" pipeline, I would like to use the output of an activity from one pipeline and then pass it to another pipeline. All of this orchestrated from the "master" pipeline.
My "master" pipeline would look something like this:
What I have tried to do is adding a parameter to "Execute Pipeline2", and I have tried passing:
#activity('Execute Pipeline1').output.pipeline.runId.output.runOutput
#activity('Execute Pipeline1').output.pipelineRunId.output.runOutput
#activity('Execute Pipeline1').output.runOutput
How would one go about doing this?
unfortunately we don't have a way to pass the output of an activity across pipelines. Right now pipelines don't have outputs (only activities).
We have a workitem that will allow a user to choose what should be the output for a pipeline (imagine a pipeline with 40 activities, user would be able to choose the output of activity 3 as pipeline output). However, this workitem is in very early stages so don't expect to see this soon.
For now, the only way would be to save the output that you want in storage (blob, for example) and then read it and pass it to the other pipeline. Another method could be a web activity that gets the pipeline run (passing run id) and you get the output using ADF SDK or REST API, and then you pass that to the next Execute Pipeline activity.
I want to deploy multiple tables creation script as one adla job to save on cost. I am using packages to get set of defined partition keys for all tables. When i try to deploy as merged script it complains that import statement is declared multiple times and fails.
While i can still deploy script one by one but wanted to see if we can merge script for faster deployment.
Thanks
Amit
I am not sure I completely get your scenario. If you want to deploy a single object by itself, then that file needs to include all the dependencies (e.g., your package). If you want to deploy several objects, you should include the dependencies only once.
You probably should set up something that generates your script from the underlying "fragments". One fragment would be the reference to the package, the other fragments would be the creation of one object. And your deployment system would concatenate the files as needed.
New to Gitlab CI/CD.
What is the proper construct to use in my .gitlab-ci.yml file to ensure that my validation job runs only when a "real" checkin happens?
What I mean is, I observe that the moment I create a merge request, say—which of course creates a new branch—the CI/CD process runs. That is, the branch creation itself, despite the fact that no files have changed, causes the .gitlab-ci.yml file to be processed and pipelines to be kicked off.
Ideally I'd only want this sort of thing to happen when there is actually a change to a file, or a file addition, etc.—in common-sense terms, I don't want CI/CD running on silly operations that don't actually really change the state of the software under development.
I'm passably familiar with except and only, but these don't seem to be able to limit things the way I want. Am I missing a fundamental category or recipe?
I'm afraid what you ask is not possible within Gitlab CI.
There could be a way to use the CI_COMMIT_SHA predefined variable since that will be the same in your new branch compared to your source branch.
Still, the pipeline will run before it can determine or compare SHA's in a custom script or condition.
Gitlab runs pipelines for branches or tags, not commits. Pushing to a repo triggers a pipeline, branching is in fact pushing a change to the repo.
My team at work is currently looking for a replacement for a rather expensive ETL tool that, at this point, we are using as a glorified scheduler. Any of the integrations offered by the ETL tool we have improved using our own python code, so I really just need its scheduling ability. One option we are looking at is Data Pipeline, which I am currently piloting.
My problem is thus: imagine we have two datasets to load - products and sales. Each of these datasets requires a number of steps to load (get source data, call a python script to transform, load to Redshift). However, product needs to be loaded before sales runs, as we need product cost, etc to calculate margin. Is it possible to have a "master" pipeline in Data Pipeline that calls products first, waits for its successful completion, and then calls sales? If so, how? I'm open to other product suggestions as well if Data Pipeline is not well-suited to this type of workflow. Appreciate the help
I think I can relate to this use case. Any how, Data Pipeline does not do this kind of dependency management on its own. It however can be simulated using file preconditions.
In this example, your child pipelines may depend on a file being present (as a precondition) before starting. A Master pipeline would create trigger files based on some logic executed in its activities. A child pipeline may create other trigger files that will start a subsequent pipeline downstream.
Another solution is to use Simple Workflow product . That has the features you are looking for - but would need custom coding using the Flow SDK.
This is a basic use case of datapipeline and should definitely be possible. You can use their graphical pipeline editor for creating this pipeline. Breaking down the problem:
There are are two datasets:
Product
Sales
Steps to load these datasets:
Get source data: Say from S3. For this, use S3DataNode
Call a python script to transform: Use ShellCommandActivity with staging. Data Pipeline does data staging implicitly for S3DataNodes attached to ShellCommandActivity. You can use them using special env variables provided: Details
Load output to Redshift: Use RedshiftDatabase
You will need to do add above components for each of the dataset you need to work with (product and sales in this case). For easy management, you can run these on an EC2 Instance.
Condition: 'product' needs to be loaded before 'sales' runs
Add dependsOn relationship. Add this field on ShellCommandActivity of Sales that refers to ShellCommandActivity of Product. See dependsOn field in documentation. It says: 'One or more references to other Activities that must reach the FINISHED state before this activity will start'.
Tip: In most cases, you would not want your next day execution to start while previous day execution is still active aka RUNNING. To avoid such a scenario, use 'maxActiveInstances' field and set it to '1'.