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.
Related
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.
I have a Pipeline job that needs to run only after an MR has been merged to a certain branch (let’s assume it’s master).
This job is supposed to make an API call to send the name of the merged source branch.
The problem I’m encountering is that CI_MERGE_REQUEST_SOURCE_BRANCH_NAME will not be available on the Pipeline that runs right after the merge (since it’s not a merge request pipeline).
Is there a way (env var) to tell what was the branch that was just merged into master?
Many thanks in advance y’all!
Better you work with hashes not with the names.
Anyway I see two options:
In the pipeline for the merge request save the hash/name in an artifact. The subsequent pipeline can access this artifact an read the hash/name.
Run a independent pipeline and read the hash/name of the previous pipeline. To do this more secure you can add tags and read the previous pipeline only if it has the correct tag.
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
I am trying to automate a process in Azure DevOps, using the REST API. I think it should go like this (at least, this is the current manual process):
fork repo
create pipeline(s) based using YAML files in newly forked repo
run pipelines in particular way
I am new to the Azure DevOps REST API and I am struggling to understand what I have done and what I should be doing.
Using the REST API, I seem to be able to create what I would call a pipeline, using the pipeline endpoint; I do notice that if I want to run it, I have to interact with its build definition instead.
Also, looking at code other colleagues have written, it seems (though I may be wrong) like they are able to achieve the same by simply creating a build definition, and not explicitly creating pipeline.
This lack of understanding is driving me bonkers so I am hoping someone can enlighten me!
Question
What is the difference, and relationship, between a Build Definition and a Pipeline?
Additional info, I am not interested in working with the older Release Pipelines and I have tried to find the answer among the Azure DevOps REST API docs, but to no avail.
If you want to create a pipeline you can do this using both of this. However, the difference is actually in terms of concept:
build definitions are part of first available flow which consist: build and release where build was responsible for building, testing and publishing artifact for later use in releases to deploy
pipeline are a new approach which leverage YAML designed process for building/testing/deploying code
More info you can find here - Whats the difference between a build pipeline and a release pipeline in Azure DevOps?
And for instance for this pipeline/build
https://dev.azure.com/thecodemanual/DevOps%20Manual/_build?definitionId=157
where definition id is 157
You will get reposnses in both endpoints:
https://dev.azure.com/{{organization}}/{{project}}/_apis/build/definitions/157?api-version=5.1
and
https://dev.azure.com/{{organization}}/{{project}}/_apis/pipelines/157?api-version=6.0-preview.1
and in that term pipeline id = build id
The pipelines endpoint is not very useful:
https://dev.azure.com/{Organization}/{ProjectName}/_apis/pipelines?api-version=6.0-preview.1
It will only give you a list of pipelines with very basic info such as name, ID, folder etc.
To create and update YAML pipelines you need to use the Build definitions endpoint. The IDs you use in the endpoint are the same IDs as the Pipelines endpoint uses.
Get definition, Get list, Create, Update:
https://dev.azure.com/{Organization}/{ProjectName}/_apis/build/definitions?api-version=6.0
(To create a working pipeline you must first Get an existing pipeline, modify the JSON you receive, then POST it as a new definition.)
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'.