I am trying to deploy an apache beam Data Flow pipeline built-in python in google cloud build. I don't find any specific details about constructing the cloud build.YAML file.
I found a link dataflow-ci-cd-with-cloudbuild, but this seems JAVA based, tried this too but did not work as my starting point is main.py
It requires a container registry. Step to build & deploy is explained in the below link
Github link
Related
We have a flink application using flink sql in aws kinesis data analytics.
Zeppline provides was to export jar to s3 and then create a application from that jar in aws.
However how do we integrate this with CI/CD.
1.We need to put our code in git. How do we export the code ? as zplyn files ? or a sql files ? zplyn files
are not recognised by sonar cube
2. How do we build those applications ?
3. Once we have jar we can create application using aws create-application command , but building jar and setting the environment properties right is what we want to know
thanks
We tried build the appliction using aws zeppline dashboard it builds and runs , but cannot integrate it with git ci/cd
The default setup for firebase functions is to run firebase deploy, which will:
Upload the whole project to Cloud Build
Cloud Build will extract the functions
It will run npm install.
Create the ZIP artefacts
Upload the ZIP artefacts to the cloud
The question is if you know of a way to make these ZIP artefacts on our side and upload them directly?
Default Cloud Build steps
List of the Cloud Build deployments
From my point of view - there are plenty of options how to deploy one or more cloud functions.
The Deploying Cloud Functions documentation provides some initial context.
The easiest way, from my point of view, to use gcloud functions deploy command - see Cloud SDK CLI - gcloud functions deploy
As a side note - from my personal point of view - an idea to use Cloud Build - is not bad, and it has many benefits (security, organization of CI/CD, etc.) but it is up to you. Personally I use Cloud Build with Terraform, and configured deployment in a such a way, that only updated Cloud Functions are redeployed.
There is different "level" of cloud build to take into consideration.
If it's the first step, I mean create a ZIP with the code of your function, no problem, you can do it on your side. Then you can deploy the zip through the console or with api calls
In both case, you need a zip and you deploy it. And, if you use the gcloud functions deploy command, it do exactly the same thing: create a zip, send it to storage and deploy the function from that ZIP!
That's was for the first stage, where you manage the ZIP creation and sending on the cloud.
HOWEVER, to deploy the ZIP code to Google Cloud Platform you need to package that code in a runnable stuff, because you only have a function and a function isn't runnable and can't handle HTTP request.
Therefore, Google Cloud run a Cloud Build under the hood and use Buildpacks to package your code in a container (yes, all is container at Google Cloud), and deploy that container on the Cloud Functions platform.
You can't discard that container creation, without container your Cloud Functions can't run. Or you can use Cloud Run and build your own container on your side and deploy it without Cloud Build.
I have a Angular project and Django (backend) project. As of now I am using Gitlab CI/CD to deploy individual app on Google Cloud Platform. But In production environment in future I want to deploy my code at the same time. How to do this on Google Cloud Platform?
There are several tools for CI/CD on Google Cloud Platform. You could use Google App Engine with Cloud Build. You can find a pretty straightforward tutorial here. Or you could take advantage of the Gitlab Google Kubernetes Engine integration. You can find an example on the official documentation by going here.
It's really confusing that every Google document for dataflow is saying that it's based on Apache Beam now and directs me to Beam website. Also, if I looked for github project, I would see the google dataflow project is empty and just all goes to apache beam repo. Say now I need to create a pipeline, from what I read from Apache Beam, I would do : from apache_beam.options.pipeline_options However, if I go with google-cloud-dataflow, I'll have error: no module named 'options' , turns out I should use from apache_beam.utils.pipeline_options. So, looks like google-cloud-dataflow is with an older beam version and is going to be deprecated?
Which one should I pick do develop my dataflow pipeline?
Ended up finding answer in Google Dataflow Release Notes
The Cloud Dataflow SDK distribution contains a subset of the Apache Beam ecosystem. This subset includes the necessary components to define your pipeline and execute it locally and on the Cloud Dataflow service, such as:
The core SDK
DirectRunner and DataflowRunner
I/O components for other Google Cloud Platform services
The Cloud Dataflow SDK distribution does not include other Beam components, such as:
Runners for other distributed processing engines
I/O components for non-Cloud Platform services
Version 2.0.0 is based on a subset of Apache Beam 2.0.0
Yes, I've had this issue recently when testing outside of GCP. This link help to determine what you need when it comes to apache-beam. If you run the below you will have no GCP components.
$ pip install apache-beam
If you run this however you will have all the cloud components.
$ pip install apache-beam[gcp]
As an aside, I use the Anaconda distribution for almost all of my python coding and packages management. As of 7/20/17 you cannot use the anaconda repos to install the necessary GCP components. Hoping to work with the Continuum folks to have this resolved not just for Apache Beam but also for Tensorflow.
I have created Pipeline in Python using Apache Beam SDK, and Dataflow jobs are running perfectly from command-line.
Now, I'd like to run those jobs from UI. For that i have to create template file for my job. I found steps to create template in Java using maven.
But how do I do it using the Python SDK?
Templates are available for creation in the Dataflow Python SDK since April of 2017. Here is the documentation.
To run a template, no SDK is needed (which is the main problem templates try to solve), so you can run them from the UI, REST API, or CL and here is how.