aws-cli/2.1.21 Python/3.7.4 Darwin/19.6.0
amplify CLI 4.41.2
macOS 10.15.6
question:
Amplify fetching process is too long.
please help to me..
I was try below.
amplify init
amplify add api -> REST API
amplify push
Fetching started when last command.
console is
'Fetching updates to backend environment: dev from the cloud. '
I waited a hour.
but process is not complete.
Please tell me I have to confirm where.
other
Previously, I manually deleted the amplify related resource.
(e.g. cloudformation, s3,and more)
This may have been bad.
What is strange about you situation is that you are pushing information, but I'm not certain that you mentioned you had a stable cloud version. Meaning this, if there was an unstable version on the cloud and a heavily modified local version it could cause issue (such is what i have seen when working on it). My advice would be to ensure you have a cleanly design cloud version with amplify studio https://docs.amplify.aws/cli/ could help with general commands though "Amplify --help" is another option.
Aside from that, "Amplify pull" would overwrite your local system, but assuming it is a clean version you could then push it. I have actually save files off to my desktop and then copied them back in and that has worked for me. Fundamentally the issue is that it is a cloud system you are relying on. Major modifications will often be ignored or overwritten. Wish I could help more.
Related
Background
I want to create the following CI/CD flow in AWS and Github, for a react app using Amplify:
A single main branch, with short-lived feature branches and PRs into main.
Each PR triggers its own test environment in Amplify, with its own temporary subdomain, which gets torn down when the PR is merged, as described here.
Merging into main does not automatically trigger a deploy to production.
Instead, there is a separate mechanism (a web page, or amplify command, or even triggers based on git tags) for manually selecting a commit from main to deploy to production.
Questions
It's not clear to me if...
Support for this flow is already built into Amplify (based on the docs I've read, I think the answer is "no", but I'm not sure).
Support for this flow is already built into AWS CodePipeline, or if it can be configured there.
There is another AWS tool that solves this.
I'm looking for answers to those questions, or specific references in the docs which address them.
The answers for Amplify are Yes, Yes, Yes, Partially.
(1) A single main branch, with short-lived feature branches and PRs into main.
Yes. Feature branch deploys. Can define which branch patterns, such as feature*/, you wish to auto-deploy.
(2) Each PR triggers its own test environment in Amplify, with its own temporary subdomain,
Yes. Web Previews for PRs. "A web preview deploys every pull request made to your GitHub repository to a unique preview URL which is completely different from the URL your main site uses."
(3) Merging into main does not automatically trigger a deploy to production.
Yes. Disable automatic builds on main.
(4) Instead, there is a separate mechanism ... for manually selecting a commit from main to deploy to production.
Partially (HEAD only?). Call the StartJob API to manually trigger a build from, say, Lambda. The job type RELEASE starts a new job with the latest change from the specified branch. I am not sure if jobType: MANUAL with a commitId starts a job from an arbitrary commit hash.
Another workaround for 3+4 is to skip the build for an arbitrary commit. Amplify will skip building if [skip-cd] appears at the end of a commit message.
In my experience, I don't think there is any easy way to meet your requirement.
If you are using Gitlab, you can try Gitlab Review Apps to achieve that (I tried before with some scripts)
Support for this flow is already built into Amplify (based on the docs I've read, I think the answer is "no", but I'm not sure).
Check below links, if this help:
https://www.youtube.com/watch?v=QV2WS535nyI
https://dev.to/rajandmr/deploying-react-app-using-aws-amplify-with-ci-cd-pipeline-setup-3lid
Support for this flow is already built into AWS CodePipeline, or if it can be configured there.
For this, you need to create a full your own pipeline. Yes, you can configure your pipeline.
There is another AWS tool that solves this.
If you are okay with Jenkins, then Jenkins will help you to achieve this.
You can deploy Jenkins docker in AWS EC2 and create your pipeline. You can also use the parameterised option for selecting your environment and git branch.
The problem
I'm approaching AWS, and the first test project will be a website, but i'm struggling on how to approach the resource and the tools to accomplish this.
AWS documentation is not really beginner-friendly, so to me it is like to being punched in the face at the first boxe training session.
First attempt
I've installed bot AWS and SAM cli tools, so what I would expect is to be able to create an empty stack at first and adding the resource one by one as the specifications are given/outlined, but instead what I see is that i need to give a template to the tool to create the new stack, but that means I need to know how to write it beforehand and therefore the template specifications for each resource type.
Second attempt
This lead me to create the stack and the related resources from the online console to get the final stack template, but then I need to test every new resource or any updated resource locally, so I have to copy the template from the online console to my machine and run the cli tools with this, but obviously it is not the desired development flow.
What I expected
Coming from a standard/classical web development I would expect to be able to create the project locally, test the related resources locally, version it, and delegate the deployment to the pipeline.
So what?
All this made me understand that "probably" I'm missing somenthing on how to use the aws cli tools and how the development for an aws-hosted application is meant to be done.
I'm not seeking for a guide on specific resource types like every single tutorial I've found online, but something on a higher level on how to handle a project development on aws, best practices and stuffs like that, I can then dig deeper on any resource later when needed.
AWS's Cloud Development Kit ticks the boxes on your specific criteria.
Caveat: the CDK has a learning curve in line with its power and flexibility. There are much easier ways to deploy a web app on AWS, like the higher-level AWS Amplify framework, with abstractions tailored to front-end devs who want to minimise the mental energy spent on the underlying infrastructure.
Each of the squillion AWS and 3rd Party deploy tools is great for somebody. Nevertheless, looking at your explicit requirements in "What I expected", we can get close to the CDK as an objective answer:
Coming from a standard/classical web development
So you know JS/Python. With the CDK, you code infrastructure as functions and classes, rather than 500 lines of YAML as with SAM. The CDK's reference implementation is in Typescript. JS/Python are also supported. There are step-by-step AWS online workshops for these and the other supported languages.
create the project locally
Most of your work will be done locally in your language of choice, with a cdk deploy CLI command to
bundle the deployment artefacts and send them up to the cloud.
test the related resources locally
The CDK has built-in testing and assertion support.
version it
"Deterministic deploy" is a CDK design goal. Commit your code and the generated deployment artefacts so you have change control over your infrastructure.
delegate the deployment to the pipeline
The CDK has good pipeline support: i.e. a push to the remote main branch can kick off a deploy.
AWS SAM is actually a good option if you are just trying to get your feet wet with AWS. SAM is an open-source wrapper around the aws-cli, which allows you to create aws resources like Lambda in say ~10 lines of code vs ~100 lines if you were to use the aws-cli directly. Yes, you'll need to learn SAM specific things like SAMtemplate and SAM-cli but it is pretty straightforward using this doc.
Once you get the hang of it, it would be easier to start looking under the hood of what/how SAM is doing things and get into the weeds with aws-cli if you wanted. Which will then allow you to build out custom solutions (using aws-cli) for your complex use cases that SAM may not support. Caveat: SAM is still pretty new and has open issues that could be a blocker for advanced features/complex use cases.
I'm seeing this Cloud Build error when I try to deploy a Cloud Function:
"Step #2 - "analyzer": [31;1mERROR: [0mfailed to initialize cache: failed to create image cache: accessing cache image "us.gcr.io/MY_PROJECT/gcf/us-central1/SOME_KEY/cache:latest": failed to get OS from config file for image 'us.gcr.io/MY_PROJECT/gcf/us-central1/SOME_KEY/cache:latest'"
I'm able to build and emulate the cloud function locally, but I can't deploy it due to this error. I was able to deploy just fine until now. I've looked everywhere and I can't find any discussion about this. Anyone know what's going on here?
UPDATE: I deployed a new function 3 days ago and now I can't seem to deploy an update to it. I get the same error. I'm fairly sure this is happening due to the lifecycle rule I set up to ensure I don't keep storing images of functions: Firebase storage artifacts is huge and keeps increasing. This rule is important to keep around because I don't want to pay for unnecessary storage, but it seems like it might be the source of our problem here. Can someone from Google look into this?
I got the same error, even for code that deployed successfully before.
A workaround is to delete the Docker images for the failing Firebase functions inside Container Registry and re-deploying the functions. (The images will be re-created upon deploying.)
The error still occurs sporadically, so I suspect this may be a bug introduced in Firebase's deployment process. Thankfully for now, the workaround above resolves the issue every time the error comes up.
I also encountered the same problem, and solved it by deleting the images in the Container Registry of Firebase Project.
I made a Script at that time, and I'll put it here. The usage is as follows. Please use it if you like.
Install the Google Cloud SDK.
Download the Script
Edit CONTAINER_REGISTRY to your registry name. For example: CONTAINER_REGISTRY=asia.gcr.io/project-name/gcf/asia-northeast1
Grant execute permission. - $ chmod +x script.sh
Execute it. - $ sh script.sh
Deploy your functions.
I'm having the same problem for the last few days and in contact with the support. I had the same log and in my case it wasn't connected to the artifacts because the artifacts rebuild themselves automatically on deploy (read below about a subtle case related to the artifacts and how to fix it), but deleting the functions and redeploying solved it for me.
Artifacts auto cleanup
Note that if the artifacts bucket is empty, then the problem is somewhere else.
But if it's not empty, what you can do to resolve any possible problems related to the artifacts auto cleanup, is to delete the whole "container" folder manually in the artifacts which should solve it. Then just redeploy again.
Make sure not to delete the artifacts bucket itself!
Dough from firebase confirmed in the question you referring to that removing the artifacts content is safe.
So, here is how to delete it:
go to the google cloud console, select your project -> storage -> browser https://console.cloud.google.com/storage/browser
Select the "artifacts" bucket
Choose "containers" and delete it
If the problem was here, it should work fine after that.
This happens because the deletion rule you refer to in your question checks the "last updated" timestamp of each file while on redeploy only some files are updated. So the next day the rule will delete some of the files while leaving the others which will lead to the inconsistent state of the bucket in this case. So you just remove everything manually.
I'm presently looking into GCP's Deployment Manager to deploy new projects, VMs and Cloud Storage buckets.
We need a web front end that authenticated users can connect to in order to deploy the required infrastructure, though I'm not sure what Dev Ops tools are recommended to work with this system. We have an instance of Jenkins and Octopus Deploy, though I see on Google's Configuration Management page (https://cloud.google.com/solutions/configuration-management) they suggest other tools like Ansible, Chef, Puppet and Saltstack.
I'm supposing that through one of these I can update something simple like a name variable in the config.yaml file and deploy a project.
Could I also ensure a chosen name for a project, VM or Cloud Storage bucket fits with a specific naming convention with one of these systems?
Which system do others use and why?
I use Deployment Manager, as all 3rd party tools are reliant upon the presence of GCP APIs, as well as trusting that those APIs are in line with the actual functionality of the underlying GCP tech.
GCP is decidedly behind the curve on API development, which means that even if you wanted to use TF or whatever, at some point you're going to be stuck inside the SDK, anyway. So that's why I went with Deployment Manager, as much as I wanted to have my whole infra/app deployment use other tools that I was more comfortable with.
To specifically answer your question about validating naming schema, what you would probably want to do is write a wrapper script that uses the gcloud deployment-manager subcommand. Do your validation in the wrapper script, then run the gcloud deployment-manager stuff.
Word of warning about Deployment Manager: it makes troubleshooting very difficult. Very often it will obscure the error that can help you actually establish the root cause of a problem. I can't tell you how many times somebody in my office has shouted "UGGH! Shut UP with your Error 400!" I hope that Google takes note from my pointed survey feedback and refactors DM to pass the original error through.
Anyway, hope this helps. GCP has come a long way, but they've still got work to do.
I'm newbie in AWS, with my free tier account I'm trying to build my nodeJS project with AWS CodeBuild but I get this error:
Build failed to start The build failed to start. The following error occured: Cannot have more than 0 builds in queue for the account
I followed the simple aws tutorial, leaving all default settings for let aws create all service, image etc for me.
Also I stored source code in a AwsCodeCommit repository.
Could anybody help me?
In my case, there was a security vulnerability in my account and AWS automatically raised a support ticket and suspended all resources that were linked to it. I had to fix it and then on chat with aws support they resumed my service.
I've seen a lot of answers around the web suggesting to call support, which is a great idea, but I was actually able to get around this on my own.
As the root user I went in and put in a current credit card. The one that was currently there was expired. I then deleted my CodeBuild project and create a new one. Now my builds work! It makes sense that AWS just needed a valid payment method before it allowed me to use premium services.
My solution may not work for you, but sure I hope it does!
My error was Project-level concurrent build limit cannot exceed the account-level concurrent build limit of 1 when I tried to increase the Concurrent build limit under checkbox Restrict number of concurrent builds this project can start in CodeBuild Project Configuration. I resolved it by writing to support to increase the limit. They increased it to 20 and it works now as expected. They increased it even though I'm on Basic plan on AWS if anyone's wondering.
My solution was to add new service role name and the concurrent build to 1. This worked
I think your issue is resolved at the moment. Any way I faced the same issue. In my case I had a "code build project" connecting to a GitHub repository. And then I added AWS Access Key and Secret hard coding the buildspec.yml file. With this AWS identified it as an unauthorized login. So they added security restrictions to the resources while opening a support issue. In such a case you can look for the emails from AWS in which they explain the reason for this behavior and the steps to get this corrected.