GCP (Google Cloud Platform) cloud storage is the object storage service provided by Google for storing many data formats from PNG files to zipped source code for web apps and cloud functions. The data is stored in a flat, key/value-like data structure where the key is your storage object's name and the value is your data.
If you're looking to store a collection of files as a single unit, either to archive a large number of log files for future audits or to bundle and store code as a part of an automated deployment cycle, it's likely you will do so by packing all of it together as a zip file.
Clouds to Code download.zip
Download: https://tinurll.com/2vKxnN
Before you can begin uploading and downloading local files to cloud storage as zip files, you will need to create the client object used in your Python code to communicate with your project's cloud storage resources in GCP.
The first option is to assign the service account to a particular resource upon deployment. For example, if your code is being deployed as a GCP cloud function, you would attach the service account to the application upon deployment using either the gcloud sdk:
Note that the service account as defined in Terraform is also being referenced in a google_project_iam_binding resource as a member that will be assigned the role of storage.objectAdmin. You will need to assign a similar role (or ideally one with the minimal permissions required for your code to perform its tasks) if you choose to create a service account using the GCP console.
With the in memory binary stream ready to be delivered, the remaining lines of code create a new Bucket object for the specified bucket and a Blob object for the storage object. The zipped files are then uploaded to cloud storage and can later retrieved using the storage object name you used to create the Blob instance.
Note The Windows-classic-samples repo contains a variety of code samples that exercise the various programming models, platforms, features, and components available in Windows and/or Windows Server. This repo provides a Visual Studio solution (SLN) file for each sample, along with the source files, assets, resources, and metadata needed to compile and run the sample. For more info about the programming models, platforms, languages, and APIs demonstrated in these samples, check out the documentation on the Windows Dev Center. This sample is provided as-is in order to indicate or demonstrate the functionality of the programming models and feature APIs for Windows and/or Windows Server.
The code for all the functions in a specific function app is located in a root project folder that contains a host configuration file. The host.json file contains runtime-specific configurations and is in the root folder of the function app. A bin folder contains packages and other library files that the function app requires. Specific folder structures required by the function app depend on language:
Bring security directly into every stage of the development process. Get real-time visibility into any security issues in their code and containers, identify vulnerability fixes early in development and monitor new risks post deployment.
Please ensure you are using the correct WD My Cloud source code for your firmware version. The GPL source code is firmware specific and is not compatible with My Cloud devices using a different firmware.
Assets are local files, directories, or Docker images that can be bundled into AWS CDK libraries and apps. For example, an asset might be a directory that contains the handler code for an AWS Lambda function. Assets can represent any artifact that the app needs to operate.
You add assets through APIs that are exposed by specific AWS constructs. For example, when you define a lambda.Function construct, the code property lets you pass an asset (directory). Function uses assets to bundle the contents of the directory and use it for the function's code. Similarly, ecs.ContainerImage.fromAsset uses a Docker image built from a local directory when defining an Amazon ECS task definition.
When the AWS CDK deploys an app that references assets (either directly by the app code or through a library), the AWS CDK CLI first prepares and publishes the assets to an Amazon S3 bucket or Amazon ECR repository. (The S3 bucket or repository is created during bootstrapping.) Only then are the resources defined in the stack deployed.
The following example uses an Amazon S3 asset to define a Python handler in the local directory handler. It also creates a Lambda function with the local directory asset as the code property. Following is the Python code for the handler.
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