berglas
gRPC
berglas | gRPC | |
---|---|---|
37 | 201 | |
1,224 | 40,775 | |
0.1% | 0.6% | |
6.9 | 9.9 | |
6 days ago | 4 days ago | |
Go | C++ | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
berglas
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How to deploy a Django app to Google Cloud Run using Terraform
Secret Manager: secure storage for sensitive data e.g passwords.
- How do you handle sensitive variables with a service-worker?
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Increasing Your Cloud Function Development Velocity Using Dynamically Loading Python Classes
Google Secret Manager
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Getting started using Google APIs: API Keys (Part 2)
API keys are easy to "leak" or compromise, so best to not only use the restrictions presented to you when you create them but physically protect them as well. Don't code them in plain-text, don't check them into GitHub, etc. Store them in a secure database or use a service like GCP Secret Manager.
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Need some advice on API key storage
I've been looking at Google Secret Manager which sounds promising but I've not been able to find any examples or tutorials that help with the actual practical details of best practice or getting this working. I'm currently reading about Cloud Functions which also sound promising but again, I'm just going deeper and deeper into GCP without feeling like I'm gaining any useful insights.
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Secure GitHub Actions by pull_request_target
In this post, I described how to build secure GitHub Actions workflows by pull_request_target event instead of pull_request event. Using pull_request_target, you can prevent malicious codes from being executed in CI. And by managing secrets in secrets management services such as AWS Secrets Manager and Google Secret Manager and access them via OIDC, you can restrict the access to secrets securely. To migrate pull_request to pull_request_target, several modifications are needed. And pull_request_target has a drawback that it's difficult to test changes of workflows, so it's good to introduce pull_request_target to repositories that require strong permissions in CI. For example, a Terraform Monorepo tends to require strong permissions for CI, so it's good to introduce pull_request_target to it.
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Need Help with Deploying Directus on Google Cloud Platform (GCP)
If you want to make these secrets more secure and get versioning and access logs for them, you may want to switch to Secret Manager later on. They can still be exposed as environment variables to your code. It's a little more setup work, so start with the simple approach at the top.
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Has anyone been able to implement the OpenAI API with a Firebase Function (which is needed for the env variable API Key)?
https://cloud.google.com/secret-manager https://aws.amazon.com/secrets-manager/
- Securely storing Social Security Numbers with Firebase?
- Dónde van las credenciales cuando voy a subir un código a la nube para correr 24/7?
gRPC
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Golang: out-of-box backpressure handling with gRPC, proven by a Grafana dashboard
gRPC, built on HTTP/2, inherently supports flow control. The server can push updates, but it must also respect flow control signals from the client, ensuring that it doesn't send data faster than what the client can handle.
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Reverse Engineering Protobuf Definitions from Compiled Binaries
Yes, grpc_cli tool uses essentially the same mechanism except implemented as a grpc service rather than as a stubby service. The basic principle of both is implementing the C++ proto library's DescriptorDatabase interface with cached recursive queries of (usually) the server's compiled in FileDescriptorProtos.
See also https://github.com/grpc/grpc/blob/master/doc/server-reflecti...
The primary difference between what grpc does and what stubby does is that grpc uses a stream to ensure that the reflection requests all go to the same server to avoid incompatible version skew and duplicate proto transmissions. With that said, in practice version skew is rarely a problem for grpc_cli style "issue a single RPC" usecases: even if requests do go to two or more different versions of a binary that might have incompatible proto graphs, it is very common for the request and response and RPC to all be in the same proto file so you only need to make one RPC in the first place unless you're using an extension mechanism like proto2 extensions or google.protobuf.Any.
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Delving Deeper: Enriching Microservices with Golang with CloudWeGo
While gRPC and Apache Thrift have served the microservice architecture well, CloudWeGo's advanced features and performance metrics set it apart as a promising open source solution for the future.
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gRPC Name Resolution & Load Balancing on Kubernetes: Everything you need to know (and probably a bit more)
The loadBalancingConfig is what we use in order to decide which policy to go for (round_robin in this case). This JSON representation is based on a protobuf message, then why does the name resolver returns it in the JSON format? The main reason is that loadBalancingConfig is a oneof field inside the proto message and so it can not contain values unknown to the gRPC if used in the proto format. The JSON representation does not have this requirement so we can use a custom loadBalancingConfig .
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Dart on the Server: Exploring Server-Side Dart Technologies in 2024
The Dart implementation of gRPC which puts mobile and HTTP/2 first. It's built and maintained by the Dart team. gRPC is a high-performance RPC (remote procedure call) framework that is optimized for efficient data transfer.
- Usando Spring Boot RestClient
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How to Build & Deploy Scalable Microservices with NodeJS, TypeScript and Docker || A Comprehesive Guide
gRPC is a high-performance, open-source RPC (Remote Procedure Call) framework initially developed by Google. It uses Protocol Buffers for serialization and supports bidirectional streaming.
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Actual SSH over HTTPS
In general, tunneling through HTTP2 turns out to be a great choice. There is a RPC protocol built on top of HTTP2: gRPC[1].
This is because HTTP2 is great at exploiting a TCP connection to transmit and receive multiple data structures concurrently - multiplexing.
There may not be a reason to use HTTP3 however, as QUIC already provides multiplexing.
I expect that in the future most communications will be over encrypted HTTP2 and QUIC simply because middleware creators can not resist to discriminate.
[1] <https://grpc.io>
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Why gRPC is not natively supported by Browsers
Even in the https://grpc.io blog says this
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SGSG (Svelte + Go + SQLite + gRPC) - open source application
gRPC
What are some alternatives?
kubernetes-external-secrets - Integrate external secret management systems with Kubernetes
ZeroMQ - ZeroMQ core engine in C++, implements ZMTP/3.1
helm-charts
Apache Thrift - Apache Thrift
kube-secrets-init - Kubernetes mutating webhook for `secrets-init` injection
Cap'n Proto - Cap'n Proto serialization/RPC system - core tools and C++ library
gitleaks - Protect and discover secrets using Gitleaks 🔑
zeroRPC - zerorpc for python
cocert - Split and distribute your private keys securely amongst untrusted network
rpclib - rpclib is a modern C++ msgpack-RPC server and client library
secrets-store-csi-driver-provider-gcp - Google Secret Manager provider for the Secret Store CSI Driver.
nanomsg - nanomsg library