nodejs-storage VS gRPC

Cloud Storage: blog storage for static assets and media files

Preevy includes built-in support for saving profiles on AWS S3 and Google Cloud Storage. You can also store the profile on the local filesystem and copy it manually before running Preevy - we won't show this method here.

Google Cloud Storage{:target="_blank"} is a globally distributed object storage service offered by Google Cloud Platform. They provide trustworthy and scalable databases for storing large amounts of blob data. They also provide a way to optimize cost and performance with different storage classes and pricing options.

Google Cloud Storage - https://cloud.google.com/storage/

Also, in terms of packing a pre-trained model you will probably want to puts weights, biases etc into S3 or similar object storage (https://cloud.google.com/storage etc) and load it on application start

An object storage system (e.g. Amazon S3, Azure Blob Storage, Google Cloud Platform Cloud Storage, etc.) makes it easy and simple to save large amounts of historical data and retrieve it for future use.

ToolJet allows you to build applications that use relational and non-relational databases, REST APIs, and cloud storage like Google Cloud Storage, AWS S3, and Minio. It is an excellent development tool helping individuals, developers, and businesses create and ship products faster.

Google Cloud Storage is a flexible, scalable, and cost-effective option for hosting a static website. It offers features like custom domains and SSL certificates, and it's easy to use. However, it can be more expensive than some other options for high-traffic websites.

There are a few ways to handle this depending on your client's preferences. If your client still wants to own the data, we support exporting to Object Storage systems like S3, GCS, R2, etc. This will keep the data out of their production systems but allow them to query it on demand.

Of course, the storage layer is the place where the data is stored. Because the amount of data to be stored is huge, we can not use HDD or SSD data storages, we need something cheaper. In this case we will be talking about AWS S3 because we are working with Amazon Web Services. For Azure, you could use Azure Data Lake Storage Gen2. If you are working with Google Cloud, you could use Google Cloud Storage. It does not matter what storage you use as long as it is cheap and can store a huge amount of data.

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.

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.

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.

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 .

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.

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.

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>

Even in the https://grpc.io blog says this

gRPC