Fluentd VS gRPC

Get Started with Fluentd

Currently, there is no cluster-wide logging. Fluentd can be used to have a unified logging layer for the cluster.

Fluentd hasn't been touched for 8 years? Looking at the repo it looks like it's alive and well. https://github.com/fluent/fluentd

Fluentd is an open-source log management and data collection tool. Just like Logstash, Fluentd uses a pipeline-based architecture. This allows it to collect data from various sources and network traffic and forward it to various destinations.

Fluentd is a powerful log management tool that offers organizations the flexibility and scalability required to handle large volumes of log data from a variety of sources and transport it to various destinations. Utilizing a flexible and modular architecture, Fluentd allows users to easily add new input and output plugins to integrate with a wide range of systems and applications. It supports a wide range of data sources and destinations, including databases, message queues, and data stores.

Substation is an affordable alternative to products like Cribl (~10x cost savings) and is easier to manage than similar open-source projects such as Logstash and fluentd. It's been used in production by the security team at Brex for 2+ years and is ready for any scale, even beyond 100,000 events per second!

There are probably too many to chose from. Logstash, Promtail, Vector, Filebeat, FluentD, Logagent and probably many more

To alleviate some of the pain, it’s a good idea to use industry standards and tooling like OpenTelemetry (https://opentelemetry.io). For data collection specific to logs, open-source tools like LogStash and Fluentd are also popular.

Created and maintained by the creators of fluentd, fluentbit is a lightweight, fast, and scalable logging and metrics processor and forwarder. Built specifically for the cloud and containerized environments, it allows users to collect data from any source, enrich it with filters and forward it to the tool of their choice.

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