liftbridge VS redpanda

Liftbridge (https://liftbridge.io/) is more or less the NATS Kafka versioning.

[Gufo Liftbridge](https://pypi.org/project/gufo-liftbridge/) is the Python asyncio Liftbridge client.

https://liftbridge.io/

Apache Pulsar might be worth a look, but it's actually more complex under the hood than Kafka, but has a lot of features built-in that either aren't in FOSS Kafka yet, like tiered storage, or won't be until Confluent doesn't dominate the PMC (like an integrated schema registry), or just can't be done very nicely, if at all, like decent multi-tenancy.

That said, it's a fast moving target, the code quality last I looked was patchy in places, ditto the documentation for both it and Bookkeeper, and the admin overhead is higher (managing bookies and brokers and Zookeepers vs. just brokers and ZK with Kafka, or when KRaft is production ready, just brokers).

This is basically an ad for GCP right?

That said, it looks like Kafka is by far and away the way to handle persistent logs/events at scale. AFAIK a company here in Japan called LINE has all their messaging flowing through a large kafka cluster themselves.

Wonder if anyone is running large NATS Jetstream[0]/Liftbridge[1] or Pulsar[2] (yahoo runs those) clusters. I guess Pulsar might be #2 in terms of adoption at large scale?

[0]: https://docs.nats.io/jetstream/jetstream

[1]: https://liftbridge.io/

[2]: https://pulsar.apache.org/

Do you need the messages to be durable, if so you can have a look at Liftbridge: - https://liftbridge.io/ - https://github.com/liftbridge-io/liftbridge

And if you want closer kafka semantics built on top of nats, check out liftbridge:

https://liftbridge.io/

Stream-processing platforms such as Apache Kafka, Apache Pulsar, or Redpanda are specifically engineered to foster event-driven communication in a distributed system and they can be a great choice for developing loosely coupled applications. Stream processing platforms analyze data in motion, offering near-zero latency advantages. For example, consider an alert system for monitoring factory equipment. If a machine's temperature exceeds a certain threshold, a streaming platform can instantly trigger an alert and engineers do timely maintenance.

Yeah it’s just the stack switching itself that is a handful of cycles, but there is not much more overhead for the full VM switch if you structure your embedding the right way. Code the code is source available if you want to peek at it!

https://github.com/redpanda-data/redpanda/blob/dev/src/v/was...

that's a littlebit of a stretch. when you say "no shortage" - outside of redpanda what product exists that actually compete in all deployment modes?

it's a misconception that redpanda is simply a better kafka. the way to think about it is that is a new storage engine, from scratch, that speaks the kafka protocol. similar to all of the pgsql companies in a different space, i.e.: big table pgsql support is not a better postgres, fundamentally different tech. you can read the src and design here: https://github.com/redpanda-data/redpanda. or an electric car is not the same as a combustion engine, but only similar in that they are cars that take you from point a to point b.

Redpanda with the MongoDB Debezium Connector installed. We use Redpanda as a Kafka broker.

And Kafka + Kafka Connect has https://www.confluent.io/ https://aiven.io/ https://upstash.com/ (and not quite Kafka, but protocol-compatible, https://redpanda.com/)

There exists a C++ project which was created after Rust the language was available. github.com/redpanda-data/redpanda/

I suggest you check out io_uring and thread per core architecture. Applications like scylladb and redpanda have thread per core architecture and use io_uring for async io.

Redpanda