Apache Pulsar VS Apache Kafka

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.

Edit: Here's a good example of what I'm looking for: https://github.com/apache/pulsar. It is a full application that happens to be deployed (or deployable) with Terraform, and the configuration files are available.

I am the founder of RisingWave (http://risingwave.com/), an open-source SQL streaming database. I am happy to see the launch of Warpstream! I just reviewed the project and here's my personal opinion:

* Apache Kafka is undoubtedly the leading product in the streaming platform space. It offers a simple yet effective API that has become the golden standard. All streaming/messaging vendors need to adhere to Kafka protocol.

* The original Kafka only used local storage to store data, which can be extremely expensive if the data volume is large. That's why many people are advocating for the development of Kafka Tiered Storage (KIP-405: https://cwiki.apache.org/confluence/display/KAFKA/KIP-405%3A...). To my best knowledge, there are at least five vendors selling Kafka or Kafka-compatible products with tiered storage support:

-- Confluent, which builds Kora, the 10X Kafka engine: https://www.confluent.io/10x-apache-kafka/;

-- Aiven, the open-source tiered storage Kafka (source code: https://github.com/Aiven-Open/tiered-storage-for-apache-kafk...

-- Redpanda Data, which cuts your TCO by 6X (https://redpanda.com/platform-tco);

-- DataStax, which commercializes Apache Pulsar (https://pulsar.apache.org/);

-- StreamNative, which commercializes Apache Pulsar (https://pulsar.apache.org/).

* WarpStream claims to be "built directly on top of S3," which I believe is a very aggressive approach that has the potential to drastically reduce costs, even compared to tiered storage. The potential tradeoff is system performance, especially in terms of latency. As new technology, WarpStream brings novelty, and definitely it also needs to convince users that the service is robust and reliable.

* BYOC (Bring Your Own Cloud) is becoming the default option. Most of the vendors listed above offer BYOC, where data is stored in customers' cloud accounts, addressing concerns about data privacy and security.

I believe WarpStream is new technology to this market, and and would encourage the team to publish some detailed numbers to confirm its performance and efficiency!

In recent years, it has become apparent that almost no production system is complete without real-time data. This can also be observed through the rise of streaming platforms such as Apache Kafka, Apache Pulsar, Redpanda, and RabbitMQ.

It's colored black on the refreshed Apache Pulsar site. https://pulsar.apache.org/

So many tools out there, its just which one do you like, I guess. I like Kafka. Works for our environment and we have a few clusters. People have brought up Cribl to replace our kafka (havent really looked into Cribl and we also run NiFi). I have even heard https://pulsar.apache.org/ , which seems to be almost another flavor of Kafka.

RisingWave is an open-source streaming database that has built-in fully-managed CDC source connectors for various databases, also it can collect data from other sources such Kafka, Pulsar, Kinesis, or Redpanda and it allows you to query real-time streams using SQL. You can get a materialized view that is always up-to-date.

Apache Kafka — a distributed event streaming platform implementing a variant of the Raft consensus protocol (written in Java, integrated with Scala);

Spring does this during autoconfiguration. For example this and this. When the user adds a configuration then it gets to overwrite the default from the template. I am looking for something similar, perhaps simpler approach.

Source and documentation on GitHub

Code is still in trunk though. https://github.com/apache/kafka/tree/trunk/connect/file/src/main/java/org/apache/kafka/connect/file

I briefly described the algorithm in this article and there is a wonderful article from Kafka that goes into more depth in their general purpose implementation. My implementation is specialized and over optimized in comparison, e.g. by using bit manipulation to avoid more expensive division/modulus instructions. Tokio rewrote their timerwheel after I showed them mine, borrowing some ideas but also staying more general. Hope that helps!

Apache Kafka