apache bookkeeper vs kafka
Apache Pulsar is significantly more complicated than Apache Kafka in terms of its protocols and storage model. Both Kafka and DistributedLog support end-to-end batching and compression. I bet that in 99% of scenarios, both will show acceptable performance for your use case. Additionally, most of the ones showing Pulsar say something like “looking for experience with Kafka, Pulsar, Kinesis or similar technologies”. I think I brought some clarity into these discussions. This does mean that if ZooKeeper goes down, everything becomes unavailable and that all Pulsar node caches will be wiped. (Note the use of Qa=2. However, this seems to be mostly due to a few posts on the internet that claim Pulsar is in some ways better than Kafka. All reads and writes are sequential. What is your strategy? Zookeeper keeps track of status of the Kafka cluster nodes and it also keeps track of Kafka topics, partitions etc.
That’s because, for each partition, only a single ledger (of the partition’s potentially many ledgers) is writable at the same time. IBM, Oracle, Amazon, Microsoft and many other software companies support Kafka and build integration capabilities and own products around it. Confluent published such a comparison of "Kafka vs. Pulsar vs. RabbitMQ: Performance, Architecture, and Features Compared". Pulsar has a converter for basic parts of the Kafka protocol. All the writes are sequentially appended to journal files on the journal disks and group committed to the disk. Developers want to write distributed applications or microservice but don’t want the hassle of managing complex message infrastructure or getting locked into a particular cloud vendor. The readers can start reading the log stream from any provided sequence number. At Kesque it’s our mission to empower developers to build cloud-native distributed applications by making cloud-agnostic, high performance messaging technology easily available to everyone. Period. So we’ve covered from a high level the logical and physical representation of messages, as well as the different actors in a Pulsar cluster and their relationships with each other. And, of course, in a fault-tolerant manner, including (and especially) for any stateful processing operations. All the other options work, too. In these simple cases, wouldn’t it be nice to not have to worry about partitions and the API and management complexity that comes along with them? Write throughput is based on the number of partitions allocated in a Pulsar topic in the exact same way it is in a Kafka topic, so Pulsar topics must be over-provisioned for exactly the same reasons. Here are the top 7 reasons why we chose Apache Pulsar over Apache Kafka. However, reads and writes now have to jump around a bit between Bookies.
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