blocks|key|707118|text|关键的区别在于卡夫卡流在输入端和输出端都需要卡夫卡主题。使用Alpakka，您可以为所有类型的输入和输出创建处理管道，而不仅仅是Kafka主题。此外，akka流DSL比Kafka流DSL更强大(也更复杂)。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|707119|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

The key difference is that Kafka streams require Kafka topics on the input side and on the output side. With Alpakka you can create processing pipeline for all kind of inputs and outputs not only Kafka topics. Also, akka streams DSL is more powerful (and complicated) than Kafka streams DSL.

blocks|key|2633320|text|通过使用Alpakka，可以将各种类型的Sink和Flow+(管道)与构建在Akka中的API连接起来。一个例子可以是同时使用Alpakka+Kafka和Alpakka+Cassandra来支持自动发布消息和保存到数据库。Akka中的测试工具包还可以使测试在整个系统中保持一致。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2633321|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

By using the Alpakka various types of Sink and Flow (pipelines) can be attached with the build in APIs in Akka Stream. An example can be using Alpakka Kafka and Alpakka Cassandra at the same time to enable automatic publishing messages and saving to databases. The test toolkit in Akka can also make tests consistent across your system.

blocks|key|2633364|text|我对两者都有经验。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2633365|对于卡夫卡流，在我看来，阿喀琉斯之踵是它使用的数据存储，RockDB，这是KTables和内部状态存储所必需的。RockDB是很好的加载单个值的方法，但是如果您对事物进行了迭代，在数据集大小为10万之后，性能会显著下降。您可以将数据存储更改为某些内容，但它实际上没有那么好的文档。|2633366|它还存在密钥/值数据库的缺点，您不能只在主键上提出问题，并且没有通配符匹配或任何东西，而且它无法实现CQRS模式。|offset|length|2633367|另一方面，Akka并不那么友好，特别是如果你没有掌握流处理的概念的话。但是它不是与单一的持久化的选项耦合，它有组件来实现。Akka开发人员制作了大量的脑风暴，以提高卡夫卡流处理的性能。|2633368|另一个非常重要的问题，虽然有人可能会说它不相关，但是Kafka流没有背压机制，但是Alpakka+Kafka有它，这在某些生产场景中是非常关键的，您可以用这个Netflix博客来说服自己。|2633369|简而言之，如果您想要快速开始流处理，从Kafka流开始，但是要准备好撞墙并切换到Alpakka流。|2633370|如果您需要示例，我有两个关于这些主题的博客，您可以查看它们，blog1+blog2|2633371|entityMap|0|LINK|mutability|MUTABLE|url|https://martinfowler.com/bliki/CQRS.html|1|https://doc.akka.io/docs/akka/current/typed/persistence.html|2|https://doc.akka.io/docs/akka-projection/current/overview.html|3|https://www.lightbend.com/blog/alpakka-kafka-flow-control-optimizations|4|https://netflixtechblog.com/towards-a-reliable-device-management-platform-4f86230ca623|5|https://mehmetsalgar.wordpress.com/2022/05/17/alpakka-kafka-stream-and-akka-finite-state-machine/|6|https://mehmetsalgar.wordpress.com/2022/04/18/a-model-driven-event-sourced-cloud-ready-application-with-akka-finite-state-machine-using-kafka-cassandra-and-elasticsearch/^0|0|0|1E|4|0|0|18|3|1|1J|2|2|23|3|3|0|27|9|4|0|0|U|5|5|10|5|6|0^^$0|@$1|2|3|4|5|6|7|19|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|1A|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|1B|8|@]|9|@$F|1C|G|1D|1|1E]]|A|$]]|$1|H|3|I|5|6|7|1F|8|@]|9|@$F|1G|G|1H|1|1I]|$F|1J|G|1K|1|1L]|$F|1M|G|1N|1|1O]]|A|$]]|$1|J|3|K|5|6|7|1P|8|@]|9|@$F|1Q|G|1R|1|1S]]|A|$]]|$1|L|3|M|5|6|7|1T|8|@]|9|@]|A|$]]|$1|N|3|O|5|6|7|1U|8|@]|9|@$F|1V|G|1W|1|1X]|$F|1Y|G|1Z|1|20]]|A|$]]|$1|P|3|-4|5|6|7|21|8|@]|9|@]|A|$]]]|Q|$R|$5|S|T|U|A|$V|W]]|X|$5|S|T|U|A|$V|Y]]|Z|$5|S|T|U|A|$V|10]]|11|$5|S|T|U|A|$V|12]]|13|$5|S|T|U|A|$V|14]]|15|$5|S|T|U|A|$V|16]]|17|$5|S|T|U|A|$V|18]]]]

Well I have experience with both.
For Kafka Streams, achilles heel in my opinion is the datastore it uses, RockDB, which is necessary for KTables and internal State Stores. RockDB is great loading single Values but if you have iterate over things, after a Dataset size of 100 000 the performance degrades significantly. You can change the datastore to something but it is really not that well documented.
It also suffers from the disadvantages of the key/value databases, you can't only ask questions over primary key and there is not wildcard matching or anything and it has no means to implement <a href="https://martinfowler.com/bliki/CQRS.html" rel="nofollow noreferrer">CQRS</a> pattern
Akka on the other hand is not that newbie friendly, specially if you don't have a grasp of stream processing concepts. But it is not coupled with single <a href="https://doc.akka.io/docs/akka/current/typed/persistence.html" rel="nofollow noreferrer">persistence</a> of option, it has <a href="https://doc.akka.io/docs/akka-projection/current/overview.html" rel="nofollow noreferrer">components</a> to implement CQRS. And Akka developers made lots of <a href="https://www.lightbend.com/blog/alpakka-kafka-flow-control-optimizations" rel="nofollow noreferrer">brain storming</a> to improve the performance Kafka for stream processing.
Another really important point, while some might say it is not relevant, Kafka Streams has no back pressure mechanism but Alpakka Kafka have it, which can be very critical in some production scenarios, you can convince yourself with this <a href="https://netflixtechblog.com/towards-a-reliable-device-management-platform-4f86230ca623" rel="nofollow noreferrer">Netflix Blog</a>.
In short, if you want to start quickly with stream processing, start with Kafka Streams but be ready to hit a wall and switch to Alpakka Streams.
If you need examples, I have two blogs about those topics, you can check it those, <a href="https://mehmetsalgar.wordpress.com/2022/05/17/alpakka-kafka-stream-and-akka-finite-state-machine/" rel="nofollow noreferrer">blog1</a> <a href="https://mehmetsalgar.wordpress.com/2022/04/18/a-model-driven-event-sourced-cloud-ready-application-with-akka-finite-state-machine-using-kafka-cassandra-and-elasticsearch/" rel="nofollow noreferrer">blog2</a>

We are building a high-throughput low-latency stream processing application. We are using Apache Kafka both as a messaging platform and as a database.

It seems like the Kafka Streams and Alpakka Kafka frameworks do a lot in common, but Kafka Streams seems to be more "native" to Kafka, while Alpakka allows us to use the power of the Akka framework.

What are the key differences between the two frameworks?

Alpakka kafka vs Kafka streams

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我们正在构建一个高吞吐量、低延迟流处理应用程序。我们使用Apache作为消息传递平台和数据库。卡夫卡流和阿尔帕卡卡夫卡框架似乎有很多共同点，但卡夫卡流似乎更像是卡夫卡的“本土化”，而Alpakka允许我们使用Akka框架的力量。这两个框架之间的主要区别是什么？

问阿尔帕卡·卡夫卡对卡夫卡流
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