DynamicData is a .NET library that brings the power of reactive programming to collections. It is built upon the principles of Reactive Extensions (Rx), extending these concepts to handle collections like lists and observables more efficiently and flexibly. DynamicData provides a set of tools and extensions that enable developers to manage collections reactively, meaning any changes in the data are automatically... - Source: dev.to / about 2 months ago

Another option is to use the RxJava library in Java. This library uses reactive programming principles to make it easy to write asynchronous and event-driven code. It's particularly well-suited for handling streams of data and allows you to write code that is both efficient and easy to read. Source: about 1 year ago

The thing that really irks me is that the generator pattern doesn't have to be an OO-first feature. Observable streams[1] work with the same basic foundation and those are awesome for FP. [1]: https://reactivex.io/. - Source: Hacker News / over 1 year ago

> I’m not sure what you mean by "Rx" in this context. From “reactive extensions”, a proper name for a family of libraries[1] (RxJava, Rx.NET, RxJS), AFAICT one of the first attempted implementations of mature FRP ideas in the imperative world and one messy enough that it took React for anything similar to reënter the mainstream. Compare the enthusiastic HN reception of “Deprecating the observer pattern” in... - Source: Hacker News / over 1 year ago

Here’s what you can do with the observer pattern — https://reactivex.io/. Source: over 1 year ago

Restate is built as a sharded replicated state machine similar to how TiKV (https://tikv.org/), Kudu (https://kudu.apache.org/kudu.pdf) or CockroachDB (https://github.com/cockroachdb/cockroach) since it makes it possible to tune the system more easily for different deployment scenarios (on-prem, cloud, cost-effective blob storage). Moreover, it allows for some other cool things like seamlessly moving from one log... - Source: Hacker News / 5 days ago

I’ve recently started my journey with Apache Flink. As I learn certain concepts, I’d like to share them. One such "learning" is the expansion of array type columns in Flink SQL. Having used ksqlDB in a previous life, I was looking for functionality similar to the EXPLODE function to "flatten" a collection type column into a row per element of the collection. Because Flink SQL is ANSI compliant, it’s no surprise... - Source: dev.to / 25 days ago

You should let the Apache Flink team know, they mention exactly-once processing on their home page (under "correctness guarantees") and in their list of features. [0] https://flink.apache.org/ [1] https://flink.apache.org/what-is-flink/flink-applications/#building-blocks-for-streaming-applications. - Source: Hacker News / about 1 month ago

Data scientists often prefer Python for its simplicity and powerful libraries like Pandas or SciPy. However, many real-time data processing tools are Java-based. Take the example of Kafka, Flink, or Spark streaming. While these tools have their Python API/wrapper libraries, they introduce increased latency, and data scientists need to manage dependencies for both Python and JVM environments. For example,... - Source: dev.to / 2 months ago

Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling. - Source: dev.to / 4 months ago