Take a look at Centrifugo - https://centrifugal.dev/ - it provides a way to build efficient real-time messaging system using standard Django without ASGI involved. Source: 12 months ago

Hello, I am an author of Centrifugo project (https://centrifugal.dev/). It's a WebSocket server which scales using Redis. Instead of the approach you described when every message is delivered to every server Centrifugo uses PUB/SUB in a way that every server subscribed only to channels which current server connections have. It should scale pretty well, and resubscribe to channels is super-efficient. All the load... Source: over 1 year ago

Hello, I am author of Centrifugo (https://centrifugal.dev/) project - WebSocket server which scales with Redis. We have several blog posts which may help to answer your questions and give you some real world numbers about using Redis for WebSocket apps. Some links:. Source: over 1 year ago

Https://centrifugal.dev/ It's go native you can even write your own using it's underlying centrifuge library. We use it currently in Production just the docker container to be honest is what we deploy and just use a small config file or flags. Source: over 1 year ago

Hey folks! Centrifugo is an open-source scalable real-time messaging server written in Go language. It's language-agnostic and can be used to build chat apps, live comments, multiplayer games, real-time data visualizations, collaborative tools, etc. In combination with any backend. Including NodeJS-based backend which is relevant to this subreddit. And while Javascript/Node ecosystem has good WebSocket tools, I... Source: almost 2 years 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