HackerReposA FIELD GUIDE TO OPEN SOURCE
ProSponsor the shelf♧ 0
← Back to top all-time
reactive-streams
reactive-streams-jvm
Java2015–2020
reactive-streams / reactive-streams-jvm

reactive-streams-jvm

Reactive Streams Specification for the JVM

4Distinct people
5Mentions
0Explicit recommenders
4.9KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Reactive Streams Specification for the JVM

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 10:01:16 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year5 dated mentions

Observed mentions: 2015-05-07 12:03:50 UTC to 2020-05-14 00:35:51 UTC.

0122015-01-01 00:00:00 UTC to 2016-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20152016-01-01 00:00:00 UTC to 2017-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments20172018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2020

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20152015-01-01 00:00:00 UTC2016-01-01 00:00:00 UTC101
20162016-01-01 00:00:00 UTC2017-01-01 00:00:00 UTC101
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC000
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC000
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC101
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC202

What Hacker News says

2 sources
> In Deno, sockets are still asynchronous, but receiving new data requires users to explicitly read() Interesting. If I understand correctly, they're essentially using pull streams[0]/reactive streams[1]. I compiled a few resources on this topic when I was digging into it a while back[2]. I've found the mental model to be very elegant to work with when needing backpressure in asynchronous systems. As for the dependencies-as-URLs, I don't mind it, and may prefer it. I've been experimenting with minimizing my dependencies lately, and vendoring them in git submodules. It's worked fairly well. [0]: https://github.com/pull-stream/pull-stream [1]: https://github.com/reactive-streams/reactive-streams-jvm [2]: https://github.com/omnistreams/omnistreams-spec
In Deno 1.0
This is awesome. The new streaming stuff is particularly interesting to me, and it's very impressive that you managed to implement it with no protocol changes. I have a couple questions. Please forgive any misconceptions as I've never used capnproto myself, since all the streaming I've done has to work in the browser, and as far as I know capnproto doesn't work over WebSocket or WebRTC transports. But I've long been impressed with and inspired by capnproto and sandstorm. Main question: is there a reason you opted for traditional window flow control a la TCP, as opposed to "request-N" style like in reactive streams[0] (see rsocket[1] for a great implementation)? So with request-N, a server->client stream would look something like this: interface MyInterface { streamingCall @0 (callback :Callback) -> (requester :Requester); interface C
Read the rest
allback { sendChunk @0 (chunk :Data) -> (); } interface Requester { request @0 (n :int) -> (); } } And the server will only ever send as much data as has been requested by the client with requester calls. This results in really elegant flow control that takes into account both the network, and the client's capacity to consume, without the necessity of tracking windows. The receiver simply calls request(1) each time it processes a message. If you want a buffer you can just start with an assumed N=10, 100 etc. I've found this worked really well when implementing omnistreams[2], which is basically a very thin streaming/multiplexing layer for WebSockets, since WS doesn't have any flow control. (fibridge[3] is a good example of it in action). I started with a window-style but once I learned about reactive streams the request model was much easier to reason about for me. [0]: https://github.com/reactive-streams/reactive-streams-jvm [1]: https://github.com/rsocket/rsocket [2]: https://github.com/omnistreams/omnistreams-spec [3]: http://iobio.io/2019/06/12/introducing-fibridge/
In Cap'n Proto 0.8: Streaming flow control, HTTP-over-RPC, fibers

MAKE YOURSELF AT HOME

A few shortcuts

/
Search the collection
?
Open this guide
Esc
Close dialogs and navigation

Your saved repositories and interests stay in this browser.