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Graph/reactor

Package

reactor

reactor

reactor

Node Details

Public graph metadata

Updated5/1/2026

PageRank0.0001

Connections

11 linked nodes

SUPPORTSLanguage

SUPPORTSPackage

RELATES_TOProblem

Mono.just(authentication.getPrincipal()) .ofType(UserDto.class) .filter(this::matchesExistingUser) // needs DTO .map(UserAuthenticationToken::from); // needs User, but. Outcome: So, there you would call your `userService.find(userDto)` and use this `Tuple2` downstream for those respective `filter` and `map` operators.

RELATES_TOSolution

I would look into `zipWhen()` operator:. Outcome: call your `userService.find(userDto)` and use this `Tuple2` downstream.

AFFECTSRootCause

ofType(UserDto.class) .filter(this::matchesExistingUser) // needs DTO .map(UserAuthenticationToken::from); // needs User, but. Outcome: use this `Tuple2` downstream for those respective `filter` and `map` operators.

AFFECTSRootCause

For smaller streams I would utilize the `groupBy` function, but since the flux hold many entries this isn't efficient. — merge large data streams available over flux which each contain a timestamp and a value. Tension: the entries in the flux are ordered but I can't find the right construct.

AFFECTSRootCause

Mono.just(getValue()). Tension: That's the reason `Mono.just()` is not good for blocking operations. Outcome: The common use cases of `Mono.just()` is when you want to compute static or pre-compute values.

REQUIRESSolution

I would look into `zipWhen()` operator:. Outcome: call your `userService.find(userDto)` and use this `Tuple2` downstream.

REQUIRESSolution

Using windowUntilChanged to group adjacent records that use the same timestamp — Combine input fluxes preserving overall date ordering using mergeComparing operator. Tension: group adjacent records that use the same timestamp. Outcome: Use `reduce` on each window to merge records as you wish.

REQUIRESSolution

You can try `Mono.fromCallable()`. Tension: `Mono.just()` is not good for blocking operations. Outcome: `subscribeOn()` is when the execution starts in a separate thread pool that you define.

OCCURS_INProblem

For smaller streams I would utilize the `groupBy` function, but since the flux hold many entries this isn't efficient. — I'm need to merge large data streams available over flux which each contain a timestamp and a value. Tension: I would like to exploit the fact that the entries in the flux are ordered but I can't find the right construct. Outcome: what I want to do.

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