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🚀 Reactive Programming in Java (Project Reactor) — All-in-One Guide

This guide includes:

• ✅ Cheat Sheet of Common Transformations
• 🔁 Step-by-Step Stream Flow Diagram
• 🧪 Practice Problems with Hints

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✅ 1. Cheat Sheet: Common Reactive Transformations

Problem	Operator(s) to Use	Resulting Type
List<T> → Flux<T>	Flux.fromIterable(list)	Flux<T>
Flux<T> → List<T>	collectList()	Mono<List<T>>
Flux<T> → Map<K, T>	collectMap(keyFn, valFn)	Mono<Map<K, T>>
Flux<T> → Map<K, List<T> >	collectMultimap(keyFn)	Mono<Map<K, List<T>>>
Transform each item	map()	Flux<R>
Transform and flatten (async calls)	flatMap()	Flux<R>
Async calls in order	concatMap()	Flux<R>
Group by key	groupBy()	GroupedFlux<K, T>
Fallback value	switchIfEmpty(Mono.just(...))	Flux<T> or Mono<T>
Combine two Flux<T>	zipWith(otherFlux)	Flux<Tuple2<T1, T2>>
Run after completion	doOnTerminate()	Same as input stream
Parallel processing	parallel().runOn(Schedulers.parallel())	ParallelFlux<T>

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🔁 2. Stream Flow Diagram (Example: Group + Transform + Return)

Problem:

You have a Flux<MyObject>, and you want to:

• Group by documentType
• For each group, process to get a DocStatusModel
• Collect all into a List<DocStatusModel>

Flow Diagram:

[Flux<MyObject>] 
    │
    ├─> collectMultimap(MyObject::getDocumentType)
    │       → Mono<Map<DocumentType, List<MyObject>>>
    │
    ├─> flatMapMany(map -> Flux.fromIterable(map.entrySet()))
    │       → Flux<Map.Entry<DocumentType, List<MyObject>>>
    │
    ├─> map(entry -> process(entry.getKey(), entry.getValue()))
    │       → Flux<DocStatusModel>
    │
    └─> collectList()
            → Mono<List<DocStatusModel>>

Each transformation stage is composable, testable, and reactive.

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🧪 3. Practice Problems with Hints

🔹 Problem 1: Group Books by Genre

Input: Flux<Book>, where Book has a getGenre() method
Goal: Mono<Map<String, List<Book>>> grouped by genre

Hint:
Use collectMultimap(Book::getGenre)

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🔹 Problem 2: Convert Orders to EnrichedOrders (with async call)

Input: Flux<Order>
Goal: Flux<EnrichedOrder>
Hint:

.flatMap(order -> enrichOrderAsync(order))

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🔹 Problem 3: Provide Default When Empty

Input: Flux<Item>
Goal: Emit default items if source is empty

Hint:

.switchIfEmpty(Flux.just(defaultItem1, defaultItem2))

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🔹 Problem 4: Retry on Error

Input: Flux<Data> with flaky source
Goal: Retry 3 times before failing

Hint:

.retry(3)

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🔹 Problem 5: Combine Two Streams

Input: Flux<User>, Flux<Account>
Goal: Flux<UserAccountSummary>

Hint:

userFlux.zipWith(accountFlux, (user, account) -> new UserAccountSummary(user, account))

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💬 Tip for Mastery

• Practice in a sandbox using main() or unit tests
• Break problems into: Input → Transform → Output
• Think in data shapes (e.g. Flux<T> , Mono<List<T> >, Map<K, V>)

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Happy streaming! 💡