Reactive Programming

 

Basic Understanding of Reactive Programming

1. What is Reactive Programming (RP) and how is it different from traditional programming paradigms?

2. What are the main principles of Reactive Programming?

3. What is the difference between synchronous and asynchronous programming?

4. How do you define "Backpressure" in Reactive Programming?

5. What are the key components of a Reactive System?

6. What is a "Publisher" in Reactive Programming?

7. Explain "Subscription" in the context of Reactive Programming.

8. What does the term "Flux" refer to in Reactive Programming?

9. What does the term "Mono" refer to in Reactive Programming?

10. What is a "Stream" in Reactive Programming?

11. Explain "Event-Driven Architecture" in the context of Reactive Programming.

12. How does Reactive Programming handle error handling and exceptions?

13. Can you explain the concept of "cold" vs "hot" observables?

14. What is the role of "Schedulers" in Reactive Programming?

15. What is "Reactive Streams" specification, and why is it important in Java?

16. What does "Backpressure" mean, and how can you handle it in a reactive system?

Reactive Programming in Java

17. What is the difference between Mono and Flux in Project Reactor?

18. How do you create a Mono or Flux in Project Reactor?

19. How can you convert a standard Java Future to a reactive type like Mono or Flux?

20. What is the role of the subscribe() method in reactive programming?

21. How does the onNext(), onComplete(), and onError() methods work in a reactive flow?

22. What is the importance of map(), flatMap(), and filter() in reactive streams?

23. What is the significance of the doOnNext(), doOnError(), and doOnTerminate() operators?

24. How do you handle timeouts in reactive streams?

25. How would you use the retry operator in Project Reactor?

26. Can you explain the use of zip() in combining multiple Mono or Flux streams?

27. How would you use merge(), concat(), and combineLatest() in reactive programming?

28. How do you handle concurrency in reactive programming in Java?

29. What is the difference between subscribeOn() and publishOn() in Project Reactor?

30. How does the buffer() operator work in Project Reactor?

31. How does the flatMap() operator handle concurrency in reactive streams?

32. What is the significance of take() and skip() in controlling the flow of data?

33. How do you use collectList() in Project Reactor, and what is it used for?

34. How would you convert a Mono into a Future in Java?

35. Can you explain how switchIfEmpty() works in a reactive flow?

36. How do you handle multiple subscribers on the same Mono or Flux in Project Reactor?

Advanced Reactive Programming Concepts

37. What is "Functional Reactive Programming" (FRP)?

38. How does "Declarative Programming" relate to Reactive Programming?

39. How do you deal with time-sensitive operations in reactive programming (e.g., timeouts)?

40. Explain "race conditions" in reactive programming and how they can be avoided.

41. Can you explain the concept of "Reactive Streams" vs "Reactive Extensions"?

42. What is the role of Schedulers.elastic() and when would you use it?

43. How do you implement parallelism in Project Reactor?

44. Can you describe the purpose of the onErrorMap() operator?

45. What is a "monoid" in the context of combining Mono or Flux elements?

46. How do you implement retry with backoff in Project Reactor?

47. What is the importance of flatMapSequential() and when would you use it over flatMap()?

48. What is the difference between concatMap() and flatMap()?

49. How would you implement "retry" with a limit and delay in reactive programming?

50. Explain the concept of "tumbling windows" in reactive streams.

51. How would you handle a "stream of events" scenario in a reactive system?

52. What is the role of "Schedulers" in Project Reactor, and how do you manage thread pooling?

53. What is takeWhile() used for in Project Reactor?

54. Explain how doFinally() is useful in a reactive stream.

55. How do you integrate Mono or Flux with blocking APIs?

56. How do you implement "Circuit Breaker" pattern with reactive streams?

57. What are "hot" and "cold" observables in the context of Project Reactor?

58. How would you deal with an operation that can throw an exception using Mono or Flux?

59. What are some common performance pitfalls in reactive programming?

60. How do you apply onErrorReturn() vs onErrorResume() in error handling?

61. How do you prevent memory leaks in a reactive application?

62. What are some best practices for testing reactive code?

63. How do you measure the performance of a reactive system?

64. How would you implement backpressure handling in a real-world scenario?

65. What are the differences between publishOn() and subscribeOn() when used with a specific thread pool?

66. What is "resource management" in reactive programming, and how do you manage resources like database connections?

67. What is the purpose of retryWhen() in Project Reactor, and how does it differ from retry()?

Reactive Design Patterns

68. What are the design patterns associated with Reactive Programming?

69. How does "Observer" pattern relate to Reactive Programming?

70. Can you describe how "Publisher-Subscriber" pattern works in Reactive Programming?

71. What are "Reactive Systems" and how does Reactive Programming help in building them?

72. How would you implement a "message queue" system using reactive programming?

73. Can you describe the "Stream" pattern in reactive programming?

74. How do you implement the "Circuit Breaker" pattern using Project Reactor?

75. How would you implement the "Retry" pattern in a reactive system?

76. Can you explain the "Bulkhead" pattern in the context of Reactive Programming?

77. How would you handle a service failure in a reactive system using fallback strategies?

78. Explain the "Event Sourcing" pattern and how reactive programming plays a role in it.

79. How do you handle timeouts in a reactive system?

80. Can you explain the "Saga" pattern in the context of reactive programming?

Integration and Frameworks

81. How would you integrate reactive programming with Spring WebFlux?

82. What is Spring WebFlux, and how does it relate to Reactive Programming?

83. How do you manage transactions in a reactive Spring application?

84. Can you explain the difference between @RequestMapping and @GetMapping in Spring WebFlux?

85. What are the pros and cons of using Spring WebFlux for building reactive APIs?

86. How does Spring WebFlux handle error handling in reactive streams?

87. Can you integrate Project Reactor with Kafka, and what are the benefits?

88. How do you integrate reactive programming with a relational database (like PostgreSQL)?

89. Can you implement a reactive service using the R2DBC (Reactive Relational Database Connectivity)?

90. How do you ensure thread-safety in a reactive Java application with Spring WebFlux?

Testing Reactive Programming

91. What tools or libraries do you use for testing reactive code in Java?

92. How do you test a Mono or Flux that completes successfully?

93. How do you test a Mono or Flux that emits an error?

94. Can you explain the role of StepVerifier in testing reactive streams?

95. How do you test the handling of backpressure in a reactive stream?

96. What is the best approach for testing a service using Mono and Flux in a Spring WebFlux application?

97. How would you simulate an error condition in a reactive stream for unit testing?

98. Can you describe a strategy for testing time-based operators like delay(), timeout(), and retry()?

99. How do you handle race conditions during testing in reactive systems?

100. What is the role of TestPublisher in testing reactive code?

Miscellaneous

101. How do you use reactive programming in microservices?

102. What are the advantages and challenges of implementing reactive programming in large-scale systems?

103. How do you handle logging in a reactive system?

104. Can you explain the trade-offs of using reactive programming for CPU-bound vs I/O-bound operations?

105. What are some common challenges in debugging reactive applications?

106. How do you handle complex data transformations in a reactive stream?

107. Can you explain how to design a reactive system that supports both batch processing and event-driven workflows?

Basic Understanding of Reactive Programming

1. What is Reactive Programming (RP) and how is it different from traditional programming paradigms?

   • Reactive Programming is a programming paradigm focused on data streams and the propagation of changes. It allows asynchronous data flows, making it more suitable for handling real-time data, concurrency, and asynchronous tasks. Unlike traditional programming, which is typically blocking, Reactive Programming allows you to build systems that respond to events and data changes in real-time.

2. What are the main principles of Reactive Programming?

   • Asynchronous and non-blocking data flows, event-driven, data-driven, backpressure handling, composability of operators, and reactive streams are the core principles.

3. What is the difference between synchronous and asynchronous programming?

   • Synchronous programming executes tasks sequentially, where one task blocks the next. Asynchronous programming allows tasks to be executed independently without blocking, often using callbacks, promises, or futures.

4. How do you define "Backpressure" in Reactive Programming?

   • Backpressure is a mechanism to handle situations where the data producer generates items faster than the consumer can handle them. It prevents overloading of resources by limiting or buffering data.

5. What are the key components of a Reactive System?

   • Publishers, Subscribers, Streams, Schedulers, Backpressure, and Operators.

6. What is a "Publisher" in Reactive Programming?

   • A Publisher is an entity that emits data over time and notifies subscribers about new data, errors, or completion.

7. Explain "Subscription" in the context of Reactive Programming.

   • Subscription represents the relationship between a publisher and a subscriber. A subscriber subscribes to a publisher to receive notifications (data, error, or completion).

8. What does the term "Flux" refer to in Reactive Programming?

   • Flux represents a stream of 0 to N asynchronous items in Project Reactor.

9. What does the term "Mono" refer to in Reactive Programming?

   • Mono represents a stream of 0 to 1 asynchronous item in Project Reactor.

10. What is a "Stream" in Reactive Programming?

    • A Stream is a sequence of data elements that are made available over time.

11. Explain "Event-Driven Architecture" in the context of Reactive Programming.

    • Event-Driven Architecture is a design where components of the system react to events, triggering actions when specific events occur, often in an asynchronous, non-blocking manner.

12. How does Reactive Programming handle error handling and exceptions?

    • Reactive Programming handles errors using operators like onErrorReturn(), onErrorResume(), or doOnError(). Errors are propagated along the stream but can be intercepted and managed.

13. Can you explain the concept of "cold" vs "hot" observables?

    • Cold observables start emitting data only when a subscriber subscribes, while hot observables emit data independently of subscribers, meaning multiple subscribers can receive the same stream of data.

14. What is the role of "Schedulers" in Reactive Programming?

    • Schedulers control the execution context of tasks in Reactive Programming, allowing developers to specify the thread or thread pool for a specific operation, such as processing or subscribing.

15. What is "Reactive Streams" specification, and why is it important in Java?

    • Reactive Streams is a standard for asynchronous stream processing with non-blocking backpressure. It is important in Java because it defines the foundation for building reactive systems, ensuring compatibility and consistent behavior across libraries.

16. What does "Backpressure" mean, and how can you handle it in a reactive system?

    • Backpressure is the situation when data production outpaces consumption. It can be handled by controlling the rate of data emission using operators like onBackpressureBuffer(), onBackpressureDrop(), or onBackpressureLatest().

Reactive Programming in Java

17. What is the difference between Mono and Flux in Project Reactor?

    • Mono represents a stream of 0 or 1 item, while Flux represents a stream of 0 to N items.

18. How do you create a Mono or Flux in Project Reactor?

    • Mono: Mono.just(value) or Mono.empty()

    • Flux: Flux.just(item1, item2) or Flux.range(1, 10)

19. How can you convert a standard Java Future to a reactive type like Mono or Flux?

    • Use Mono.fromFuture() for a Mono and Flux.fromStream() for a Flux.

20. What is the role of the subscribe() method in reactive programming?

    • subscribe() initiates the execution of the reactive pipeline and subscribes to the data source to receive emitted items.

21. How does the onNext(), onComplete(), and onError() methods work in a reactive flow?

    • onNext() is invoked for each emitted item, onComplete() is called when the stream finishes emitting, and onError() is invoked if an error occurs.

22. What is the importance of map(), flatMap(), and filter() in reactive streams?

    • map() transforms the emitted items, flatMap() is used for asynchronous transformations, and filter() filters items based on a condition.

23. What is the significance of the doOnNext(), doOnError(), and doOnTerminate() operators?

    • These operators allow side effects without altering the flow of the data. doOnNext() executes a side effect for each item, doOnError() handles errors, and doOnTerminate() is triggered after the stream completes or errors.

24. How do you handle timeouts in reactive streams?

    • Use operators like timeout(), timeoutAfter(), or timeoutWith() to manage timeouts in a reactive stream.

25. How would you use the retry operator in Project Reactor?

    • The retry() operator automatically retries a failed operation a specified number of times.

26. Can you explain the use of zip() in combining multiple Mono or Flux streams?

    • zip() combines multiple streams and emits a tuple of values from all the streams once each emits at least one item.

27. How would you use merge(), concat(), and combineLatest() in reactive programming?

    • merge() combines multiple sources into a single stream, concat() emits items sequentially from multiple sources, and combineLatest() emits the latest item from each source as they arrive.

28. How do you handle concurrency in reactive programming in Java?

    • By using flatMap(), subscribeOn(), and publishOn() operators to control concurrency and execution context.

29. What is the difference between subscribeOn() and publishOn() in Project Reactor?

    • subscribeOn() specifies the thread on which the subscription should happen, while publishOn() specifies the thread on which the data is processed.

30. How does the buffer() operator work in Project Reactor?

    • The buffer() operator collects emitted items into a list and emits that list to the subscriber.

31. How does the flatMap() operator handle concurrency in reactive streams?

    • flatMap() creates multiple parallel asynchronous streams and merges them into a single stream, often used for non-blocking operations.

32. What is the significance of take() and skip() in controlling the flow of data?

    • take() limits the number of items emitted by the stream, while skip() skips the first N items before starting emission.

33. How do you use collectList() in Project Reactor, and what is it used for?

    • collectList() collects all emitted items into a List and emits the final list once the stream completes.

34. How would you convert a Mono into a Future in Java?

    • Use mono.toFuture() to convert a Mono into a Future.

35. Can you explain how switchIfEmpty() works in a reactive flow?

    • switchIfEmpty() provides a fallback flow to be executed if the original Mono or Flux is empty.

36. How do you handle multiple subscribers on the same Mono or Flux in Project Reactor?

    • Mono and Flux are "cold" by default, so they emit data individually for each subscriber. Use publish() to convert them to "hot" observables if necessary.

Advanced Reactive Programming Concepts

37. What is "Functional Reactive Programming" (FRP)?

    • FRP combines the principles of Reactive Programming and functional programming to create more declarative and composable reactive systems using higher-order functions and immutable data.

38. How does "Declarative Programming" relate to Reactive Programming?

    • Reactive programming is a form of declarative programming where developers specify what they want to happen with streams of data, not how it should be processed.

39. How do you deal with time-sensitive operations in reactive programming (e.g., timeouts)?

    • Use time-related operators such as timeout(), delay(), or timeoutAfter() to manage time-sensitive operations.

40. Explain "race conditions" in reactive programming and how they can be avoided.

    • Race conditions occur when two or more asynchronous operations attempt to update the same resource concurrently. They can be avoided by ensuring proper synchronization, using flatMapSequential(), or leveraging atomic operations.

41. Can you explain the concept of "Reactive Streams" vs "Reactive Extensions"?

    • Reactive Streams is a specification for asynchronous stream processing with backpressure, while Reactive Extensions (Rx) is a library for composing asynchronous and event-based programs using observable sequences.

42. What is the role of Schedulers.elastic() and when would you use it?

    • Schedulers.elastic() provides a scheduler with a dynamically resizing thread pool for handling tasks that don't fit into a fixed-size pool. Use it for tasks that require unbounded resources.

43. How do you implement parallelism in Project Reactor?

    • You can implement parallelism by using flatMap() with publishOn() or parallel() operators.

44. Can you describe the purpose of the onErrorMap() operator?

    • onErrorMap() transforms the exception to another exception, typically used for exception mapping in a pipeline.

45. What is a "monoid" in the context of combining Mono or Flux elements?

    • A monoid is a mathematical concept that allows for combining elements of a collection in a way that is associative and has an identity element (e.g., combining lists).

46. How do you implement retry with backoff in Project Reactor?

    • Use the retryWhen() operator combined with Mono.delay() for adding delays between retries.

47. What is the importance of flatMapSequential() and when would you use it over flatMap()?

    • flatMapSequential() ensures that operations are executed sequentially, unlike flatMap() which can execute them concurrently.

48. What is the difference between concatMap() and flatMap()?

    • concatMap() preserves the order of operations and executes them sequentially, whereas flatMap() may execute operations concurrently.

49. How would you implement "retry" with a limit and delay in reactive programming?

    • Use retryWhen() along with Mono.delay() to apply a delay and a maximum number of retries.

50. Explain the concept of "tumbling windows" in reactive streams.

    • Tumbling windows are fixed-size windows that process a batch of elements before sliding to the next batch. They are often used in event-driven systems for time-based aggregation.

51. How would you handle a "stream of events" scenario in a reactive system?

    • By using reactive streams to asynchronously process and react to the incoming events in real-time.

52. What is the role of "Schedulers" in Project Reactor, and how do you manage thread pooling?

    • Schedulers control the execution context for reactive streams, allowing developers to define which thread pool or thread should be used for specific tasks. Managing thread pooling involves choosing the appropriate scheduler like Schedulers.parallel(), Schedulers.boundedElastic(), etc.

Advanced Reactive Programming Concepts (continued)

53. What is takeWhile() used for in Project Reactor?

    • takeWhile() emits elements from the source stream as long as the condition is true. Once the condition fails, the emission stops and no more elements are produced.

54. Explain how doFinally() is useful in a reactive stream.

    • doFinally() is a terminal operator that allows you to run a side-effect action when a stream completes or terminates (whether due to an error or successful completion). It's useful for cleanup actions like closing resources.

55. How do you integrate Mono or Flux with blocking APIs?

    • To integrate with blocking APIs, use Mono.fromCallable() or Mono.fromSupplier() to wrap blocking operations, allowing them to be executed asynchronously within a reactive flow.

56. How do you implement "Circuit Breaker" pattern with reactive streams?

    • Use a CircuitBreaker (e.g., from libraries like Resilience4j) in conjunction with a Mono or Flux to monitor the failure rate of a service. When a failure threshold is reached, the circuit breaker opens and blocks further requests until it is closed again.

57. What are "hot" and "cold" observables in the context of Project Reactor?

    • Cold observables start emitting data when a subscriber subscribes, meaning each subscriber will receive their own sequence of data. Hot observables emit data continuously, and all subscribers receive the same data (i.e., they share the same stream).

58. How would you deal with an operation that can throw an exception using Mono or Flux?

    • You can use the onErrorResume() or onErrorReturn() operators to handle exceptions by providing a fallback value or stream in case of an error.

59. What are some common performance pitfalls in reactive programming?

    • Overusing operators that create excessive context switching, creating too many threads, improper thread pool management, unneeded use of operators like flatMap() when not needed, and lack of proper backpressure handling can all lead to performance issues.

60. How do you apply onErrorReturn() vs onErrorResume() in error handling?

    • onErrorReturn() returns a predefined fallback value if an error occurs, while onErrorResume() allows the developer to supply a different stream or fallback logic, providing more flexibility for error recovery.

61. How do you prevent memory leaks in a reactive application?

    • Ensure that resources like database connections, files, and network streams are properly closed and disposed of, often through operators like doFinally(). Also, make sure to unsubscribe from Mono and Flux when they are no longer needed.

62. What are some best practices for testing reactive code?

    • Use tools like StepVerifier for testing, mock external dependencies, and make sure to account for timing-based operations with proper time-related assertions. Avoid using blocking calls and always assert the completion and error conditions.

63. How do you measure the performance of a reactive system?

    • Measure throughput (items processed per unit time), latency (time taken for items to flow through the system), and backpressure handling. Tools like metrics libraries or custom logging can help monitor performance.

64. How would you implement backpressure handling in a real-world scenario?

    • Use operators like onBackpressureBuffer(), onBackpressureDrop(), and onBackpressureLatest() to handle excessive data production. Additionally, adjust the producer-consumer rate appropriately.

65. What are the differences between publishOn() and subscribeOn() when used with a specific thread pool?

    • publishOn() changes the thread pool on which the downstream operations will execute, while subscribeOn() determines the thread on which the subscription and the upstream operations are executed.

66. What is "resource management" in reactive programming, and how do you manage resources like database connections?

    • Resource management in reactive programming ensures that resources (like database connections) are used efficiently and released properly. This can be done by using resource management libraries, ensuring a proper connection pool, or employing reactive database drivers like R2DBC.

67. What is the purpose of retryWhen() in Project Reactor, and how does it differ from retry()?

    • retryWhen() allows you to provide custom retry logic (e.g., delay between retries), whereas retry() retries a failed operation a specific number of times without custom delay logic.

Reactive Design Patterns

68. What are the design patterns associated with Reactive Programming?

    • Observer pattern (for subscription to streams), Publisher-Subscriber pattern, Circuit Breaker, Retry, Bulkhead, and Event Sourcing are common patterns in reactive systems.

69. How does "Observer" pattern relate to Reactive Programming?

    • In Reactive Programming, the Observer pattern allows subscribers to observe data streams (or Publishers) and react when new data is emitted, errors occur, or the stream completes.

70. Can you describe how "Publisher-Subscriber" pattern works in Reactive Programming?

    • The Publisher-Subscriber pattern involves a Publisher that emits events/data and multiple Subscribers who receive the emitted data asynchronously. The pattern decouples data producers from consumers.

71. What are "Reactive Systems" and how does Reactive Programming help in building them?

    • A Reactive System is an architectural style that is responsive, resilient, elastic, and message-driven. Reactive programming supports this style by enabling systems that are non-blocking, handle errors gracefully, and can scale effectively.

72. How would you implement a "message queue" system using reactive programming?

    • Use reactive messaging systems like Kafka or RabbitMQ with reactive clients, or use a reactive stream API like Project Reactor to implement message queues. Messages can be processed asynchronously with backpressure handling.

73. Can you describe the "Stream" pattern in reactive programming?

    • The Stream pattern involves a continuous flow of data where operations like transformation, filtering, and aggregation can be applied in a declarative way, similar to the concept of a data stream.

74. How do you implement the "Circuit Breaker" pattern using Project Reactor?

    • You can use the Resilience4j library or other tools to create a circuit breaker around your reactive stream. This will prevent continuous failures by opening the circuit and providing fallback responses when a threshold is exceeded.

75. How would you implement the "Retry" pattern in a reactive system?

    • Use retry() or retryWhen() in Project Reactor to automatically retry failed operations, with optional backoff strategies or limits.

76. Can you explain the "Bulkhead" pattern in the context of Reactive Programming?

    • The Bulkhead pattern isolates failures within a system to prevent cascading failures. In reactive systems, this can be implemented by isolating different parts of a system (like network calls or services) into separate reactive flows to handle failure independently.

77. How would you handle a service failure in a reactive system using fallback strategies?

    • Use operators like onErrorResume() or onErrorReturn() to provide fallback streams or default values when a service fails.

78. Explain the "Event Sourcing" pattern and how reactive programming plays a role in it.

    • Event Sourcing stores all changes to an application's state as a sequence of events. Reactive programming can help in handling events asynchronously, processing event streams efficiently, and providing a responsive system.

79. How do you handle timeouts in a reactive system?

    • Use the timeout() operator to apply a timeout to specific operations and provide fallback behavior using onErrorReturn() or onErrorResume() if a timeout occurs.

80. Can you explain the "Saga" pattern in the context of reactive programming?

    • The Saga pattern manages long-running transactions by splitting them into smaller, isolated steps. Each step in the saga can be implemented as a reactive stream that handles different parts of the business logic asynchronously.

Integration and Frameworks

81. How would you integrate reactive programming with Spring WebFlux?

    • Spring WebFlux is designed for building reactive APIs. You can use Project Reactor’s Mono and Flux types for handling request and response bodies asynchronously in controllers.

82. What is Spring WebFlux, and how does it relate to Reactive Programming?

    • Spring WebFlux is a reactive web framework built on Project Reactor that enables non-blocking, asynchronous communication for building highly scalable web applications.

83. How do you manage transactions in a reactive Spring application?

    • In reactive Spring applications, R2DBC (Reactive Relational Database Connectivity) is used to manage database transactions. You can use @Transactional with reactive types to ensure the consistency of your transactions.

84. Can you explain the difference between @RequestMapping and @GetMapping in Spring WebFlux?

    • @RequestMapping is a more general annotation that can handle all types of HTTP requests, while @GetMapping is a shortcut for handling HTTP GET requests specifically.

85. What are the pros and cons of using Spring WebFlux for building reactive APIs?

    • Pros: Scalability, non-blocking, asynchronous handling, and better performance under high load.

    • Cons: Complexity, potential learning curve, and the need for an entirely non-blocking I/O stack.

86. How does Spring WebFlux handle error handling in reactive streams?

    • Spring WebFlux uses the onErrorResume() and onErrorReturn() operators for handling errors in the reactive streams, and the @ExceptionHandler annotation for controller-level error handling.

87. Can you integrate Project Reactor with Kafka, and what are the benefits?

    • Yes, you can integrate Project Reactor with Kafka using libraries like ReactiveKafka. The benefits include the ability to process Kafka messages reactively, enabling non-blocking, scalable consumption and production of Kafka messages.

88. How do you integrate reactive programming with a relational database (like PostgreSQL)?

    • Use R2DBC (Reactive Relational Database Connectivity) to integrate reactive programming with relational databases in Java.

89. Can you implement a reactive service using the R2DBC (Reactive Relational Database Connectivity)?

    • Yes, by using R2dbcRepository in Spring Data R2DBC, you can implement reactive services that interact with a relational database asynchronously.

90. How do you ensure thread-safety in a reactive Java application with Spring WebFlux?

    • Ensure thread safety by using immutable data structures, properly managing shared resources, and avoiding shared mutable state. Schedulers in Spring WebFlux can also help manage concurrent tasks.

Testing Reactive Programming

91. What tools or libraries do you use for testing reactive code in Java?

    • Common tools include StepVerifier (for testing reactive streams), Mockito for mocking dependencies, and JUnit 5 for unit testing.

92. How do you test a Mono or Flux that completes successfully?

    • Use StepVerifier.create() and assert that expectNext() and expectComplete() are called to test successful completion.

93. How do you test a Mono or Flux that emits an error?

    • Use StepVerifier.create() and assert that expectError() is called to verify error emissions.

94. Can you explain the role of StepVerifier in testing reactive streams?

    • StepVerifier is used to test Mono and Flux by defining expectations for emitted items, completion, or errors, and ensuring that the reactive stream behaves as expected.

95. How do you test the handling of backpressure in a reactive stream?

    • Use StepVerifier with expectNoEvent() to simulate backpressure and test how your system behaves when the stream is under load.

96. What is the best approach for testing a service using Mono and Flux in a Spring WebFlux application?

    • Use MockWebServer to mock HTTP requests, WebTestClient to perform HTTP assertions, and StepVerifier to test the reactive streams.

97. How would you simulate an error condition in a reactive stream for unit testing?

    • Use operators like Mono.error() or Flux.error() to simulate error conditions in your test.

98. Can you describe a strategy for testing time-based operators like delay(), timeout(), and retry()?

    • Use StepVerifier and control the virtual time (e.g., using VirtualTimeScheduler) to simulate and test time-based operations.

99. How do you handle race conditions during testing in reactive systems?

    • Ensure the correct order of execution by using operators like flatMapSequential() and controlling the execution order during testing with tools like StepVerifier or TestScheduler.

100. What is the role of TestPublisher in testing reactive code?
     - TestPublisher is used to simulate the behavior of a reactive stream, allowing you to emit items, errors, and completion events programmatically during tests.

Miscellaneous

101. How do you use reactive programming in microservices?
     - In microservices, reactive programming is used to handle asynchronous communication between services, manage concurrency effectively, and build non-blocking APIs using technologies like WebFlux.

102. What are the advantages and challenges of implementing reactive programming in large-scale systems?
     - Advantages: Scalability, non-blocking I/O, resilience, better resource utilization.
     - Challenges: Complexity, debugging difficulties, and the need for a full non-blocking infrastructure.

103. How do you handle logging in a reactive system?
     - Use tools like SLF4J with reactive-friendly loggers and operators like doOnNext() or doOnError() to log events without blocking the reactive flow.

104. Can you explain the trade-offs of using reactive programming for CPU-bound vs I/O-bound operations?
     - Reactive programming shines in I/O-bound operations (e.g., network requests, database calls) but may introduce unnecessary complexity and overhead for CPU-bound tasks, where traditional synchronous programming might be more efficient.

105. What are some common challenges in debugging reactive applications?
     - Stack traces can be hard to trace due to the asynchronous nature, and race conditions can cause unpredictable behavior. Using proper logging, debugging tools, and breakpoints is essential.

106. How do you handle complex data transformations in a reactive stream?
     - Use operators like flatMap(), map(), reduce(), and scan() to transform and aggregate data efficiently in a reactive flow.

107. Can you explain how to design a reactive system that supports both batch processing and event-driven workflows?
     - Combine batch processing techniques like buffer() with event-driven approaches to handle streams of data. Use a hybrid architecture that allows the system to process events as they come while handling batches efficiently.