What is a Reactor Scheduler, and what are the four built-in Schedulers (boundedElastic, parallel, single, immediate) each meant for?
answer
- boundedElastic = blocking I/O, capped 10×cores
- parallel = CPU work, fixed = #cores
- single = one thread; immediate = no switch
- never block the event loop
- Mono.fromCallable + subscribeOn(boundedElastic)
basics
~10 sA Scheduler decides which thread(s) run reactive work. boundedElastic is for blocking I/O, parallel for CPU work, single for one shared thread, and immediate runs on the current thread (no switch).
solid answer
~40 sA `Scheduler` is Reactor's abstraction over thread pools; operators like `subscribeOn`/`publishOn` take one to move work off the calling thread. `Schedulers.boundedElastic()` — a capped, elastic pool for blocking/long I/O (JDBC, blocking HTTP); it caps threads (default ~10× CPU cores) and queues extra tasks, evicting idle threads. `Schedulers.parallel()` — a fixed pool sized to CPU cores for short CPU-bound work; never block on it. `Schedulers.single()` — one reusable thread for low-latency serialized tasks. `Schedulers.immediate()` — a no-op that runs on the current thread, useful as a null-object when an API requires a Scheduler but you want no switch. In WebFlux the golden rule is: keep the event-loop threads non-blocking and push any blocking call to boundedElastic.
code
java · 13 linesimport reactor.core.publisher.Mono;
import reactor.core.scheduler.Schedulers;
// Blocking JDBC call wrapped and pushed off the event loop
Mono<User> user = Mono.fromCallable(() -> userJdbcDao.findById(id)) // blocking
.subscribeOn(Schedulers.boundedElastic()); // run on I/O pool
// CPU-bound, non-blocking transform on the parallel pool
Mono<BigInteger> hash = Mono.fromSupplier(() -> expensiveHash(data))
.subscribeOn(Schedulers.parallel());
// immediate(): required by an API but we want NO thread switch
flux.publishOn(Schedulers.immediate()); // stays on current threadgo deeper
Name the four Schedulers and the one-line purpose of each; know 'blocking → boundedElastic'.
Explain the bound (10×cores + queue) and why the event loop must stay non-blocking.
Discuss isolating heavy subsystems with newBoundedElastic, daemon-thread naming, BlockHound detection.
Reason about pool sizing under SLA/backpressure, thread-starvation deadlocks, and when to wrap a custom ExecutorService.
## What a Scheduler is Reactor is thread-agnostic: by default a reactive pipeline runs on whatever thread called `subscribe()` (or emitted the signal). A `reactor.core.scheduler.Scheduler` is an abstraction over a source of threads (usually a thread pool) that lets you *explicitly* decide where work runs. You never create threads by hand — you hand a `Scheduler` to `subscribeOn(...)` or `publishOn(...)` and Reactor dispatches the signals onto that Scheduler's worker(s). `Schedulers` (the factory class) exposes shared, lazily-initialised singletons. ## The four built-ins **`Schedulers.boundedElastic()`** — the workhorse for **blocking** or long-running I/O (legacy JDBC, `RestTemplate`, blocking file/network calls). It's *elastic*: it creates worker threads on demand and reuses them, but it's *bounded* — by default the thread cap is **10 × the number of CPU cores**, and once all threads are busy, extra tasks are **queued** (default queue cap 100 000) rather than spawning unbounded threads. Idle threads are evicted after 60s. The bound is deliberate: it prevents the classic "thread explosion" that unbounded elastic pools caused, while still letting blocking work park a thread without starving the rest of the app. **`Schedulers.parallel()`** — a **fixed-size** pool with one thread per CPU core (`Runtime.availableProcessors()`), tuned for **short, non-blocking, CPU-bound** work (computation, fan-out with `flatMap`, parallel streams). Because it's small and fixed, **blocking on it starves the whole pool** and can deadlock the app. **`Schedulers.single()`** — a **single** reusable thread for tasks that must be serialized or need a dedicated low-latency thread (e.g., a periodic timer). `Schedulers.newSingle(...)` gives a fresh private one. **`Schedulers.immediate()`** — not a pool at all: it executes the task **synchronously on the current thread**. It's a *null-object* — used when an operator requires a `Scheduler` argument but you explicitly want no thread switch. There's also `Schedulers.fromExecutor(...)`/`fromExecutorService(...)` to wrap your own pool. ## Why this matters in WebFlux Spring WebFlux serves requests on a **small number of non-blocking event-loop threads** (Reactor Netty). If you block one of those threads (a synchronous DB/HTTP call), you stall every request that thread was multiplexing — throughput collapses. The rule: **never block the event loop; offload blocking calls to `boundedElastic`** (via `subscribeOn`/`publishOn`). CPU-bound work that's non-blocking can go to `parallel`. ## Gotchas - boundedElastic threads are **daemon** threads named `boundedElastic-…`; parallel threads are `parallel-…` — handy when reading stack traces/logs. - The bound means boundedElastic is **not infinite**: if you flood it with slow blocking calls you can still exhaust it (queued tasks pile up). Size/isolate with `Schedulers.newBoundedElastic(...)` for heavy subsystems. - Schedulers are shared singletons; `Schedulers.parallel()` returns the *same* instance every call. Don't `dispose()` the global ones. - `Mono.fromCallable(blockingCall).subscribeOn(Schedulers.boundedElastic())` is the canonical wrapper for a blocking method.
- Why is boundedElastic bounded instead of unbounded elastic?Unbounded elastic pools spawned a thread per blocking task and could exhaust OS threads/memory under load. boundedElastic caps threads (default 10×cores) and queues overflow tasks, so blocking work parks a thread safely without a thread explosion.
- What happens if you run a blocking call on Schedulers.parallel()?You occupy one of the few fixed CPU-core threads. Under concurrency you starve the pool, stalling other non-blocking work and risking deadlock. BlockHound can detect such blocking calls on non-blocking Schedulers.
saying these in an interview costs you the question
- Thinking boundedElastic is unbounded/infinite
- Using parallel() for blocking JDBC/HTTP calls
- Believing immediate() uses a thread pool
- Assuming parallel() grows under load like a cached pool