Concurrency Comparisons

Side-by-side trade-offs where neither column wins. The workload, the runtime and how much correctness risk you can carry decide — and each comparison ends with the verdict that follows from that, not from a preference.

Async vs threads

Two ways to have many things in flight. Async multiplexes tasks over one thread at explicit suspension points; threads let the OS preempt anywhere. That single difference decides where your bugs are and how many concurrent operations you can afford.

DimensionAsync tasksThreads
Cost per unit in flightA small heap object — thousands are routineA stack, typically measured in hundreds of kilobytes
Where a switch can happenOnly at await — you can see every one of themAnywhere, including mid-increment
Data races on plain variablesImpossible on one loop between suspension pointsPossible on every unsynchronized access
Race conditionsVery much possible — across await boundariesPossible everywhere
CPU-bound workStalls the entire loop and every other task on itPreempted by the scheduler; other threads continue
Blocking callsPoison — one blocking call freezes all tasksAbsorbed, at the cost of a parked thread
DebuggingAsync stack traces, orphaned tasks, "who never resolved this?"Thread dumps, lock cycles, timing-dependent corruption
Ecosystem requirementEvery library on the hot path must be non-blockingAnything works, blocking or not
Use Async tasks when
  • Tens of thousands of concurrent I/O operations.
  • The whole stack is non-blocking, or blocking parts can be offloaded.
  • You want the set of possible interleavings to be small and visible.
Use Threads when
  • The libraries you must use are blocking and will not change.
  • Concurrency is in the hundreds, not the tens of thousands.
  • Some tasks are CPU-heavy and must not stall the others.
Verdict

Async where the concurrency is large and the work is waiting; threads where the concurrency is modest or the code blocks. Most real servers end up hybrid — an async edge with a bounded thread pool for the blocking and compute-heavy parts.