Concurrency is about dealing with many things at once; parallelism is about doing many things at once. Concurrency is a way to structure a program so multiple tasks make progress by interleaving; parallelism is actually executing them simultaneously on multiple cores.
A single CPU core can be but not parallel: it rapidly switches between tasks, so they overlap in time even though only one runs at any instant. needs multiple cores running instructions at the same moment.
Concurrency (1 core, interleaved): A--B--A--B--A--B
Parallelism (2 cores, simultaneous): A--A--A--A
B--B--B--B
# Concurrency: one thread juggles two downloads by waiting on I/O
async def main():
await asyncio.gather(fetch(url1), fetch(url2)) # overlap, one core
# Parallelism: two CPU cores crunch numbers at the same time
with ProcessPoolExecutor() as pool:
pool.map(heavy_compute, data) # truly simultaneous
Rob Pike's summary: "Concurrency is about structure, parallelism is about execution." You can have concurrency without parallelism (async I/O on one core) and they often combine.
Interviewers use this to check whether you understand why threads help. I/O-bound work benefits from concurrency (overlap waiting); CPU-bound work needs parallelism (more cores). Mixing them up leads to designs that add threads but see no speedup.
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