Python 3, an illustrated tour Transcripts
Chapter: Asynchronous Programming
Lecture: asyncio Definitions
0:00 We're going to look at asyncio, specifically pep 526
0:04 and the tooling that came in Python 3.6.
0:08 Asyncio came about during Python 3.4 time frame
0:12 and Python 3.6 added some new syntax to make it nicer to use.
0:15 Let's talk about some terms first to get these straight
0:19 what is concurrency, concurrency means that we're sharing resources.
0:23 One way to visualize this is a juggler who has multiple balls.
0:28 Each of those balls is a resource and he's juggling them.
0:30 Similarly, a CPU on a computer juggles multiple resources.
0:35 It can run multiple things,
0:38 a single CPU cannot run multiple things at the same time,
0:40 but what it's doing is it's shifting between those very quickly.
0:43 Parallelism, which some people confuse for concurrency, but it's different,
0:47 parallelism means doing multiple things at the same time.
0:50 So if I have some parallel code,
0:52 it can run something faster than doing something at once.
0:57 So an example here would be a CPU that has multiple cores.
1:03 It can run code twice as fast as that code is run to take advantage of both CPUs.
1:08 If we're going back to our juggler example,
1:11 this would be multiple jugglers juggling multiple balls,
1:14 presumably multiple jugglers who are juggling can juggle more balls
1:18 than just one juggler, if they don't have to coordinate among themselves per se.
1:22 A couple of other things to be aware of,
1:24 one is a thread, a thread is a operating system construct for doing something,
1:29 a thread runs on a CPU, if you have multiple CPUs
1:33 and your code can run in multiple threads,
1:36 it's possible that each of those threads could take advantage
1:39 of a single CPU and make it run faster.
1:42 Now, this isn't the case in Python,
1:44 Python has what's called the gil or global interpreter lock
1:47 which limits multi-threaded code in Python to only run on one CPU
1:51 regardless of how many CPUs are on the system.
1:54 Another thing to be aware of is what's called a green thread,
1:57 green threads are VM level threads,
2:00 so they're not done at the operating system level
2:02 but they're done at a programming or user level
2:05 and these are a little bit lighter weight, but they don't scale across CPUs.
2:09 And we'll see how asyncio basically allows you
2:13 to use green threads or run different contexts across a CPU inside of a VM,
2:21 but doesn't necessarily allow you to take advantage of multiple CPUs.
2:25 A couple other terms we'll talk about here,
2:28 synchronous, synchronous means if I'm going to run something,
2:31 if I'm synchronous I wait till the execution is done before I run something else.
2:35 And asynchronous code is where I kick off execution
2:37 and maybe I have a callback or some way to figure out when it's done
2:41 and after I kicked off execution, I'm going to move on to some other code
2:46 until I get this call back or mechanism that tells me that it's done.
2:49 That's asynchronous code.