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https://codeberg.org/ziglings/exercises.git
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revival of the async-io functions
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@@ -1131,9 +1131,12 @@ const exercises = [_]Exercise{
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},
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.{
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.main_file = "087_async4.zig",
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.output = "1 2 3 4 5",
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.skip = true,
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.skip_hint = "async has not been implemented in the current compiler version.",
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.output =
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\\Task 1 done.
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\\Task 2 done.
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\\Task 3 done.
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\\All tasks finished!
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, // pay attention to the comma
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},
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.{
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.main_file = "088_async5.zig",
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@@ -1,30 +1,50 @@
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//
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// It has probably not escaped your attention that we are no
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// longer capturing a return value from foo() because the 'async'
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// keyword returns the frame instead.
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// When you have many tasks that don't return individual values,
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// use a Group! A Group is an unordered set of tasks that can
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// only be awaited or canceled as a whole:
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//
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// One way to solve this is to use a global variable.
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// var group: std.Io.Group = .init;
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// group.async(io, myTask, .{arg1});
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// group.async(io, myTask, .{arg2});
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// try group.await(io); // blocks until ALL tasks finish
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//
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// See if you can make this program print "1 2 3 4 5".
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// Important rules:
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// * The return type of functions spawned in a group must be
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// coercible to Cancelable!void (i.e. void, or error{Canceled}!void).
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// * Once you call group.async(), you MUST eventually call
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// group.await() or group.cancel() to release resources.
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// * group.cancel() requests cancellation on ALL members,
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// then waits for them to finish.
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//
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const print = @import("std").debug.print;
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// Unlike Future, Group tasks don't return values to the caller.
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// They're ideal for parallel work that communicates through
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// shared state or side effects (like printing).
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//
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// Fix this program to await all tasks in the group.
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//
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const std = @import("std");
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const print = std.debug.print;
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var global_counter: i32 = 0;
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pub fn main(init: std.process.Init) !void {
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const io = init.io;
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pub fn main() void {
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var foo_frame = async foo();
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var group: std.Io.Group = .init;
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while (global_counter <= 5) {
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print("{} ", .{global_counter});
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???
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// Spawn 3 tasks in any order. Each sleeps for (id * 1) seconds
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// before printing, so the output order is deterministic.
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group.async(io, doWork, .{ io, 1 });
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group.async(io, doWork, .{ io, 3 });
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group.async(io, doWork, .{ io, 2 });
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// Wait for all tasks to finish.
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// What Group method blocks until all tasks complete?
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try group.???
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print("All tasks finished!\n", .{});
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}
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print("\n", .{});
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}
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fn foo() void {
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while (true) {
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???
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???
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}
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fn doWork(io: std.Io, id: u32) void {
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// Sleep ensures deterministic output order.
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io.sleep(std.Io.Duration.fromSeconds(id), .awake) catch return;
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print("Task {} done.\n", .{id});
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}
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@@ -1,31 +1,22 @@
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//
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// Whenever there is a lot to calculate, the question arises as to how
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// tasks can be carried out simultaneously. We have already learned about
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// one possibility, namely asynchronous processes, in Exercises 84-91.
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// In Exercises 84-91, we learned about Zig's Io interface for
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// concurrent execution: io.async(), Group, Select, and Futures.
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// Under the hood, the Threaded backend manages a pool of real
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// OS threads for you - including scheduling, cancellation, and
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// resource cleanup.
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//
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// However, the computing power of the processor is only distributed to
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// the started and running tasks, which always reaches its limits when
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// pure computing power is called up.
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// But sometimes you need direct control over threads:
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// * Long-lived dedicated workers
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// * Specific stack sizes or thread counts
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// * Code that doesn't have an Io interface available
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// * Fine-grained synchronization patterns
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//
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// For example, in blockchains based on proof of work, the miners have
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// to find a nonce for a certain character string so that the first m bits
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// in the hash of the character string and the nonce are zeros.
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// As the miner who can solve the task first receives the reward, everyone
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// tries to complete the calculations as quickly as possible.
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// That's where std.Thread comes in. It gives you a raw OS thread
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// that you spawn, manage, and join yourself. No pool, no Futures,
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// no automatic cancellation - but full control.
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//
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// This is where multithreading comes into play, where tasks are actually
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// distributed across several cores of the CPU or GPU, which then really
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// means a multiplication of performance.
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//
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// The following diagram roughly illustrates the difference between the
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// various types of process execution.
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// The 'Overall Time' column is intended to illustrate how the time is
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// affected if, instead of one core as in synchronous and asynchronous
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// processing, a second core now helps to complete the work in multithreading.
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//
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// In the ideal case shown, execution takes only half the time compared
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// to the synchronous single thread. And even asynchronous processing
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// is only slightly faster in comparison.
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// The following diagram roughly illustrates the difference between
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// the various types of process execution:
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//
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//
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// Synchronous Asynchronous
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@@ -108,7 +99,7 @@ pub fn main() !void {
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// they run in parallel and we can still do some work in between.
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var io_instance: std.Io.Threaded = .init_single_threaded;
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const io = io_instance.io();
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try io.sleep(std.Io.Duration.fromSeconds(4), .awake);
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try io.sleep(std.Io.Duration.fromMilliseconds(400), .awake);
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std.debug.print("Some weird stuff, after starting the threads.\n", .{});
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}
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// After we have left the closed area, we wait until
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@@ -118,17 +109,17 @@ pub fn main() !void {
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// This function is started with every thread that we set up.
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// In our example, we pass the number of the thread as a parameter.
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fn thread_function(num: usize) !void {
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fn thread_function(id: usize) !void {
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var io_instance: std.Io.Threaded = .init_single_threaded;
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const io = io_instance.io();
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try io.sleep(std.Io.Duration.fromSeconds(1 * @as(isize, @intCast(num))), .awake);
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std.debug.print("thread {d}: {s}\n", .{ num, "started." });
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try io.sleep(std.Io.Duration.fromMilliseconds(100 * @as(isize, @intCast(id))), .awake);
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std.debug.print("thread {d}: {s}\n", .{ id, "started." });
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// This timer simulates the work of the thread.
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const work_time = 3 * ((5 - num % 3) - 2);
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try io.sleep(std.Io.Duration.fromSeconds(@intCast(work_time)), .awake);
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const work_time = 300 * ((5 - id % 3) - 2);
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try io.sleep(std.Io.Duration.fromMilliseconds(@intCast(work_time)), .awake);
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std.debug.print("thread {d}: {s}\n", .{ num, "finished." });
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std.debug.print("thread {d}: {s}\n", .{ id, "finished." });
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}
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// This is the easiest way to run threads in parallel.
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// In general, however, more management effort is required,
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11
patches/patches/087_async4.patch
Normal file
11
patches/patches/087_async4.patch
Normal file
@@ -0,0 +1,11 @@
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--- exercises/087_async4.zig 2026-04-01 23:17:31.066443941 +0200
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+++ answers/087_async4.zig 2026-04-01 23:17:39.251612131 +0200
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@@ -38,7 +38,7 @@
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// Wait for all tasks to finish.
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// What Group method blocks until all tasks complete?
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- try group.???
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+ try group.await(io);
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print("All tasks finished!\n", .{});
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}
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@@ -1,6 +1,6 @@
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--- exercises/104_threading.zig 2025-11-28 14:17:31.552529679 +0100
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+++ answers/104_threading.zig 2025-11-28 14:15:36.823931851 +0100
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@@ -97,12 +97,12 @@
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--- exercises/104_threading.zig 2026-04-01 23:31:10.073198955 +0200
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+++ answers/104_threading.zig 2026-04-01 23:29:51.314585919 +0200
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@@ -88,12 +88,12 @@
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defer handle.join();
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// Second thread
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