When do I use this in practice?

Use it when choosing server/backend stacks, designing microservices, or comparing concurrency models — the scenarios, comparison tables, and selection flowcharts are meant for real decisions.

What should I read first in this series?

Follow the “previous post” links at the bottom of each article for order, or open the C++ series index for the full roadmap.

C++ vs Go | Performance, Concurrency, and Selection Guide [#47-1]

Q: Where do I go deeper?

cppreference, the official Go documentation, and Effective Go are the primary references. The links at the end of this article are a good starting set.

2026년 3월 12일 · 22분 읽기 · 수정 2026년 4월 17일 intermediate tutorial

이 글의 핵심

C++ vs Go from a production angle: failure scenarios from bad stack choices, concurrency models (threads/Asio vs goroutines), memory and switching costs, benchmarks, mistakes to avoid, selection guide, and patterns.

Introduction: the “C++ or Go?” moment

Why compare them?

In backend and server work, C++ and Go both advertise high performance and concurrency. C++ keeps control in the developer’s hands with threads and event loops (Asio); Go’s goroutines (lightweight tasks scheduled by the Go runtime) and channels (communication between goroutines) let the runtime schedule tens of thousands of tasks with an M:N model. This article compares the two from a practical angle: real failure scenarios, a full comparison table, common mistakes, a selection guide, and production patterns.

What this article covers:

Problem scenarios: what goes wrong when the stack choice mismatches the requirements
Concurrency models: C++ threads/Asio vs Go goroutines and M:N scheduling
Full comparison: performance, memory, types, ecosystem, builds
Common mistakes: patterns to avoid in each language
Selection guide: when to pick which
Production patterns: designs that show up in real systems

Related posts: C++ in practice #7 — threads, Understanding Go through a C++ developer’s mental model.

A mental model

Treat the topic as a system of interlocking parts. Choices in one layer (storage, networking, observability) affect others, so the article grounds trade-offs in numbers and patterns.

Production note: this draws on large-scale C++ experience — pitfalls and debugging angles that textbooks often skip.

Problem scenarios: when stack choice fails
Concurrency model comparison
Context switching and memory cost
Performance and trade-offs
Full C++ vs Go comparison
Common mistakes
Selection guide: what to choose when
Production patterns
Summary and checklist

1. Problem scenarios: when stack choice fails

Wrong technology choices tend to produce problems like these:

flowchart LR
    subgraph Mismatch[Stack mismatch]
        A[Requirements] -->|choose| B[Language / stack]
        B -->|expect| C[Behavior]
        A -.->|mismatch| C
        C --> D[Slowdowns / delays / ops pain]
    end

Scenario 1: “We built a web API in C++ and velocity collapsed”

Situation: A startup chose C++ for a REST API because “performance matters.” They used one thread per connection; at ~10k concurrent connections memory passed 80GB, and shipping slipped three months while they hand-rolled JSON, HTTP, and data access.

Why: For web/API servers, delivery speed and operational simplicity usually dominate. You may need C++’s peak performance, but most CRUD and microservices are fine in Go or Node.js. C++ often means assembling networking, parsing, and persistence yourself — high upfront cost.

What to do: Split “performance matters” into nanosecond-scale latency vs throughput. If you are in the millisecond range, Go or Rust may fit better. Reserve C++ for tight control: games, HFT, embedded, and similar domains.

Scenario 2: “We built HFT routing in Go and GC pauses broke latency”

Situation: A firm implemented order routing in Go because “Go is great at concurrency.” Under load, GC pauses reached milliseconds, failing a nanosecond-class latency target.

Why: Go’s GC can introduce multi-millisecond stop-the-world pauses. HFT and ultra-low-latency trading usually need GC-free C++ or Rust. Go fits web/API/microservices; not extreme tail latency.

What to do: If latency must stay below microseconds, prefer C++ or Rust. Go fits millisecond-class APIs and batch-style work.

Scenario 3: “One thread per connection for 100k users — OOM”

Situation: A chat server used one std::thread per connection. Around 10k connections, thread stacks alone exceeded 80GB and the process OOM’d.

Why: OS threads default to ~1–8MB stack each. 100k threads implies hundreds of GB. One thread per connection does not scale.

What to do: In C++, use an Asio-style event loop with a small thread pool and non-blocking I/O for many sockets. In Go, one goroutine per connection is idiomatic; stacks start around kilobytes so 100k is feasible.

Scenario 4: “We spawned goroutines for CPU work and got no speedup”

Situation: An image-resizing service launched one goroutine per request for CPU-heavy work. Only GOMAXPROCS (defaults to CPU count) ran in parallel; the rest waited — throughput capped by core count.

Why: Goroutines use M:N scheduling; OS threads stay near core count. Extra goroutines do not create extra parallel CPU execution beyond cores. They shine for I/O-bound work.

What to do: Use a worker pool with about one goroutine per core for CPU-bound stages, or move hot paths to C++ and call via cgo (mind cgo overhead).

Scenario 5: “Asio callbacks nested five levels deep — maintenance hell”

Situation: An Asio HTTP server chained async_read → async_write → async_read five levels deep; error handling and timeouts scattered across callbacks.

Why: Classic Asio is callback-heavy; complex async flows hurt readability. Go’s go func() plus channels often read like synchronous code.

What to do: Consider C++20 coroutines or a coroutine library; or split I/O-heavy services into Go microservices.

Scenario 6: “Our Go CLI binary is 50MB+”

Situation: A Go CLI produced a single static binary — great — but 15–30MB+ after linking the runtime and standard library, painful for embedded or small Lambda bundles.

Why: Go statically links dependencies by default. Even with -ldflags="-s -w", runtime, GC, and scheduler stay in the binary.

What to do: If size is critical, C++ or Rust static binaries can be much smaller. In Go, minimize with -trimpath, -ldflags="-s -w", and optionally UPX (with care).

Scenario 7: “C++ rebuilds take 30 minutes and CI times out”

Situation: Touching one header triggered 20–30 minute full rebuilds; CI hit job limits on every commit.

Why: C++ header dependencies are heavy. Popular headers (<iostream>, Boost, …) inflate compile units; templates live in headers and instantiate widely.

What to do: PCH, C++20 modules, ccache, incremental build tuning, or split services so hot code builds in smaller units. Go’s package-level incremental builds are usually fast.

2. Concurrency model comparison

Analogy: Concurrency is like one cook switching between pots; parallelism is multiple cooks on different dishes.

Model comparison

C++: OS threads + event loop

std::thread: 1:1 with OS threads. Creation/teardown is costly; each thread has a large stack (often 1–8MB). Tens of thousands of one thread per connection is painful.
Asio: One (or few) threads run an event loop handling many sockets with non-blocking I/O. Completion handlers may fan out to a pool — thread count stays small vs connection count.
Control: You design scheduling, memory, and locking — more complexity, finer latency/throughput tuning.

flowchart TB
    subgraph Cpp[C++ model]
        T1[OS thread 1]
        T2[OS thread 2]
        T3[OS thread N]
        E[Event loop]
        S[~10k sockets]
        E --> S
        T1 --> E
        T2 --> E
        T3 --> E
    end

Runnable example (minimal C++ thread):

// Paste and run: g++ -std=c++17 -pthread -o cpp_concurrent cpp_concurrent.cpp && ./cpp_concurrent
#include <iostream>
#include <thread>
int main() {
    std::thread t([]{ std::cout << "C++ OS thread\n"; });
    std::cout << "main\n";
    t.join();
    return 0;
}

Go: goroutines + M:N scheduling

Goroutine: lightweight coroutine; KB-scale stack that grows as needed. Tens or hundreds of thousands of goroutines still map to roughly core-count OS threads.
M:N: Many goroutines (N) map to M OS threads; context switches often happen in user space and cost less than full thread switches.
Channels: idiomatic synchronization and communication — prefer “message passing” over “shared memory + locks” when possible.

flowchart TB
    subgraph Go[Go model]
        M1[OS thread 1]
        M2[OS thread 2]
        G1[Goroutine 1]
        G2[Goroutine 2]
        G3[Goroutine ...]
        GN[Goroutine ~100k]
        M1 --> G1
        M1 --> G2
        M2 --> G3
        M2 --> GN
    end

Runnable example (Go goroutine):

// go run main.go
package main
import (
    "fmt"
    "sync"
)
func main() {
    var wg sync.WaitGroup
    wg.Add(1)
    go func() {
        defer wg.Done()
        fmt.Println("Go goroutine")
    }()
    fmt.Println("main")
    wg.Wait()
}

Same job, different style

Task: fetch 10 URLs concurrently and collect results.

C++ (std::async):

#include <future>
#include <vector>
#include <string>
std::vector<std::string> fetchAll(const std::vector<std::string>& urls) {
    std::vector<std::future<std::string>> futures;
    for (const auto& url : urls) {
        futures.push_back(std::async(std::launch::async, [url]() {
            return fetchUrl(url);  // HTTP request
        }));
    }
    std::vector<std::string> results;
    for (auto& f : futures) {
        results.push_back(f.get());
    }
    return results;
}

Go (goroutines + wait group):

func fetchAll(urls []string) []string {
    results := make([]string, len(urls))
    var wg sync.WaitGroup
    for i, url := range urls {
        wg.Add(1)
        go func(idx int, u string) {
            defer wg.Done()
            results[idx] = fetchURL(u)
        }(i, url)
    }
    wg.Wait()
    return results
}

Difference: C++ gathers with std::future; Go synchronizes with sync.WaitGroup. Passing i and url as parameters avoids classic loop-variable capture bugs.

In one line

C++: you design threads and event loops — maximum control, higher complexity.
Go: spawn many goroutines; the runtime schedules — easier for I/O-heavy work and many concurrent connections.

3. Context switching and memory cost

C++ threads

Stack: often 1–8MB per thread by default. 10k threads can mean tens of GB of stack alone — one thread per connection rarely scales.
Context switch: kernel involvement; cache/TLB effects can land in microseconds. More threads → more switching cost.

Go goroutines

Stack: starts around KB; grows on demand. 100k goroutines still use far less stack memory than 100k OS threads.
Switching: user-space scheduler; often nanoseconds to microseconds — lighter than full thread switches. Blocking on I/O parks a goroutine and runs others — one goroutine per connection is natural.

Stack size (conceptual)

Item	C++ `std::thread`	Go goroutine
Initial stack	1–8MB (typical default)	~2KB
Growth	fixed (tunable)	grows as needed
~10k instances	~10–80GB stacks	~20MB order of magnitude
~100k	impractical	~200MB order of magnitude

Takeaway

Many concurrent connections, I/O-heavy: goroutines win on memory and switching.
CPU-bound or nanosecond latency: tune C++ thread counts, event loops, lock-free structures, etc.

Analogy: think of memory like a building — stacks are fast but small “elevators”; the heap is a large “warehouse.” Pointers are addresses on a slip of paper.

4. Performance and trade-offs

CPU-bound

C++: native code, compile-time optimization, direct cache/layout control — usually ahead on raw compute vs Go. Keep thread count near core count to limit switching.
Go: GC and runtime overhead; for extreme CPU performance, C++ often wins.

I/O-bound and many connections

C++: Asio event loop + few threads saves memory and switching — but callbacks and strand design add implementation cost.
Go: one goroutine per connection is idiomatic; blocking-style code is fine — simple and often fast enough for web/API workloads.

Latency

Ultra-low (sub-microsecond): C++ with explicit control; no GC pauses matters.
Milliseconds: Go can hit targets with tuning.

Benchmarks (illustrative)

Workload	C++	Go	Notes
Pure compute (~1e9 ops)	~100ms	~150ms	C++ ahead
HTTP ~10k QPS	similar	similar	depends on implementation
~100k concurrent echo	Asio typical	goroutines natural	faster to build in Go
GC pauses	none	ms possible	C++ for ultra-low tail

Echo server sketch (~10k connections)

C++ Asio: one io_context + thread pool (e.g. 4–8 threads); register ~10k sockets; chain async_accept → async_read → async_write. Memory: a few threads × MB stacks + socket buffers — tens of MB order.

Go: go handleConn(conn) per connection; ~10k goroutines → ~20MB stack total; use net.Conn in blocking style — simple; runtime schedules work.

Conclusion: both can handle ~10k connections; Go is quicker to write; C++ offers finer control at higher engineering cost.

5. Full C++ vs Go comparison

Overview table

Topic	C++	Go
Memory	Manual / RAII / smart pointers	GC
Concurrency	`std::thread`, Asio	Goroutines, channels
Types	Static, strong, templates	Static, strong, interfaces
Generics	Templates (compile time)	Generics (Go 1.18+)
Errors	`try`/`catch`	`error` returns, `panic`/`recover`
Build	Slower (headers)	Faster (packages)
Binary	static/dynamic link	single static binary
Deploy	dependency complexity	`go build`
Learning curve	steep	gentler
Ecosystem	Boost, Qt, huge library space	strong stdlib, `go get`
Performance	extreme control	“enough” for many workloads
Latency	no GC, nanosecond control	GC pauses possible

Types and memory

C++:

Explicit memory control: new/delete or smart pointers.
RAII for resource lifetimes.
Compile-time polymorphism with templates.

Go:

GC reclaims memory; focus on allocation patterns.
defer for cleanup.
Runtime polymorphism with interfaces.

Ecosystem

C++: Boost.Asio, nlohmann/json, spdlog, gRPC, Protobuf, etc. — usually CMake + vcpkg/Conan.

Go: rich standard library — net/http, encoding/json, log, context. Dependencies via modules.

Syntax cheat sheet

Feature	C++	Go
Concurrent execution	`std::thread t(f); t.join();`	`go f()`
Synchronization	`std::mutex`, `std::atomic`	`sync.Mutex`, channels
Async results	`std::future`, `std::async`	channels, `errgroup`
Cleanup	RAII, destructors	`defer`
Errors	`try`/`catch`, `optional`	`error` returns
Modules	`#include`, namespaces	`import`, packages

Error handling

C++: exceptions or std::expected (C++23), error codes — avoid exception cost with result types.

// C++: exception or error-style
std::optional<int> parse(const std::string& s) {
    try {
        return std::stoi(s);
    } catch (...) {
        return std::nullopt;
    }
}

Go: error returns — if err != nil is idiomatic.

// Go: error return
func parse(s string) (int, error) {
    n, err := strconv.Atoi(s)
    if err != nil {
        return 0, err
    }
    return n, nil
}

Build and deploy

Topic	C++	Go
Build time	slow (headers, templates)	fast (incremental packages)
Dependencies	vcpkg, Conan, system packages	`go.mod`, `go.sum`
Cross-compile	per-toolchain setup	`GOOS`, `GOARCH`
Deploy	watch dynamic libs	single binary
Docker	often larger base + toolchain	scratch + binary works

6. Common mistakes

C++ mistakes

Mistake 1: one thread per connection

// Bad: 10k connections => 10k threads
void handle_client(int fd) {
    std::thread([fd]() {
        // ~1–8MB stack each => 10k threads => 10–80GB stacks
        process_request(fd);
    }).detach();
}

Fix: use an Asio event loop with a small thread pool.

// Better: event loop + thread pool
boost::asio::io_context ioc;
for (int i = 0; i < std::thread::hardware_concurrency(); ++i) {
    std::thread([&ioc]() { ioc.run(); }).detach();
}
// register many sockets on ioc

Mistake 2: `shared_ptr` everywhere

// Bad: shared_ptr on everything
void process(std::shared_ptr<Request> req) {
    auto resp = std::make_shared<Response>();  // atomic refcounts
    // ...
}

Fix: prefer unique_ptr for single ownership; shared_ptr only when sharing is required.

Mistake 3: shared data without synchronization

// Bad: data race
int counter = 0;
std::thread t1([&]() { ++counter; });
std::thread t2([&]() { ++counter; });

Fix: std::mutex or std::atomic.

Go mistakes

Mistake 1: unbounded goroutines for CPU work

// Bad: 10k goroutines doing CPU work => scheduling overhead
for i := 0; i < 10000; i++ {
    go cpuHeavyTask()
}

Fix: worker pool sized to cores.

// Better: worker pool
jobs := make(chan int, 100)
for w := 0; w < runtime.NumCPU(); w++ {
    go func() {
        for j := range jobs {
            cpuHeavyTask(j)
        }
    }()
}

Mistake 2: goroutine leak — channel never closed

// Bad: range waits forever if ch never closed
ch := make(chan int)
go func() {
    for v := range ch {
        process(v)
    }
}()

Fix: sender calls close(ch) when work is done.

Mistake 3: loop variable captured in goroutine

// Bad: data race / wrong index — loop variable captured
for i := 0; i < 10; i++ {
    go func() {
        process(items[i])
    }()
}

Fix: copy or pass by parameter.

// Better
for i := 0; i < 10; i++ {
    go func(idx int) {
        process(items[idx])
    }(i)
}

Mistake 4: send/receive on nil channel

// Bad: nil channel blocks forever
var ch chan int
ch <- 1
<-ch

Fix: ch := make(chan int) before use.

Mistake 5: unbuffered channel deadlock

// Bad: no receiver => sender blocks forever
ch := make(chan int)
ch <- 1

Fix: buffer, or start receiver before send.

Shared pitfalls

Mistake 6: deadlock

C++: lock A then B in one thread and B then A in another.

Go: two channels waiting on each other.

Fix: consistent lock order; unidirectional channel patterns; select with timeouts.

7. Selection guide: what to choose when

Decision flow

flowchart TD
    A[Analyze requirements] --> B{Latency target?}
    B -->|Sub-microsecond| C[C++]
    B -->|Milliseconds| D{Concurrent connections?}
    D -->|Very high| E{Velocity priority?}
    E -->|Yes| F[Go]
    E -->|No| G[C++ Asio]
    D -->|Thousands| H{Team skills?}
    H -->|Strong C++| I[C++]
    H -->|Mixed| J[Go]

Goals

Goal	C++	Go
Peak CPU / low latency	strong	limited
Many connections + speed	needs design	strong
Memory / thread control	strong (event loop)	strong (goroutines)
Team ramp / maintenance	heavier	lighter
Single-binary deploy	possible with static link	strong
Existing C/C++ stack	strong	via cgo

Domains

Prefer C++: game servers, HFT, embedded, heavy legacy C++, nanosecond-level control.
Prefer Go: web, APIs, microservices, fast shipping, simple operations.

Migration

C++ → Go: cgo works but watch cgo cost and GC pauses blocking native threads. Common pattern: split with gRPC/HTTP — C++ core, Go edge.

Go → C++: rewrite hot modules; keep Go for the rest — typical hybrid.

Hiring

C++: harder to hire; steep curve; demand in games, finance, systems.
Go: easier onboarding; strong cloud/DevOps demand.

Scenario summary

Scenario	Likely choice	Why
REST / microservices	Go	speed of development, stdlib, single binary
Game server (100k+ CCU)	C++	memory/latency control, engine integration
HFT / ultra-low latency	C++	no GC, fine control
K8s / Docker tooling	Go	ecosystem
Image/video CPU pipeline	C++ or Go	CPU-bound → C++; I/O-heavy → Go
Chat / realtime fanout	Go	goroutine-per-conn ergonomics
Embedded / IoT edge	C++	constraints, direct hardware

8. Production patterns

C++ patterns

Pattern 1: Asio + thread pool

boost::asio::io_context ioc;
boost::asio::signal_set signals(ioc, SIGINT, SIGTERM);
signals.async_wait([&](auto, auto) { ioc.stop(); });
std::vector<std::thread> threads;
for (unsigned i = 0; i < std::thread::hardware_concurrency(); ++i) {
    threads.emplace_back([&ioc]() { ioc.run(); });
}
for (auto& t : threads) t.join();

Pattern 2: strand for per-connection ordering

auto strand = boost::asio::make_strand(ioc);
boost::asio::async_read(socket, buffer, boost::asio::bind_executor(strand,  {
    // runs sequentially on this strand
}));

Pattern 3: pool allocator for hot paths

template<typename T>
using pool_alloc = boost::pool_allocator<T>;
std::vector<int, pool_alloc<int>> vec;

Go patterns

Pattern 1: `context` for cancel and timeout

ctx, cancel := context.WithTimeout(context.Background(), 5*time.Second)
defer cancel()
req, _ := http.NewRequestWithContext(ctx, "GET", "https://api.example.com", nil)
resp, err := http.DefaultClient.Do(req)
if err != nil {
    if ctx.Err() == context.DeadlineExceeded {
        // handle timeout
    }
}
defer resp.Body.Close()

Pattern 2: `errgroup` for goroutine groups

g, ctx := errgroup.WithContext(ctx)
for _, url := range urls {
    url := url
    g.Go(func() error {
        resp, err := fetch(ctx, url)
        if err != nil {
            return err
        }
        return process(resp)
    })
}
if err := g.Wait(); err != nil {
    return err
}

Pattern 3: worker pool

func worker(id int, jobs <-chan Job, results chan<- Result) {
    for j := range jobs {
        results <- process(j)
    }
}
jobs := make(chan Job, 100)
results := make(chan Result, 100)
for w := 0; w < runtime.NumCPU(); w++ {
    go worker(w, jobs, results)
}

Hybrid architecture

C++ core + Go API: HFT engine in C++; REST/gRPC in Go.
Go service + C++ lib: cgo — mind overhead and GC blocking native code.

Pattern 4: C++ RAII for connection cleanup

class Connection {
    boost::asio::ip::tcp::socket socket_;
public:
    Connection(boost::asio::io_context& ioc) : socket_(ioc) {}
    ~Connection() {
        boost::system::error_code ec;
        socket_.shutdown(boost::asio::ip::tcp::socket::shutdown_both, ec);
    }
};

Pattern 5: Go `defer`

func processFile(path string) error {
    f, err := os.Open(path)
    if err != nil {
        return err
    }
    defer f.Close()
    return nil
}

Pattern 6: C++ `thread_local`

thread_local std::mt19937 rng(std::random_device{}());

Pattern 7: Go `sync.Once`

var once sync.Once
var config *Config
func getConfig() *Config {
    once.Do(func() {
        config = loadConfig()
    })
    return config
}

Pattern 8: channel vs mutex

Channels: passing data between goroutines; “don’t share memory, communicate.”

ch := make(chan int, 10)
go producer(ch)
go consumer(ch)

Mutex: protecting shared mutable state.

var mu sync.Mutex
var cache map[string]string
func get(key string) string {
    mu.Lock()
    defer mu.Unlock()
    return cache[key]
}

Go proverb: “Don’t communicate by sharing memory; share memory by communicating.”

Pattern 9: C++ `atomic` counter

std::atomic<int> counter{0};
counter.fetch_add(1, std::memory_order_relaxed);

Pattern 10: C++ `shared_mutex` reader/writer

std::shared_mutex mtx;
std::shared_lock read_lock(mtx);
std::unique_lock write_lock(mtx);

9. Summary and checklist

Highlights

Topic	C++	Go
Concurrency	threads, Asio	goroutines, channels
Memory	manual / RAII	GC
Latency	ultra-low possible	GC pauses
Velocity	slower to ship	faster for many services
Deploy	complex deps	single binary

Principles:

Measure latency, throughput, and team skills first.
Ultra-low latency → C++; fast iteration and ops → consider Go.
Avoid one thread per connection; use Asio in C++, goroutines in Go.
CPU-bound → worker pools; I/O-bound → goroutines / event loops.

Checklist

Sub-microsecond latency? → consider C++
Tens of thousands of connections? → Go goroutines or C++ Asio
No senior C++ engineers? → lean Go
Heavy existing C++ stack? → likely stay on C++
Single-binary deploy critical? → Go shines
GC pauses unacceptable? → C++

Profiling

C++: perf, VTune, gprof; Valgrind for leaks/races; std::chrono::high_resolution_clock for timing.

Go: go tool pprof, -race, runtime/debug for GC stats.

import "runtime/debug"
var stats debug.GCStats
debug.ReadGCStats(&stats)

FAQ

When do I use this at work?

A. When choosing server stacks, designing microservices, or comparing concurrency models — use the scenarios, tables, and patterns above as a checklist.

What should I read next in order?

A. Follow previous post links at the bottom of each article, or open the C++ series index for the full sequence.

Where do I go deeper?

A. cppreference, Go documentation, Effective Go, plus Boost.Asio and Go net docs.

Can I use C++ and Go together?

A. Yes — gRPC/HTTP hybrids are common: C++ core (engine/HFT) + Go APIs. cgo is possible; account for call overhead and GC blocking native threads.

Next steps

C++ depth: C++ thread basics, Data races, mutex, atomic
Go intro: Understanding Go through a C++ lens
System design: C++ system design

One-line summary: knowing how C++ and Go differ on performance and concurrency makes stack choices clearer. Next, read Go for C++ developers (#47-2).

Next post: [C++ vs other languages #47-2] Understanding Go through a C++ developer’s mental model

References

Two-week Go curriculum for C++ developers · Go series index
[Go advanced #09] Context, timeouts, graceful shutdown
C++ std::thread basics — common mistakes
Understanding Go through a C++ developer’s mental model [#47-2]

Keywords

C++ vs Go, language comparison, goroutines, concurrency, performance, Asio, channels — useful search terms for this topic.