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denoland-deno/cli/tools/serve.rs

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feat(serve): Opt-in parallelism for `deno serve` (#24920) Adds a `parallel` flag to `deno serve`. When present, we spawn multiple workers to parallelize serving requests. ```bash deno serve --parallel main.ts ``` Currently on linux we use `SO_REUSEPORT` and rely on the fact that the kernel will distribute connections in a round-robin manner. On mac and windows, we sort of emulate this by cloning the underlying file descriptor and passing a handle to each worker. The connections will not be guaranteed to be fairly distributed (and in practice almost certainly won't be), but the distribution is still spread enough to provide a significant performance increase. --- (Run on an Macbook Pro with an M3 Max, serving `deno.com` baseline:: ``` ❯ wrk -d 30s -c 125 --latency http://127.0.0.1:8000 Running 30s test @ http://127.0.0.1:8000 2 threads and 125 connections Thread Stats Avg Stdev Max +/- Stdev Latency 239.78ms 13.56ms 330.54ms 79.12% Req/Sec 258.58 35.56 360.00 70.64% Latency Distribution 50% 236.72ms 75% 248.46ms 90% 256.84ms 99% 268.23ms 15458 requests in 30.02s, 2.47GB read Requests/sec: 514.89 Transfer/sec: 84.33MB ``` this PR (`with --parallel` flag) ``` ❯ wrk -d 30s -c 125 --latency http://127.0.0.1:8000 Running 30s test @ http://127.0.0.1:8000 2 threads and 125 connections Thread Stats Avg Stdev Max +/- Stdev Latency 117.40ms 142.84ms 590.45ms 79.07% Req/Sec 1.33k 175.19 1.77k 69.00% Latency Distribution 50% 22.34ms 75% 223.67ms 90% 357.32ms 99% 460.50ms 79636 requests in 30.07s, 12.74GB read Requests/sec: 2647.96 Transfer/sec: 433.71MB ```
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// Copyright 2018-2024 the Deno authors. All rights reserved. MIT license.
use std::sync::Arc;
use deno_core::error::AnyError;
use deno_core::futures::TryFutureExt;
use deno_core::ModuleSpecifier;
use super::run::check_permission_before_script;
use super::run::maybe_npm_install;
use crate::args::Flags;
use crate::args::ServeFlags;
use crate::args::WatchFlagsWithPaths;
use crate::factory::CliFactory;
use crate::util::file_watcher::WatcherRestartMode;
use crate::worker::CliMainWorkerFactory;
pub async fn serve(
flags: Arc<Flags>,
serve_flags: ServeFlags,
) -> Result<i32, AnyError> {
check_permission_before_script(&flags);
if let Some(watch_flags) = serve_flags.watch {
return serve_with_watch(flags, watch_flags, serve_flags.worker_count)
.await;
}
let factory = CliFactory::from_flags(flags);
let cli_options = factory.cli_options()?;
let deno_dir = factory.deno_dir()?;
let http_client = factory.http_client_provider();
// Run a background task that checks for available upgrades or output
// if an earlier run of this background task found a new version of Deno.
#[cfg(feature = "upgrade")]
super::upgrade::check_for_upgrades(
http_client.clone(),
deno_dir.upgrade_check_file_path(),
);
let main_module = cli_options.resolve_main_module()?;
maybe_npm_install(&factory).await?;
let worker_factory = factory.create_cli_main_worker_factory().await?;
let hmr = serve_flags
.watch
.map(|watch_flags| watch_flags.hmr)
.unwrap_or(false);
feat(serve): Opt-in parallelism for `deno serve` (#24920) Adds a `parallel` flag to `deno serve`. When present, we spawn multiple workers to parallelize serving requests. ```bash deno serve --parallel main.ts ``` Currently on linux we use `SO_REUSEPORT` and rely on the fact that the kernel will distribute connections in a round-robin manner. On mac and windows, we sort of emulate this by cloning the underlying file descriptor and passing a handle to each worker. The connections will not be guaranteed to be fairly distributed (and in practice almost certainly won't be), but the distribution is still spread enough to provide a significant performance increase. --- (Run on an Macbook Pro with an M3 Max, serving `deno.com` baseline:: ``` ❯ wrk -d 30s -c 125 --latency http://127.0.0.1:8000 Running 30s test @ http://127.0.0.1:8000 2 threads and 125 connections Thread Stats Avg Stdev Max +/- Stdev Latency 239.78ms 13.56ms 330.54ms 79.12% Req/Sec 258.58 35.56 360.00 70.64% Latency Distribution 50% 236.72ms 75% 248.46ms 90% 256.84ms 99% 268.23ms 15458 requests in 30.02s, 2.47GB read Requests/sec: 514.89 Transfer/sec: 84.33MB ``` this PR (`with --parallel` flag) ``` ❯ wrk -d 30s -c 125 --latency http://127.0.0.1:8000 Running 30s test @ http://127.0.0.1:8000 2 threads and 125 connections Thread Stats Avg Stdev Max +/- Stdev Latency 117.40ms 142.84ms 590.45ms 79.07% Req/Sec 1.33k 175.19 1.77k 69.00% Latency Distribution 50% 22.34ms 75% 223.67ms 90% 357.32ms 99% 460.50ms 79636 requests in 30.07s, 12.74GB read Requests/sec: 2647.96 Transfer/sec: 433.71MB ```
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do_serve(
worker_factory,
main_module.clone(),
feat(serve): Opt-in parallelism for `deno serve` (#24920) Adds a `parallel` flag to `deno serve`. When present, we spawn multiple workers to parallelize serving requests. ```bash deno serve --parallel main.ts ``` Currently on linux we use `SO_REUSEPORT` and rely on the fact that the kernel will distribute connections in a round-robin manner. On mac and windows, we sort of emulate this by cloning the underlying file descriptor and passing a handle to each worker. The connections will not be guaranteed to be fairly distributed (and in practice almost certainly won't be), but the distribution is still spread enough to provide a significant performance increase. --- (Run on an Macbook Pro with an M3 Max, serving `deno.com` baseline:: ``` ❯ wrk -d 30s -c 125 --latency http://127.0.0.1:8000 Running 30s test @ http://127.0.0.1:8000 2 threads and 125 connections Thread Stats Avg Stdev Max +/- Stdev Latency 239.78ms 13.56ms 330.54ms 79.12% Req/Sec 258.58 35.56 360.00 70.64% Latency Distribution 50% 236.72ms 75% 248.46ms 90% 256.84ms 99% 268.23ms 15458 requests in 30.02s, 2.47GB read Requests/sec: 514.89 Transfer/sec: 84.33MB ``` this PR (`with --parallel` flag) ``` ❯ wrk -d 30s -c 125 --latency http://127.0.0.1:8000 Running 30s test @ http://127.0.0.1:8000 2 threads and 125 connections Thread Stats Avg Stdev Max +/- Stdev Latency 117.40ms 142.84ms 590.45ms 79.07% Req/Sec 1.33k 175.19 1.77k 69.00% Latency Distribution 50% 22.34ms 75% 223.67ms 90% 357.32ms 99% 460.50ms 79636 requests in 30.07s, 12.74GB read Requests/sec: 2647.96 Transfer/sec: 433.71MB ```
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serve_flags.worker_count,
hmr,
feat(serve): Opt-in parallelism for `deno serve` (#24920) Adds a `parallel` flag to `deno serve`. When present, we spawn multiple workers to parallelize serving requests. ```bash deno serve --parallel main.ts ``` Currently on linux we use `SO_REUSEPORT` and rely on the fact that the kernel will distribute connections in a round-robin manner. On mac and windows, we sort of emulate this by cloning the underlying file descriptor and passing a handle to each worker. The connections will not be guaranteed to be fairly distributed (and in practice almost certainly won't be), but the distribution is still spread enough to provide a significant performance increase. --- (Run on an Macbook Pro with an M3 Max, serving `deno.com` baseline:: ``` ❯ wrk -d 30s -c 125 --latency http://127.0.0.1:8000 Running 30s test @ http://127.0.0.1:8000 2 threads and 125 connections Thread Stats Avg Stdev Max +/- Stdev Latency 239.78ms 13.56ms 330.54ms 79.12% Req/Sec 258.58 35.56 360.00 70.64% Latency Distribution 50% 236.72ms 75% 248.46ms 90% 256.84ms 99% 268.23ms 15458 requests in 30.02s, 2.47GB read Requests/sec: 514.89 Transfer/sec: 84.33MB ``` this PR (`with --parallel` flag) ``` ❯ wrk -d 30s -c 125 --latency http://127.0.0.1:8000 Running 30s test @ http://127.0.0.1:8000 2 threads and 125 connections Thread Stats Avg Stdev Max +/- Stdev Latency 117.40ms 142.84ms 590.45ms 79.07% Req/Sec 1.33k 175.19 1.77k 69.00% Latency Distribution 50% 22.34ms 75% 223.67ms 90% 357.32ms 99% 460.50ms 79636 requests in 30.07s, 12.74GB read Requests/sec: 2647.96 Transfer/sec: 433.71MB ```
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)
.await
}
async fn do_serve(
worker_factory: CliMainWorkerFactory,
main_module: ModuleSpecifier,
worker_count: Option<usize>,
hmr: bool,
) -> Result<i32, AnyError> {
let mut worker = worker_factory
.create_main_worker(
deno_runtime::WorkerExecutionMode::Serve {
is_main: true,
worker_count,
},
main_module.clone(),
)
.await?;
let worker_count = match worker_count {
None | Some(1) => return worker.run().await,
Some(c) => c,
};
let main = deno_core::unsync::spawn(async move { worker.run().await });
let extra_workers = worker_count.saturating_sub(1);
let mut channels = Vec::with_capacity(extra_workers);
for i in 0..extra_workers {
let worker_factory = worker_factory.clone();
let main_module = main_module.clone();
let (tx, rx) = tokio::sync::oneshot::channel();
channels.push(rx);
std::thread::Builder::new()
.name(format!("serve-worker-{i}"))
.spawn(move || {
deno_runtime::tokio_util::create_and_run_current_thread(async move {
let result = run_worker(i, worker_factory, main_module, hmr).await;
feat(serve): Opt-in parallelism for `deno serve` (#24920) Adds a `parallel` flag to `deno serve`. When present, we spawn multiple workers to parallelize serving requests. ```bash deno serve --parallel main.ts ``` Currently on linux we use `SO_REUSEPORT` and rely on the fact that the kernel will distribute connections in a round-robin manner. On mac and windows, we sort of emulate this by cloning the underlying file descriptor and passing a handle to each worker. The connections will not be guaranteed to be fairly distributed (and in practice almost certainly won't be), but the distribution is still spread enough to provide a significant performance increase. --- (Run on an Macbook Pro with an M3 Max, serving `deno.com` baseline:: ``` ❯ wrk -d 30s -c 125 --latency http://127.0.0.1:8000 Running 30s test @ http://127.0.0.1:8000 2 threads and 125 connections Thread Stats Avg Stdev Max +/- Stdev Latency 239.78ms 13.56ms 330.54ms 79.12% Req/Sec 258.58 35.56 360.00 70.64% Latency Distribution 50% 236.72ms 75% 248.46ms 90% 256.84ms 99% 268.23ms 15458 requests in 30.02s, 2.47GB read Requests/sec: 514.89 Transfer/sec: 84.33MB ``` this PR (`with --parallel` flag) ``` ❯ wrk -d 30s -c 125 --latency http://127.0.0.1:8000 Running 30s test @ http://127.0.0.1:8000 2 threads and 125 connections Thread Stats Avg Stdev Max +/- Stdev Latency 117.40ms 142.84ms 590.45ms 79.07% Req/Sec 1.33k 175.19 1.77k 69.00% Latency Distribution 50% 22.34ms 75% 223.67ms 90% 357.32ms 99% 460.50ms 79636 requests in 30.07s, 12.74GB read Requests/sec: 2647.96 Transfer/sec: 433.71MB ```
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let _ = tx.send(result);
});
})?;
}
let (main_result, worker_results) = tokio::try_join!(
main.map_err(AnyError::from),
deno_core::futures::future::try_join_all(
channels.into_iter().map(|r| r.map_err(AnyError::from))
)
)?;
let mut exit_code = main_result?;
for res in worker_results {
let ret = res?;
if ret != 0 && exit_code == 0 {
exit_code = ret;
}
}
Ok(exit_code)
}
async fn run_worker(
worker_count: usize,
worker_factory: CliMainWorkerFactory,
main_module: ModuleSpecifier,
hmr: bool,
) -> Result<i32, AnyError> {
let mut worker: crate::worker::CliMainWorker = worker_factory
feat(serve): Opt-in parallelism for `deno serve` (#24920) Adds a `parallel` flag to `deno serve`. When present, we spawn multiple workers to parallelize serving requests. ```bash deno serve --parallel main.ts ``` Currently on linux we use `SO_REUSEPORT` and rely on the fact that the kernel will distribute connections in a round-robin manner. On mac and windows, we sort of emulate this by cloning the underlying file descriptor and passing a handle to each worker. The connections will not be guaranteed to be fairly distributed (and in practice almost certainly won't be), but the distribution is still spread enough to provide a significant performance increase. --- (Run on an Macbook Pro with an M3 Max, serving `deno.com` baseline:: ``` ❯ wrk -d 30s -c 125 --latency http://127.0.0.1:8000 Running 30s test @ http://127.0.0.1:8000 2 threads and 125 connections Thread Stats Avg Stdev Max +/- Stdev Latency 239.78ms 13.56ms 330.54ms 79.12% Req/Sec 258.58 35.56 360.00 70.64% Latency Distribution 50% 236.72ms 75% 248.46ms 90% 256.84ms 99% 268.23ms 15458 requests in 30.02s, 2.47GB read Requests/sec: 514.89 Transfer/sec: 84.33MB ``` this PR (`with --parallel` flag) ``` ❯ wrk -d 30s -c 125 --latency http://127.0.0.1:8000 Running 30s test @ http://127.0.0.1:8000 2 threads and 125 connections Thread Stats Avg Stdev Max +/- Stdev Latency 117.40ms 142.84ms 590.45ms 79.07% Req/Sec 1.33k 175.19 1.77k 69.00% Latency Distribution 50% 22.34ms 75% 223.67ms 90% 357.32ms 99% 460.50ms 79636 requests in 30.07s, 12.74GB read Requests/sec: 2647.96 Transfer/sec: 433.71MB ```
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.create_main_worker(
deno_runtime::WorkerExecutionMode::Serve {
is_main: false,
worker_count: Some(worker_count),
},
main_module,
)
.await?;
if hmr {
worker.run_for_watcher().await?;
Ok(0)
} else {
worker.run().await
}
}
async fn serve_with_watch(
flags: Arc<Flags>,
watch_flags: WatchFlagsWithPaths,
worker_count: Option<usize>,
) -> Result<i32, AnyError> {
let hmr = watch_flags.hmr;
crate::util::file_watcher::watch_recv(
flags,
crate::util::file_watcher::PrintConfig::new_with_banner(
if watch_flags.hmr { "HMR" } else { "Watcher" },
"Process",
!watch_flags.no_clear_screen,
),
WatcherRestartMode::Automatic,
move |flags, watcher_communicator, changed_paths| {
watcher_communicator.show_path_changed(changed_paths.clone());
feat(serve): Opt-in parallelism for `deno serve` (#24920) Adds a `parallel` flag to `deno serve`. When present, we spawn multiple workers to parallelize serving requests. ```bash deno serve --parallel main.ts ``` Currently on linux we use `SO_REUSEPORT` and rely on the fact that the kernel will distribute connections in a round-robin manner. On mac and windows, we sort of emulate this by cloning the underlying file descriptor and passing a handle to each worker. The connections will not be guaranteed to be fairly distributed (and in practice almost certainly won't be), but the distribution is still spread enough to provide a significant performance increase. --- (Run on an Macbook Pro with an M3 Max, serving `deno.com` baseline:: ``` ❯ wrk -d 30s -c 125 --latency http://127.0.0.1:8000 Running 30s test @ http://127.0.0.1:8000 2 threads and 125 connections Thread Stats Avg Stdev Max +/- Stdev Latency 239.78ms 13.56ms 330.54ms 79.12% Req/Sec 258.58 35.56 360.00 70.64% Latency Distribution 50% 236.72ms 75% 248.46ms 90% 256.84ms 99% 268.23ms 15458 requests in 30.02s, 2.47GB read Requests/sec: 514.89 Transfer/sec: 84.33MB ``` this PR (`with --parallel` flag) ``` ❯ wrk -d 30s -c 125 --latency http://127.0.0.1:8000 Running 30s test @ http://127.0.0.1:8000 2 threads and 125 connections Thread Stats Avg Stdev Max +/- Stdev Latency 117.40ms 142.84ms 590.45ms 79.07% Req/Sec 1.33k 175.19 1.77k 69.00% Latency Distribution 50% 22.34ms 75% 223.67ms 90% 357.32ms 99% 460.50ms 79636 requests in 30.07s, 12.74GB read Requests/sec: 2647.96 Transfer/sec: 433.71MB ```
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Ok(async move {
let factory = CliFactory::from_flags_for_watcher(
flags,
watcher_communicator.clone(),
);
let cli_options = factory.cli_options()?;
let main_module = cli_options.resolve_main_module()?;
maybe_npm_install(&factory).await?;
let _ = watcher_communicator.watch_paths(cli_options.watch_paths());
let worker_factory = factory.create_cli_main_worker_factory().await?;
do_serve(worker_factory, main_module.clone(), worker_count, hmr)
feat(serve): Opt-in parallelism for `deno serve` (#24920) Adds a `parallel` flag to `deno serve`. When present, we spawn multiple workers to parallelize serving requests. ```bash deno serve --parallel main.ts ``` Currently on linux we use `SO_REUSEPORT` and rely on the fact that the kernel will distribute connections in a round-robin manner. On mac and windows, we sort of emulate this by cloning the underlying file descriptor and passing a handle to each worker. The connections will not be guaranteed to be fairly distributed (and in practice almost certainly won't be), but the distribution is still spread enough to provide a significant performance increase. --- (Run on an Macbook Pro with an M3 Max, serving `deno.com` baseline:: ``` ❯ wrk -d 30s -c 125 --latency http://127.0.0.1:8000 Running 30s test @ http://127.0.0.1:8000 2 threads and 125 connections Thread Stats Avg Stdev Max +/- Stdev Latency 239.78ms 13.56ms 330.54ms 79.12% Req/Sec 258.58 35.56 360.00 70.64% Latency Distribution 50% 236.72ms 75% 248.46ms 90% 256.84ms 99% 268.23ms 15458 requests in 30.02s, 2.47GB read Requests/sec: 514.89 Transfer/sec: 84.33MB ``` this PR (`with --parallel` flag) ``` ❯ wrk -d 30s -c 125 --latency http://127.0.0.1:8000 Running 30s test @ http://127.0.0.1:8000 2 threads and 125 connections Thread Stats Avg Stdev Max +/- Stdev Latency 117.40ms 142.84ms 590.45ms 79.07% Req/Sec 1.33k 175.19 1.77k 69.00% Latency Distribution 50% 22.34ms 75% 223.67ms 90% 357.32ms 99% 460.50ms 79636 requests in 30.07s, 12.74GB read Requests/sec: 2647.96 Transfer/sec: 433.71MB ```
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.await?;
Ok(())
})
},
)
.await?;
Ok(0)
}