Update tests, examples, benches
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@@ -21,7 +21,7 @@ pub fn bench_config_broadcast(c: &mut Criterion) {
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// Configuration source - framework wraps HashMap in Arc automatically
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let config: TaskHandle<_> = dag
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.add_task(task_fn(|_: ()| async {
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.add_task(task_fn::<(), _, _>(|_: ()| {
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HashMap::from([
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("max_connections".to_string(), "100".to_string()),
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("timeout_ms".to_string(), "5000".to_string()),
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@@ -37,20 +37,22 @@ pub fn bench_config_broadcast(c: &mut Criterion) {
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// Workers receive and process based on shared config
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for worker_id in 0..workers {
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dag.add_task(task_fn(move |cfg: HashMap<String, String>| async move {
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// Simulate work based on configuration
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let batch_size: usize = cfg
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.get("batch_size")
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.and_then(|s| s.parse().ok())
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.unwrap_or(10);
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let timeout: usize = cfg
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.get("timeout_ms")
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.and_then(|s| s.parse().ok())
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.unwrap_or(1000);
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dag.add_task(task_fn::<HashMap<_, _>, _, _>(
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move |cfg: &HashMap<String, String>| {
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// Simulate work based on configuration
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let batch_size: usize = cfg
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.get("batch_size")
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.and_then(|s| s.parse().ok())
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.unwrap_or(10);
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let timeout: usize = cfg
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.get("timeout_ms")
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.and_then(|s| s.parse().ok())
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.unwrap_or(1000);
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// Simulate some computation
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black_box(worker_id * batch_size + timeout / 100)
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}))
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// Simulate some computation
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black_box(worker_id * batch_size + timeout / 100)
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},
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))
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.depends_on(config);
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}
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+22
-20
@@ -21,24 +21,26 @@ pub fn bench_etl_pipeline(c: &mut Criterion) {
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let dag = DagRunner::new();
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// Extract: Load JSON-like data (framework wraps in Arc automatically)
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let extract: TaskHandle<_> = dag.add_task(task_fn(move |_: ()| async move {
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(0..count)
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.map(|i| {
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format!(
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"{{\"id\":{},\"name\":\"User{}\",\"score\":{}}}",
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i,
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i,
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i * 10
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)
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})
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.collect::<Vec<_>>()
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})).into();
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let extract: TaskHandle<_> = dag
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.add_task(task_fn::<(), _, _>(move |_: ()| {
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(0..count)
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.map(|i| {
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format!(
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"{{\"id\":{},\"name\":\"User{}\",\"score\":{}}}",
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i,
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i,
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i * 10
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)
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})
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.collect::<Vec<_>>()
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}))
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.into();
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// Transform: Three parallel transformations
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// T1: Parse and validate
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let validate = dag
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.add_task(task_fn(move |data: Vec<String>| async move {
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.add_task(task_fn::<Vec<_>, _, _>(move |data: &Vec<String>| {
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data.iter()
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.filter(|s| {
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s.contains("\"id\"")
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@@ -51,7 +53,7 @@ pub fn bench_etl_pipeline(c: &mut Criterion) {
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// T2: Extract scores
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let scores = dag
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.add_task(task_fn(move |data: Vec<String>| async move {
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.add_task(task_fn::<Vec<_>, _, _>(move |data: &Vec<String>| {
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data.iter()
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.filter_map(|s| {
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s.split("\"score\":").nth(1).and_then(|part| {
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@@ -64,7 +66,7 @@ pub fn bench_etl_pipeline(c: &mut Criterion) {
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// T3: Extract names
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let names = dag
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.add_task(task_fn(move |data: Vec<String>| async move {
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.add_task(task_fn::<Vec<_>, _, _>(move |data: &Vec<String>| {
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data.iter()
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.filter_map(|s| {
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s.split("\"name\":\"")
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@@ -77,8 +79,8 @@ pub fn bench_etl_pipeline(c: &mut Criterion) {
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.depends_on(extract);
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// Load: Aggregate all transformations
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dag.add_task(task_fn(
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move |(valid, scores, names): (usize, Vec<i32>, Vec<String>)| async move {
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dag.add_task(task_fn::<(usize, Vec<_>, Vec<_>), _, _>(
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move |(valid, scores, names): (&usize, &Vec<i32>, &Vec<String>)| {
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let avg_score =
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scores.iter().sum::<i32>() / scores.len().max(1) as i32;
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format!(
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@@ -91,9 +93,9 @@ pub fn bench_etl_pipeline(c: &mut Criterion) {
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))
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.depends_on((&validate, &scores, &names));
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dag.run(|fut| tokio::spawn(fut).map(Result::unwrap))
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.await
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.unwrap();
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dag.run(|fut| tokio::spawn(fut).map(Result::unwrap))
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.await
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.unwrap();
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})
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});
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},
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@@ -20,7 +20,7 @@ pub fn bench_memory_efficiency(c: &mut Criterion) {
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let dag = DagRunner::new();
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let source: TaskHandle<_> = dag
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.add_task(task_fn(move |_: ()| async move {
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.add_task(task_fn::<(), _, _>(move |_: ()| {
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// Create MB of data - framework wraps in Arc automatically
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vec![0u8; mb * 1024 * 1024]
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}))
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@@ -28,7 +28,7 @@ pub fn bench_memory_efficiency(c: &mut Criterion) {
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// 20 consumers share the data via automatic Arc wrapping
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for i in 0..20 {
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dag.add_task(task_fn(move |data: Vec<u8>| async move {
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dag.add_task(task_fn::<Vec<_>, _, _>(move |data: &Vec<u8>| {
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// Process a small portion (data extracted from Arc)
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let sample_sum: u32 = data
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.iter()
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@@ -14,7 +14,7 @@ pub fn bench_mixed_patterns(c: &mut Criterion) {
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// Stage 1: Load data (framework wraps in Arc automatically)
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let data: TaskHandle<_> = dag
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.add_task(task_fn(|_: ()| async {
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.add_task(task_fn::<(), _, _>(|_: ()| {
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(0..1000)
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.map(|i| format!("Record-{:04}", i))
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.collect::<Vec<_>>()
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@@ -23,31 +23,33 @@ pub fn bench_mixed_patterns(c: &mut Criterion) {
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// Stage 2: Fan-out for parallel analysis
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let analysis1 = dag
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.add_task(task_fn(|d: Vec<String>| async move {
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.add_task(task_fn::<Vec<_>, _, _>(|d: &Vec<String>| {
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d.iter().filter(|s| s.contains("00")).count()
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}))
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.depends_on(data);
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let analysis2 = dag
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.add_task(task_fn(|d: Vec<String>| async move {
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.add_task(task_fn::<Vec<_>, _, _>(|d: &Vec<String>| {
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d.iter().map(|s| s.len()).sum::<usize>()
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}))
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.depends_on(data);
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let analysis3 = dag
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.add_task(task_fn(|d: Vec<String>| async move { d.len() }))
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.add_task(task_fn::<Vec<_>, _, _>(|d: &Vec<String>| d.len()))
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.depends_on(data);
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// Stage 3: Fan-in to aggregate
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let summary = dag
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.add_task(task_fn(|(a1, a2, a3): (usize, usize, usize)| async move {
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format!("Matches: {}, Total bytes: {}, Count: {}", a1, a2, a3)
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}))
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.add_task(task_fn::<(usize, usize, usize), _, _>(
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|(a1, a2, a3): (&usize, &usize, &usize)| {
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format!("Matches: {}, Total bytes: {}, Count: {}", a1, a2, a3)
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},
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))
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.depends_on((&analysis1, &analysis2, &analysis3));
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// Stage 4: Fan-out final report to multiple destinations
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for i in 0..3 {
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dag.add_task(task_fn(move |report: String| async move {
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dag.add_task(task_fn::<String, _, _>(move |report: &String| {
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format!("Destination {}: {}", i, report)
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}))
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.depends_on(summary);
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