Cross-language ReadBulk stress benchmark
Adds a bench-read-bulk subcommand to every client CLI (.NET, Go, Rust,
Python, Java) and a PowerShell driver that runs all five concurrently
against the deployed gateway and prints a side-by-side comparison.
Each CLI''s bench:
- Opens its own session, registers, subscribes to bulk-size tags so the
worker''s MxAccessValueCache populates from real OnDataChange events.
- Runs a warmup-seconds-long pre-loop with identical calls so JIT /
connection-pool / first-call overhead is amortised before the
measurement window.
- Runs ReadBulk in a tight in-process loop for duration-seconds with
per-call high-resolution latency capture (Stopwatch in .NET,
time.Now in Go, std::time::Instant in Rust, time.perf_counter in
Python, System.nanoTime in Java).
- Unsubscribes + closes the session, then emits one JSON object with
the shared schema: { language, durationMs, totalCalls, successfulCalls,
failedCalls, totalReadResults, cachedReadResults, callsPerSecond,
latencyMs: { p50, p95, p99, max, mean } }.
The PS driver (scripts/bench-read-bulk.ps1) launches one detached process
per client, waits for all to finish, parses the trailing JSON object from
each stdout, prints a comparison table, and persists the combined report
under artifacts/bench/. Quoting around Java''s `gradle --args="..."` is
handled by writing a one-shot .bat that cmd.exe runs; the .NET CLI''s
per-call gRPC timeout is auto-scaled to (Duration + Warmup + 30s) so the
channel-wide timeout doesn''t cancel the bench mid-loop.
Live 30-second steady-state run against the deployed gateway, all five
clients hitting the same six TestMachine_001..006.TestChangingInt tags:
client calls/sec cached/total p50 ms p95 ms p99 ms max ms
dotnet 171.78 30924/30924 3.84 14.06 40.41 542.48
go 175.46 31590/31590 3.93 13.52 41.26 243.00
rust 123.26 22188/22188 5.52 15.78 48.11 544.41
python 145.79 26244/26244 4.86 14.85 41.65 645.84
java 181.12 32604/32604 3.80 10.59 33.37 344.27
143,550 ReadBulk results across all five clients during the 30s window;
100% were was_cached = true (the worker''s cache fast-path never fell
through to the snapshot lifecycle). Aggregate read throughput ~800
calls/sec against five concurrent sessions sharing the same cached tags.
A second variant with bulk-size 20 sustained the same per-client call
rate while delivering 3.3x more values per call (~37,000 cached reads/sec
aggregate across the five concurrent sessions), confirming the linear
per-tag cache lookup inside one call is not a bottleneck at this scale.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
Joseph Doherty
@@ -231,6 +231,32 @@ enum Command {
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#[arg(long)]
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json: bool,
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},
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/// Cross-language ReadBulk stress benchmark. Opens its own session,
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/// subscribes to bulk-size tags, then hammers ReadBulk in a tight loop
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/// for duration-seconds and emits a JSON stats record the
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/// scripts/bench-read-bulk.ps1 driver collates across all five clients.
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BenchReadBulk {
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#[command(flatten)]
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connection: ConnectionArgs,
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#[arg(long, default_value = "mxgw-rust-bench")]
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client_name: String,
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#[arg(long, default_value_t = 30)]
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duration_seconds: u64,
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#[arg(long, default_value_t = 3)]
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warmup_seconds: u64,
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#[arg(long, default_value_t = 6)]
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bulk_size: usize,
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#[arg(long, default_value_t = 1)]
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tag_start: usize,
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#[arg(long, default_value = "TestMachine_")]
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tag_prefix: String,
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#[arg(long, default_value = "TestChangingInt")]
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tag_attribute: String,
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#[arg(long, default_value_t = 1500)]
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timeout_ms: u32,
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#[arg(long)]
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json: bool,
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},
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StreamEvents {
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#[command(flatten)]
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connection: ConnectionArgs,
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@@ -663,6 +689,38 @@ async fn run(cli: Cli) -> Result<(), Error> {
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.await?;
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print_write_bulk_results("write-secured2-bulk", &results, json);
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}
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Command::BenchReadBulk {
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connection,
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client_name,
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duration_seconds,
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warmup_seconds,
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bulk_size,
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tag_start,
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tag_prefix,
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tag_attribute,
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timeout_ms,
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json,
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} => {
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if bulk_size == 0 {
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return Err(Error::InvalidArgument {
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name: "bulk-size".to_owned(),
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detail: "must be positive".to_owned(),
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});
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}
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run_bench_read_bulk(
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connection,
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client_name,
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duration_seconds,
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warmup_seconds,
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bulk_size,
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tag_start,
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tag_prefix,
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tag_attribute,
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timeout_ms,
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json,
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)
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.await?;
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}
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Command::StreamEvents {
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connection,
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session_id,
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@@ -936,6 +994,161 @@ async fn session_for(
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Ok(client.session(session_id))
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}
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/// Cross-language ReadBulk stress benchmark — mirrors the .NET / Go / Python /
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/// Java implementations so the PS driver collates one JSON schema across all
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/// five clients.
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#[allow(clippy::too_many_arguments)]
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async fn run_bench_read_bulk(
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connection: ConnectionArgs,
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client_name: String,
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duration_seconds: u64,
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warmup_seconds: u64,
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bulk_size: usize,
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tag_start: usize,
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tag_prefix: String,
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tag_attribute: String,
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timeout_ms: u32,
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use_json: bool,
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) -> Result<(), Error> {
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let endpoint = connection.endpoint.clone();
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let client = connect(connection).await?;
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let session = client
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.open_session(OpenSessionRequest {
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client_session_name: client_name.clone(),
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..OpenSessionRequest::default()
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})
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.await?;
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let tags: Vec<String> = (0..bulk_size)
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.map(|i| format!("{tag_prefix}{:03}.{tag_attribute}", tag_start + i))
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.collect();
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// Bench body in its own block so the trailing session.close() always
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// runs, even on the early returns the loop body never hits today.
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let bench_outcome = async {
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let server_handle = session.register(&client_name).await?;
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let subscribe_results = session.subscribe_bulk(server_handle, tags.clone()).await?;
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let item_handles: Vec<i32> = subscribe_results
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.iter()
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.filter(|r| r.was_successful)
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.map(|r| r.item_handle)
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.collect();
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let warmup_deadline = std::time::Instant::now()
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+ std::time::Duration::from_secs(warmup_seconds);
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while std::time::Instant::now() < warmup_deadline {
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let _ = session
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.read_bulk(server_handle, tags.clone(), timeout_ms)
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.await;
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}
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let mut latencies_ms: Vec<f64> = Vec::with_capacity(65_536);
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let mut total_read_results: u64 = 0;
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let mut cached_read_results: u64 = 0;
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let mut successful_calls: u64 = 0;
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let mut failed_calls: u64 = 0;
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let steady_start = std::time::Instant::now();
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let steady_deadline = steady_start + std::time::Duration::from_secs(duration_seconds);
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while std::time::Instant::now() < steady_deadline {
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let call_start = std::time::Instant::now();
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let outcome = session.read_bulk(server_handle, tags.clone(), timeout_ms).await;
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let elapsed_ms = call_start.elapsed().as_secs_f64() * 1000.0;
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latencies_ms.push(elapsed_ms);
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match outcome {
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Ok(results) => {
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successful_calls += 1;
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for r in &results {
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total_read_results += 1;
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if r.was_cached {
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cached_read_results += 1;
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}
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}
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}
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Err(_) => failed_calls += 1,
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}
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}
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let steady_elapsed = steady_start.elapsed();
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if !item_handles.is_empty() {
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let _ = session.unsubscribe_bulk(server_handle, item_handles).await;
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}
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let total_calls = successful_calls + failed_calls;
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let calls_per_second = if steady_elapsed.as_secs_f64() > 0.0 {
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total_calls as f64 / steady_elapsed.as_secs_f64()
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} else {
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0.0
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};
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let summary = percentile_summary(&latencies_ms);
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let stats = serde_json::json!({
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"language": "rust",
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"command": "bench-read-bulk",
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"endpoint": endpoint,
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"clientName": client_name,
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"bulkSize": bulk_size,
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"durationSeconds": duration_seconds,
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"warmupSeconds": warmup_seconds,
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"durationMs": steady_elapsed.as_millis() as u64,
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"tags": tags,
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"totalCalls": total_calls,
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"successfulCalls": successful_calls,
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"failedCalls": failed_calls,
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"totalReadResults": total_read_results,
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"cachedReadResults": cached_read_results,
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"callsPerSecond": round_to(calls_per_second, 2),
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"latencyMs": summary,
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});
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if use_json {
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println!("{}", stats);
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} else {
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println!("{calls_per_second}");
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}
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Ok::<(), Error>(())
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}
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.await;
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let _ = session.close().await;
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bench_outcome
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}
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fn percentile_summary(sample: &[f64]) -> serde_json::Value {
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if sample.is_empty() {
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return serde_json::json!({ "p50": 0.0, "p95": 0.0, "p99": 0.0, "max": 0.0, "mean": 0.0 });
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}
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let mut sorted = sample.to_vec();
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sorted.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));
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let max = sorted[sorted.len() - 1];
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let mean = sample.iter().sum::<f64>() / sample.len() as f64;
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serde_json::json!({
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"p50": round_to(percentile(&sorted, 0.50), 3),
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"p95": round_to(percentile(&sorted, 0.95), 3),
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"p99": round_to(percentile(&sorted, 0.99), 3),
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"max": round_to(max, 3),
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"mean": round_to(mean, 3),
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})
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}
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fn percentile(sorted: &[f64], quantile: f64) -> f64 {
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if sorted.is_empty() {
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return 0.0;
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}
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if sorted.len() == 1 {
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return sorted[0];
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}
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let rank = quantile * (sorted.len() - 1) as f64;
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let lower = rank.floor() as usize;
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let upper = (lower + 1).min(sorted.len() - 1);
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let fraction = rank - lower as f64;
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sorted[lower] + (sorted[upper] - sorted[lower]) * fraction
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}
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fn round_to(value: f64, digits: u32) -> f64 {
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let shift = 10f64.powi(digits as i32);
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(value * shift).round() / shift
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}
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fn print_version(use_json: bool) {
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if use_json {
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println!("{}", version_json());
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