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104 lines
4.5 KiB
Markdown
104 lines
4.5 KiB
Markdown
# Benchmarks
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What this library sustains, and against what. Run them before setting or changing a scale goal.
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```bash
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docker compose run --rm node node benchmarks/run.ts # this library, both ends
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COUNT=100000 docker compose run --rm node node benchmarks/run.ts # longer, steadier
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```
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`run.ts` spawns `smsc-sink.ts` — a server that answers every `submit_sm` `ESME_ROK` and stores
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nothing — and drives it with `submit-load.ts` at four window sizes. Point `submit-load.ts` at any
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SMSC to compare:
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```bash
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docker compose -f compose.yaml -f interop-tests/compose.jasmin.yaml run --rm node \
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node benchmarks/submit-load.ts --host=jasmin --port=2775 --username=esme1 --password=esme1pw \
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--count=20000 --concurrency=50
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```
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`GATE=1` fails the run when a window falls under goal 6's floor, and refuses to judge at all on
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fewer than four cores.
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**A short run measures the JIT, not the library.** At 2,000 messages per point the same build
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reported 16k/s where 100,000 messages reported 40k/s. Give each point several seconds.
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**Cores matter, though the library is single-threaded.** The run is two Node processes, and past
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them V8 marks and compiles on threads of its own while the kernel carries loopback TCP. Window 200,
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100,000 messages:
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| Cores | msgs/s |
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| --- | --- |
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| 1 | 20,476 |
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| 2 | 29,603 |
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| 4 | 32,869 |
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| 8 | 40,046 |
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A window of 1 is unmoved by any of it (7,280–8,659 throughout) because it waits on the round trip
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rather than the CPU. The windowed figures are for the pair: one process alone reaches about half.
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## Results, 2026-09-20
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Single host, 8 cores, Node 24.18.0 in the project container, loopback. Client and SMSC are separate
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processes competing for the same CPUs, so these are a floor for split hosts.
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`maxOutstanding` is the send window, and it is the setting that matters most:
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| Window | msgs/s | with `dlr: true` |
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| --- | --- | --- |
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| 1 | 7,385 | 7,289 |
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| 10 (default) | 25,358 | 24,282 |
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| 50 | 37,125 | 37,502 |
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| 200 | 40,046 | 39,730 |
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Requesting delivery receipts costs nothing measurable at any window. A window of 1 — one request in
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flight at a time — costs 5x, which is the round trip rather than the codec.
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Against real peers, same driver, window 50, 20,000 messages:
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| SMSC | msgs/s | failed |
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| --- | --- | --- |
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| this library's sink | 37,125 | 0 |
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| Jasmin 0.11.0 | 2,207 | 0 |
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| SMPPSim 2.6.11 | — | 19,000 of 20,000 |
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## Against the other client libraries
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Same sink, same 100,000 single-segment messages, same host. This is the comparison that means
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something: every client is measured pushing into *our* server, so the server's work is common to all
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three and only the client differs.
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```bash
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docker compose -f compose.yaml -f benchmarks/compose.jsmpp.yaml up -d --build
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docker compose -f compose.yaml -f benchmarks/compose.jsmpp.yaml run --rm node \
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node benchmarks/peer-load.ts --driver=http://jsmpp:8080 --count=100000 --concurrency=50
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```
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| Window | this library | jsmpp 3.0.3 | Cloudhopper 5.0.10 |
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| --- | --- | --- | --- |
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| 10 | 25,358 | 30,771 | 27,945 |
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| 50 | 38,675 | 40,934 | 32,384 |
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| 200 | 40,046 | 42,105 | 25,497 |
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**We are slowest at the default window**, which is the setting most callers will ever run — 25,358
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against jsmpp's 30,771. That is the throughput work worth doing, and it is worth doing there.
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Two things the table does not show. This library does it on one event loop where both Java peers
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spend one OS thread per in-flight request, which is why Cloudhopper falls off at 200 threads and we
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do not. And all three are pushing into the same Node sink, whose own cost is in every number, so the
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differences between clients are compressed rather than exaggerated here.
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Kannel is absent deliberately: it is a gateway rather than a client library, wired here as an ESME
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that forwards from its own spool, so loading it would measure its HTTP frontend and queue rather
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than an SMPP client. The number would not belong in this table.
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Jasmin routes and persists where the sink does neither, so the gap is not an efficiency ratio
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between two comparable things — what it establishes is that this library is not the bottleneck
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against a production SMSC, by more than an order of magnitude. SMPPSim's store fills at roughly a
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thousand messages and it then refuses the rest, so it cannot be loaded; that is a property of the
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simulator, not a result.
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`smppload`, the one purpose-built SMPP load generator among the peers, is blocked by a bind defect
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of its own ([findings/07-load.md](../interop-tests/findings/07-load.md)), so no third-party load
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tool drives these numbers.
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