## 4. Study methodology

### 4.1 Question

On the NS1 test stand, what message **throughput** and **delay** does the three-node `verae` JetStream cluster deliver at several loads, and which part of the stack is the limiter for product traffic (jobs, events, webhooks, archive)?

### 4.2 Hypotheses (stated before the run)

1. **H1 — Core vs JetStream.** Fire-and-forget core NATS is at least an order of magnitude faster than JetStream **file + replicas=3**, because durable publish waits for a majority disk replica.
2. **H2 — JetStream parallelism.** Adding publishers does **not** linearly increase JetStream write rate once the replica log is saturated.
3. **H3 — Quiet delay.** Sequential pub→sub round trip on `vmbr1` is well under 1 ms p99 when the consumer is waiting.
4. **H4 — Burst delay.** If publishers dump a batch before the subscriber drains, observed delay is **queueing time**, roughly linear in backlog, not in cluster hop count.
5. **H5 — Payload.** Moving 128 B → 1 KiB lowers message rate and raises byte rate on core NATS; JetStream in this size band stays replica/fsync bound.

### 4.3 Independent variables (what we changed)

| Factor | Levels |
|--------|--------|
| Transport | Core NATS pub/sub vs JetStream file replicas=3 |
| Publisher count | 1, 2, 4, 8 |
| Subscriber count | 0 (JS publish-only), 1, 2, 4, 8 |
| Message count | 1k, 5k, 10k, 20k, 50k, 100k, 200k (by ladder step) |
| Payload | 128 B, 1024 B |
| Delay mode | **ping** (publish, wait, repeat) vs **flood** (publish all, then drain) |

### 4.4 Dependent variables (what we recorded)

| Metric | Instrument | Unit |
|--------|------------|------|
| Publish rate | `nats bench` 0.1.6 Pub stats | msgs/s, MB/s |
| Subscribe rate | `nats bench` Sub stats | msgs/s, MB/s |
| Aggregate | `nats bench` NATS Pub/Sub stats | msgs/s (fan-out counts both sides) |
| Publisher spread | nats min/avg/max **msgs/s** | not delay |
| One-way-ish RTT | `latency.mjs` header timestamp | min, avg, p50, p90, p99, max |
| Host load | `/proc/loadavg` before and after | load average |
| Broker counters | `http://127.0.0.1:8222/varz` inside each nats LXC | connections, in/out msgs, cpu, mem |

**Important:** nats CLI 0.1.6 min/avg/max are **rate spread across publishers**, not microseconds of delay. Delay is only `latency.mjs`.

### 4.5 Controls and constants

- Cluster name `verae`, three routes, client `:4222`, cluster `:6222`, monitor loopback `:8222`.
- Client URL always the three-node list on `vmbr1` (never host `127.0.0.1:4222`, never `vmbr0`).
- Bench client is LXC **510**, not a nats-* server.
- JetStream bench stream name `benchstream`, **file** storage, **replicas=3**, deleted between JS loads (`nats stream rm --force`) so names do not collide.
- Product streams were **not** the bench target (no load test on `ZAPIER_*` / `VERAE_ARCHIVE`).
- No TLS, no nkeys, no account isolation (isolation is `vmbr1`).
- Same nats CLI version (0.1.6) and `nats@2` Node client as the first ladder.

### 4.6 Procedure

1. Confirm this script is executing on **NS1.GEORGELAMBERT.ORG**. Refuse otherwise.
2. Snapshot host load, memory, LXC configs, and each nats `varz`.
3. From NS1, `pct exec 510` the core ladder (1p1s, 4p4s, 8p8s at 128 B; 4p4s at 1 KiB).
4. Delete `benchstream`; JS ladder (1p, 4p, 4p×1 KiB, 2p2s pull) at replicas=3 file.
5. Copy `latency.mjs` into 510; ping then flood at several batch sizes.
6. Snapshot host/`varz` again.
7. Parse logs on **this host**; draw charts; write HTML and PDF on **this host**.

No publish, subscribe, chart, or PDF process runs on the operator laptop for this study.

### 4.7 Instrumentation path

```text
[NS1 host 70.88.205.138]
   study-on-ns1.sh  (bash + python3)
        |
        | sudo pct exec 510
        v
[LXC 510 verae-px-worker 10.10.10.20]
   nats bench / node latency.mjs
        |
        | NATS client protocol to
        v
[LXC 511/512/513  10.10.10.21-23 :4222]
   nats-server -js  cluster routes :6222
```

The hypervisor issues the guest commands. The messages themselves never leave `vmbr1`.

### 4.8 Threats to validity

| Threat | Effect on numbers |
|--------|-------------------|
| **One physical host** | Three “replicas” share CPU, memory, and usually the same datastore. This measures process/LXC HA, not disk HA. |
| **Shared load** | NS1 also runs Caddy, Forgejo, keep, fleet, portal, and other CTs. Load average during a run is part of the result, not noise to ignore. |
| **Single bench client** | All publishers live in 510. Per-publisher rate spread is contention in that guest. |
| **Short runs** | Seconds of traffic. No compaction, no multi-hour page-cache eviction, no snapshot during load. |
| **No TLS/nkeys** | Production auth will cost CPU. Do not treat these rates as post-nkeys rates. |
| **Fan-out aggregate** | Core aggregate msgs/s counts pub+sub. Do not compare that column to JetStream unique writes. |
| **Flood ≠ RTT** | Mixing flood averages with ping p99 produces a fake “NATS is slow” story. |
| **Lab only** | Not a Zapier HTTPS bench and not live `api.veraetime.net`. |

### 4.9 Ethics / safety

Bench uses throwaway subjects (`bench.core.*`, `bench.js.*`, `bench.lat.*`) and a throwaway stream. It does not purge product streams. Zapier cloud has no NATS socket.

---

## 5. Suggestions for fine-tuning

These follow from the method and from the first ladder on this stand (JetStream ~16k durable 128 B pubs/s; ping ~0.3 ms; flood hundreds of ms). Apply in order of leverage. Re-run **this NS1 study** after each change so the delta is measured the same way.

### 5.1 Treat JetStream as the product limiter

Product jobs/events/webhooks/archive are durable. Tuning core NATS to 2M msgs/s will not move a timestamp Zap. Put effort into **replica write path** and **consumer lag**, not core fan-out.

### 5.2 Split storage class by stream

| Stream | Suggested store | Why |
|--------|-----------------|-----|
| `ZAPIER_JOBS` | file, r=3 | Work queue; lose-a-job is bad |
| `ZAPIER_EVENTS` | file r=3, or memory r=3 if events are rebuildable from job status | Hot waiters; measure both |
| `ZAPIER_WEBHOOKS` | file, r=3, workqueue | HTTPS to Zapier is the slow consumer |
| `ZAPIER_USAGE` | file, r=3, limits + max-age | Telemetry |
| `VERAE_ARCHIVE` | file, r=3, on the **best disk** | Puts are larger and must survive |

Try `ZAPIER_EVENTS` as memory store in a maintenance window and re-run only the JS + ping/flood steps. If ping stays ~0.3 ms and durable events still ack at a higher rate, keep it; if a CT restart drops in-flight waiters, revert.

### 5.3 Give JetStream real disks

Today r=3 on three LXC guests on **one Proxmox host** is three files, one failure domain.

- Bind-mount a distinct SSD/NVMe (or ZFS dataset with its own vdev) into each nats LXC `store_dir`.
- Set `sync: always` only on archive if you need it; default sync is often enough for jobs and is faster. Measure.
- Do not put JetStream `store_dir` on the same busy rootfs as Forgejo/Caddy if we can avoid it.
- When moving to three metal boxes: same configs, private NIC, one disk (or mirror) **per node**. That is the first change that makes r=3 mean “two boxes can die.”

### 5.4 Isolate the nats CTs from the rest of NS1

Host load on this box is often already several. Pin:

- `nats-a/b/c`: dedicated cores, no steal from keep/fleet Node processes.
- Memory high enough that file-backed streams stay cache-hot for the working set.
- `cpuunits` / cpuset in `pct config` so a Zapier-facing Node GC pause does not stall fsync.

Re-run this study after pinning; H1/H2 should move more than ping.

### 5.5 Consumer and mailbox tuning (delay H4)

Flood delay is backlog / consume_rate. Fine-tune the **waiters**, not the broker RTT.

- `jobs.events` and `webhooks.deliver`: raise `max_ack_pending` so a slow HTTPS hook does not stall the whole consumer; cap it so a poison message cannot unbounded-buffer RAM.
- Pull consumers: larger batch, shorter `expires`, more pullers horizontally (fleet replica floors) instead of one fat subscriber.
- Middleware should **not** flood-publish then wait; it already does per-job publish. Keep that. The flood test is the outage profile when a consumer is stopped.
- Alert on **consumer lag** (pending + ack pending) from JetStream, not on ping RTT.

### 5.6 Publisher-side batching in middleware

A timestamp job is one small JSON. 16k msgs/s is ample. Still:

- Avoid per-byte publishes; one message per job/event.
- Reuse NATS connections (connection churn showed up as publisher spread in the core 4p/8p runs).
- Idempotent `msg id` / duplicate window sized to Verae retry window, not default-only.

### 5.7 nats-server knobs worth measuring (A/B with this script)

| Knob | Why try it |
|------|------------|
| `max_payload` | Keep default unless archive puts grow |
| `write_deadline` | Slow consumer protection for webhooks |
| `max_pending` | Bound memory on a stuck Zapier hook |
| `max_connections` | Fleet workers + keep + middleware |
| JetStream `max_file_store` / `max_memory_store` | Prevent one stream from filling the CT |
| `max_outstanding_catchup` | Replica restart after a nats-c blip |
| GOMAXPROCS = LXC cores | Do not overthread a 2-core CT |

Change **one** knob, re-run `study-on-ns1.sh`, compare JetStream 1p 128 B and ping p99.

### 5.8 Network

- Keep NATS off `vmbr0`. No change.
- When on metal: dedicated NIC or VLAN for cluster `:6222` vs client `:4222` if possible (replication vs client load).
- Check virtio queue counts on the LXC nics if core 1 KiB byte rate plateaus.

### 5.9 Security cost (when nkeys/mTLS flip)

`verae-nats-accounts` is still a sketch. Enabling accounts will add CPU on publish. Budget: re-run this exact study **after** creds are in every `NATS_URL`, and accept a drop on both core and JS. Do not flip without that measurement.

### 5.10 Operational fine-tuning (lag, not peak msgs/s)

1. Scrape `varz` / `jsz` from the host over `vmbr1` (not public). Monitor loopback `:8222` is invisible to Prometheus on NS1 unless we add a host-side proxy on `10.10.10.21:8222` bound only to `vmbr1`.
2. Keep replica floors for webhook-deliver and job-poller — they are the flood defense.
3. Backup/restore drill of JetStream **during idle**, then a short JS 1p run to see catchup cost.
4. A 15–30 minute soak (not in this ladder) for page cache and compaction; add that as a third study when disks are dedicated.

### 5.11 What not to tune

- Do not chase core 8p8s aggregate. It is fan-out on a lab bridge.
- Do not treat flood 400 ms as “cluster RTT.” Fix consumers.
- Do not load-test on `ZAPIER_*` streams.
- Do not bind client NATS to `0.0.0.0` on `vmbr0`.

### 5.12 Recommended next experiments (same method, one change each)

1. CPU pin nats-a/b/c → re-run JS 1p + ping.
2. `ZAPIER_EVENTS`-shaped memory stream vs file (throwaway stream, same flags as this JS ladder).
3. Distinct `store_dir` disks per node.
4. nkeys on, same ladder.
5. Three hardware boxes, same `cluster.env` IPs updated.

Each experiment should produce a new `results/<utc>/` on NS1 and a new progress-repo report so we can diff H1–H5 instead of arguing from memory.