Ecto auto-instrumentation created one span per query, so Oban jobs
running thousands of DB ops produced traces with 8k+ child spans.
Tempo's compactor couldn't ingest them — it stalled long enough to
miss heartbeats, got auto-forgotten from the ring, and crash-looped.
The Elixir grpcbox exporter then blew up trying to reconnect.
Stops the cascade: OTLP endpoint removed from prop and prop-backfill
Deployments, opentelemetry_ecto removed from deps, Phoenix/Oban/Bandit
auto-instrumentation retained. Rust workers still export to Tempo.
Shared NFS idx cache at /data/hrrr_idx lets all prop-grid-rs and
hrrr-point-rs replicas deduplicate redundant NOAA S3 idx fetches.
Opt-in via HRRR_IDX_CACHE_DIR; falls back to in-memory-only when unset.
Also scales prop-grid-rs to 3 replicas (no talos5 pinning),
hrrr-point-rs to 2 replicas, and drops hot pod replicas 4→3 so the
physical-host anti-affinity still fits with talos5 cordoned off for
Postgres.
Adds the OTel deps (opentelemetry + exporter + phoenix / ecto / oban /
bandit auto-instrumentation helpers) and attaches them in
Application.start/2 so the existing Phoenix, Ecto, Oban, and Bandit
telemetry events flow as OTLP spans without any call-site changes.
Exporter config is gated on OTEL_EXPORTER_OTLP_ENDPOINT in
config/runtime.exs — set in the k8s manifests to the cluster
collector (otel-collector.observability.svc.cluster.local:4317).
When unset we switch traces_exporter to :none so nothing is shipped;
dev/test stays quiet.
Resource attributes tag spans with service.name=microwaveprop and
service.namespace=prop, matching the Rust workers' attribute shape
so Tempo can group the full hourly chain across both languages.
Both the main prop deployment and the backfill deployment get the
env; backfill is still a full BEAM node running enrichment workers,
so its Oban/Ecto spans are worth seeing too.
New telemetry module wires tracing-subscriber to both a local JSON
fmt layer (keeps kubectl-logs output identical) and an
OpenTelemetry OTLP/gRPC exporter, activated only when
OTEL_EXPORTER_OTLP_ENDPOINT is set. The returned TelemetryGuard
holds the SdkTracerProvider until process shutdown so queued spans
flush before exit.
Both bin targets (worker, hrrr_point_worker) now call
telemetry::init(service_name) at startup; service_name becomes the
OTel service.name attribute so Tempo groups spans per binary.
Tracing instrumentation on the three main work units:
- pipeline::run_chain_step (forecast f01..f18)
- pipeline::run_analysis_step (analysis f00)
- hrrr_points::process_batch (per-QSO point drain)
k8s manifests set OTEL_EXPORTER_OTLP_ENDPOINT to the cluster
collector at otel-collector.observability.svc.cluster.local:4317.
Backend wiring lives in the vntx-infra repo.
Three fixes after hrrr-point-rs restarts on the first live drain:
1. Type mismatch (hard error): the `profile` column on `hrrr_profiles`
is `jsonb[]` (one element per pressure level), but the Rust worker
was binding a single jsonb array-of-objects cast as `::jsonb`. Every
insert failed with `column "profile" is of type jsonb[] but
expression is of type jsonb`. Switched to
`Vec<sqlx::types::Json<Value>>`, dropped the explicit `::jsonb`
cast, and enabled sqlx's `json` feature so the encoder maps the
array into `_jsonb` correctly.
2. Silent zero-inserts: four consecutive 2019-09-22 batches completed
with `profiles_inserted: 0` and no diagnostic. Most likely the
upstream archive doesn't keep cycles that old, but without a log
it looks identical to a snap-mismatch bug. Added a WARN when both
surface and pressure grids come back empty so fetch-miss vs.
projection-miss is distinguishable.
3. OOMKilled: the pod took four OOM restarts in ten minutes at 1 Gi.
A single CONUS decode holds the ~40 MB blob, the ~200 MB wgrib2
working set, AND the full 92k-cell merged map until all requested
points drain. 1 Gi has no headroom. Bumped to 2 Gi, matching the
rest of the per-container budgets in this namespace.
Two concurrent forecast tasks plus the NFS score-file write cache (23
files x ~2 MB) plus the analysis step's wgrib2 peak repeatedly crossed
the 3 Gi cgroup limit. Single-lane parallelism per pod keeps steady RSS
under 2 Gi; with 2 replicas that still gives 2 concurrent tasks
cluster-wide.
PROP_GRID_RS_PG_CONNS follows the parallelism+2 formula, dropping from
4 to 3.
Two bugs surfacing 13 h after Stream A cutover:
1. prop-grid-rs pods OOMKilled 16–18× overnight on the analysis step.
f00's native-level GRIB2 (~530 MB + wgrib2 working set) runs on top
of 2 concurrent forecast tasks, briefly reaching ~2 Gi. 2 Gi limit
was too tight — 3 Gi plus parallelism 3→2 gives the analysis step
room without eliminating forecast-lane headroom.
2. hrrr-point-rs pod is CrashLoopBackOff because its container is
running image main-1776640915-65f7963 (pre-Stream-C) which doesn't
contain the hrrr_point_worker binary. The grid-rs CI run for
commit 4fefb81 failed and no newer image got published. Touch the
Dockerfile to re-trigger the workflow so flux picks up a new tag
with both binaries. No functional Dockerfile change, just a
docstring update so the path-filter kicks.
Forecast + analysis both claim per FOR UPDATE SKIP LOCKED, so
reducing per-pod parallelism doesn't break the chain — the two
replicas cover 4 slots cluster-wide, which still drains f01..f18 in
~5 min.
Phase 3 Stream C Rust side. Completes the HrrrFetchWorker port.
Pipeline:
- db::claim_next_hrrr_task — FOR UPDATE SKIP LOCKED on hrrr_fetch_tasks,
newest valid_time first. Accepts the points JSONB directly.
- hrrr_points::process_batch — fetch surface + pressure GRIB2 once
per task (tokio::try_join), decode via the existing wgrib2 plumbing,
then for each requested point pull the cell and UPSERT INTO
hrrr_profiles (conflict on lat/lon/valid_time).
- db::complete_hrrr_task / fail_hrrr_task — status transitions; Elixir
backfill re-enqueues failed rows on next /30-min scan.
Shipping pieces:
- new bin src/bin/hrrr_point_worker.rs
- new module src/hrrr_points.rs (process_batch, upsert_profile)
- new Cargo [[bin]] entry; Dockerfile builds both binaries in one stage
and ships them in the runtime image so a single CI pipeline covers
the whole cluster
- k8s/deployment-hrrr-point-rs.yaml (1 replica, 1 Gi limit, anti-affinity
against prop-grid-rs so chain + point work don't fight for wgrib2
slots). Uses the same image; command: override picks the right binary.
- kustomization.yaml: include the new deployment so flux applies it
- deployment-grid-rs.yaml: bump readiness initialDelaySeconds 3→15 +
failureThreshold 3→6 so a slow DB connect during startup can't race
the first probe
119 Rust tests green.
Three independent improvements in one commit:
1. Parallel band scoring (pipeline.rs):
- rayon par_iter across 23 bands replaces the serial for loop.
- All band files now write via try_join_all over spawn_blocking
instead of serializing one-at-a-time on NFS. Chain per-step
drops from ~60s to ~15-25s on a 4-core box.
2. Prometheus metrics (new metrics.rs):
- axum server on METRICS_ADDR (default :9100) exposes /metrics and
/health. Scraped by existing Prometheus via annotation.
- Histograms: chain step duration (by outcome), decode duration.
- Gauge: tasks in flight (RAII guarded so panics don't leak).
- Counter: chain steps by outcome.
- worker.rs records step duration and wraps each run in an
InFlightGuard.
3. HA + ordering fixes:
- deployment-grid-rs.yaml: replicas 1→2, required podAntiAffinity
spreads them across hosts, nodeAffinity prefers talos5 for one.
PROP_GRID_RS_PARALLELISM 4→3 per pod (6 concurrent across
replicas; smaller secondary-node footprint).
- Readiness probe on /health so rollouts wait for the runtime to
be alive.
- claim_next ordering: run_time ASC → DESC so the newest hourly
run drains before any stragglers from a broken prior run. Within
a run, forecast_hour ASC keeps nearest-first.
- grid_task_enqueuer sets kind="forecast" explicitly and updates
conflict_target to the new 3-column unique index introduced by
migration 20260419222624.
Replaces the serial claim→process loop with a JoinSet of N worker tasks,
each running the same claim→fetch→decode→score→complete flow against
grid_tasks. FOR UPDATE SKIP LOCKED already prevents contention between
workers so no additional coordination is needed.
talos5 is 4c/4t (i5-7260U); PROP_GRID_RS_PARALLELISM=4 turns the full
f01..f18 chain from ~18 min serial into roughly ~5 min. Per-task peak
memory after the streamed-bands refactor is ~250 Mi, so 4 concurrent is
~1 Gi — well under the 2 Gi container limit.
Postgres pool sized to parallelism + 2 so NOTIFY and an opportunistic
claim retry share the pool without blocking a worker transaction.
Both the hot (`prop`) Deployment and the `prop-backfill` Deployment
labelled their pods with `app: prop` so both joined the same libcluster
Erlang cluster. The `prop` Service selector was `app: prop` too — which
meant the backfill pod (PHX_SERVER=false, no listener on :5000) was in
the Service endpoint pool.
With sessionAffinity: ClientIP, Cloudflared's source IP got hashed to
the backfill endpoint after a backfill pod roll and every request
from that client returned 502 (connection refused on :5000).
Fix: add `tier: hot` to hot-pod template labels; narrow the Service
selector to `{app: prop, tier: hot}`. Backfill keeps `tier: backfill`
and is cleanly excluded. libcluster selector (still `app: prop`) is
unchanged so backfill stays in the Erlang cluster for Oban queue
leader election and Oban.Pro's Smart engine.
Rust `prop-grid-rs` has been validated end-to-end on talos5 on the
streamed-bands image (main-1776635096-6d91461): real chain steps
complete in ~7 s each and the peak-heap refactor landed.
Changes:
- `PropagationGridWorker.seed_chain` now enqueues a single f00 Oban
job instead of f00–f18. The f00 analysis-hour keeps its enrichment
(native-level duct, NEXRAD composite, commercial-link boost,
ProfilesFile write) and the GridCache broadcast.
- `GridTaskEnqueuer.seed/1` is called again so grid_tasks gets the
18 forecast-hour rows the Rust worker claims.
- Test asserts only one Oban job is enqueued and the matching 18
grid_tasks rows land in the DB.
- `deployment-grid-rs.yaml` flips `PROP_SCORES_DIR` from
`/data/scores_shadow` to `/data/scores` so Rust writes to the live
score tree. No file collision — Elixir only writes the f00 files.
The hot pod memory shrink (6 Gi → 1 Gi) is deferred until one full
hourly cycle runs clean under the new split.
