helixobs 0.1.0

Entity-centric observability client for scientific instrument pipelines

helixobs — Python client

Entity-centric observability for scientific instrument pipelines.

Track domain entities (data blocks, beam candidates, astrophysical events) across disjoint asynchronous pipeline stages. The library attaches provenance links between entities, so you can trace any scientific result all the way back to the raw sensor data that produced it.

Built on OpenTelemetry. Signals are standard OTLP — the HelixObs gateway adds entity intelligence on top.

Installation

pip install helixobs

Quick start

from helixobs import Instrument
from helixobs.logging import configure_logging

# Once at startup
configure_logging()
tel = Instrument(
    service_name="chime.frb-classifier",
    instrument_id="CHIME",
    endpoint="interceptor.local:4317",
)

# Track a pipeline stage
with tel.stage("frb_classifier", id=cand_id, parents=[block_id]) as span:
    result = classify(candidate)
    if result.confidence > 0.95:
        span.add_event("helix.event.candidate_promoted", {
            "snr": result.snr,
            "dm":  result.dm,
        })
# span ends here — exported asynchronously, pipeline never blocked

Three integration layers

All three layers emit identical OTLP signals. Choose the one that fits your code structure.

Layer 0 — Primitive

For long-running daemons, event loops, or any structure where a context manager is awkward.

token = tel.track("x-engine", id="block-1234", parents=[])
result = process_data_block(block)
tel.complete(token)

# On failure:
tel.error(token, metadata={"type": "BUFFER_OVERFLOW", "rack": "gpu-rack-3"})

Layer 1 — Context manager (recommended)

complete() is called automatically on normal exit. error() is called automatically on any unhandled exception.

with tel.stage("frb_classifier", id=cand_id, parents=[block_id]) as span:
    result = classify(candidate)

    # Attach scientifically notable events to the span
    if result.rfi_probability > 0.9:
        span.add_event("helix.event.rfi_flagged", {
            "fraction_flagged": result.rfi_probability,
            "algorithm":        "spectral_kurtosis",
        })

Layer 2 — Decorator

For functions that map cleanly to a single pipeline stage. Use callables for id and parents when they depend on the function's arguments.

@tel.stage(
    "frb_classifier",
    id=lambda cand: cand.id,
    parents=lambda cand: [cand.block_id],
)
def classify_candidate(cand):
    return run_classifier(cand)

N-to-1 provenance

The defining feature of HelixObs: a single entity can have multiple parents from independent processing chains. Standard OTel tracing can't model this.

# Three beam candidates, processed independently
for beam_id in [41, 42, 43]:
    with tel.stage("beam-processor", id=f"cand-beam{beam_id}"):
        process_beam(beam_id)

# One astrophysical event formed from all three
with tel.stage("clustering", id="frb-20260415-042",
               parents=["cand-beam41", "cand-beam42", "cand-beam43"]) as span:
    event = cluster_candidates(candidates)

The gateway resolves all three parent IDs into OTel span links, so Tempo shows the full DAG.

CHIME

Pre-configured instrument with semantic convention helpers:

from helixobs.chime import CHIMEInstrument

tel = CHIMEInstrument(
    service_name="chime.x-engine",
    endpoint="interceptor.local:4317",
)

# data block
token = tel.track("x-engine", id="block-400mhz-001")
tel.data_block_metadata(token, fpga_rack="gpu-rack-3", duration_s=8.0)
tel.complete(token)

# beam candidate
token = tel.track("frb-classifier", id="cand-beam42-001", parents=["block-400mhz-001"])
tel.candidate_metadata(token, beam_id=42, dm=341.2, snr=18.3)
tel.complete(token)

# astrophysical event (N-to-1)
token = tel.track("clustering", id="frb-20260415-042",
                  parents=["cand-beam41-001", "cand-beam42-001", "cand-beam43-001"])
tel.event_metadata(token, ra=12.4, dec=33.2, classification="FRB")
tel.complete(token)

Log context injection

One call at startup injects trace_id, span_id, entity_id, and instrument_id into every log record emitted anywhere in the process — including from third-party libraries — while a span is active.

from helixobs.logging import configure_logging
configure_logging()

import logging
log = logging.getLogger("chime.pipeline")

with tel.stage("frb_classifier", id=cand_id, parents=[block_id]):
    log.info("classifying candidate")
    # Record now carries: otelTraceID, otelSpanID, helixEntityID, helixInstrumentID

These fields are indexed by Loki and enable the Grafana cross-signal navigation path: metric anomaly → Loki log line → Tempo trace → parent entity trace.

Span events

Use token.add_event() or span.add_event() (the context manager yields a Token) to attach named events to an entity's span:

with tel.stage("classifier", id=cand_id) as span:
    span.add_event("helix.event.rfi_flagged",       {"fraction_flagged": 0.92})
    span.add_event("helix.event.candidate_promoted", {"snr": 23.4, "confidence": 0.97})
    span.add_event("helix.event.voevent_issued",     {"ivorn": "ivo://chime/frb/2026a"})
    span.add_event("helix.event.archived",           {"path": "/data/chime/2026/frb042"})

The gateway extracts every helix.* event and writes it to TimescaleDB using the event's own timestamp, producing a complete queryable timeline for each entity.

Non-blocking guarantee

track(), complete(), error(), and the context manager enter/exit never perform network I/O on the calling thread. All OTLP exports happen in the OTel SDK's BatchSpanProcessor background thread. If the gateway is unreachable, spans queue locally and are retried silently. The instrument pipeline is never aware of any observability infrastructure failure.

Development

pip install -e ".[dev]"
pytest