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Choose explicit decorators or automatic launcher instrumentation
| Approach | What changes | Dagster definitions | Trade-off |
|---|---|---|---|
dagster-otel |
Add @traced() to selected definitions and configure OpenTelemetry export. |
Keep @op and @asset; the project describes this route as avoiding monkeypatching. |
Opt-in control, but you must edit each definition you want traced. Project page. |
opentelemetry-instrumentation-dagster |
Configure OpenTelemetry and prefix a Dagster command with opentelemetry-instrument. |
No changes to definitions, but the package patches Dagster to provide automatic coverage. | Convenient for zero-code instrumentation, with the patching trade-off; PyPI classifies it as Alpha. Project page. |
For the specific goal of adding tracing without patching Dagster or replacing its decorators, the explicit dagster-otel route is the closer fit.
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Add tracing to an op or asset
Keep Dagster’s decorator outermost and put @traced() directly beneath it. This abbreviated example shows placement only; consult the current package README for decorator arguments and exporter configuration.
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from dagster_otel import traced
@op
@traced()
def upstream_op(context) -> int:
...
@asset
@traced()
def downstream_asset(context):
...The outer Dagster decorator receives the function already wrapped by @traced(). You can choose which operations and assets to instrument rather than enabling framework-wide automatic patching.
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The package author says the first traced step in a run becomes the run trace root and that trace context is propagated through Dagster’s run storage when steps execute in separate processes. These are maintainer-described behaviors; deployment and verification caveats are important, particularly for Kubernetes execution.
What the automatic launcher documents
The separate opentelemetry-instrumentation-dagster project documents a zero-code alternative for people who accept its patching approach. Its PyPI page lists version 0.2.0, released September 22, 2026, Python 3.10 or later, and Dagster 1.5 or later. It also says it is not independently version-matrix-tested beyond the coverage provided by dagster-otel. These are project-stated details, not an independent compatibility guarantee. Check the current package page before adopting it.
Basic setup
The documented quick start installs the launcher package, starts a local Jaeger instance, sets OTEL_SERVICE_NAME and OTEL_EXPORTER_OTLP_ENDPOINT, then runs a Dagster command through opentelemetry-instrument. The package says an endpoint (or traces-specific endpoint) is needed for an exporter to attach. It documents grpc as the default protocol and http/protobuf as another option; set OTEL_SDK_DISABLED=true to disable export. Follow its current quick-start instructions for the exact command and environment values.
Documented coverage and gaps
@op,@assetand@multi_asset: listed as covered, including bare and named forms of@opand@asset.@asset_check: listed as covered whendagster-otel0.4.0 or later is used. The project reports checking a realmaterialize()run, but says multiprocess and Kubernetes execution were not independently re-verified for this case.@dbt_assets: covered through its use ofmulti_asset, with one span per dbt run. The project says end-to-end verification against a real dbt project remained outstanding.@graph_asset: deliberately excluded because its decorated function does not receive a runtime context. The project recommends tracing the ops it composes to cover executed work.
Those coverage and verification statements come from the package maintainers. They should not be read as independent testing of every Dagster version or deployment configuration.
Account for how Dagster executes steps
Multiprocess execution
The launcher project says its instrumentation is inherited by fresh Python subprocesses under the multiprocess executor and reports verification in a real run. That is the launcher’s stated behavior, not a guarantee for every customized process environment.
Kubernetes job execution
With k8s_job_executor, each step runs in a separate pod, so prefixing a command in one shell does not by itself instrument new pods. The launcher project recommends baking instrumentation into the image; its example is copying OpenTelemetry’s sitecustomize.py into the environment’s site-packages. It reports verification on a real kind cluster. Treat both the guidance and verification as project-maintainer claims, and test the image and export path used in your deployment.
How this fits Dagster’s telemetry options
Dagster describes itself as a data orchestrator with integrated lineage and observability, and its documentation presents assets defined with @asset. A request for a built-in, globally available OpenTelemetry telemetry provider was opened on December 16, 2022 and remains open on the accessed issue page. That is the status of this particular feature request, not proof that Dagster has no other tracing capabilities or third-party integrations. View the Dagster issue.
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A December 2025 community discussion shows another pattern: a resource constructs a SpanContext from the Dagster run ID and ops are wrapped in a tracing decorator. A later note describes a local check on Dagster 1.11.16 using a local span stub to check decorator forms and resource setup. It did not verify Logfire export or multiprocess trace propagation, so it is evidence about the mechanics, not an end-to-end production tracing design. Read the community discussion.
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