What's the issue?
When running new build code location with dagster version 1.12.22 in kubernetes, it raises error with following:
protobuf runtime issue: google.protobuf.runtime_version.VersionError. Detected incompatible Protobuf Gencode/Runtime versions when loading grpc_health/v1/health.pr. gencode 7.35.0 runtime 6.33.6. Runtime version cannot be older than the linked gencode version. See Protobuf version guarantees at https://protobuf.dev/support/cross-version-runtime-guarantee.
Then, all code locations remain in state CrashBackoff all the time, and keep restarting.
What did you expect to happen?
dependencies = [
"dagster>=1.12,<1.13",
"dagster-webserver>=1.12,<1.13",
"dagster-postgres>=0.28,<0.29",
"dagster-aws>=0.28,<0.29",
"dagster-graphql>=1.12,<1.13",
"dagster-slack>=0.28,<0.29",
"dagster-duckdb>=0.28,<0.29",
]
When uv sync those dependencies, we can build image accordingly. But when runtime, it start failure. Based on code base review, I can get following conclusion:
- protobuf==6.33.6 satisfies Dagster’s protobuf<7
- grpcio-health-checking==1.82.0 is allowed by Dagster’s >=1.44.0 floor
- But the 1.82 wheel embeds gencode 7.35.0 while runtime is 6.33.6 → exact error
Dagster imports grpc_health at code-server startup and pins protobuf <7 but does not cap grpcio-health-checking / grpcio. After grpc 1.82.0 (2026-07-06), fresh installs can resolve a broken combo.
This issue can be simply resolved by adding constraint-dependencies like:
[tool.uv]
constraint-dependencies = [
"grpcio-health-checking<1.82",
"grpcio<1.82",
]
Or directly add to dependencies:
dependencies = [
"dagster>=1.12,<1.13",
"dagster-webserver>=1.12,<1.13",
"dagster-postgres>=0.28,<0.29",
"dagster-aws>=0.28,<0.29",
"dagster-graphql>=1.12,<1.13",
"dagster-slack>=0.28,<0.29",
"dagster-duckdb>=0.28,<0.29",
"grpcio-health-checking<1.82",
"grpcio<1.82",
]
But still require caps added into code base to prevent inconsistency.
How to reproduce?
Try to spin up code location with following example pyproject.toml dependencies and start in kubernetes with 1.12.22 version
[project]
dependencies = [
"dagster>=1.12,<1.13",
"dagster-webserver>=1.12,<1.13",
"dagster-postgres>=0.28,<0.29",
"dagster-aws>=0.28,<0.29",
"dagster-graphql>=1.12,<1.13",
"dagster-slack>=0.28,<0.29",
"dagster-duckdb>=0.28,<0.29",
]
Dagster version
1.12.22
Deployment type
Dagster Helm chart
Deployment details
Simply build docker image by python:3.12-slim, and install pyproject.toml through uv venv and uv sync --refresh. After that, we spin up a dagster/dagster-celery-k8s with version 1.12.22 to connect this code location via new build image, simulate in my kubernetes using rancher-desktop
Additional information
No response
Message from the maintainers
Impacted by this issue? Give it a 👍! We factor engagement into prioritization.
What's the issue?
When running new build code location with dagster version 1.12.22 in kubernetes, it raises error with following:
Then, all code locations remain in state CrashBackoff all the time, and keep restarting.
What did you expect to happen?
When uv sync those dependencies, we can build image accordingly. But when runtime, it start failure. Based on code base review, I can get following conclusion:
Dagster imports grpc_health at code-server startup and pins protobuf <7 but does not cap grpcio-health-checking / grpcio. After grpc 1.82.0 (2026-07-06), fresh installs can resolve a broken combo.
This issue can be simply resolved by adding constraint-dependencies like:
Or directly add to dependencies:
But still require caps added into code base to prevent inconsistency.
How to reproduce?
Try to spin up code location with following example pyproject.toml dependencies and start in kubernetes with 1.12.22 version
Dagster version
1.12.22
Deployment type
Dagster Helm chart
Deployment details
Simply build docker image by python:3.12-slim, and install pyproject.toml through
uv venvanduv sync --refresh. After that, we spin up adagster/dagster-celery-k8swith version 1.12.22 to connect this code location via new build image, simulate in my kubernetes using rancher-desktopAdditional information
No response
Message from the maintainers
Impacted by this issue? Give it a 👍! We factor engagement into prioritization.