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I am encountering an issue that resembles GitHub issue #20177. I am using Dagster version 1.7.9 on a Windows VM with SQLlite database. Most of the time, asset refreshing works fine, but occasionally some assets fail to update, remaining stuck with the status "waiting on upstream data to be up to date."
When I investigate the upstream assets listed as ancestors, they appear to be up-to-date. However, Dagster does not recognize this and prevents the dependent assets from materializing. This results in an unnecessary blockage in the asset pipeline.
I would like to ask the following:
Is this a known issue in version 1.7.9?
If so, is there a fix available or planned for this problem?
Is there a way to manually trigger Dagster to update its metadata to recognize that the upstream assets are up-to-date, even if the pipeline currently does not detect this correctly?
Are there workarounds or recommendations for ensuring that assets refresh correctly when this issue occurs? For example, should I be manually materializing assets more often, or is there a configuration setting I might have overlooked?
What did you expect to happen?
If a Asset is refreshed, I expect that all the downstream upstream assets will be refreshed also. This is what I see most days also.
My policy is:
########################################################################################
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The text was updated successfully, but these errors were encountered:
What's the issue?
Hi Dagster Team,
I am encountering an issue that resembles GitHub issue #20177. I am using Dagster version 1.7.9 on a Windows VM with SQLlite database. Most of the time, asset refreshing works fine, but occasionally some assets fail to update, remaining stuck with the status "waiting on upstream data to be up to date."
When I investigate the upstream assets listed as ancestors, they appear to be up-to-date. However, Dagster does not recognize this and prevents the dependent assets from materializing. This results in an unnecessary blockage in the asset pipeline.
I would like to ask the following:
Is this a known issue in version 1.7.9?
If so, is there a fix available or planned for this problem?
Is there a way to manually trigger Dagster to update its metadata to recognize that the upstream assets are up-to-date, even if the pipeline currently does not detect this correctly?
Are there workarounds or recommendations for ensuring that assets refresh correctly when this issue occurs? For example, should I be manually materializing assets more often, or is there a configuration setting I might have overlooked?
What did you expect to happen?
If a Asset is refreshed, I expect that all the downstream upstream assets will be refreshed also. This is what I see most days also.
My policy is:
########################################################################################
Policy for DBT materializations
parent_updated_policy = AutoMaterializePolicy.eager().with_rules(
AutoMaterializeRule.skip_on_not_all_parents_updated()
)
How to reproduce?
I am currently unsure what causes this behavior. Any insights into potential triggers or diagnostic steps would be greatly appreciated.
Dagster version
1.7.9
Deployment type
Local
Deployment details
pip freeze:
agate==1.7.1
alembic==1.13.1
aniso8601==9.0.1
annotated-types==0.7.0
anyio==4.4.0
attrs==23.1.0
azure-core==1.30.2
azure-identity==1.16.1
azure-keyvault-secrets==4.8.0
azure-storage-blob==12.20.0
azure-storage-file-datalake==12.15.0
Babel==2.15.0
backoff==2.2.1
cachetools==5.3.3
certifi==2024.6.2
cffi==1.15.1
charset-normalizer==3.3.2
click==8.1.7
colorama==0.4.6
coloredlogs==14.0
croniter==2.0.5
cryptography==42.0.8
dagster==1.7.9
dagster-azure==0.23.9
dagster-cloud==1.7.9
dagster-cloud-cli==1.7.9
dagster-databricks==0.23.9
dagster-dbt==0.23.9
dagster-graphql==1.7.9
dagster-msteams==0.23.9
dagster-pipes==1.7.9
dagster-pyspark==0.23.9
dagster-spark==0.23.9
dagster-webserver==1.7.9
databricks-api==0.9.0
databricks-cli==0.18.0
databricks-sdk==0.17.0
databricks-sql-connector==2.9.6
dbt-core==1.7.15
dbt-databricks==1.7.8
dbt-extractor==0.5.1
dbt-semantic-interfaces==0.4.4
dbt-spark==1.7.1
docstring_parser==0.16
et-xmlfile==1.1.0
filelock==3.14.0
fsspec==2024.6.0
github3.py==4.0.1
google-auth==2.30.0
gql==3.5.0
graphene==3.3
graphql-core==3.2.3
graphql-relay==3.2.0
greenlet==3.0.3
grpcio==1.64.1
grpcio-health-checking==1.62.2
h11==0.14.0
httptools==0.6.1
humanfriendly==10.0
idna==3.7
importlib-metadata==6.11.0
isodate==0.6.1
jaraco.classes==3.3.1
Jinja2==3.1.4
jsonschema==4.22.0
jsonschema-specifications==2023.11.2
keyring==24.3.0
leather==0.4.0
Logbook==1.5.3
lz4==4.3.3
Mako==1.3.5
markdown-it-py==3.0.0
MarkupSafe==2.1.5
mashumaro==3.13
mdurl==0.1.2
minimal-snowplow-tracker==0.0.2
more-itertools==10.1.0
msal==1.29.0
msal-extensions==1.2.0
msgpack==1.0.8
multidict==6.0.5
networkx==3.3
numpy==1.26.4
oauthlib==3.2.2
openpyxl==3.1.2
orjson==3.10.3
packaging==24.1
pandas==2.1.4
parsedatetime==2.6
pathspec==0.11.1
pendulum==2.1.2
pex==2.3.3
portalocker==2.10.0
prompt-toolkit==3.0.36
protobuf==4.25.3
psutil==5.9.8
py4j==0.10.9.7
pyarrow==15.0.0
pyasn1==0.6.0
pyasn1_modules==0.4.0
pycparser==2.22
pydantic==2.7.3
pydantic_core==2.18.4
Pygments==2.18.0
PyJWT==2.8.0
pyodbc==5.1.0
pyreadline3==3.4.1
pyspark==3.5.1
python-dateutil==2.9.0.post0
python-dotenv==1.0.1
python-slugify==8.0.4
pytimeparse==1.1.8
pytz==2024.1
pytzdata==2020.1
pywin32==306
pywin32-ctypes==0.2.1
PyYAML==6.0.1
questionary==2.0.1
referencing==0.35.1
requests==2.32.3
requests-toolbelt==1.0.0
rich==13.7.1
rpds-py==0.18.1
rsa==4.9
shellingham==1.5.4
six==1.16.0
sniffio==1.3.1
SQLAlchemy==1.4.51
sqlglot==25.0.3
sqlglotrs==0.2.5
sqlparams==6.0.1
sqlparse==0.5.0
starlette==0.37.2
structlog==24.2.0
tabulate==0.9.0
text-unidecode==1.3
thrift==0.16.0
tomli==2.0.1
toposort==1.10
tqdm==4.66.4
typer==0.12.3
typing_extensions==4.12.2
tzdata==2024.1
universal_pathlib==0.2.2
uritemplate==4.1.1
urllib3==1.26.19
uvicorn==0.30.1
watchdog==4.0.1
watchfiles==0.22.0
wcwidth==0.2.13
websockets==12.0
yarl==1.9.4
zipp==3.19.2
Additional information
SQLLite database
Message from the maintainers
Impacted by this issue? Give it a 👍! We factor engagement into prioritization.
By submitting this issue, you agree to follow Dagster's Code of Conduct.
The text was updated successfully, but these errors were encountered: