fix(ingest): clarify s3/s3a requirements and platform defaults #4263
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jjoyce0510
reviewed
Mar 2, 2022
| def read_file_spark(self, file: str, is_aws: bool) -> Optional[DataFrame]: | ||
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| if is_aws: | ||
| file = f"s3a://{file}" |
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or is the "a" intentional?
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Not a typo—Spark uses the s3a protocol rather than s3 (see https://spark.apache.org/docs/latest/cloud-integration.html)
jjoyce0510
reviewed
Mar 2, 2022
| @@ -40,6 +41,15 @@ def ensure_profiling_pattern_is_passed_to_profiling( | |||
| profiling.allow_deny_patterns = values["profile_patterns"] | |||
| return values | |||
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| @pydantic.validator("platform", always=True) | |||
| def validate_platform(cls, value: str, values: Dict[str, Any]) -> Optional[str]: | |||
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Where is "values" coming from here?
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Is this the rest of the config values?
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values are the previously-validated fields here since this is a Pydantic validator — see https://pydantic-docs.helpmanual.io/usage/validators/
jjoyce0510
reviewed
Mar 2, 2022
shirshanka
approved these changes
Mar 2, 2022
maggiehays
pushed a commit
to maggiehays/datahub
that referenced
this pull request
Aug 1, 2022
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Adds a few sentences to the docs clarifying the need to grant S3A permissions when running profiling on files in AWS S3. Also sets the
platformtos3orfiledepending on thebase_pathifplatformis unspecified.Checklist