Accept Pandas dataframe as input for historical feature retrieval #1071
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Signed-off-by: Khor Shu Heng <[email protected]>
khorshuheng
requested review from
davidheryanto,
pyalex,
woop and
zhilingc
as code owners
Signed-off-by: Khor Shu Heng <[email protected]>
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/test test-end-to-end-auth |
woop
approved these changes
Oct 19, 2020
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[APPROVALNOTIFIER] This PR is APPROVED This pull-request has been approved by: khorshuheng, woop The full list of commands accepted by this bot can be found here. The pull request process is described here
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/lgtm |
pyalex
reviewed
Oct 20, 2020
| "event_timestamp", | ||
| "created_timestamp", | ||
| ParquetFormat(), | ||
| entity_staging_uri.path, |
pyalex
reviewed
Oct 20, 2020
| entity_source.to_parquet(df_export_path.name) | ||
| bucket = ( | ||
| None | ||
| if entity_staging_uri.scheme == "fs" |
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what's fs scheme? maybe file?
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Signed-off-by: Khor Shu Heng [email protected]
What this PR does / why we need it:
Currently, historical feature retrieval accepts only Datasource as input. This PR enables the user to supply a Panda dataframe instead, which will then be uploaded to a staging location.
Staging location configuration for the different spark clusters are also merged into a single key in this PR.
Which issue(s) this PR fixes:
Fixes #
Does this PR introduce a user-facing change?: