Accept Pandas dataframe as input for historical feature retrieval - #1071
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feast-ci-bot merged 2 commits intoOct 19, 2020
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Signed-off-by: Khor Shu Heng <khor.heng@gojek.com>
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October 19, 2020 13:46
Signed-off-by: Khor Shu Heng <khor.heng@gojek.com>
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/test test-end-to-end-auth |
woop
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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 DetailsNeeds approval from an approver in each of these files:
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/lgtm |
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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 khor.heng@gojek.com
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?: