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Update metadata export logic for join derivation #879
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tableUtils.createDatabase(namespace) | ||
val viewsGroupBy = getViewsGroupBy(suffix = "cumulative", makeCumulative = true, namespace) | ||
val joinConf = getEventsEventsTemporal("cumulative", namespace) | ||
joinConf.setJoinParts(Seq(Builders.JoinPart(groupBy = viewsGroupBy)).asJava) |
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can you add a test case that contains external parts?
StructType(derivedDummyOutputDf.schema.filterNot(f => keyAndPartitionFields.map(_.name).contains(f.name)))) | ||
ListBuffer(columns.map { tup => toAggregationMetadata(tup._1, tup._2, joinConf.hasDerivations) }: _*) | ||
} else { | ||
aggregationsMetadata |
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nit: rename agg because agg is specific to group_by, but now we have external parts and derivations
aggMetadata
=>joinOutputFieldsMetadata
aggregationsMetadata
=>joinIntermediateFieldsMetadata
expression = "*" | ||
), Derivation( | ||
name = "test_feature_name", | ||
expression = f"${viewsGroupBy.metaData.name}_time_spent_ms_average" |
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Can you add some derivations that use ts
and ds
as inputs?
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Similarly, can we add a test case for key columns as output?
such as
Derivation(
name = "event_id",
expression = "ext_contextual_event_id"
)
val finalOutputColumns = joinConf.derivationsScala.finalOutputColumn(dummyOutputDf.columns).toSeq | ||
val derivedDummyOutputDf = dummyOutputDf.select(finalOutputColumns: _*) | ||
val columns = SparkConversions.toChrononSchema( | ||
StructType(derivedDummyOutputDf.schema.filterNot(f => keyAndPartitionFields.map(_.name).contains(f.name)))) |
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Is the filterNot
necessary here? I think we should keep everything that users included in derivations. For example, we should allow key columns to be in the output if users explicitly included it in derivations.
Summary
Update the metadata export logic for join with derivation. Use derived columns as exported features.
Why / Goal
Test Plan
Checklist
Reviewers
@hzding621 @pengyu-hou @SophieYu41