Package org.apache.druid.data.input
Class InputRowSchema
java.lang.Object
org.apache.druid.data.input.InputRowSchema
Schema of
InputRow.-
Constructor Summary
ConstructorsConstructorDescriptionInputRowSchema(TimestampSpec timestampSpec, DimensionsSpec dimensionsSpec, ColumnsFilter columnsFilter) InputRowSchema(TimestampSpec timestampSpec, DimensionsSpec dimensionsSpec, ColumnsFilter columnsFilter, Set<String> metricNames) -
Method Summary
Modifier and TypeMethodDescriptionAColumnsFilterthat can filter down the list of columns that must be read after flattening.
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Constructor Details
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InputRowSchema
public InputRowSchema(TimestampSpec timestampSpec, DimensionsSpec dimensionsSpec, ColumnsFilter columnsFilter) -
InputRowSchema
public InputRowSchema(TimestampSpec timestampSpec, DimensionsSpec dimensionsSpec, ColumnsFilter columnsFilter, Set<String> metricNames)
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Method Details
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getMetricNames
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getTimestampSpec
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getDimensionsSpec
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getColumnsFilter
AColumnsFilterthat can filter down the list of columns that must be read after flattening. Logically, Druid applies ingestion spec components in a particular order: first flattenSpec (if any), then timestampSpec, then transformSpec, and finally dimensionsSpec and metricsSpec. If a flattenSpec is provided, this method returns a filter that should be applied after flattening. So, it will be based on what needs to pass between the flattenSpec and everything beyond it.
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