object SparkUtils extends LazyLogging
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- case class SimpleFeatureRowMapping(sft: SimpleFeatureType, mappings: Seq[(Int, Int, Boolean)], id: (Row) => String) extends Product with Serializable
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- def createFeatureType(name: String, struct: StructType): SimpleFeatureType
- def createStructType(sft: SimpleFeatureType): StructType
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- val ff: FilterFactory2
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- def getExtractors(requiredColumns: Array[String], schema: StructType): Array[(SimpleFeature) => AnyRef]
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- def joinedSf2row(schema: StructType, sf1: SimpleFeature, sf2: SimpleFeature, extractors: Array[(SimpleFeature) => AnyRef]): Row
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- def rowsToFeatures(name: String, schema: StructType): SimpleFeatureRowMapping
Creates a function to convert a row to a simple feature, which will be based on the columns in the row schema.
Creates a function to convert a row to a simple feature, which will be based on the columns in the row schema.
If the row has a
fidcolumn, it will be used for the feature id. Otherwise, it will use a random id prefixed with the current time- name
simple feature type name to use
- schema
row schema
- def rowsToFeatures(sft: SimpleFeatureType, schema: StructType): SimpleFeatureRowMapping
Creates a function to convert a row to a simple feature.
Creates a function to convert a row to a simple feature. Columns will be mapped to attributes based on matching names.
If the row has a
fidcolumn, it will be used for the feature id. Otherwise, it will use a random id prefixed with the current time- sft
simple feature type
- schema
dataframe schema
- def sf2row(schema: StructType, sf: SimpleFeature, extractors: Array[(SimpleFeature) => AnyRef]): Row
- def sparkFilterToCQLFilter(filt: Filter): Option[Filter]
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