[SQL] [SPARK-6620] Speed up toDF() and rdd() functions by constructing converters in ScalaReflection #5279
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| /* | ||
| * Licensed to the Apache Software Foundation (ASF) under one or more | ||
| * contributor license agreements. See the NOTICE file distributed with | ||
| * this work for additional information regarding copyright ownership. | ||
| * The ASF licenses this file to You under the Apache License, Version 2.0 | ||
| * (the "License"); you may not use this file except in compliance with | ||
| * the License. You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| */ | ||
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| package org.apache.spark.sql.catalyst | ||
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| import java.util.{Map => JavaMap} | ||
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| import scala.collection.mutable.HashMap | ||
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| import org.apache.spark.sql.catalyst.expressions._ | ||
| import org.apache.spark.sql.types._ | ||
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| /** | ||
| * Functions to convert Scala types to Catalyst types and vice versa. | ||
| */ | ||
| object CatalystTypeConverters { | ||
| // The Predef.Map is scala.collection.immutable.Map. | ||
| // Since the map values can be mutable, we explicitly import scala.collection.Map at here. | ||
| import scala.collection.Map | ||
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| /** | ||
| * Converts Scala objects to catalyst rows / types. This method is slow, and for batch | ||
| * conversion you should be using converter produced by createToCatalystConverter. | ||
| * Note: This is always called after schemaFor has been called. | ||
| * This ordering is important for UDT registration. | ||
| */ | ||
| def convertToCatalyst(a: Any, dataType: DataType): Any = (a, dataType) match { | ||
| // Check UDT first since UDTs can override other types | ||
|
There was a problem hiding this comment. I don't quite get this comment, why do we say "UDTs can override other types"? I ask this because I may face similar issue in #5422. There was a problem hiding this comment. The object could be There was a problem hiding this comment. Ah, I see. Thanks @davies! |
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| case (obj, udt: UserDefinedType[_]) => | ||
| udt.serialize(obj) | ||
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| case (o: Option[_], _) => | ||
| o.map(convertToCatalyst(_, dataType)).orNull | ||
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| case (s: Seq[_], arrayType: ArrayType) => | ||
| s.map(convertToCatalyst(_, arrayType.elementType)) | ||
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| case (s: Array[_], arrayType: ArrayType) => | ||
| s.toSeq.map(convertToCatalyst(_, arrayType.elementType)) | ||
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| case (m: Map[_, _], mapType: MapType) => | ||
| m.map { case (k, v) => | ||
| convertToCatalyst(k, mapType.keyType) -> convertToCatalyst(v, mapType.valueType) | ||
| } | ||
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| case (jmap: JavaMap[_, _], mapType: MapType) => | ||
| val iter = jmap.entrySet.iterator | ||
| var listOfEntries: List[(Any, Any)] = List() | ||
| while (iter.hasNext) { | ||
| val entry = iter.next() | ||
| listOfEntries :+= (convertToCatalyst(entry.getKey, mapType.keyType), | ||
| convertToCatalyst(entry.getValue, mapType.valueType)) | ||
| } | ||
| listOfEntries.toMap | ||
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| case (p: Product, structType: StructType) => | ||
| val ar = new Array[Any](structType.size) | ||
| val iter = p.productIterator | ||
| var idx = 0 | ||
| while (idx < structType.size) { | ||
| ar(idx) = convertToCatalyst(iter.next(), structType.fields(idx).dataType) | ||
| idx += 1 | ||
| } | ||
| new GenericRowWithSchema(ar, structType) | ||
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| case (d: BigDecimal, _) => | ||
| Decimal(d) | ||
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| case (d: java.math.BigDecimal, _) => | ||
| Decimal(d) | ||
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| case (d: java.sql.Date, _) => | ||
| DateUtils.fromJavaDate(d) | ||
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| case (r: Row, structType: StructType) => | ||
| val converters = structType.fields.map { | ||
| f => (item: Any) => convertToCatalyst(item, f.dataType) | ||
| } | ||
| convertRowWithConverters(r, structType, converters) | ||
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| case (other, _) => | ||
| other | ||
| } | ||
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| /** | ||
| * Creates a converter function that will convert Scala objects to the specified catalyst type. | ||
| * Typical use case would be converting a collection of rows that have the same schema. You will | ||
| * call this function once to get a converter, and apply it to every row. | ||
| */ | ||
| private[sql] def createToCatalystConverter(dataType: DataType): Any => Any = { | ||
| def extractOption(item: Any): Any = item match { | ||
| case opt: Option[_] => opt.orNull | ||
| case other => other | ||
| } | ||
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| dataType match { | ||
| // Check UDT first since UDTs can override other types | ||
| case udt: UserDefinedType[_] => | ||
| (item) => extractOption(item) match { | ||
| case null => null | ||
| case other => udt.serialize(other) | ||
| } | ||
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| case arrayType: ArrayType => | ||
| val elementConverter = createToCatalystConverter(arrayType.elementType) | ||
| (item: Any) => { | ||
| extractOption(item) match { | ||
| case a: Array[_] => a.toSeq.map(elementConverter) | ||
| case s: Seq[_] => s.map(elementConverter) | ||
| case null => null | ||
| } | ||
| } | ||
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| case mapType: MapType => | ||
| val keyConverter = createToCatalystConverter(mapType.keyType) | ||
| val valueConverter = createToCatalystConverter(mapType.valueType) | ||
| (item: Any) => { | ||
| extractOption(item) match { | ||
| case m: Map[_, _] => | ||
| m.map { case (k, v) => | ||
| keyConverter(k) -> valueConverter(v) | ||
| } | ||
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| case jmap: JavaMap[_, _] => | ||
| val iter = jmap.entrySet.iterator | ||
| val convertedMap: HashMap[Any, Any] = HashMap() | ||
| while (iter.hasNext) { | ||
| val entry = iter.next() | ||
| convertedMap(keyConverter(entry.getKey)) = valueConverter(entry.getValue) | ||
| } | ||
| convertedMap | ||
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| case null => null | ||
| } | ||
| } | ||
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| case structType: StructType => | ||
| val converters = structType.fields.map(f => createToCatalystConverter(f.dataType)) | ||
| (item: Any) => { | ||
| extractOption(item) match { | ||
| case r: Row => | ||
| convertRowWithConverters(r, structType, converters) | ||
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| case p: Product => | ||
| val ar = new Array[Any](structType.size) | ||
| val iter = p.productIterator | ||
| var idx = 0 | ||
| while (idx < structType.size) { | ||
| ar(idx) = converters(idx)(iter.next()) | ||
| idx += 1 | ||
| } | ||
| new GenericRowWithSchema(ar, structType) | ||
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| case null => | ||
| null | ||
| } | ||
| } | ||
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| case dateType: DateType => (item: Any) => extractOption(item) match { | ||
| case d: java.sql.Date => DateUtils.fromJavaDate(d) | ||
| case other => other | ||
| } | ||
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| case _ => | ||
| (item: Any) => extractOption(item) match { | ||
| case d: BigDecimal => Decimal(d) | ||
| case d: java.math.BigDecimal => Decimal(d) | ||
| case other => other | ||
| } | ||
| } | ||
| } | ||
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| /** Converts Catalyst types used internally in rows to standard Scala types | ||
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| * This method is slow, and for batch conversion you should be using converter | ||
| * produced by createToScalaConverter. | ||
| */ | ||
| def convertToScala(a: Any, dataType: DataType): Any = (a, dataType) match { | ||
| // Check UDT first since UDTs can override other types | ||
| case (d, udt: UserDefinedType[_]) => | ||
| udt.deserialize(d) | ||
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| case (s: Seq[_], arrayType: ArrayType) => | ||
| s.map(convertToScala(_, arrayType.elementType)) | ||
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| case (m: Map[_, _], mapType: MapType) => | ||
| m.map { case (k, v) => | ||
| convertToScala(k, mapType.keyType) -> convertToScala(v, mapType.valueType) | ||
| } | ||
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| case (r: Row, s: StructType) => | ||
| convertRowToScala(r, s) | ||
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| case (d: Decimal, _: DecimalType) => | ||
| d.toJavaBigDecimal | ||
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| case (i: Int, DateType) => | ||
| DateUtils.toJavaDate(i) | ||
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| case (other, _) => | ||
| other | ||
| } | ||
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| /** | ||
| * Creates a converter function that will convert Catalyst types to Scala type. | ||
|
There was a problem hiding this comment. Also add a note in this guy and its counterpart the pattern that they're expected to be used in. I was just reading executeCollect() and realized it may not be obvious to someone coming from there. Just something like "use this during batch conversion, such as within a mapPartitions, to generate a function which efficiently converts Catalyst types back to Scala types for a particular schema." |
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| * Typical use case would be converting a collection of rows that have the same schema. You will | ||
| * call this function once to get a converter, and apply it to every row. | ||
| */ | ||
| private[sql] def createToScalaConverter(dataType: DataType): Any => Any = dataType match { | ||
| // Check UDT first since UDTs can override other types | ||
| case udt: UserDefinedType[_] => | ||
| (item: Any) => if (item == null) null else udt.deserialize(item) | ||
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| case arrayType: ArrayType => | ||
| val elementConverter = createToScalaConverter(arrayType.elementType) | ||
| (item: Any) => if (item == null) null else item.asInstanceOf[Seq[_]].map(elementConverter) | ||
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| case mapType: MapType => | ||
| val keyConverter = createToScalaConverter(mapType.keyType) | ||
| val valueConverter = createToScalaConverter(mapType.valueType) | ||
| (item: Any) => if (item == null) { | ||
| null | ||
| } else { | ||
| item.asInstanceOf[Map[_, _]].map { case (k, v) => | ||
| keyConverter(k) -> valueConverter(v) | ||
| } | ||
| } | ||
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| case s: StructType => | ||
| val converters = s.fields.map(f => createToScalaConverter(f.dataType)) | ||
| (item: Any) => { | ||
| if (item == null) { | ||
| null | ||
| } else { | ||
| convertRowWithConverters(item.asInstanceOf[Row], s, converters) | ||
| } | ||
| } | ||
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| case _: DecimalType => | ||
| (item: Any) => item match { | ||
| case d: Decimal => d.toJavaBigDecimal | ||
| case other => other | ||
| } | ||
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| case DateType => | ||
| (item: Any) => item match { | ||
| case i: Int => DateUtils.toJavaDate(i) | ||
| case other => other | ||
| } | ||
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| case other => | ||
| (item: Any) => item | ||
| } | ||
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| def convertRowToScala(r: Row, schema: StructType): Row = { | ||
| val ar = new Array[Any](r.size) | ||
| var idx = 0 | ||
| while (idx < r.size) { | ||
| ar(idx) = convertToScala(r(idx), schema.fields(idx).dataType) | ||
| idx += 1 | ||
| } | ||
| new GenericRowWithSchema(ar, schema) | ||
| } | ||
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| /** | ||
| * Converts a row by applying the provided set of converter functions. It is used for both | ||
| * toScala and toCatalyst conversions. | ||
| */ | ||
| private[sql] def convertRowWithConverters( | ||
| row: Row, | ||
| schema: StructType, | ||
| converters: Array[Any => Any]): Row = { | ||
| val ar = new Array[Any](row.size) | ||
| var idx = 0 | ||
| while (idx < row.size) { | ||
| ar(idx) = converters(idx)(row(idx)) | ||
| idx += 1 | ||
| } | ||
| new GenericRowWithSchema(ar, schema) | ||
| } | ||
| } | ||
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Add a note to this and its like that the method is considered slow and that batch conversions should be done using createToCatalystConverter. Also maybe list this one after its converter counterpart.