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Split up JsonRDD2 into multiple objects
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Nathan Howell
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May 4, 2015
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171 changes: 171 additions & 0 deletions
171
sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala
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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.json | ||
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| import com.fasterxml.jackson.core._ | ||
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| import org.apache.spark.rdd.RDD | ||
| import org.apache.spark.sql.catalyst.analysis.HiveTypeCoercion | ||
| import org.apache.spark.sql.json.JacksonUtils.nextUntil | ||
| import org.apache.spark.sql.types._ | ||
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| private[sql] object InferSchema { | ||
| /** | ||
| * Infer the type of a collection of json records in three stages: | ||
| * 1. Infer the type of each record | ||
| * 2. Merge types by choosing the lowest type necessary to cover equal keys | ||
| * 3. Replace any remaining null fields with string, the top type | ||
| */ | ||
| def apply( | ||
| json: RDD[String], | ||
| samplingRatio: Double = 1.0, | ||
| columnNameOfCorruptRecords: String): StructType = { | ||
| require(samplingRatio > 0, s"samplingRatio ($samplingRatio) should be greater than 0") | ||
| val schemaData = if (samplingRatio > 0.99) { | ||
| json | ||
| } else { | ||
| json.sample(withReplacement = false, samplingRatio, 1) | ||
| } | ||
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| // perform schema inference on each row and merge afterwards | ||
| schemaData.mapPartitions { iter => | ||
| val factory = new JsonFactory() | ||
| iter.map { row => | ||
| try { | ||
| val parser = factory.createParser(row) | ||
| parser.nextToken() | ||
| inferField(parser) | ||
| } catch { | ||
| case _: JsonParseException => | ||
| StructType(Seq(StructField(columnNameOfCorruptRecords, StringType))) | ||
| } | ||
| } | ||
| }.treeAggregate[DataType](StructType(Seq()))(compatibleRootType, compatibleRootType) match { | ||
| case st: StructType => nullTypeToStringType(st) | ||
| } | ||
| } | ||
|
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| /** | ||
| * Infer the type of a json document from the parser's token stream | ||
| */ | ||
| private def inferField(parser: JsonParser): DataType = { | ||
| import com.fasterxml.jackson.core.JsonToken._ | ||
| parser.getCurrentToken match { | ||
| case null | VALUE_NULL => NullType | ||
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| case FIELD_NAME => | ||
| parser.nextToken() | ||
| inferField(parser) | ||
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| case VALUE_STRING if parser.getTextLength < 1 => | ||
| // Zero length strings and nulls have special handling to deal | ||
| // with JSON generators that do not distinguish between the two. | ||
| // To accurately infer types for empty strings that are really | ||
| // meant to represent nulls we assume that the two are isomorphic | ||
| // but will defer treating null fields as strings until all the | ||
| // record fields' types have been combined. | ||
| NullType | ||
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| case VALUE_STRING => StringType | ||
| case START_OBJECT => | ||
| val builder = Seq.newBuilder[StructField] | ||
| while (nextUntil(parser, END_OBJECT)) { | ||
| builder += StructField(parser.getCurrentName, inferField(parser), nullable = true) | ||
| } | ||
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| StructType(builder.result().sortBy(_.name)) | ||
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| case START_ARRAY => | ||
| // If this JSON array is empty, we use NullType as a placeholder. | ||
| // If this array is not empty in other JSON objects, we can resolve | ||
| // the type as we pass through all JSON objects. | ||
| var elementType: DataType = NullType | ||
| while (nextUntil(parser, END_ARRAY)) { | ||
| elementType = compatibleType(elementType, inferField(parser)) | ||
| } | ||
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| ArrayType(elementType) | ||
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| case VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT => | ||
| import JsonParser.NumberType._ | ||
| parser.getNumberType match { | ||
| // For Integer values, use LongType by default. | ||
| case INT | LONG => LongType | ||
| // Since we do not have a data type backed by BigInteger, | ||
| // when we see a Java BigInteger, we use DecimalType. | ||
| case BIG_INTEGER | BIG_DECIMAL => DecimalType.Unlimited | ||
| case FLOAT | DOUBLE => DoubleType | ||
| } | ||
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| case VALUE_TRUE | VALUE_FALSE => BooleanType | ||
| } | ||
| } | ||
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| private def nullTypeToStringType(struct: StructType): StructType = { | ||
| val fields = struct.fields.map { | ||
| case StructField(fieldName, dataType, nullable, _) => | ||
| val newType = dataType match { | ||
| case NullType => StringType | ||
| case ArrayType(NullType, containsNull) => ArrayType(StringType, containsNull) | ||
| case ArrayType(struct: StructType, containsNull) => | ||
| ArrayType(nullTypeToStringType(struct), containsNull) | ||
| case struct: StructType =>nullTypeToStringType(struct) | ||
| case other: DataType => other | ||
| } | ||
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| StructField(fieldName, newType, nullable) | ||
| } | ||
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| StructType(fields) | ||
| } | ||
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| /** | ||
| * Remove top-level ArrayType wrappers and merge the remaining schemas | ||
| */ | ||
| private def compatibleRootType: (DataType, DataType) => DataType = { | ||
| case (ArrayType(ty1, _), ty2) => compatibleRootType(ty1, ty2) | ||
| case (ty1, ArrayType(ty2, _)) => compatibleRootType(ty1, ty2) | ||
| case (ty1, ty2) => compatibleType(ty1, ty2) | ||
| } | ||
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| /** | ||
| * Returns the most general data type for two given data types. | ||
| */ | ||
| private[json] def compatibleType(t1: DataType, t2: DataType): DataType = { | ||
| HiveTypeCoercion.findTightestCommonType(t1, t2).getOrElse { | ||
| // t1 or t2 is a StructType, ArrayType, or an unexpected type. | ||
| (t1, t2) match { | ||
| case (other: DataType, NullType) => other | ||
| case (NullType, other: DataType) => other | ||
| case (StructType(fields1), StructType(fields2)) => | ||
| val newFields = (fields1 ++ fields2).groupBy(field => field.name).map { | ||
| case (name, fieldTypes) => | ||
| val dataType = fieldTypes.view.map(_.dataType).reduce(compatibleType) | ||
| StructField(name, dataType, nullable = true) | ||
| } | ||
| StructType(newFields.toSeq.sortBy(_.name)) | ||
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| case (ArrayType(elementType1, containsNull1), ArrayType(elementType2, containsNull2)) => | ||
| ArrayType(compatibleType(elementType1, elementType2), containsNull1 || containsNull2) | ||
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| // strings and every string is a Json object. | ||
| case (_, _) => StringType | ||
| } | ||
| } | ||
| } | ||
| } |
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77 changes: 77 additions & 0 deletions
77
sql/core/src/main/scala/org/apache/spark/sql/json/JacksonGenerator.scala
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,77 @@ | ||
| /* | ||
| * 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.json | ||
|
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| import scala.collection.Map | ||
|
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| import com.fasterxml.jackson.core._ | ||
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| import org.apache.spark.sql.catalyst.expressions._ | ||
| import org.apache.spark.sql.types._ | ||
|
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| private[sql] object JacksonGenerator { | ||
| /** Transforms a single Row to JSON using Jackson | ||
| * | ||
| * @param rowSchema the schema object used for conversion | ||
| * @param gen a JsonGenerator object | ||
| * @param row The row to convert | ||
| */ | ||
| def apply(rowSchema: StructType, gen: JsonGenerator)(row: Row): Unit = { | ||
| def valWriter: (DataType, Any) => Unit = { | ||
| case (_, null) | (NullType, _) => gen.writeNull() | ||
| case (StringType, v: String) => gen.writeString(v) | ||
| case (TimestampType, v: java.sql.Timestamp) => gen.writeString(v.toString) | ||
| case (IntegerType, v: Int) => gen.writeNumber(v) | ||
| case (ShortType, v: Short) => gen.writeNumber(v) | ||
| case (FloatType, v: Float) => gen.writeNumber(v) | ||
| case (DoubleType, v: Double) => gen.writeNumber(v) | ||
| case (LongType, v: Long) => gen.writeNumber(v) | ||
| case (DecimalType(), v: java.math.BigDecimal) => gen.writeNumber(v) | ||
| case (ByteType, v: Byte) => gen.writeNumber(v.toInt) | ||
| case (BinaryType, v: Array[Byte]) => gen.writeBinary(v) | ||
| case (BooleanType, v: Boolean) => gen.writeBoolean(v) | ||
| case (DateType, v) => gen.writeString(v.toString) | ||
| case (udt: UserDefinedType[_], v) => valWriter(udt.sqlType, udt.serialize(v)) | ||
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| case (ArrayType(ty, _), v: Seq[_] ) => | ||
| gen.writeStartArray() | ||
| v.foreach(valWriter(ty,_)) | ||
| gen.writeEndArray() | ||
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| case (MapType(kv,vv, _), v: Map[_,_]) => | ||
| gen.writeStartObject() | ||
| v.foreach { p => | ||
| gen.writeFieldName(p._1.toString) | ||
| valWriter(vv,p._2) | ||
| } | ||
| gen.writeEndObject() | ||
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| case (StructType(ty), v: Row) => | ||
| gen.writeStartObject() | ||
| ty.zip(v.toSeq).foreach { | ||
| case (_, null) => | ||
| case (field, v) => | ||
| gen.writeFieldName(field.name) | ||
| valWriter(field.dataType, v) | ||
| } | ||
| gen.writeEndObject() | ||
| } | ||
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| valWriter(rowSchema, row) | ||
| } | ||
| } |
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