Repository: spark
Updated Branches:
refs/heads/master cfac17ee1 -> 01dd00830
[SPARK-17764][SQL] Add `to_json` supporting to convert nested struct column to
JSON string
## What changes were proposed in this pull request?
This PR proposes to add `to_json` function in contrast with `from_json` in
Scala, Java and Python.
It'd be useful if we can convert a same column from/to json. Also, some
datasources do not support nested types. If we are forced to save a dataframe
into those data sources, we might be able to work around by this function.
The usage is as below:
``` scala
val df = Seq(Tuple1(Tuple1(1))).toDF("a")
df.select(to_json($"a").as("json")).show()
```
``` bash
+--------+
| json|
+--------+
|{"_1":1}|
+--------+
```
## How was this patch tested?
Unit tests in `JsonFunctionsSuite` and `JsonExpressionsSuite`.
Author: hyukjinkwon <gurwls...@gmail.com>
Closes #15354 from HyukjinKwon/SPARK-17764.
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/01dd0083
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/01dd0083
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/01dd0083
Branch: refs/heads/master
Commit: 01dd0083011741c2bbe5ae1d2a25f2c9a1302b76
Parents: cfac17e
Author: hyukjinkwon <gurwls...@gmail.com>
Authored: Tue Nov 1 12:46:41 2016 -0700
Committer: Michael Armbrust <mich...@databricks.com>
Committed: Tue Nov 1 12:46:41 2016 -0700
----------------------------------------------------------------------
python/pyspark/sql/functions.py | 23 +++
python/pyspark/sql/readwriter.py | 2 +-
python/pyspark/sql/streaming.py | 2 +-
.../catalyst/expressions/jsonExpressions.scala | 48 ++++-
.../sql/catalyst/json/JacksonGenerator.scala | 197 ++++++++++++++++++
.../spark/sql/catalyst/json/JacksonUtils.scala | 26 +++
.../expressions/JsonExpressionsSuite.scala | 9 +
.../scala/org/apache/spark/sql/Dataset.scala | 2 +-
.../datasources/json/JacksonGenerator.scala | 198 -------------------
.../datasources/json/JsonFileFormat.scala | 2 +-
.../scala/org/apache/spark/sql/functions.scala | 44 ++++-
.../apache/spark/sql/JsonFunctionsSuite.scala | 30 ++-
12 files changed, 372 insertions(+), 211 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/spark/blob/01dd0083/python/pyspark/sql/functions.py
----------------------------------------------------------------------
diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 7fa3fd2..45e3c22 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -1744,6 +1744,29 @@ def from_json(col, schema, options={}):
return Column(jc)
+@ignore_unicode_prefix
+@since(2.1)
+def to_json(col, options={}):
+ """
+ Converts a column containing a [[StructType]] into a JSON string. Throws
an exception,
+ in the case of an unsupported type.
+
+ :param col: name of column containing the struct
+ :param options: options to control converting. accepts the same options as
the json datasource
+
+ >>> from pyspark.sql import Row
+ >>> from pyspark.sql.types import *
+ >>> data = [(1, Row(name='Alice', age=2))]
+ >>> df = spark.createDataFrame(data, ("key", "value"))
+ >>> df.select(to_json(df.value).alias("json")).collect()
+ [Row(json=u'{"age":2,"name":"Alice"}')]
+ """
+
+ sc = SparkContext._active_spark_context
+ jc = sc._jvm.functions.to_json(_to_java_column(col), options)
+ return Column(jc)
+
+
@since(1.5)
def size(col):
"""
http://git-wip-us.apache.org/repos/asf/spark/blob/01dd0083/python/pyspark/sql/readwriter.py
----------------------------------------------------------------------
diff --git a/python/pyspark/sql/readwriter.py b/python/pyspark/sql/readwriter.py
index bc786ef..b0c51b1 100644
--- a/python/pyspark/sql/readwriter.py
+++ b/python/pyspark/sql/readwriter.py
@@ -161,7 +161,7 @@ class DataFrameReader(OptionUtils):
mode=None, columnNameOfCorruptRecord=None, dateFormat=None,
timestampFormat=None):
"""
Loads a JSON file (`JSON Lines text format or newline-delimited JSON
- <[http://jsonlines.org/>`_) or an RDD of Strings storing JSON objects
(one object per
+ <http://jsonlines.org/>`_) or an RDD of Strings storing JSON objects
(one object per
record) and returns the result as a :class`DataFrame`.
If the ``schema`` parameter is not specified, this function goes
http://git-wip-us.apache.org/repos/asf/spark/blob/01dd0083/python/pyspark/sql/streaming.py
----------------------------------------------------------------------
diff --git a/python/pyspark/sql/streaming.py b/python/pyspark/sql/streaming.py
index 559647b..1c94413 100644
--- a/python/pyspark/sql/streaming.py
+++ b/python/pyspark/sql/streaming.py
@@ -641,7 +641,7 @@ class DataStreamReader(OptionUtils):
timestampFormat=None):
"""
Loads a JSON file stream (`JSON Lines text format or newline-delimited
JSON
- <[http://jsonlines.org/>`_) and returns a :class`DataFrame`.
+ <http://jsonlines.org/>`_) and returns a :class`DataFrame`.
If the ``schema`` parameter is not specified, this function goes
through the input once to determine the input schema.
http://git-wip-us.apache.org/repos/asf/spark/blob/01dd0083/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/jsonExpressions.scala
----------------------------------------------------------------------
diff --git
a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/jsonExpressions.scala
b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/jsonExpressions.scala
index 65dbd6a..244a5a3 100644
---
a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/jsonExpressions.scala
+++
b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/jsonExpressions.scala
@@ -17,16 +17,17 @@
package org.apache.spark.sql.catalyst.expressions
-import java.io.{ByteArrayOutputStream, StringWriter}
+import java.io.{ByteArrayOutputStream, CharArrayWriter, StringWriter}
import scala.util.parsing.combinator.RegexParsers
import com.fasterxml.jackson.core._
+import org.apache.spark.sql.AnalysisException
import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.json.{JacksonParser, JSONOptions,
SparkSQLJsonProcessingException}
+import org.apache.spark.sql.catalyst.json._
import org.apache.spark.sql.catalyst.util.ParseModes
import org.apache.spark.sql.types._
import org.apache.spark.unsafe.types.UTF8String
@@ -494,3 +495,46 @@ case class JsonToStruct(schema: StructType, options:
Map[String, String], child:
override def inputTypes: Seq[AbstractDataType] = StringType :: Nil
}
+
+/**
+ * Converts a [[StructType]] to a json output string.
+ */
+case class StructToJson(options: Map[String, String], child: Expression)
+ extends Expression with CodegenFallback with ExpectsInputTypes {
+ override def nullable: Boolean = true
+
+ @transient
+ lazy val writer = new CharArrayWriter()
+
+ @transient
+ lazy val gen =
+ new JacksonGenerator(child.dataType.asInstanceOf[StructType], writer)
+
+ override def dataType: DataType = StringType
+ override def children: Seq[Expression] = child :: Nil
+
+ override def checkInputDataTypes(): TypeCheckResult = {
+ if (StructType.acceptsType(child.dataType)) {
+ try {
+ JacksonUtils.verifySchema(child.dataType.asInstanceOf[StructType])
+ TypeCheckResult.TypeCheckSuccess
+ } catch {
+ case e: UnsupportedOperationException =>
+ TypeCheckResult.TypeCheckFailure(e.getMessage)
+ }
+ } else {
+ TypeCheckResult.TypeCheckFailure(
+ s"$prettyName requires that the expression is a struct expression.")
+ }
+ }
+
+ override def eval(input: InternalRow): Any = {
+ gen.write(child.eval(input).asInstanceOf[InternalRow])
+ gen.flush()
+ val json = writer.toString
+ writer.reset()
+ UTF8String.fromString(json)
+ }
+
+ override def inputTypes: Seq[AbstractDataType] = StructType :: Nil
+}
http://git-wip-us.apache.org/repos/asf/spark/blob/01dd0083/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JacksonGenerator.scala
----------------------------------------------------------------------
diff --git
a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JacksonGenerator.scala
b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JacksonGenerator.scala
new file mode 100644
index 0000000..4b548e0
--- /dev/null
+++
b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JacksonGenerator.scala
@@ -0,0 +1,197 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.sql.catalyst.json
+
+import java.io.Writer
+
+import com.fasterxml.jackson.core._
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.SpecializedGetters
+import org.apache.spark.sql.catalyst.util.{ArrayData, DateTimeUtils, MapData}
+import org.apache.spark.sql.types._
+
+private[sql] class JacksonGenerator(
+ schema: StructType,
+ writer: Writer,
+ options: JSONOptions = new JSONOptions(Map.empty[String, String])) {
+ // A `ValueWriter` is responsible for writing a field of an `InternalRow` to
appropriate
+ // JSON data. Here we are using `SpecializedGetters` rather than
`InternalRow` so that
+ // we can directly access data in `ArrayData` without the help of
`SpecificMutableRow`.
+ private type ValueWriter = (SpecializedGetters, Int) => Unit
+
+ // `ValueWriter`s for all fields of the schema
+ private val rootFieldWriters: Array[ValueWriter] =
schema.map(_.dataType).map(makeWriter).toArray
+
+ private val gen = new
JsonFactory().createGenerator(writer).setRootValueSeparator(null)
+
+ private def makeWriter(dataType: DataType): ValueWriter = dataType match {
+ case NullType =>
+ (row: SpecializedGetters, ordinal: Int) =>
+ gen.writeNull()
+
+ case BooleanType =>
+ (row: SpecializedGetters, ordinal: Int) =>
+ gen.writeBoolean(row.getBoolean(ordinal))
+
+ case ByteType =>
+ (row: SpecializedGetters, ordinal: Int) =>
+ gen.writeNumber(row.getByte(ordinal))
+
+ case ShortType =>
+ (row: SpecializedGetters, ordinal: Int) =>
+ gen.writeNumber(row.getShort(ordinal))
+
+ case IntegerType =>
+ (row: SpecializedGetters, ordinal: Int) =>
+ gen.writeNumber(row.getInt(ordinal))
+
+ case LongType =>
+ (row: SpecializedGetters, ordinal: Int) =>
+ gen.writeNumber(row.getLong(ordinal))
+
+ case FloatType =>
+ (row: SpecializedGetters, ordinal: Int) =>
+ gen.writeNumber(row.getFloat(ordinal))
+
+ case DoubleType =>
+ (row: SpecializedGetters, ordinal: Int) =>
+ gen.writeNumber(row.getDouble(ordinal))
+
+ case StringType =>
+ (row: SpecializedGetters, ordinal: Int) =>
+ gen.writeString(row.getUTF8String(ordinal).toString)
+
+ case TimestampType =>
+ (row: SpecializedGetters, ordinal: Int) =>
+ val timestampString =
+
options.timestampFormat.format(DateTimeUtils.toJavaTimestamp(row.getLong(ordinal)))
+ gen.writeString(timestampString)
+
+ case DateType =>
+ (row: SpecializedGetters, ordinal: Int) =>
+ val dateString =
+
options.dateFormat.format(DateTimeUtils.toJavaDate(row.getInt(ordinal)))
+ gen.writeString(dateString)
+
+ case BinaryType =>
+ (row: SpecializedGetters, ordinal: Int) =>
+ gen.writeBinary(row.getBinary(ordinal))
+
+ case dt: DecimalType =>
+ (row: SpecializedGetters, ordinal: Int) =>
+ gen.writeNumber(row.getDecimal(ordinal, dt.precision,
dt.scale).toJavaBigDecimal)
+
+ case st: StructType =>
+ val fieldWriters = st.map(_.dataType).map(makeWriter)
+ (row: SpecializedGetters, ordinal: Int) =>
+ writeObject(writeFields(row.getStruct(ordinal, st.length), st,
fieldWriters))
+
+ case at: ArrayType =>
+ val elementWriter = makeWriter(at.elementType)
+ (row: SpecializedGetters, ordinal: Int) =>
+ writeArray(writeArrayData(row.getArray(ordinal), elementWriter))
+
+ case mt: MapType =>
+ val valueWriter = makeWriter(mt.valueType)
+ (row: SpecializedGetters, ordinal: Int) =>
+ writeObject(writeMapData(row.getMap(ordinal), mt, valueWriter))
+
+ // For UDT values, they should be in the SQL type's corresponding value
type.
+ // We should not see values in the user-defined class at here.
+ // For example, VectorUDT's SQL type is an array of double. So, we should
expect that v is
+ // an ArrayData at here, instead of a Vector.
+ case t: UserDefinedType[_] =>
+ makeWriter(t.sqlType)
+
+ case _ =>
+ (row: SpecializedGetters, ordinal: Int) =>
+ val v = row.get(ordinal, dataType)
+ sys.error(s"Failed to convert value $v (class of ${v.getClass}}) " +
+ s"with the type of $dataType to JSON.")
+ }
+
+ private def writeObject(f: => Unit): Unit = {
+ gen.writeStartObject()
+ f
+ gen.writeEndObject()
+ }
+
+ private def writeFields(
+ row: InternalRow, schema: StructType, fieldWriters: Seq[ValueWriter]):
Unit = {
+ var i = 0
+ while (i < row.numFields) {
+ val field = schema(i)
+ if (!row.isNullAt(i)) {
+ gen.writeFieldName(field.name)
+ fieldWriters(i).apply(row, i)
+ }
+ i += 1
+ }
+ }
+
+ private def writeArray(f: => Unit): Unit = {
+ gen.writeStartArray()
+ f
+ gen.writeEndArray()
+ }
+
+ private def writeArrayData(
+ array: ArrayData, fieldWriter: ValueWriter): Unit = {
+ var i = 0
+ while (i < array.numElements()) {
+ if (!array.isNullAt(i)) {
+ fieldWriter.apply(array, i)
+ } else {
+ gen.writeNull()
+ }
+ i += 1
+ }
+ }
+
+ private def writeMapData(
+ map: MapData, mapType: MapType, fieldWriter: ValueWriter): Unit = {
+ val keyArray = map.keyArray()
+ val valueArray = map.valueArray()
+ var i = 0
+ while (i < map.numElements()) {
+ gen.writeFieldName(keyArray.get(i, mapType.keyType).toString)
+ if (!valueArray.isNullAt(i)) {
+ fieldWriter.apply(valueArray, i)
+ } else {
+ gen.writeNull()
+ }
+ i += 1
+ }
+ }
+
+ def close(): Unit = gen.close()
+
+ def flush(): Unit = gen.flush()
+
+ /**
+ * Transforms a single InternalRow to JSON using Jackson
+ *
+ * @param row The row to convert
+ */
+ def write(row: InternalRow): Unit = {
+ writeObject {
+ writeFields(row, schema, rootFieldWriters)
+ }
+ }
+}
http://git-wip-us.apache.org/repos/asf/spark/blob/01dd0083/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JacksonUtils.scala
----------------------------------------------------------------------
diff --git
a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JacksonUtils.scala
b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JacksonUtils.scala
index c4d9abb..3b23c6c 100644
---
a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JacksonUtils.scala
+++
b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JacksonUtils.scala
@@ -19,6 +19,8 @@ package org.apache.spark.sql.catalyst.json
import com.fasterxml.jackson.core.{JsonParser, JsonToken}
+import org.apache.spark.sql.types._
+
object JacksonUtils {
/**
* Advance the parser until a null or a specific token is found
@@ -29,4 +31,28 @@ object JacksonUtils {
case x => x != stopOn
}
}
+
+ /**
+ * Verify if the schema is supported in JSON parsing.
+ */
+ def verifySchema(schema: StructType): Unit = {
+ def verifyType(name: String, dataType: DataType): Unit = dataType match {
+ case NullType | BooleanType | ByteType | ShortType | IntegerType |
LongType | FloatType |
+ DoubleType | StringType | TimestampType | DateType | BinaryType |
_: DecimalType =>
+
+ case st: StructType => st.foreach(field => verifyType(field.name,
field.dataType))
+
+ case at: ArrayType => verifyType(name, at.elementType)
+
+ case mt: MapType => verifyType(name, mt.keyType)
+
+ case udt: UserDefinedType[_] => verifyType(name, udt.sqlType)
+
+ case _ =>
+ throw new UnsupportedOperationException(
+ s"Unable to convert column $name of type ${dataType.simpleString} to
JSON.")
+ }
+
+ schema.foreach(field => verifyType(field.name, field.dataType))
+ }
}
http://git-wip-us.apache.org/repos/asf/spark/blob/01dd0083/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/JsonExpressionsSuite.scala
----------------------------------------------------------------------
diff --git
a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/JsonExpressionsSuite.scala
b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/JsonExpressionsSuite.scala
index 8462393..f9db649 100644
---
a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/JsonExpressionsSuite.scala
+++
b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/JsonExpressionsSuite.scala
@@ -343,4 +343,13 @@ class JsonExpressionsSuite extends SparkFunSuite with
ExpressionEvalHelper {
null
)
}
+
+ test("to_json") {
+ val schema = StructType(StructField("a", IntegerType) :: Nil)
+ val struct = Literal.create(create_row(1), schema)
+ checkEvaluation(
+ StructToJson(Map.empty, struct),
+ """{"a":1}"""
+ )
+ }
}
http://git-wip-us.apache.org/repos/asf/spark/blob/01dd0083/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
----------------------------------------------------------------------
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
b/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
index 6e0a247..eb2b20a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
@@ -37,6 +37,7 @@ import org.apache.spark.sql.catalyst.analysis._
import org.apache.spark.sql.catalyst.encoders._
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.expressions.aggregate._
+import org.apache.spark.sql.catalyst.json.JacksonGenerator
import org.apache.spark.sql.catalyst.optimizer.CombineUnions
import org.apache.spark.sql.catalyst.plans._
import org.apache.spark.sql.catalyst.plans.logical._
@@ -45,7 +46,6 @@ import org.apache.spark.sql.catalyst.util.usePrettyExpression
import org.apache.spark.sql.execution.{FileRelation, LogicalRDD,
QueryExecution, SQLExecution}
import org.apache.spark.sql.execution.command.{CreateViewCommand,
ExplainCommand, GlobalTempView, LocalTempView}
import org.apache.spark.sql.execution.datasources.LogicalRelation
-import org.apache.spark.sql.execution.datasources.json.JacksonGenerator
import org.apache.spark.sql.execution.python.EvaluatePython
import org.apache.spark.sql.streaming.DataStreamWriter
import org.apache.spark.sql.types._
http://git-wip-us.apache.org/repos/asf/spark/blob/01dd0083/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala
----------------------------------------------------------------------
diff --git
a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala
b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala
deleted file mode 100644
index 5b55b70..0000000
---
a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala
+++ /dev/null
@@ -1,198 +0,0 @@
-/*
- * 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.
- */
-
-package org.apache.spark.sql.execution.datasources.json
-
-import java.io.Writer
-
-import com.fasterxml.jackson.core._
-
-import org.apache.spark.sql.catalyst.expressions.SpecializedGetters
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.json.JSONOptions
-import org.apache.spark.sql.catalyst.util.{ArrayData, DateTimeUtils, MapData}
-import org.apache.spark.sql.types._
-
-private[sql] class JacksonGenerator(
- schema: StructType,
- writer: Writer,
- options: JSONOptions = new JSONOptions(Map.empty[String, String])) {
- // A `ValueWriter` is responsible for writing a field of an `InternalRow` to
appropriate
- // JSON data. Here we are using `SpecializedGetters` rather than
`InternalRow` so that
- // we can directly access data in `ArrayData` without the help of
`SpecificMutableRow`.
- private type ValueWriter = (SpecializedGetters, Int) => Unit
-
- // `ValueWriter`s for all fields of the schema
- private val rootFieldWriters: Array[ValueWriter] =
schema.map(_.dataType).map(makeWriter).toArray
-
- private val gen = new
JsonFactory().createGenerator(writer).setRootValueSeparator(null)
-
- private def makeWriter(dataType: DataType): ValueWriter = dataType match {
- case NullType =>
- (row: SpecializedGetters, ordinal: Int) =>
- gen.writeNull()
-
- case BooleanType =>
- (row: SpecializedGetters, ordinal: Int) =>
- gen.writeBoolean(row.getBoolean(ordinal))
-
- case ByteType =>
- (row: SpecializedGetters, ordinal: Int) =>
- gen.writeNumber(row.getByte(ordinal))
-
- case ShortType =>
- (row: SpecializedGetters, ordinal: Int) =>
- gen.writeNumber(row.getShort(ordinal))
-
- case IntegerType =>
- (row: SpecializedGetters, ordinal: Int) =>
- gen.writeNumber(row.getInt(ordinal))
-
- case LongType =>
- (row: SpecializedGetters, ordinal: Int) =>
- gen.writeNumber(row.getLong(ordinal))
-
- case FloatType =>
- (row: SpecializedGetters, ordinal: Int) =>
- gen.writeNumber(row.getFloat(ordinal))
-
- case DoubleType =>
- (row: SpecializedGetters, ordinal: Int) =>
- gen.writeNumber(row.getDouble(ordinal))
-
- case StringType =>
- (row: SpecializedGetters, ordinal: Int) =>
- gen.writeString(row.getUTF8String(ordinal).toString)
-
- case TimestampType =>
- (row: SpecializedGetters, ordinal: Int) =>
- val timestampString =
-
options.timestampFormat.format(DateTimeUtils.toJavaTimestamp(row.getLong(ordinal)))
- gen.writeString(timestampString)
-
- case DateType =>
- (row: SpecializedGetters, ordinal: Int) =>
- val dateString =
-
options.dateFormat.format(DateTimeUtils.toJavaDate(row.getInt(ordinal)))
- gen.writeString(dateString)
-
- case BinaryType =>
- (row: SpecializedGetters, ordinal: Int) =>
- gen.writeBinary(row.getBinary(ordinal))
-
- case dt: DecimalType =>
- (row: SpecializedGetters, ordinal: Int) =>
- gen.writeNumber(row.getDecimal(ordinal, dt.precision,
dt.scale).toJavaBigDecimal)
-
- case st: StructType =>
- val fieldWriters = st.map(_.dataType).map(makeWriter)
- (row: SpecializedGetters, ordinal: Int) =>
- writeObject(writeFields(row.getStruct(ordinal, st.length), st,
fieldWriters))
-
- case at: ArrayType =>
- val elementWriter = makeWriter(at.elementType)
- (row: SpecializedGetters, ordinal: Int) =>
- writeArray(writeArrayData(row.getArray(ordinal), elementWriter))
-
- case mt: MapType =>
- val valueWriter = makeWriter(mt.valueType)
- (row: SpecializedGetters, ordinal: Int) =>
- writeObject(writeMapData(row.getMap(ordinal), mt, valueWriter))
-
- // For UDT values, they should be in the SQL type's corresponding value
type.
- // We should not see values in the user-defined class at here.
- // For example, VectorUDT's SQL type is an array of double. So, we should
expect that v is
- // an ArrayData at here, instead of a Vector.
- case t: UserDefinedType[_] =>
- makeWriter(t.sqlType)
-
- case _ =>
- (row: SpecializedGetters, ordinal: Int) =>
- val v = row.get(ordinal, dataType)
- sys.error(s"Failed to convert value $v (class of ${v.getClass}}) " +
- s"with the type of $dataType to JSON.")
- }
-
- private def writeObject(f: => Unit): Unit = {
- gen.writeStartObject()
- f
- gen.writeEndObject()
- }
-
- private def writeFields(
- row: InternalRow, schema: StructType, fieldWriters: Seq[ValueWriter]):
Unit = {
- var i = 0
- while (i < row.numFields) {
- val field = schema(i)
- if (!row.isNullAt(i)) {
- gen.writeFieldName(field.name)
- fieldWriters(i).apply(row, i)
- }
- i += 1
- }
- }
-
- private def writeArray(f: => Unit): Unit = {
- gen.writeStartArray()
- f
- gen.writeEndArray()
- }
-
- private def writeArrayData(
- array: ArrayData, fieldWriter: ValueWriter): Unit = {
- var i = 0
- while (i < array.numElements()) {
- if (!array.isNullAt(i)) {
- fieldWriter.apply(array, i)
- } else {
- gen.writeNull()
- }
- i += 1
- }
- }
-
- private def writeMapData(
- map: MapData, mapType: MapType, fieldWriter: ValueWriter): Unit = {
- val keyArray = map.keyArray()
- val valueArray = map.valueArray()
- var i = 0
- while (i < map.numElements()) {
- gen.writeFieldName(keyArray.get(i, mapType.keyType).toString)
- if (!valueArray.isNullAt(i)) {
- fieldWriter.apply(valueArray, i)
- } else {
- gen.writeNull()
- }
- i += 1
- }
- }
-
- def close(): Unit = gen.close()
-
- def flush(): Unit = gen.flush()
-
- /**
- * Transforms a single InternalRow to JSON using Jackson
- *
- * @param row The row to convert
- */
- def write(row: InternalRow): Unit = {
- writeObject {
- writeFields(row, schema, rootFieldWriters)
- }
- }
-}
http://git-wip-us.apache.org/repos/asf/spark/blob/01dd0083/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JsonFileFormat.scala
----------------------------------------------------------------------
diff --git
a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JsonFileFormat.scala
b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JsonFileFormat.scala
index 5a409c0..0e38aef 100644
---
a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JsonFileFormat.scala
+++
b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JsonFileFormat.scala
@@ -32,7 +32,7 @@ import org.apache.spark.internal.Logging
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{AnalysisException, Row, SparkSession}
import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.json.{JacksonParser, JSONOptions}
+import org.apache.spark.sql.catalyst.json.{JacksonGenerator, JacksonParser,
JSONOptions}
import org.apache.spark.sql.catalyst.util.CompressionCodecs
import org.apache.spark.sql.execution.datasources._
import org.apache.spark.sql.execution.datasources.text.TextOutputWriter
http://git-wip-us.apache.org/repos/asf/spark/blob/01dd0083/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
----------------------------------------------------------------------
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 5f1efd2..944a476 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -2883,10 +2883,10 @@ object functions {
* (Scala-specific) Parses a column containing a JSON string into a
[[StructType]] with the
* specified schema. Returns `null`, in the case of an unparseable string.
*
+ * @param e a string column containing JSON data.
* @param schema the schema to use when parsing the json string
* @param options options to control how the json is parsed. accepts the
same options and the
* json data source.
- * @param e a string column containing JSON data.
*
* @group collection_funcs
* @since 2.1.0
@@ -2936,6 +2936,48 @@ object functions {
def from_json(e: Column, schema: String, options: java.util.Map[String,
String]): Column =
from_json(e, DataType.fromJson(schema).asInstanceOf[StructType], options)
+
+ /**
+ * (Scala-specific) Converts a column containing a [[StructType]] into a
JSON string with the
+ * specified schema. Throws an exception, in the case of an unsupported type.
+ *
+ * @param e a struct column.
+ * @param options options to control how the struct column is converted into
a json string.
+ * accepts the same options and the json data source.
+ *
+ * @group collection_funcs
+ * @since 2.1.0
+ */
+ def to_json(e: Column, options: Map[String, String]): Column = withExpr {
+ StructToJson(options, e.expr)
+ }
+
+ /**
+ * (Java-specific) Converts a column containing a [[StructType]] into a JSON
string with the
+ * specified schema. Throws an exception, in the case of an unsupported type.
+ *
+ * @param e a struct column.
+ * @param options options to control how the struct column is converted into
a json string.
+ * accepts the same options and the json data source.
+ *
+ * @group collection_funcs
+ * @since 2.1.0
+ */
+ def to_json(e: Column, options: java.util.Map[String, String]): Column =
+ to_json(e, options.asScala.toMap)
+
+ /**
+ * Converts a column containing a [[StructType]] into a JSON string with the
+ * specified schema. Throws an exception, in the case of an unsupported type.
+ *
+ * @param e a struct column.
+ *
+ * @group collection_funcs
+ * @since 2.1.0
+ */
+ def to_json(e: Column): Column =
+ to_json(e, Map.empty[String, String])
+
/**
* Returns length of array or map.
*
http://git-wip-us.apache.org/repos/asf/spark/blob/01dd0083/sql/core/src/test/scala/org/apache/spark/sql/JsonFunctionsSuite.scala
----------------------------------------------------------------------
diff --git
a/sql/core/src/test/scala/org/apache/spark/sql/JsonFunctionsSuite.scala
b/sql/core/src/test/scala/org/apache/spark/sql/JsonFunctionsSuite.scala
index 518d6e9..59ae889 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/JsonFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/JsonFunctionsSuite.scala
@@ -17,9 +17,9 @@
package org.apache.spark.sql
-import org.apache.spark.sql.functions.from_json
+import org.apache.spark.sql.functions.{from_json, struct, to_json}
import org.apache.spark.sql.test.SharedSQLContext
-import org.apache.spark.sql.types.{IntegerType, StructType}
+import org.apache.spark.sql.types.{CalendarIntervalType, IntegerType,
StructType}
class JsonFunctionsSuite extends QueryTest with SharedSQLContext {
import testImplicits._
@@ -31,7 +31,6 @@ class JsonFunctionsSuite extends QueryTest with
SharedSQLContext {
Row("alice", "5"))
}
-
val tuples: Seq[(String, String)] =
("1", """{"f1": "value1", "f2": "value2", "f3": 3, "f5": 5.23}""") ::
("2", """{"f1": "value12", "f3": "value3", "f2": 2, "f4": 4.01}""") ::
@@ -97,7 +96,7 @@ class JsonFunctionsSuite extends QueryTest with
SharedSQLContext {
checkAnswer(expr, expected)
}
- test("json_parser") {
+ test("from_json") {
val df = Seq("""{"a": 1}""").toDS()
val schema = new StructType().add("a", IntegerType)
@@ -106,7 +105,7 @@ class JsonFunctionsSuite extends QueryTest with
SharedSQLContext {
Row(Row(1)) :: Nil)
}
- test("json_parser missing columns") {
+ test("from_json missing columns") {
val df = Seq("""{"a": 1}""").toDS()
val schema = new StructType().add("b", IntegerType)
@@ -115,7 +114,7 @@ class JsonFunctionsSuite extends QueryTest with
SharedSQLContext {
Row(Row(null)) :: Nil)
}
- test("json_parser invalid json") {
+ test("from_json invalid json") {
val df = Seq("""{"a" 1}""").toDS()
val schema = new StructType().add("a", IntegerType)
@@ -123,4 +122,23 @@ class JsonFunctionsSuite extends QueryTest with
SharedSQLContext {
df.select(from_json($"value", schema)),
Row(null) :: Nil)
}
+
+ test("to_json") {
+ val df = Seq(Tuple1(Tuple1(1))).toDF("a")
+
+ checkAnswer(
+ df.select(to_json($"a")),
+ Row("""{"_1":1}""") :: Nil)
+ }
+
+ test("to_json unsupported type") {
+ val df = Seq(Tuple1(Tuple1("interval -3 month 7 hours"))).toDF("a")
+ .select(struct($"a._1".cast(CalendarIntervalType).as("a")).as("c"))
+ val e = intercept[AnalysisException]{
+ // Unsupported type throws an exception
+ df.select(to_json($"c")).collect()
+ }
+ assert(e.getMessage.contains(
+ "Unable to convert column a of type calendarinterval to JSON."))
+ }
}
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