Repository: spark
Updated Branches:
refs/heads/master 5585c5765 -> 776f299fc
[SPARK-24709][SQL] schema_of_json() - schema inference from an example
## What changes were proposed in this pull request?
In the PR, I propose to add new function - *schema_of_json()* which infers
schema of JSON string literal. The result of the function is a string
containing a schema in DDL format.
One of the use cases is using of *schema_of_json()* in the combination with
*from_json()*. Currently, _from_json()_ requires a schema as a mandatory
argument. The *schema_of_json()* function will allow to point out an JSON
string as an example which has the same schema as the first argument of
_from_json()_. For instance:
```sql
select from_json(json_column, schema_of_json('{"c1": [0], "c2": [{"c3":0}]}'))
from json_table;
```
## How was this patch tested?
Added new test to `JsonFunctionsSuite`, `JsonExpressionsSuite` and SQL tests to
`json-functions.sql`
Author: Maxim Gekk <[email protected]>
Closes #21686 from MaxGekk/infer_schema_json.
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/776f299f
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/776f299f
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/776f299f
Branch: refs/heads/master
Commit: 776f299fc8146b400e97185b1577b0fc8f06e14b
Parents: 5585c57
Author: Maxim Gekk <[email protected]>
Authored: Wed Jul 4 09:38:18 2018 +0800
Committer: hyukjinkwon <[email protected]>
Committed: Wed Jul 4 09:38:18 2018 +0800
----------------------------------------------------------------------
python/pyspark/sql/functions.py | 27 ++
.../catalyst/analysis/FunctionRegistry.scala | 1 +
.../catalyst/expressions/jsonExpressions.scala | 52 ++-
.../sql/catalyst/json/JsonInferSchema.scala | 348 ++++++++++++++++++
.../expressions/JsonExpressionsSuite.scala | 7 +
.../datasources/json/JsonDataSource.scala | 2 +-
.../datasources/json/JsonInferSchema.scala | 349 -------------------
.../scala/org/apache/spark/sql/functions.scala | 42 +++
.../sql-tests/inputs/json-functions.sql | 4 +
.../sql-tests/results/json-functions.sql.out | 20 +-
.../apache/spark/sql/JsonFunctionsSuite.scala | 17 +-
.../execution/datasources/json/JsonSuite.scala | 4 +-
12 files changed, 509 insertions(+), 364 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/spark/blob/776f299f/python/pyspark/sql/functions.py
----------------------------------------------------------------------
diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 9652d3e..4d37197 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -2189,11 +2189,16 @@ def from_json(col, schema, options={}):
>>> df = spark.createDataFrame(data, ("key", "value"))
>>> df.select(from_json(df.value, schema).alias("json")).collect()
[Row(json=[Row(a=1)])]
+ >>> schema = schema_of_json(lit('''{"a": 0}'''))
+ >>> df.select(from_json(df.value, schema).alias("json")).collect()
+ [Row(json=Row(a=1))]
"""
sc = SparkContext._active_spark_context
if isinstance(schema, DataType):
schema = schema.json()
+ elif isinstance(schema, Column):
+ schema = _to_java_column(schema)
jc = sc._jvm.functions.from_json(_to_java_column(col), schema, options)
return Column(jc)
@@ -2235,6 +2240,28 @@ def to_json(col, options={}):
return Column(jc)
+@ignore_unicode_prefix
+@since(2.4)
+def schema_of_json(col):
+ """
+ Parses a column containing a JSON string and infers its schema in DDL
format.
+
+ :param col: string column in json format
+
+ >>> from pyspark.sql.types import *
+ >>> data = [(1, '{"a": 1}')]
+ >>> df = spark.createDataFrame(data, ("key", "value"))
+ >>> df.select(schema_of_json(df.value).alias("json")).collect()
+ [Row(json=u'struct<a:bigint>')]
+ >>> df.select(schema_of_json(lit('{"a": 0}')).alias("json")).collect()
+ [Row(json=u'struct<a:bigint>')]
+ """
+
+ sc = SparkContext._active_spark_context
+ jc = sc._jvm.functions.schema_of_json(_to_java_column(col))
+ return Column(jc)
+
+
@since(1.5)
def size(col):
"""
http://git-wip-us.apache.org/repos/asf/spark/blob/776f299f/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
----------------------------------------------------------------------
diff --git
a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index a574d8a..80a0af6 100644
---
a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++
b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -505,6 +505,7 @@ object FunctionRegistry {
// json
expression[StructsToJson]("to_json"),
expression[JsonToStructs]("from_json"),
+ expression[SchemaOfJson]("schema_of_json"),
// cast
expression[Cast]("cast"),
http://git-wip-us.apache.org/repos/asf/spark/blob/776f299f/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 f6d74f5..8cd8605 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
@@ -17,7 +17,7 @@
package org.apache.spark.sql.catalyst.expressions
-import java.io.{ByteArrayInputStream, ByteArrayOutputStream, CharArrayWriter,
InputStreamReader, StringWriter}
+import java.io._
import scala.util.parsing.combinator.RegexParsers
@@ -28,7 +28,8 @@ import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
import org.apache.spark.sql.catalyst.json._
-import org.apache.spark.sql.catalyst.util.{ArrayBasedMapData, ArrayData,
BadRecordException, FailFastMode, GenericArrayData, MapData}
+import org.apache.spark.sql.catalyst.json.JsonInferSchema.inferField
+import org.apache.spark.sql.catalyst.util._
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.types._
import org.apache.spark.unsafe.types.UTF8String
@@ -525,17 +526,19 @@ case class JsonToStructs(
override def nullable: Boolean = true
// Used in `FunctionRegistry`
- def this(child: Expression, schema: Expression) =
+ def this(child: Expression, schema: Expression, options: Map[String,
String]) =
this(
- schema = JsonExprUtils.validateSchemaLiteral(schema),
- options = Map.empty[String, String],
+ schema = JsonExprUtils.evalSchemaExpr(schema),
+ options = options,
child = child,
timeZoneId = None,
forceNullableSchema =
SQLConf.get.getConf(SQLConf.FROM_JSON_FORCE_NULLABLE_SCHEMA))
+ def this(child: Expression, schema: Expression) = this(child, schema,
Map.empty[String, String])
+
def this(child: Expression, schema: Expression, options: Expression) =
this(
- schema = JsonExprUtils.validateSchemaLiteral(schema),
+ schema = JsonExprUtils.evalSchemaExpr(schema),
options = JsonExprUtils.convertToMapData(options),
child = child,
timeZoneId = None,
@@ -744,11 +747,44 @@ case class StructsToJson(
override def inputTypes: Seq[AbstractDataType] = TypeCollection(ArrayType,
StructType) :: Nil
}
+/**
+ * A function infers schema of JSON string.
+ */
+@ExpressionDescription(
+ usage = "_FUNC_(json[, options]) - Returns schema in the DDL format of JSON
string.",
+ examples = """
+ Examples:
+ > SELECT _FUNC_('[{"col":0}]');
+ array<struct<col:int>>
+ """,
+ since = "2.4.0")
+case class SchemaOfJson(child: Expression)
+ extends UnaryExpression with String2StringExpression with CodegenFallback {
+
+ private val jsonOptions = new JSONOptions(Map.empty, "UTC")
+ private val jsonFactory = new JsonFactory()
+ jsonOptions.setJacksonOptions(jsonFactory)
+
+ override def convert(v: UTF8String): UTF8String = {
+ val dt = Utils.tryWithResource(CreateJacksonParser.utf8String(jsonFactory,
v)) { parser =>
+ parser.nextToken()
+ inferField(parser, jsonOptions)
+ }
+
+ UTF8String.fromString(dt.catalogString)
+ }
+}
+
object JsonExprUtils {
- def validateSchemaLiteral(exp: Expression): DataType = exp match {
+ def evalSchemaExpr(exp: Expression): DataType = exp match {
case Literal(s, StringType) => DataType.fromDDL(s.toString)
- case e => throw new AnalysisException(s"Expected a string literal instead
of $e")
+ case e @ SchemaOfJson(_: Literal) =>
+ val ddlSchema = e.eval().asInstanceOf[UTF8String]
+ DataType.fromDDL(ddlSchema.toString)
+ case e => throw new AnalysisException(
+ "Schema should be specified in DDL format as a string literal" +
+ s" or output of the schema_of_json function instead of ${e.sql}")
}
def convertToMapData(exp: Expression): Map[String, String] = exp match {
http://git-wip-us.apache.org/repos/asf/spark/blob/776f299f/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JsonInferSchema.scala
----------------------------------------------------------------------
diff --git
a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JsonInferSchema.scala
b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JsonInferSchema.scala
new file mode 100644
index 0000000..491ca00
--- /dev/null
+++
b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JsonInferSchema.scala
@@ -0,0 +1,348 @@
+/*
+ * 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.util.Comparator
+
+import com.fasterxml.jackson.core._
+
+import org.apache.spark.SparkException
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.analysis.TypeCoercion
+import org.apache.spark.sql.catalyst.json.JacksonUtils.nextUntil
+import org.apache.spark.sql.catalyst.util.{DropMalformedMode, FailFastMode,
ParseMode, PermissiveMode}
+import org.apache.spark.sql.types._
+import org.apache.spark.util.Utils
+
+private[sql] object JsonInferSchema {
+
+ /**
+ * 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 infer[T](
+ json: RDD[T],
+ configOptions: JSONOptions,
+ createParser: (JsonFactory, T) => JsonParser): StructType = {
+ val parseMode = configOptions.parseMode
+ val columnNameOfCorruptRecord = configOptions.columnNameOfCorruptRecord
+
+ // In each RDD partition, perform schema inference on each row and merge
afterwards.
+ val typeMerger = compatibleRootType(columnNameOfCorruptRecord, parseMode)
+ val mergedTypesFromPartitions = json.mapPartitions { iter =>
+ val factory = new JsonFactory()
+ configOptions.setJacksonOptions(factory)
+ iter.flatMap { row =>
+ try {
+ Utils.tryWithResource(createParser(factory, row)) { parser =>
+ parser.nextToken()
+ Some(inferField(parser, configOptions))
+ }
+ } catch {
+ case e @ (_: RuntimeException | _: JsonProcessingException) =>
parseMode match {
+ case PermissiveMode =>
+ Some(StructType(Seq(StructField(columnNameOfCorruptRecord,
StringType))))
+ case DropMalformedMode =>
+ None
+ case FailFastMode =>
+ throw new SparkException("Malformed records are detected in
schema inference. " +
+ s"Parse Mode: ${FailFastMode.name}.", e)
+ }
+ }
+ }.reduceOption(typeMerger).toIterator
+ }
+
+ // Here we get RDD local iterator then fold, instead of calling `RDD.fold`
directly, because
+ // `RDD.fold` will run the fold function in DAGScheduler event loop
thread, which may not have
+ // active SparkSession and `SQLConf.get` may point to the wrong configs.
+ val rootType =
mergedTypesFromPartitions.toLocalIterator.fold(StructType(Nil))(typeMerger)
+
+ canonicalizeType(rootType, configOptions) match {
+ case Some(st: StructType) => st
+ case _ =>
+ // canonicalizeType erases all empty structs, including the only one
we want to keep
+ StructType(Nil)
+ }
+ }
+
+ private[this] val structFieldComparator = new Comparator[StructField] {
+ override def compare(o1: StructField, o2: StructField): Int = {
+ o1.name.compareTo(o2.name)
+ }
+ }
+
+ private def isSorted(arr: Array[StructField]): Boolean = {
+ var i: Int = 0
+ while (i < arr.length - 1) {
+ if (structFieldComparator.compare(arr(i), arr(i + 1)) > 0) {
+ return false
+ }
+ i += 1
+ }
+ true
+ }
+
+ /**
+ * Infer the type of a json document from the parser's token stream
+ */
+ def inferField(parser: JsonParser, configOptions: JSONOptions): DataType = {
+ import com.fasterxml.jackson.core.JsonToken._
+ parser.getCurrentToken match {
+ case null | VALUE_NULL => NullType
+
+ case FIELD_NAME =>
+ parser.nextToken()
+ inferField(parser, configOptions)
+
+ 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
+
+ case VALUE_STRING => StringType
+ case START_OBJECT =>
+ val builder = Array.newBuilder[StructField]
+ while (nextUntil(parser, END_OBJECT)) {
+ builder += StructField(
+ parser.getCurrentName,
+ inferField(parser, configOptions),
+ nullable = true)
+ }
+ val fields: Array[StructField] = builder.result()
+ // Note: other code relies on this sorting for correctness, so don't
remove it!
+ java.util.Arrays.sort(fields, structFieldComparator)
+ StructType(fields)
+
+ 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, configOptions))
+ }
+
+ ArrayType(elementType)
+
+ case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT) if
configOptions.primitivesAsString => StringType
+
+ case (VALUE_TRUE | VALUE_FALSE) if configOptions.primitivesAsString =>
StringType
+
+ 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 =>
+ val v = parser.getDecimalValue
+ if (Math.max(v.precision(), v.scale()) <=
DecimalType.MAX_PRECISION) {
+ DecimalType(Math.max(v.precision(), v.scale()), v.scale())
+ } else {
+ DoubleType
+ }
+ case FLOAT | DOUBLE if configOptions.prefersDecimal =>
+ val v = parser.getDecimalValue
+ if (Math.max(v.precision(), v.scale()) <=
DecimalType.MAX_PRECISION) {
+ DecimalType(Math.max(v.precision(), v.scale()), v.scale())
+ } else {
+ DoubleType
+ }
+ case FLOAT | DOUBLE =>
+ DoubleType
+ }
+
+ case VALUE_TRUE | VALUE_FALSE => BooleanType
+ }
+ }
+
+ /**
+ * Recursively canonicalizes inferred types, e.g., removes StructTypes with
no fields,
+ * drops NullTypes or converts them to StringType based on provided options.
+ */
+ private def canonicalizeType(tpe: DataType, options: JSONOptions):
Option[DataType] = tpe match {
+ case at: ArrayType =>
+ canonicalizeType(at.elementType, options)
+ .map(t => at.copy(elementType = t))
+
+ case StructType(fields) =>
+ val canonicalFields = fields.filter(_.name.nonEmpty).flatMap { f =>
+ canonicalizeType(f.dataType, options)
+ .map(t => f.copy(dataType = t))
+ }
+ // SPARK-8093: empty structs should be deleted
+ if (canonicalFields.isEmpty) {
+ None
+ } else {
+ Some(StructType(canonicalFields))
+ }
+
+ case NullType =>
+ if (options.dropFieldIfAllNull) {
+ None
+ } else {
+ Some(StringType)
+ }
+
+ case other => Some(other)
+ }
+
+ private def withCorruptField(
+ struct: StructType,
+ other: DataType,
+ columnNameOfCorruptRecords: String,
+ parseMode: ParseMode) = parseMode match {
+ case PermissiveMode =>
+ // If we see any other data type at the root level, we get records that
cannot be
+ // parsed. So, we use the struct as the data type and add the corrupt
field to the schema.
+ if (!struct.fieldNames.contains(columnNameOfCorruptRecords)) {
+ // If this given struct does not have a column used for corrupt
records,
+ // add this field.
+ val newFields: Array[StructField] =
+ StructField(columnNameOfCorruptRecords, StringType, nullable = true)
+: struct.fields
+ // Note: other code relies on this sorting for correctness, so don't
remove it!
+ java.util.Arrays.sort(newFields, structFieldComparator)
+ StructType(newFields)
+ } else {
+ // Otherwise, just return this struct.
+ struct
+ }
+
+ case DropMalformedMode =>
+ // If corrupt record handling is disabled we retain the valid schema and
discard the other.
+ struct
+
+ case FailFastMode =>
+ // If `other` is not struct type, consider it as malformed one and
throws an exception.
+ throw new SparkException("Malformed records are detected in schema
inference. " +
+ s"Parse Mode: ${FailFastMode.name}. Reasons: Failed to infer a common
schema. " +
+ s"Struct types are expected, but `${other.catalogString}` was found.")
+ }
+
+ /**
+ * Remove top-level ArrayType wrappers and merge the remaining schemas
+ */
+ private def compatibleRootType(
+ columnNameOfCorruptRecords: String,
+ parseMode: ParseMode): (DataType, DataType) => DataType = {
+ // Since we support array of json objects at the top level,
+ // we need to check the element type and find the root level data type.
+ case (ArrayType(ty1, _), ty2) =>
+ compatibleRootType(columnNameOfCorruptRecords, parseMode)(ty1, ty2)
+ case (ty1, ArrayType(ty2, _)) =>
+ compatibleRootType(columnNameOfCorruptRecords, parseMode)(ty1, ty2)
+ // Discard null/empty documents
+ case (struct: StructType, NullType) => struct
+ case (NullType, struct: StructType) => struct
+ case (struct: StructType, o) if !o.isInstanceOf[StructType] =>
+ withCorruptField(struct, o, columnNameOfCorruptRecords, parseMode)
+ case (o, struct: StructType) if !o.isInstanceOf[StructType] =>
+ withCorruptField(struct, o, columnNameOfCorruptRecords, parseMode)
+ // If we get anything else, we call compatibleType.
+ // Usually, when we reach here, ty1 and ty2 are two StructTypes.
+ case (ty1, ty2) => compatibleType(ty1, ty2)
+ }
+
+ private[this] val emptyStructFieldArray = Array.empty[StructField]
+
+ /**
+ * Returns the most general data type for two given data types.
+ */
+ def compatibleType(t1: DataType, t2: DataType): DataType = {
+ TypeCoercion.findTightestCommonType(t1, t2).getOrElse {
+ // t1 or t2 is a StructType, ArrayType, or an unexpected type.
+ (t1, t2) match {
+ // Double support larger range than fixed decimal, DecimalType.Maximum
should be enough
+ // in most case, also have better precision.
+ case (DoubleType, _: DecimalType) | (_: DecimalType, DoubleType) =>
+ DoubleType
+
+ case (t1: DecimalType, t2: DecimalType) =>
+ val scale = math.max(t1.scale, t2.scale)
+ val range = math.max(t1.precision - t1.scale, t2.precision -
t2.scale)
+ if (range + scale > 38) {
+ // DecimalType can't support precision > 38
+ DoubleType
+ } else {
+ DecimalType(range + scale, scale)
+ }
+
+ case (StructType(fields1), StructType(fields2)) =>
+ // Both fields1 and fields2 should be sorted by name, since
inferField performs sorting.
+ // Therefore, we can take advantage of the fact that we're merging
sorted lists and skip
+ // building a hash map or performing additional sorting.
+ assert(isSorted(fields1), s"StructType's fields were not sorted:
${fields1.toSeq}")
+ assert(isSorted(fields2), s"StructType's fields were not sorted:
${fields2.toSeq}")
+
+ val newFields = new java.util.ArrayList[StructField]()
+
+ var f1Idx = 0
+ var f2Idx = 0
+
+ while (f1Idx < fields1.length && f2Idx < fields2.length) {
+ val f1Name = fields1(f1Idx).name
+ val f2Name = fields2(f2Idx).name
+ val comp = f1Name.compareTo(f2Name)
+ if (comp == 0) {
+ val dataType = compatibleType(fields1(f1Idx).dataType,
fields2(f2Idx).dataType)
+ newFields.add(StructField(f1Name, dataType, nullable = true))
+ f1Idx += 1
+ f2Idx += 1
+ } else if (comp < 0) { // f1Name < f2Name
+ newFields.add(fields1(f1Idx))
+ f1Idx += 1
+ } else { // f1Name > f2Name
+ newFields.add(fields2(f2Idx))
+ f2Idx += 1
+ }
+ }
+ while (f1Idx < fields1.length) {
+ newFields.add(fields1(f1Idx))
+ f1Idx += 1
+ }
+ while (f2Idx < fields2.length) {
+ newFields.add(fields2(f2Idx))
+ f2Idx += 1
+ }
+ StructType(newFields.toArray(emptyStructFieldArray))
+
+ case (ArrayType(elementType1, containsNull1), ArrayType(elementType2,
containsNull2)) =>
+ ArrayType(compatibleType(elementType1, elementType2), containsNull1
|| containsNull2)
+
+ // The case that given `DecimalType` is capable of given
`IntegralType` is handled in
+ // `findTightestCommonType`. Both cases below will be executed only
when the given
+ // `DecimalType` is not capable of the given `IntegralType`.
+ case (t1: IntegralType, t2: DecimalType) =>
+ compatibleType(DecimalType.forType(t1), t2)
+ case (t1: DecimalType, t2: IntegralType) =>
+ compatibleType(t1, DecimalType.forType(t2))
+
+ // strings and every string is a Json object.
+ case (_, _) => StringType
+ }
+ }
+ }
+}
http://git-wip-us.apache.org/repos/asf/spark/blob/776f299f/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 00e9763..52203b9 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
@@ -706,4 +706,11 @@ class JsonExpressionsSuite extends SparkFunSuite with
ExpressionEvalHelper with
assert(schemaToCompare == schema)
}
}
+
+ test("SPARK-24709: infer schema of json strings") {
+ checkEvaluation(SchemaOfJson(Literal.create("""{"col":0}""")),
"struct<col:bigint>")
+ checkEvaluation(
+ SchemaOfJson(Literal.create("""{"col0":["a"], "col1": {"col2":
"b"}}""")),
+ "struct<col0:array<string>,col1:struct<col2:string>>")
+ }
}
http://git-wip-us.apache.org/repos/asf/spark/blob/776f299f/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JsonDataSource.scala
----------------------------------------------------------------------
diff --git
a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JsonDataSource.scala
b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JsonDataSource.scala
index 3b6df45..2fee212 100644
---
a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JsonDataSource.scala
+++
b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JsonDataSource.scala
@@ -33,7 +33,7 @@ import org.apache.spark.input.{PortableDataStream,
StreamInputFormat}
import org.apache.spark.rdd.{BinaryFileRDD, RDD}
import org.apache.spark.sql.{Dataset, Encoders, SparkSession}
import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.json.{CreateJacksonParser, JacksonParser,
JSONOptions}
+import org.apache.spark.sql.catalyst.json.{CreateJacksonParser, JacksonParser,
JsonInferSchema, JSONOptions}
import org.apache.spark.sql.execution.SQLExecution
import org.apache.spark.sql.execution.datasources._
import org.apache.spark.sql.execution.datasources.text.TextFileFormat
http://git-wip-us.apache.org/repos/asf/spark/blob/776f299f/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JsonInferSchema.scala
----------------------------------------------------------------------
diff --git
a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JsonInferSchema.scala
b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JsonInferSchema.scala
deleted file mode 100644
index 8e1b430..0000000
---
a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JsonInferSchema.scala
+++ /dev/null
@@ -1,349 +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.util.Comparator
-
-import com.fasterxml.jackson.core._
-
-import org.apache.spark.SparkException
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.catalyst.analysis.TypeCoercion
-import org.apache.spark.sql.catalyst.json.JacksonUtils.nextUntil
-import org.apache.spark.sql.catalyst.json.JSONOptions
-import org.apache.spark.sql.catalyst.util.{DropMalformedMode, FailFastMode,
ParseMode, PermissiveMode}
-import org.apache.spark.sql.types._
-import org.apache.spark.util.Utils
-
-private[sql] object JsonInferSchema {
-
- /**
- * 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 infer[T](
- json: RDD[T],
- configOptions: JSONOptions,
- createParser: (JsonFactory, T) => JsonParser): StructType = {
- val parseMode = configOptions.parseMode
- val columnNameOfCorruptRecord = configOptions.columnNameOfCorruptRecord
-
- // In each RDD partition, perform schema inference on each row and merge
afterwards.
- val typeMerger = compatibleRootType(columnNameOfCorruptRecord, parseMode)
- val mergedTypesFromPartitions = json.mapPartitions { iter =>
- val factory = new JsonFactory()
- configOptions.setJacksonOptions(factory)
- iter.flatMap { row =>
- try {
- Utils.tryWithResource(createParser(factory, row)) { parser =>
- parser.nextToken()
- Some(inferField(parser, configOptions))
- }
- } catch {
- case e @ (_: RuntimeException | _: JsonProcessingException) =>
parseMode match {
- case PermissiveMode =>
- Some(StructType(Seq(StructField(columnNameOfCorruptRecord,
StringType))))
- case DropMalformedMode =>
- None
- case FailFastMode =>
- throw new SparkException("Malformed records are detected in
schema inference. " +
- s"Parse Mode: ${FailFastMode.name}.", e)
- }
- }
- }.reduceOption(typeMerger).toIterator
- }
-
- // Here we get RDD local iterator then fold, instead of calling `RDD.fold`
directly, because
- // `RDD.fold` will run the fold function in DAGScheduler event loop
thread, which may not have
- // active SparkSession and `SQLConf.get` may point to the wrong configs.
- val rootType =
mergedTypesFromPartitions.toLocalIterator.fold(StructType(Nil))(typeMerger)
-
- canonicalizeType(rootType, configOptions) match {
- case Some(st: StructType) => st
- case _ =>
- // canonicalizeType erases all empty structs, including the only one
we want to keep
- StructType(Nil)
- }
- }
-
- private[this] val structFieldComparator = new Comparator[StructField] {
- override def compare(o1: StructField, o2: StructField): Int = {
- o1.name.compareTo(o2.name)
- }
- }
-
- private def isSorted(arr: Array[StructField]): Boolean = {
- var i: Int = 0
- while (i < arr.length - 1) {
- if (structFieldComparator.compare(arr(i), arr(i + 1)) > 0) {
- return false
- }
- i += 1
- }
- true
- }
-
- /**
- * Infer the type of a json document from the parser's token stream
- */
- private def inferField(parser: JsonParser, configOptions: JSONOptions):
DataType = {
- import com.fasterxml.jackson.core.JsonToken._
- parser.getCurrentToken match {
- case null | VALUE_NULL => NullType
-
- case FIELD_NAME =>
- parser.nextToken()
- inferField(parser, configOptions)
-
- 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
-
- case VALUE_STRING => StringType
- case START_OBJECT =>
- val builder = Array.newBuilder[StructField]
- while (nextUntil(parser, END_OBJECT)) {
- builder += StructField(
- parser.getCurrentName,
- inferField(parser, configOptions),
- nullable = true)
- }
- val fields: Array[StructField] = builder.result()
- // Note: other code relies on this sorting for correctness, so don't
remove it!
- java.util.Arrays.sort(fields, structFieldComparator)
- StructType(fields)
-
- 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, configOptions))
- }
-
- ArrayType(elementType)
-
- case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT) if
configOptions.primitivesAsString => StringType
-
- case (VALUE_TRUE | VALUE_FALSE) if configOptions.primitivesAsString =>
StringType
-
- 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 =>
- val v = parser.getDecimalValue
- if (Math.max(v.precision(), v.scale()) <=
DecimalType.MAX_PRECISION) {
- DecimalType(Math.max(v.precision(), v.scale()), v.scale())
- } else {
- DoubleType
- }
- case FLOAT | DOUBLE if configOptions.prefersDecimal =>
- val v = parser.getDecimalValue
- if (Math.max(v.precision(), v.scale()) <=
DecimalType.MAX_PRECISION) {
- DecimalType(Math.max(v.precision(), v.scale()), v.scale())
- } else {
- DoubleType
- }
- case FLOAT | DOUBLE =>
- DoubleType
- }
-
- case VALUE_TRUE | VALUE_FALSE => BooleanType
- }
- }
-
- /**
- * Recursively canonicalizes inferred types, e.g., removes StructTypes with
no fields,
- * drops NullTypes or converts them to StringType based on provided options.
- */
- private def canonicalizeType(tpe: DataType, options: JSONOptions):
Option[DataType] = tpe match {
- case at: ArrayType =>
- canonicalizeType(at.elementType, options)
- .map(t => at.copy(elementType = t))
-
- case StructType(fields) =>
- val canonicalFields = fields.filter(_.name.nonEmpty).flatMap { f =>
- canonicalizeType(f.dataType, options)
- .map(t => f.copy(dataType = t))
- }
- // SPARK-8093: empty structs should be deleted
- if (canonicalFields.isEmpty) {
- None
- } else {
- Some(StructType(canonicalFields))
- }
-
- case NullType =>
- if (options.dropFieldIfAllNull) {
- None
- } else {
- Some(StringType)
- }
-
- case other => Some(other)
- }
-
- private def withCorruptField(
- struct: StructType,
- other: DataType,
- columnNameOfCorruptRecords: String,
- parseMode: ParseMode) = parseMode match {
- case PermissiveMode =>
- // If we see any other data type at the root level, we get records that
cannot be
- // parsed. So, we use the struct as the data type and add the corrupt
field to the schema.
- if (!struct.fieldNames.contains(columnNameOfCorruptRecords)) {
- // If this given struct does not have a column used for corrupt
records,
- // add this field.
- val newFields: Array[StructField] =
- StructField(columnNameOfCorruptRecords, StringType, nullable = true)
+: struct.fields
- // Note: other code relies on this sorting for correctness, so don't
remove it!
- java.util.Arrays.sort(newFields, structFieldComparator)
- StructType(newFields)
- } else {
- // Otherwise, just return this struct.
- struct
- }
-
- case DropMalformedMode =>
- // If corrupt record handling is disabled we retain the valid schema and
discard the other.
- struct
-
- case FailFastMode =>
- // If `other` is not struct type, consider it as malformed one and
throws an exception.
- throw new SparkException("Malformed records are detected in schema
inference. " +
- s"Parse Mode: ${FailFastMode.name}. Reasons: Failed to infer a common
schema. " +
- s"Struct types are expected, but `${other.catalogString}` was found.")
- }
-
- /**
- * Remove top-level ArrayType wrappers and merge the remaining schemas
- */
- private def compatibleRootType(
- columnNameOfCorruptRecords: String,
- parseMode: ParseMode): (DataType, DataType) => DataType = {
- // Since we support array of json objects at the top level,
- // we need to check the element type and find the root level data type.
- case (ArrayType(ty1, _), ty2) =>
- compatibleRootType(columnNameOfCorruptRecords, parseMode)(ty1, ty2)
- case (ty1, ArrayType(ty2, _)) =>
- compatibleRootType(columnNameOfCorruptRecords, parseMode)(ty1, ty2)
- // Discard null/empty documents
- case (struct: StructType, NullType) => struct
- case (NullType, struct: StructType) => struct
- case (struct: StructType, o) if !o.isInstanceOf[StructType] =>
- withCorruptField(struct, o, columnNameOfCorruptRecords, parseMode)
- case (o, struct: StructType) if !o.isInstanceOf[StructType] =>
- withCorruptField(struct, o, columnNameOfCorruptRecords, parseMode)
- // If we get anything else, we call compatibleType.
- // Usually, when we reach here, ty1 and ty2 are two StructTypes.
- case (ty1, ty2) => compatibleType(ty1, ty2)
- }
-
- private[this] val emptyStructFieldArray = Array.empty[StructField]
-
- /**
- * Returns the most general data type for two given data types.
- */
- def compatibleType(t1: DataType, t2: DataType): DataType = {
- TypeCoercion.findTightestCommonType(t1, t2).getOrElse {
- // t1 or t2 is a StructType, ArrayType, or an unexpected type.
- (t1, t2) match {
- // Double support larger range than fixed decimal, DecimalType.Maximum
should be enough
- // in most case, also have better precision.
- case (DoubleType, _: DecimalType) | (_: DecimalType, DoubleType) =>
- DoubleType
-
- case (t1: DecimalType, t2: DecimalType) =>
- val scale = math.max(t1.scale, t2.scale)
- val range = math.max(t1.precision - t1.scale, t2.precision -
t2.scale)
- if (range + scale > 38) {
- // DecimalType can't support precision > 38
- DoubleType
- } else {
- DecimalType(range + scale, scale)
- }
-
- case (StructType(fields1), StructType(fields2)) =>
- // Both fields1 and fields2 should be sorted by name, since
inferField performs sorting.
- // Therefore, we can take advantage of the fact that we're merging
sorted lists and skip
- // building a hash map or performing additional sorting.
- assert(isSorted(fields1), s"StructType's fields were not sorted:
${fields1.toSeq}")
- assert(isSorted(fields2), s"StructType's fields were not sorted:
${fields2.toSeq}")
-
- val newFields = new java.util.ArrayList[StructField]()
-
- var f1Idx = 0
- var f2Idx = 0
-
- while (f1Idx < fields1.length && f2Idx < fields2.length) {
- val f1Name = fields1(f1Idx).name
- val f2Name = fields2(f2Idx).name
- val comp = f1Name.compareTo(f2Name)
- if (comp == 0) {
- val dataType = compatibleType(fields1(f1Idx).dataType,
fields2(f2Idx).dataType)
- newFields.add(StructField(f1Name, dataType, nullable = true))
- f1Idx += 1
- f2Idx += 1
- } else if (comp < 0) { // f1Name < f2Name
- newFields.add(fields1(f1Idx))
- f1Idx += 1
- } else { // f1Name > f2Name
- newFields.add(fields2(f2Idx))
- f2Idx += 1
- }
- }
- while (f1Idx < fields1.length) {
- newFields.add(fields1(f1Idx))
- f1Idx += 1
- }
- while (f2Idx < fields2.length) {
- newFields.add(fields2(f2Idx))
- f2Idx += 1
- }
- StructType(newFields.toArray(emptyStructFieldArray))
-
- case (ArrayType(elementType1, containsNull1), ArrayType(elementType2,
containsNull2)) =>
- ArrayType(compatibleType(elementType1, elementType2), containsNull1
|| containsNull2)
-
- // The case that given `DecimalType` is capable of given
`IntegralType` is handled in
- // `findTightestCommonType`. Both cases below will be executed only
when the given
- // `DecimalType` is not capable of the given `IntegralType`.
- case (t1: IntegralType, t2: DecimalType) =>
- compatibleType(DecimalType.forType(t1), t2)
- case (t1: DecimalType, t2: IntegralType) =>
- compatibleType(t1, DecimalType.forType(t2))
-
- // strings and every string is a Json object.
- case (_, _) => StringType
- }
- }
- }
-}
http://git-wip-us.apache.org/repos/asf/spark/blob/776f299f/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 acca957..614f65f 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
@@ -3382,6 +3382,48 @@ object functions {
}
/**
+ * (Scala-specific) Parses a column containing a JSON string into a
`MapType` with `StringType`
+ * as keys type, `StructType` or `ArrayType` of `StructType`s 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
+ *
+ * @group collection_funcs
+ * @since 2.4.0
+ */
+ def from_json(e: Column, schema: Column): Column = {
+ from_json(e, schema, Map.empty[String, String].asJava)
+ }
+
+ /**
+ * (Java-specific) Parses a column containing a JSON string into a `MapType`
with `StringType`
+ * as keys type, `StructType` or `ArrayType` of `StructType`s 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.
+ *
+ * @group collection_funcs
+ * @since 2.4.0
+ */
+ def from_json(e: Column, schema: Column, options: java.util.Map[String,
String]): Column = {
+ withExpr(new JsonToStructs(e.expr, schema.expr, options.asScala.toMap))
+ }
+
+ /**
+ * Parses a column containing a JSON string and infers its schema.
+ *
+ * @param e a string column containing JSON data.
+ *
+ * @group collection_funcs
+ * @since 2.4.0
+ */
+ def schema_of_json(e: Column): Column = withExpr(new SchemaOfJson(e.expr))
+
+ /**
* (Scala-specific) Converts a column containing a `StructType`, `ArrayType`
of `StructType`s,
* a `MapType` or `ArrayType` of `MapType`s into a JSON string with the
specified schema.
* Throws an exception, in the case of an unsupported type.
http://git-wip-us.apache.org/repos/asf/spark/blob/776f299f/sql/core/src/test/resources/sql-tests/inputs/json-functions.sql
----------------------------------------------------------------------
diff --git a/sql/core/src/test/resources/sql-tests/inputs/json-functions.sql
b/sql/core/src/test/resources/sql-tests/inputs/json-functions.sql
index dc15d13..79fdd58 100644
--- a/sql/core/src/test/resources/sql-tests/inputs/json-functions.sql
+++ b/sql/core/src/test/resources/sql-tests/inputs/json-functions.sql
@@ -35,3 +35,7 @@ DROP VIEW IF EXISTS jsonTable;
-- from_json - complex types
select from_json('{"a":1, "b":2}', 'map<string, int>');
select from_json('{"a":1, "b":"2"}', 'struct<a:int,b:string>');
+
+-- infer schema of json literal
+select schema_of_json('{"c1":0, "c2":[1]}');
+select from_json('{"c1":[1, 2, 3]}', schema_of_json('{"c1":[0]}'));
http://git-wip-us.apache.org/repos/asf/spark/blob/776f299f/sql/core/src/test/resources/sql-tests/results/json-functions.sql.out
----------------------------------------------------------------------
diff --git
a/sql/core/src/test/resources/sql-tests/results/json-functions.sql.out
b/sql/core/src/test/resources/sql-tests/results/json-functions.sql.out
index 2b3288d..3d49323 100644
--- a/sql/core/src/test/resources/sql-tests/results/json-functions.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/json-functions.sql.out
@@ -1,5 +1,5 @@
-- Automatically generated by SQLQueryTestSuite
--- Number of queries: 28
+-- Number of queries: 30
-- !query 0
@@ -183,7 +183,7 @@ select from_json('{"a":1}', 1)
struct<>
-- !query 17 output
org.apache.spark.sql.AnalysisException
-Expected a string literal instead of 1;; line 1 pos 7
+Schema should be specified in DDL format as a string literal or output of the
schema_of_json function instead of 1;; line 1 pos 7
-- !query 18
@@ -274,3 +274,19 @@ select from_json('{"a":1, "b":"2"}',
'struct<a:int,b:string>')
struct<jsontostructs({"a":1, "b":"2"}):struct<a:int,b:string>>
-- !query 27 output
{"a":1,"b":"2"}
+
+
+-- !query 28
+select schema_of_json('{"c1":0, "c2":[1]}')
+-- !query 28 schema
+struct<schemaofjson({"c1":0, "c2":[1]}):string>
+-- !query 28 output
+struct<c1:bigint,c2:array<bigint>>
+
+
+-- !query 29
+select from_json('{"c1":[1, 2, 3]}', schema_of_json('{"c1":[0]}'))
+-- !query 29 schema
+struct<jsontostructs({"c1":[1, 2, 3]}):struct<c1:array<bigint>>>
+-- !query 29 output
+{"c1":[1,2,3]}
http://git-wip-us.apache.org/repos/asf/spark/blob/776f299f/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 7bf17cb..d3b2701 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
@@ -17,7 +17,7 @@
package org.apache.spark.sql
-import org.apache.spark.sql.functions.{from_json, lit, map, struct, to_json}
+import org.apache.spark.sql.functions._
import org.apache.spark.sql.test.SharedSQLContext
import org.apache.spark.sql.types._
@@ -311,7 +311,7 @@ class JsonFunctionsSuite extends QueryTest with
SharedSQLContext {
val errMsg1 = intercept[AnalysisException] {
df3.selectExpr("from_json(value, 1)")
}
- assert(errMsg1.getMessage.startsWith("Expected a string literal instead
of"))
+ assert(errMsg1.getMessage.startsWith("Schema should be specified in DDL
format as a string"))
val errMsg2 = intercept[AnalysisException] {
df3.selectExpr("""from_json(value, 'time InvalidType')""")
}
@@ -392,4 +392,17 @@ class JsonFunctionsSuite extends QueryTest with
SharedSQLContext {
checkAnswer(Seq("""{"{"f": 1}": "a"}""").toDS().select(from_json($"value",
schema)),
Row(null))
}
+
+ test("SPARK-24709: infers schemas of json strings and pass them to
from_json") {
+ val in = Seq("""{"a": [1, 2, 3]}""").toDS()
+ val out = in.select(from_json('value, schema_of_json(lit("""{"a":
[1]}"""))) as "parsed")
+ val expected = StructType(StructField(
+ "parsed",
+ StructType(StructField(
+ "a",
+ ArrayType(LongType, true), true) :: Nil),
+ true) :: Nil)
+
+ assert(out.schema == expected)
+ }
}
http://git-wip-us.apache.org/repos/asf/spark/blob/776f299f/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala
----------------------------------------------------------------------
diff --git
a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala
b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala
index 897424d..eab15b3 100644
---
a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala
+++
b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala
@@ -31,11 +31,11 @@ import org.apache.hadoop.io.compress.GzipCodec
import org.apache.spark.{SparkException, TestUtils}
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{functions => F, _}
-import org.apache.spark.sql.catalyst.json.{CreateJacksonParser, JacksonParser,
JSONOptions}
+import org.apache.spark.sql.catalyst.json.{CreateJacksonParser, JacksonParser,
JsonInferSchema, JSONOptions}
+import org.apache.spark.sql.catalyst.json.JsonInferSchema.compatibleType
import org.apache.spark.sql.catalyst.util.DateTimeUtils
import org.apache.spark.sql.execution.ExternalRDD
import org.apache.spark.sql.execution.datasources.DataSource
-import
org.apache.spark.sql.execution.datasources.json.JsonInferSchema.compatibleType
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.test.SharedSQLContext
import org.apache.spark.sql.types._
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