object JsonFileTest {
def main(args: Array[String]): Unit = {
val spark = SparkSession
.builder()
.master("local")
.appName("JsonFileTest")
.getOrCreate()
import spark.implicits._
//将parquet文件数据转化成json文件数据
val sessionDf = spark.read.parquet(s"${BASE_PATH}/trackerSession")
sessionDf.show()
sessionDf.write.json(s"${BASE_PATH}/json")
//读取json文件数据
val jsonDF = spark.read.json(s"${BASE_PATH}/json")
jsonDF.show()
//可以从JSON Dataset(类型为String)中创建一个DF
val jsonDataset = spark.createDataset(
"""{"name":"Yin","address":{"city":"Columbus","state":"Ohio"}}""" :: Nil)
val otherJsonDF = spark.read.json(jsonDataset)
otherJsonDF.show()
//primitivesAsString(默认为false) 表示将基本类型转化为string类型，这里的基本类型包括：boolean、int、long、float、double
//prefersDecimal(默认是false)表示在primitivesAsString为false的时候，将float，double转成DecimalType
val jsonDataset_1 = spark.createDataset(
"""{"name":"Yin","address":{"is_old":true,"area":23000.34}}""" :: Nil)
var otherJsonDF_1 = spark.read.json(jsonDataset_1)
otherJsonDF_1.printSchema()
/*
root
|-- address: struct (nullable = true)
| |-- area: double (nullable = true)
| |-- is_old: boolean (nullable = true)
|-- name: string (nullable = true)
*/
var optsMap = Map("primitivesAsString" -> "true", "prefersDecimal" -> "true")
otherJsonDF_1 = spark.read.options(optsMap).json(jsonDataset_1)
otherJsonDF_1.printSchema()
/*
root
|-- address: struct (nullable = true)
| |-- area: string (nullable = true)
| |-- is_old: string (nullable = true)
|-- name: string (nullable = true)
*/
optsMap = Map("primitivesAsString" -> "false", "prefersDecimal" -> "true")
otherJsonDF_1 = spark.read.options(optsMap).json(jsonDataset_1)
otherJsonDF_1.printSchema()
/*
root
|-- address: struct (nullable = true)
| |-- area: decimal(7,2) (nullable = true)
| |-- is_old: boolean (nullable = true)
|-- name: string (nullable = true)
*/
//allowComments(默认是false)，表示是否支持json中含有java/c格式的注释
spark.read.option("allowComments", "true").json(Seq("""{"name":/* hello */"Yin","address":{"is_old":true,"area":23000.34}}""").toDS()).show()
//allowUnquotedFieldNames(默认是false)，表示是否支持json中含有没有引号的域名
spark.read.option("allowUnquotedFieldNames", "true").json(Seq("""{name:"Yin","address":{"is_old":true,"area":23000.34}}""").toDS()).show()
//allowSingleQuotes(默认是true)，表示是否支持json中含有单引号的域名或者值
spark.read.option("allowSingleQuotes", "true").json(Seq("""{'name':'Yin',"address":{"is_old":true,"area":23000.34}}""").toDS()).show()
//allowNumericLeadingZeros(默认是false)，表示是否支持json中含有以0开头的数值
spark.read.option("allowNumericLeadingZeros", "true").json(Seq("""{'name':'Yin',"address":{"is_old":true,"area":0023000.34}}""").toDS()).show()
//allowNonNumericNumbers(默认是false)，表示是否支持json中含有NaN(not a number)
spark.read.option("allowNonNumericNumbers", "true").json(Seq("""{'name':'Yin',"address":{"is_old":true,"area":NaN}}""").toDS()).show()
//allowBackslashEscapingAnyCharacter(默认是false)，表示是否支持json中含有反斜杠，且将反斜杠忽略掉
spark.read.option("allowBackslashEscapingAnyCharacter", "true").json(Seq("""{'name':'Yin',"address":{"is_old":true,"area":"\$23000"}}""").toDS()).show()
//mode(默认是PERMISSIVE)，表是碰到格式解析错误的json的处理行为是：
//PERMISSIVE 表示比较宽容的。如果某条格式错误，则新增一个字段，字段名为columnNameOfCorruptRecord的值，字段的值是错误格式的json字符串，其他的是null
spark.read.option("mode", "PERMISSIVE").json(Seq("""{'name':'Yin',"address":{"is_old":true,"area":3000}}""",
"""{'name':'Yin',"address":{"is_old":true,"area":\3000}}""").toDS()).show()
/*
+--------------------+-----------+----+
| _corrupt_record| address|name|
+--------------------+-----------+----+
| null|[3000,true]| Yin|
|{'name':'Yin',"ad...| null|null|
+--------------------+-----------+----+
*/
spark.read.option("mode", "PERMISSIVE").option("columnNameOfCorruptRecord", "customer_column").json(
Seq("""{'name':'Yin',"address":{"is_old":true,"area":3000}}""",
"""{'name':'Yin',"address":{"is_old":true,"area":\3000}}""").toDS()).show()
/*
+-----------+--------------------+----+
| address| customer_column|name|
+-----------+--------------------+----+
|[3000,true]| null| Yin|
| null|{'name':'Yin',"ad...|null|
+-----------+--------------------+----+
*/
//DROPMALFORMED 表示丢掉错误格式的那条记录
spark.read.option("mode", "DROPMALFORMED").json(Seq("""{'name':'Yin',"address":{"is_old":true,"area":3000}}""",
"""{'name':'Yin',"address":{"is_old":true,"area":\3000}}""").toDS()).show()
/*
+-----------+----+
| address|name|
+-----------+----+
|[3000,true]| Yin|
+-----------+----+
*/
//FAILFAST 碰到解析错误的记录直接报错
spark.read.option("mode", "FAILFAST").json(Seq("""{'name':'Yin',"address":{"is_old":true,"area":3000}}""",
"""{'name':'Yin',"address":{"is_old":true,"area":\3000}}""").toDS()).show()
//dateFormat(默认值为yyyy-MM-dd) 表示json中时间的字符串格式(对应着DataType)
val customSchema = new StructType(Array(StructField("name", StringType, true),
StructField("date", DateType, true)))
val dataFormatDF =
spark.read.schema(customSchema).option("dateFormat", "dd/MM/yyyy HH:mm").json(Seq(
"""{'name':'Yin',"date":"26/08/2015 18:00"}""").toDS())
dataFormatDF.write.mode(SaveMode.Overwrite).option("dateFormat", "yyyy/MM/dd").json("testjson")
spark.read.json("testjson").show()
//timestampFormat(默认值为yyyy-MM-dd'T'HH:mm:ss.SSSZZ) 表示json中时间的字符串格式(对应着TimestampType)
val customSchema_1 = new StructType(Array(StructField("name", StringType, true),
StructField("date", TimestampType, true)))
val timestampFormatDf =
spark.read.schema(customSchema_1).option("timestampFormat", "dd/MM/yyyy HH:mm").json(Seq(
"""{'name':'Yin',"date":"26/08/2015 18:00"}""").toDS())
val optMap = Map("timestampFormat" -> "yyyy/MM/dd HH:mm", DateTimeUtils.TIMEZONE_OPTION -> "GMT")
timestampFormatDf.write.mode(SaveMode.Overwrite).format("json").options(optMap).save("test.json")
spark.read.json("test.json").show()
//compression 压缩格式，支持的压缩格式有：
//none 和 uncompressed表示不压缩
//bzip2、deflate、gzip、lz4、snappy
timestampFormatDf.write.mode(SaveMode.Overwrite).option("compression", "gzip").json("test.json")
//multiLine 表示是否支持一条json记录拆分成多行
val primitiveFieldAndType: Dataset[String] = spark.createDataset(spark.sparkContext.parallelize(
"""{"string":"this is a simple string.",
"integer":10,
"long":21474836470,
"bigInteger":92233720368547758070,
"double":1.7976931348623157E308,
"boolean":true,
"null":null
}""" ::
"""{"string":"this is a simple string.",
| "integer":10,
| "long":21474836470,
| "bigInteger":92233720368547758070,
| "double":1.7976931348623157E308,
| "boolean":true,
| "null":null
| }""" :: Nil))(Encoders.STRING)
primitiveFieldAndType.toDF("value").write.mode(SaveMode.Overwrite).option("compression", "GzIp").text(s"${BASE_PATH}/primitiveFieldAndType")
val multiLineDF = spark.read.option("multiLine", false).json(s"${BASE_PATH}/primitiveFieldAndType")
multiLineDF.show()
spark.stop()
}
}