最后,我们使用`from_json`函数将JSON字符串解析为结构化的数据,并将结果保存在DataFrame`parsedDF`中。##总结在Spark SQL中,使用`from_json`函数解析JSON数据是一种常见的操作。然而,当解析出现报错时,我们需要检查JSON格式、模式设置、数据类型对齐和编码问题等方面,以确保解析能够顺利进行。通过正确设置模式和处理数...
scala> import org.apache.spark.sql.{Row} scala> val st = new StructType().add("c1", LongType).add("c2", ArrayType(new StructType().add("c3", LongType).add("c4", StringType))) scala> val df1 = Seq("""{"c2": [19], "c1": 123456}""").toDF("c0") scala> df1.write.mo...
您可以使用DataType并“验证” JSON字符串(使用Spark在from_json的幕后使用的DataType.fromJson)。 import org.apache.spark.sql.types.DataType val dt = DataType.fromJson(schemaAsJson) scala> println(dt.sql) STRUCT<`firstName`: STRING, `lastName`: STRING, `email`: STRING, `addresses`: ARRAY<ST...
让我们首先使用Spark API for Scala定义模式。
schema_spark_3 = ArrayType(StructType([StructField("id",StringType(),True),StructField("name",StringType(),True)])) from pyspark.sql.functions import col, from_json display( df.select(col('value'), from_json(col('value'), schema_spark_3, {"mode" : "PERMISSIVE"})) ...
在IDEA中运行Scala脚本访问执行SparkSQL时: df.show() 出现报错信息: 119/12/06 14:26:17 INFO SparkContext: Created broadcast 2 from show at Student.scala:162Exception in thread "main"org.apache.spark.sql.AnalysisException: Since Spark 2.3, the queries from raw JSON/CSV files are disallowed when...
json from_unixtime函数在spark-sql中没有给出正确的输出我得到了解决方案，Arrival_Time和Creation_Time...
GeoAnalytics Engine is an interface for Apache Spark that provides a collection of spatial SQL functions and spatial analysis tools that can be run in a distributed environment using Python code.
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PythonPythonSQLScala Use dark colors for code blocksCopy fromgeoanalytics_fabric.sqlimportfunctionsasSTline_geojson ='{"type": "LineString","coordinates": [[-7489594.84,5178779.67],[-7474281.07,5176558.51],[-7465977.43,5179778.83]]}'df = spark.createDataFrame([(line_geojson,)],["geojson"])df...