from pyspark.sql.typesimportStructType,StructField,StringType,IntegerType spark=SparkSession.builder.master("local[1]")\.appName('SparkByExamples.com')\.getOrCreate()data=[("James","","Smith","36636","M",3000),("Michael","Rose","","40288","M",4000),("Robert","","Williams","4211...
在Spark中,可以使用编程方式为所有字段生成StructType作为StringType。首先,需要导入必要的Spark相关库: 代码语言:txt 复制 from pyspark.sql.types import StructField, StructType, StringType 接下来,可以通过读取数据源,如CSV文件,获取数据集的schema。假设我们有一个CSV文件data.csv,其内容如下...
I would like to get the seperatedfper level fromJSONfile. Below code allows me to go 2 level deep but later I cant useexplodefunctiondue to data type mismatch: input to function explode should be array or map type, not structerror ondf_4. My code andJSONstructure: start_df = spark.r...
这里有一种方法可以遍历这些字段并动态修改它们的名称。首先使用main.schema.fields[0].dataType.fields...
这里有一种方法可以遍历这些字段并动态修改它们的名称。首先使用main.schema.fields[0].dataType.fields...
The return type of the udf has been changed to a sequence of string tuples. This udf can now be registered in Pyspark with frompyspark.sqlimporttypesasT rt = T.ArrayType(T.StructType([T.StructField("_1",T.StringType()), T.StructField("_2",T.StringType())]))...
但我想补充的是，为了让您的工作更轻松，您应该将两个JSON文件放在同一个目录中，然后将它们读入，...
# 需要导入模块: from pyspark.sql import types [as 别名]# 或者: from pyspark.sql.types importStructField[as 别名]defregister_udfs(self, sess, sc):"""Register UDFs to be used in SQL queries. :type sess: `pyspark.sql.SparkSession` ...
在配置单元中，Map列的键必须是基元（即不是结构）。https://cwiki.apache.org/confluence/display/...
but it should've been `"Lee"`. In this case, we need to be able to infer the schema with a `StructType` instead of a `MapType`. Therefore, this PR proposes adding an new configuration `spark.sql.pyspark.inferNestedDictAsStruct.enabled` to handle which type is used for inferring neste...