from pyspark.sql import SparkSession from pyspark.sql.functions import explode, col from pyspark.sql.types import StructType, StructField, StringType, ArrayType spark = SparkSession.builder \ .appName("Read Nested JSON with Schema") \ .getOrCreate() # Read the JSON file with specified options...
你有这个功能from_json那就行了。它将转换您的字符串，然后您可以使用explode。
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Using xplode array and map columns torows Explode nested array into rows Using External Data Sources In real-time applications, Data Frames are created from external sources, such as files from the local system, HDFS, S3 Azure, HBase, MySQL table, etc. Supported file formats Apache Spark, by...
将单行或多行（多行）JSON 文件读取到 PySpark DataFrame 并 write.json("path") 保存或写入 JSON 文...
对于spark 2.4+，可以使用拆分和变换的组合将字符串转换为二维数组。然后可以将此数组的单个条目分别转换...
F.array_distinct('my_array'))# Map over & transform array elements – F.transform(col, func: col -> col)df=df.withColumn('elem_ids',F.transform(F.col('my_array'),lambdax:x.getField('id')))# Return a row per array element – F.explode(col)df=df.select(F.explode('my_array'...
Explode nested array into rows Using External Data Sources In real-time applications, Data Frames are created from external sources, such as files from the local system, HDFS, S3 Azure, HBase, MySQL table, etc. Supported file formats Apache Spark, by default, supports a rich set of APIs ...
1.解压缩的文件有多大？Gzip在压缩json和文本方面做得很好。当你加载gzip文件时，spark将解压缩并将结果...
由于您的JSON具有相同的结构，您可以尝试以下变通方案，使用select划分JSON