For Spark 2.1+, you can use from_json
which allows the preservation of the other non-json columns within the dataframe as follows:
from pyspark.sql.functions import from_json, col
json_schema = spark.read.json(df.rdd.map(lambda row: row.json)).schema
df.withColumn('json', from_json(col('json'), json_schema))
You let Spark derive the schema of the json string column. Then the df.json
column is no longer a StringType, but the correctly decoded json structure, i.e., nested StrucType
and all the other columns of df
are preserved as-is.
You can access the json content as follows:
df.select(col('json.header').alias('header'))