![]() While writing a JSON file you can use several options. json("s3a://sparkbyexamples/json/ziprecords-new.json") Use the Spark DataFrameWriter object write() method on DataFrame to write a JSON file to Amazon S3 bucket. Write Spark DataFrame to JSON file on Amazon S3 Bucket Note: Besides the above options, the Spark JSON dataset also supports many other options, please refer to Spark documentation for the latest documents. dateFormatĭateFormat option to used to set the format of the input DateType and TimestampType columns. For example, if you want to consider a date column with a value “” set null on DataFrame. Using nullValues option you can specify the string in a JSON to consider as null. Options while reading JSON file nullValues " (path 's3a://sparkbyexamples/json/simple_zipcodes.json')") ![]() ("CREATE TEMPORARY VIEW zipcode USING json OPTIONS" Spark SQL also provides a way to read a JSON file by creating a temporary view directly from reading file using (“load json to temporary view”) json("s3a://sparkbyexamples/json/simple_zipcodes.json") ![]() Use the StructType class to create a custom schema, below we initiate this class and use add a method to add columns to it by providing the column name, data type and nullable option. If you know the schema of the file ahead and do not want to use the default inferSchema option for column names and types, use user-defined custom column names and type using schema option. Spark SQL provides StructType
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