Writing a CSV With Column Names and Reading a CSV File Which Is Being Generated from a Sparksql Dataframe in Pyspark

writing a csv with column names and reading a csv file which is being generated from a sparksql dataframe in Pyspark

Try

df.coalesce(1).write.format('com.databricks.spark.csv').save('path+my.csv',header = 'true')

Note that this may not be an issue on your current setup, but on extremely large datasets, you can run into memory problems on the driver. This will also take longer (in a cluster scenario) as everything has to push back to a single location.

Pyspark: Write CSV from JSON file with struct column

Use explode on array and select("struct.*") on struct.

df.select("trial", "id", explode('history').alias('history')),
  .select('id', 'history.*', 'trial.*'))

Write csv file as per column name in spark

You can specify the output to be partitioned by date:

result.repartition("date")\
          .write\
          .partitionBy("date")\
          .mode ("overwrite")\
          .format("com.databricks.spark.csv")\
          .option("header", "true")\
          .save("hdfs path")

which should give you folder names like date=01-01-2021.

Pyspark create temp view from dataframe

Spark operations like sql() do not process anything by default. You need to add .show() or .collect() to get results.

---

Related Topics