Reading and Writing Data

Reading in Data

...from Mounted Storage

dataset = sqlContext.read.format('csv') \
.options(header='true', inferSchema='true', delimiter= ',') \
.load('/mnt/<FOLDERNAME>/<FILENAME>.csv')
## or spark.read.format('csv')...
## Formats: json, parquet, jdbc, orc, libsvm, csv, text, avro

...when Schema Inference Fails

from pyspark.sql.types import *
schema = StructType([StructField('ID', IntegerType(), True),
StructField('Value', DoubleType(), True),
StructField('Category', StringType(), True),
StructField('Date', DateType(), True)])
dataset = sqlContext.read.format('csv') \
.schema(schema) \
.options(header='true', delimiter= ',') \
.load('/mnt/<FOLDERNAME>/<FILENAME>.csv')

Writing out Data

df.coalesce(1) \
.write.format("com.databricks.spark.csv") \
.option("header", "true") \
.save("file.csv")

Other Resources

Apache Spark Data Sources Documentation: https://spark.apache.org/docs/latest/sql-data-sources.html