EMRNotes from researching how to quickly increase PySpark DataFrame data by vertically concatenating DataFrames. This simply reads a CSV from S3 then merges multiple DataFrames into one.
from pyspark.sql.types import *
from functools import reduce
from pyspark.sql import DataFrame
schema = StructType([
StructField('file', StringType(), False),
StructField('num', IntegerType(), False),
StructField('row', IntegerType(), False),
StructField('word', StringType(), False),
StructField('subtype1', StringType(), False),
StructField('subtype2', StringType(), False),
StructField('subtype3', StringType(), False),
StructField('subtype4', StringType(), False),
StructField('conjtype', StringType(), False),
StructField('conjugation', StringType(), False),
StructField('basic', StringType(), False),
StructField('ruby', StringType(), False),
StructField('pronunce', StringType(), False)
])
df1 = spark.read.csv(f's3://xxxxx/aozora_data.csv', schema=schema, header=True)
df2 = spark.read.csv(f's3://xxxxx/aozora_data.csv', schema=schema, header=True)
df3 = spark.read.csv(f's3://xxxxx/aozora_data.csv', schema=schema, header=True)
df4 = spark.read.csv(f's3://xxxxx/aozora_data.csv', schema=schema, header=True)
df5 = spark.read.csv(f's3://xxxxx/aozora_data.csv', schema=schema, header=True)
df6 = spark.read.csv(f's3://xxxxx/aozora_data.csv', schema=schema, header=True)
df7 = spark.read.csv(f's3://xxxxx/aozora_data.csv', schema=schema, header=True)
df8 = spark.read.csv(f's3://xxxxx/aozora_data.csv', schema=schema, header=True)
df9 = spark.read.csv(f's3://xxxxx/aozora_data.csv', schema=schema, header=True)
df10 = spark.read.csv(f's3://xxxxx/aozora_data.csv', schema=schema, header=True)
df_union=reduce(DataFrame.unionByName, [df1, df2, df3, df4, df5, df6, df7, df8, df9, df10])
df_union.count()