ALTER TABLE | Databricks on AWS

split_col = pyspark.sql.functions.split(df['my_str_col'], '-')
df = df.withColumn('NAME1', split_col.getItem(0))
df = df.withColumn('NAME2', split_col.getItem(1))

ALTER TABLE table_identifier ADD [IF NOT EXISTS]
    ( partition_spec [ partition_spec ... ] )

from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('abc').getOrCreate()

"""
A simple example demonstrating basic Spark SQL features using fictional
data inspired by a paper on determining the optimum length of chopsticks.
https://www.ncbi.nlm.nih.gov/pubmed/15676839
Run with:
  ./bin/spark-submit OptimumChopstick.py
"""
from __future__ import print_function
from pyspark.sql import SparkSession
from pyspark.sql.types import *
from pyspark.storagelevel import StorageLevel
#rdd.persist(StorageLevel.MEMORY_ONLY_SER)
​
# Get avg Food pinching effeciency by length
def AvgEffeciencyByLength(df):
    meansDf = df.groupby('ChopstickLength').mean('FoodPinchingEffeciency').orderBy('avg(FoodPinchingEffeciency)',ascending=0)
    return meansDf
​
# init
spark = SparkSession.builder.appName("Optimum Chopstick").getOrCreate()
sc = spark.sparkContext
input_loc = "s3://llubbe-gdelt-open-data/ChopstickEffeciency/"
​
# Read input by line
lines = sc.textFile(input_loc)
parts = lines.map(lambda l: l.split(","))
parts.persist(StorageLevel.MEMORY_ONLY_SER)
# Each line is converted to a tuple.
chopstickItems = parts.map(lambda p: (str(p[0]), float(p[1]), int(p[2]), int(p[3].strip())))
​
# Define a schema
fields = [StructField("TestID", StringType()),
          StructField("FoodPinchingEffeciency", DoubleType()), 
          StructField("Individual", IntegerType()), 
          StructField("ChopstickLength", IntegerType())]
schema = StructType(fields)
​
# Apply the schema to the RDD
chopsticksDF = spark.createDataFrame(chopstickItems, schema)
​
effeciencyByLength = AvgEffeciencyByLength(chopsticksDF)
effeciencyByLength.distinct().count()
​
moar_chopsticksDF = spark.read.load(input_loc, format="csv", schema=schema)
moar_effeciencyByLength = AvgEffeciencyByLength(moar_chopsticksDF)
moar_effeciencyByLength.distinct().count()
​
spark.stop()

val columnsToSum = List(col("var1"), col("var2"), col("var3"), col("var4"), col("var5"))

val output = input.withColumn("sums", columnsToSum.reduce(_ + _))

val classes = Seq(
  getClass,                   // To get the jar with our own code.
  classOf[mysql.jdbc.Driver]  // To get the connector.
)
val jars = classes.map(_.getProtectionDomain().getCodeSource().getLocation().getPath())
val conf = new SparkConf().setJars(jars)