-- We are defining the properties of a monadic data type ( aka the Box ) -- it should support the bind (>>=) operator. -- The bind operator takes care of peeking inside the box -- and deciding if the function should be applied -- it should support the return function -- Just to put the contents in the box class Monad m where (>>=) :: m a -> (a -> m b) -> m b return :: a -> m a -- The Maybe data type. It can be either Nothing or can have Just x ( like Just 10 ) Maybe = Nothing | Just x -- Defining the Maybe monadic type instance Monad Maybe where Nothing >>= f = Nothing -- if the Maybe box is Nothing, don't apply the function, return Nothing Just x >>= f = Just (f x) -- if it contains a value, apply the function and put it inside the box ( Just result ) return x = Just x -- put the content inside a box

/* Full Screen Background Image CSS3 */ #container { background: url(images/bg.jpg) no-repeat center center fixed; /* around 1900px wide*/ -webkit-background-size: cover; -moz-background-size: cover; -o-background-size: cover; background-size: cover; } @media only screen and (max-width: 1024px) and (orientation:landscape) { #container { background: url(images/mediumBG.jpg) 50% no-repeat fixed; !important; background-size: 100% auto; -webkit-background-size: cover; -moz-background-size: cover; -o-background-size: cover; background-size: cover; } } @media only screen and (min-width: 768px) and (max-width: 991px) { #container { background: url(images/mediumTallBG.jpg) 50% 80% no-repeat fixed !important -webkit-background-size: cover; -moz-background-size: cover; -o-background-size: cover; background-size: cover; } } @media only screen and (min-width: 0px) and (max-width: 767px) { #container { background: url(images/smallBG.jpg) 50% 80% no-repeat fixed !important; background-size: auto !important; -webkit-background-size: cover; -moz-background-size: cover; -o-background-size: cover; background-size: cover; } }

import com.databricks.spark.avro._ import org.apache.spark.sql.SparkSession val spark = SparkSession.builder().master("yarn").getOrCreate() val df = spark.read.avro("*.avro").select(explode(col("extraOptions"))).select("extraOptions.*")

beeline -u $CRM_CONNECTION -n $CRM_USER -p $CRM_PASSWORD --showHeader=false --silent=true --outputformat=csv2 --isolation=TRANSACTION_READ_COMMITTED --fastConnect=true -e "$1" 2>/dev/null