Consider the following Spark Code to construction a Dataframe.

a $=[(\text{'}$Chris$\text{'},$ 'Budweiser', $15),(\text{'}$Chris$\text{'},$ 'Becks', $5),(\text{'}$Chris$\text{'},$ 'Heineken', $2),(\text{'}$Bob$\text{'},$ 'Becks', $15),(\text{'}$Bob$\text{'},$ 'Budweiser', $10),(\text{'}$Bob$\text{'},$

'Heineken', $2),(\text{'}$Alice$\text{'},$ 'Heineken', $8)]$

rdd $=$ sc$.$ parallelize$($a$)$

df $=$ sqIContext.createDataFrame$($rdd$,[\text{'}$drinker$\text{'},$ 'beer', 'score'$])$

sqIContext.registerDataFrameAsTable$($df$,$ "drinkers"$)$

How can we get the total score of each beer brand?

We want to have the following answer from the above example: $($Your print out may be different $-$ values are important$)$

beer$=$'Becks', total score$=20$

beer$=$'Budweiser', total score $=25$

beer$=$'Heineken', total score $=12$