4 .runQueries.txt

SELECT COUNT(*) FROM flights;


SELECT *
FROM flights
ORDER BY random()
LIMIT 10;

This query actually assigns a random number to all 96 million rows, sorts them by the random number and then returns the first 10 results.



Now that you have the data loaded, the next step is to perform queries to find underlying patterns in the data and to help drive business decisions.


SELECT
  carrier,
  SUM (departures)
FROM flights
GROUP BY carrier
ORDER BY 2 DESC
LIMIT 10;


Run the above query.
Question: Who are the top 3 carriers by number of departures?

Change departures to 
passengers
 and run it again to view top carriers by passengers carried.
Question: Who are the top 3 carriers by passengers carried?

Change departures to 
miles
 and run it again to view top carriers by miles flown.
Question: Who are the top 3 carriers by miles flown?

Change departures to 
passengers * miles
 and run it again to view top carriers by passenger-miles.
Question: Who are the top 3 carriers by passenger-miles?

Change departures to 
freight_pounds
 and run it again to view top carriers by freight transported.
Question: Who are the top 3 carriers of freight? (You should be able to guess this one!)

Each of these queries is performing calculations against almost 100 million rows of data, but they each take only a few seconds to run. Adding additional compute nodes will make the queries run even faster.