Implement distributed query planner for simple aggregate queries about ballista HOT 2 CLOSED

== Parsed Logical Plan ==
'Aggregate ['passenger_count], ['passenger_count, unresolvedalias('MIN('fare_amount), None), unresolvedalias('MAX('fare_amount), None)]
+- 'UnresolvedRelation `tripdata`

== Analyzed Logical Plan ==
passenger_count: int, min(fare_amount): double, max(fare_amount): double
Aggregate [passenger_count#3], [passenger_count#3, min(fare_amount#10) AS min(fare_amount)#44, max(fare_amount#10) AS max(fare_amount)#45]
+- SubqueryAlias `tripdata`
   +- Relation[VendorID#0,tpep_pickup_datetime#1,tpep_dropoff_datetime#2,passenger_count#3,trip_distance#4,RatecodeID#5,store_and_fwd_flag#6,PULocationID#7,DOLocationID#8,payment_type#9,fare_amount#10,extra#11,mta_tax#12,tip_amount#13,tolls_amount#14,improvement_surcharge#15,total_amount#16] csv

== Optimized Logical Plan ==
Aggregate [passenger_count#3], [passenger_count#3, min(fare_amount#10) AS min(fare_amount)#44, max(fare_amount#10) AS max(fare_amount)#45]
+- Project [passenger_count#3, fare_amount#10]
   +- Relation[VendorID#0,tpep_pickup_datetime#1,tpep_dropoff_datetime#2,passenger_count#3,trip_distance#4,RatecodeID#5,store_and_fwd_flag#6,PULocationID#7,DOLocationID#8,payment_type#9,fare_amount#10,extra#11,mta_tax#12,tip_amount#13,tolls_amount#14,improvement_surcharge#15,total_amount#16] csv

== Physical Plan ==
*(2) HashAggregate(keys=[passenger_count#3], functions=[min(fare_amount#10), max(fare_amount#10)], output=[passenger_count#3, min(fare_amount)#44, max(fare_amount)#45])
+- Exchange hashpartitioning(passenger_count#3, 200)
   +- *(1) HashAggregate(keys=[passenger_count#3], functions=[partial_min(fare_amount#10), partial_max(fare_amount#10)], output=[passenger_count#3, min#48, max#49])
      +- *(1) FileScan csv [passenger_count#3,fare_amount#10] Batched: false, Format: CSV, Location: InMemoryFileIndex[file:/mnt/ssd/nyc_taxis/csv], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<passenger_count:int,fare_amount:double>