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sparktacle's Issues

Remove internal detail steps

(5) Exchange
Input [2]: [user_id#505, sum#533]
Arguments: hashpartitioning(user_id#505, 16), ENSURE_REQUIREMENTS, [id=#149]

(6) ShuffleQueryStage
Output [2]: [user_id#505, sum#533]
Arguments: 0

(7) AQEShuffleRead
Input [2]: [user_id#505, sum#533]
Arguments: coalesced

(13) AdaptiveSparkPlan
Output [2]: [product_id#505, sum(sales_quantity)#529]
Arguments: isFinalPlan=true

These can be removed safely for now since they are computation details

Parse scan parquet

(1) Scan parquet 
Output [5]: [user_id#499, date#505, sales_amount#502, timestamp#504]
Batched: true
Location: TahoeLogFileIndex [file:/file/path]
ReadSchema: struct<...>
(1) Scan csv 
Output [1]: [_c0#678]
Batched: false
Location: InMemoryFileIndex [file:/workspaces/dev/rocks/tmp]
ReadSchema: struct<_c0:string>

We should be able to parse these as a node in the graph.

Parse HashAggregate

Parse HashAggregate and collapse into a single node

(10) HashAggregate
Input [2]: [sales_amount#499, user_id#505]
Keys [1]: [user_id#505]
Functions [1]: [partial_sum(sales_amount#499)]
Aggregate Attributes [1]: [sum#532]
Results [2]: [user_id#505, sum#533]

(11) Exchange
Input [2]: [user_id#505, sum#533]
Arguments: hashpartitioning(user_id#505, 16), ENSURE_REQUIREMENTS, [id=#131]

(12) HashAggregate
Input [2]: [user_id#505, sum#533]
Keys [1]: [user_id#505]
Functions [1]: [sum(sales_amount#499)]
Aggregate Attributes [1]: [sum(sales_amount#499)#528]
Results [2]: [user_id#505, sum(sales_amount#499)#528 AS sum(sales_amount)#529]

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