Credit Card Data Analysis using PySpark (Get the total amount spent by each user) with execution plan

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Input details:

#● File has json records
#● Each record has fields:
#○ user_id
#○ card_num
#○ merchant
#○ category
#○ amount
#○ ts
### Below analysis to be done

Sample data:

+------+--------+---------+--------+----------+-------+|amount|card_num| category|merchant|        ts|user_id|+------+--------+---------+--------+----------+-------+|   243|   C_108|     food|   M_102|1579532902|  U_104||   699|   C_106|cosmetics|   M_103|1581759040|  U_103||   228|   C_104| children|   M_110|1584161986|  U_103|

Application: Get the total amount spent by each user

Solution:

cardTnDF = spark.read.json(“card_transactions.json”)
cardTnDF.show(3)
cardTnDF.groupBy(‘user_id’).sum(‘amount’).collect()

Output:

+------+--------+---------+--------+----------+-------+|amount|card_num| category|merchant|        ts|user_id|+------+--------+---------+--------+----------+-------+|   243|   C_108|     food|   M_102|1579532902|  U_104||   699|   C_106|cosmetics|   M_103|1581759040|  U_103||   228|   C_104| children|   M_110|1584161986|  U_103|+------+--------+---------+--------+----------+-------+only showing top 3 rows
Out[3]:
[Row(user_id='U_102', sum(amount)=126475), Row(user_id='U_104', sum(amount)=110939), Row(user_id='U_101', sum(amount)=125784), Row(user_id='U_103', sum(amount)=131539)]

Execution Plan:

== Physical Plan ==AdaptiveSparkPlan (10)+- == Final Plan ==   * HashAggregate (6)   +- AQEShuffleRead (5)      +- ShuffleQueryStage (4)         +- Exchange (3)            +- * HashAggregate (2)               +- Scan json  (1)+- == Initial Plan ==   HashAggregate (9)   +- Exchange (8)      +- HashAggregate (7)         +- Scan json  (1)(1) Scan json Output [2]: [amount#277L, user_id#282]Batched: falseLocation: InMemoryFileIndex [file:/Users/dpq/Practice/card_transactions.json]ReadSchema: struct<amount:bigint,user_id:string>(2) HashAggregate [codegen id : 1]Input [2]: [amount#277L, user_id#282]Keys [1]: [user_id#282]Functions [1]: [partial_sum(amount#277L)]Aggregate Attributes [1]: [sum#330L]Results [2]: [user_id#282, sum#331L](3) ExchangeInput [2]: [user_id#282, sum#331L]Arguments: hashpartitioning(user_id#282, 200), ENSURE_REQUIREMENTS, [id=#469](4) ShuffleQueryStageOutput [2]: [user_id#282, sum#331L]Arguments: 0(5) AQEShuffleReadInput [2]: [user_id#282, sum#331L]Arguments: coalesced(6) HashAggregate [codegen id : 2]Input [2]: [user_id#282, sum#331L]Keys [1]: [user_id#282]Functions [1]: [sum(amount#277L)]Aggregate Attributes [1]: [sum(amount#277L)#326L]Results [2]: [user_id#282, sum(amount#277L)#326L AS sum(amount)#327L](7) HashAggregateInput [2]: [amount#277L, user_id#282]Keys [1]: [user_id#282]Functions [1]: [partial_sum(amount#277L)]Aggregate Attributes [1]: [sum#330L]Results [2]: [user_id#282, sum#331L](8) ExchangeInput [2]: [user_id#282, sum#331L]Arguments: hashpartitioning(user_id#282, 200), ENSURE_REQUIREMENTS, [id=#459](9) HashAggregateInput [2]: [user_id#282, sum#331L]Keys [1]: [user_id#282]Functions [1]: [sum(amount#277L)]Aggregate Attributes [1]: [sum(amount#277L)#326L]Results [2]: [user_id#282, sum(amount#277L)#326L AS sum(amount)#327L](10) AdaptiveSparkPlanOutput [2]: [user_id#282, sum(amount)#327L]Arguments: isFinalPlan=true

 

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