Hi,
Need some ideas.
*Summary:*
I am working on a tool to slice and dice the amount of money I have spent
so far (meaning the whole data sample) on a given retailer so I have a
better idea of where I am wasting the money
*Approach*
Downloaded my bank statements from a given account in csv format from
inception till end of July. Read the data and stored it in ORC table.
I am interested for all bills that I paid using Debit Card (
transactiontype = "DEB") that comes out the account directly.
Transactiontype is the three character code lookup that I download as well.
scala> ll_18740868.printSchema
root
|-- transactiondate: date (nullable = true)
|-- transactiontype: string (nullable = true)
|-- sortcode: string (nullable = true)
|-- accountnumber: string (nullable = true)
|-- transactiondescription: string (nullable = true)
|-- debitamount: double (nullable = true)
|-- creditamount: double (nullable = true)
|-- balance: double (nullable = true)
The important fields are transactiondate, transactiontype,
transactiondescription and debitamount
So using analytics. windowing I can do all sorts of things. For example
this one gives me the last time I spent money on retailer XYZ and the amount
SELECT *
FROM (
select transactiondate, transactiondescription, debitamount
, rank() over (order by transactiondate desc) AS rank
from accounts.ll_18740868 where transactiondescription like '%XYZ%'
) tmp
where rank <= 1
And its equivalent using Windowing in FP
import org.apache.spark.sql.expressions.Window
val wSpec =
Window.partitionBy("transactiontype").orderBy(desc("transactiondate"))
ll_18740868.filter(col("transactiondescription").contains("XYZ")).select($"transactiondate",$"transactiondescription",
rank().over(wSpec).as("rank")).filter($"rank"===1).show
+---------------+----------------------+----+
|transactiondate|transactiondescription|rank|
+---------------+----------------------+----+
| 2015-12-15| XYZ LTD CD 4636 | 1|
+---------------+----------------------+----+
So far so good. But if I want to find all I spent on each retailer, then it
gets trickier as a retailer appears like below in the column
transactiondescription:
ll_18740868.where($"transactiondescription".contains("SAINSBURY")).select($"transactiondescription").show(5)
+----------------------+
|transactiondescription|
+----------------------+
| SAINSBURYS SMKT C...|
| SACAT SAINSBURYS ...|
| SAINSBURY'S SMKT ...|
| SAINSBURYS S/MKT ...|
| SACAT SAINSBURYS ...|
+----------------------+
If I look at them I know they all belong to SAINBURYS (food retailer). I
have done some crude grouping and it works somehow
//define UDF here to handle substring
val SubstrUDF = udf { (s: String, start: Int, end: Int) =>
s.substring(start, end) }
var cutoff = "CD" // currently used in the statement
val wSpec2 =
Window.partitionBy(SubstrUDF($"transactiondescription",lit(0),instr($"transactiondescription",
cutoff)-1))
ll_18740868.where($"transactiontype" === "DEB" &&
($"transactiondescription").isNotNull).select(SubstrUDF($"transactiondescription",lit(0),instr($"transactiondescription",
cutoff)-1).as("Retailer"),sum($"debitamount").over(wSpec2).as("Spent")).distinct.orderBy($"Spent").collect.foreach(println)
However, I really need to extract the "keyword" retailer name from
transactiondescription column And I need some ideas about the best way of
doing it. Is this possible in Spark?
Thanks
Dr Mich Talebzadeh
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