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54 changes: 17 additions & 37 deletions script.spark
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Original file line number Diff line number Diff line change
@@ -1,4 +1,3 @@

import com.amazon.deequ.{VerificationSuite, VerificationResult}
import com.amazon.deequ.VerificationResult.checkResultsAsDataFrame
import com.amazon.deequ.checks.{Check, CheckLevel}
Expand All @@ -8,21 +7,12 @@ import com.amazon.deequ.schema
import com.amazon.deequ.schema.{RowLevelSchema,RowLevelSchemaValidator}

var demo = """


BasicEvaluation
StatisticalEvaluation

ReportViolations


var suggestions = SuggestConstraints()
suggestions.select("_1", "_2").show(100, 50)


suggestions.select("_1", "_2", "_3").show(100, 100)
showEvaluation
EvaluateDataQuality

"""

val args = sc.getConf.get("spark.driver.args").split("\\s+")
Expand Down Expand Up @@ -76,75 +66,67 @@ def StatisticalEvaluation(): Unit = {

def SuggestConstraints(): org.apache.spark.sql.Dataset[(String, String, String)] = {
val suggestionResult = { ConstraintSuggestionRunner().onData(dataset).addConstraintRules(Rules.DEFAULT).run()}
suggestionResult.constraintSuggestions.flatMap {
case (column, suggestions) =>
suggestionResult.constraintSuggestions.flatMap {
case (column, suggestions) =>
suggestions.map { constraint =>
(column, constraint.description, constraint.codeForConstraint)
}
}
}.toSeq.toDS()

}
var suggestions = SuggestConstraints()
// suggestions.coalesce(1).write.mode("overwrite").format("com.databricks.spark.csv").option("header", "true").save(output_bucket)
/////////////////////////////////////////////////////////////////////////////////////////

def showEvaluation = """
def EvaluateDataQuality(): Unit = {


val verificationResult: VerificationResult = { VerificationSuite().onData(dataset).addCheck(
Check(CheckLevel.Error, "Review Check")

.isUnique("id")

.isUnique("id")
.isComplete("purpose_cat")
.isComplete("annual_inc")
.isComplete("total_pymnt")
.isComplete("grade_cat")

.isComplete("interest_rate")
.isComplete("interest_rate")
.isNonNegative("interest_rate")

.hasMax("loan_amount", _ == 5000)

.isContainedIn("term", Array(" 36 months", " 60 x months"))
.isContainedIn("loan_condition", Array("Good Loan"), _ >= 0.95, Some("It should be above 0.95!"))
.isContainedIn("term", Array(" 36 months", " 60 months"))
.isContainedIn("loan_condition", Array("Good Loan"), _ >= 0.95, Some("It should be above 0.95!"))

).run()
).run()
}
"""


def EvaluateDataQuality(): Unit = {


val verificationResult: VerificationResult = { VerificationSuite().onData(dataset).addCheck(
Check(CheckLevel.Error, "Review Check")

.isUnique("id")

.isComplete("purpose_cat")
.isComplete("annual_inc")
.isComplete("total_pymnt")
.isComplete("grade_cat")

.isComplete("interest_rate")
.isComplete("interest_rate")
.isNonNegative("interest_rate")

.hasMax("loan_amount", _ == 5000)

.isContainedIn("term", Array(" 36 months", " 60 months"))
.isContainedIn("loan_condition", Array("Good Loan"), _ >= 0.95, Some("It should be above 0.95!"))
.isContainedIn("loan_condition", Array("Good Loan"), _ >= 0.95, Some("It should be above 0.95!"))

).run()
).run()
}

val resultDataFrame = checkResultsAsDataFrame(spark, verificationResult)

// resultDataFrame.show(20, 100)
resultDataFrame.select("constraint", "constraint_status", "constraint_message").show()
resultDataFrame.select("constraint_message").show(100,100)

resultDataFrame.select("constraint", "constraint_status", "constraint_message").show(100,100)

sc.hadoopConfiguration.set("mapreduce.fileoutputcommitter.marksuccessfuljobs", "false")
sc.hadoopConfiguration.set("parquet.enable.summary-metadata", "false")
Expand All @@ -163,5 +145,3 @@ def ReportViolations(): Unit = {
println("Rows: ")
result.invalidRows.show(truncate=true)
}