[CCFD-RF] Credit Card Fraudulent Detection with Random Forest

This is a project for Credit Card Fraudulent Detection with Random Forest using Spark Structured Streaming

In the code:

There are 3 options if you want to run CCFD-RF

1. Option 1: Run job locally, reading from a file and writing to console
2. Option 2: Run job locally, reading from a kafka source and writing to a kafka sink
3. Option 3: Run job in SoftNet cluster, reading from HDFS and writing to HDFS

Notes:
We propose to run the project with Option 2 because it is easier to test:
The attached code is written in Option 2

Configure SparkSession

Option 1 & 2 Run locally:

In line 25-30 [StructuredRandomForest]: Configure SparkSession variable

    val spark = SparkSession.builder()
      .appName("SparkStructuredStreamingExample")
      .master("local[*]")
      .config("spark.sql.streaming.checkpointLocation", "checkpoint_saves/")
      .getOrCreate()

Option 3 Run on the cluster:

In line 25-30 [StructuredRandomForest]: Configure SparkSession variable

    val spark = SparkSession.builder()
       .appName("SparkStructuredRandomForest")
       .config("spark.sql.streaming.checkpointLocation", "/user/vvittis")
       .getOrCreate()

Read

Option 1 Read from file:

In line 35-43 [StructuredRandomForest]: Read from Source

 val rawData = spark.readStream.text("dataset_source/")

Option 2 Read from kafka:

In line 35-43 [StructuredRandomForest]: Read from Source

 val rawData = spark.readStream
          .format("kafka")
          .option("kafka.bootstrap.servers", "localhost:9092")
          .option("subscribe", "testSource")
          .option("startingOffsets", "earliest")
          .load()
          .selectExpr("CAST(value AS STRING)")

Note: of course you have to execute:

Open 2 command line windows and cd on “C:\kafka_2.12-2.3.0”
1st window
bin\windows\zookeeper-server-start.bat config\zookeeper.properties
2nd window
bin\windows\kafka-server-start.bat config\server.properties

Option 3 Read from an HDFS file:

In line 35-43 [StructuredRandomForest]: Read from Source

val rawData = spark.readStream.text("/user/vvittis/numbers")

Note: /user/vvittis/numbers is a path to a HDFS folder

Write

Option 1 Write to console:

In line 212 [StructuredRandomForest]: Write to Console

  val query = kafkaResult
      .writeStream
      .outputMode("update")
      .option("truncate", "false")
      .format("console")
      .queryName("TestStatefulOperator")
      .start()

Option 2 Write to kafka:

In line 215-230 [StructuredRandomForest]: Write to kafka sink

        val query = kafkaResult
          .selectExpr("CAST(value AS STRING)")
          .writeStream.outputMode("update")
          .format("kafka")
          .option("kafka.bootstrap.servers", "localhost:9092")
          .option("topic", "testSink")
          .queryName("RandomForest")
          .start()

Option 3 Write to HDFS file:

In line 224-230 [StructuredRandomForest]: Write to HDFS sink

        val query = kafkaResult
            .writeStream
            .outputMode("append")
            .format("csv")
            .option("path","/user/vvittis/results/")          
            .queryName("RandomForest")
            .start()

Note: /user/vvittis/results is a path to a HDFS folder

RUN the project.

In Intellij

Step 1: Clone CCFD-RF File > New > Project From Version Control... 
Step 2: In the URL: copy https://github.com/vvittis/CCFD-RF.git 
        In the Directory: Add your preferred directory
Step 3: Click the build button or Build > Build Project
Step 4: Go to src > main > scala > StructuredRandomForest.scala and click Run

• A typical Console showing the state:

• A typical Console showing the output:

In Cluster

You will find the sbt folder

Step 1: Run sbt assembly and create a .jar file
Step 2: Run
        ./bin/spark-submit 
        --class StructuredRandomForest 
        --master yarn-client 
        --num-executors 10 
        --driver-memory 512m 
        --executor-memory 512m 
        --executor-cores 1 /home/vvittis/StructuredRandomForest-assembly-0.1.jar

• A typical Cluster showing that each executor takes one Hoeffding Tree of the Random Forest:
• This test executed with 10 executors and 10 HT.

Licensed under the MIT Licence.