[Merged by Bors] - Spark anomaly detection demo

demos/demos-v1.yaml

@@ -53,6 +53,21 @@ demos:
       - plainYaml: https://raw.githubusercontent.com/stackabletech/stackablectl/main/demos/nifi-kafka-druid-water-level-data/create-nifi-ingestion-job.yaml
       - plainYaml: https://raw.githubusercontent.com/stackabletech/stackablectl/main/demos/nifi-kafka-druid-water-level-data/create-druid-ingestion-job.yaml
       - plainYaml: https://raw.githubusercontent.com/stackabletech/stackablectl/main/demos/nifi-kafka-druid-water-level-data/setup-superset.yaml
+  spark-k8s-anomaly-detection-taxi-data:
+    description: Demo loading New York taxi data into an S3 bucket and carrying out an anomaly detection analysis on it
+    documentation: https://docs.stackable.tech/stackablectl/stable/demos/spark-k8s-anomaly-detection-taxi-data.html
+    stackableStack: spark-trino-superset-s3
+    labels:
+      - trino
+      - superset
+      - minio
+      - s3
+      - ny-taxi-data
+    manifests:
+      - plainYaml: demos/spark-k8s-anomaly-detection-taxi-data/serviceaccount.yaml
+      - plainYaml: demos/spark-k8s-anomaly-detection-taxi-data/load-test-data.yaml
+      - plainYaml: demos/spark-k8s-anomaly-detection-taxi-data/create-spark-anomaly-detection-job.yaml
+      - plainYaml: demos/spark-k8s-anomaly-detection-taxi-data/setup-superset.yaml
   trino-taxi-data:
     description: Demo loading 2.5 years of New York taxi data into S3 bucket, creating a Trino table and a Superset dashboard
     documentation: https://docs.stackable.tech/stackablectl/stable/demos/trino-taxi-data.html

demos/spark-k8s-anomaly-detection-taxi-data/Dockerfile

@@ -0,0 +1,10 @@
+FROM docker.stackable.tech/stackable/pyspark-k8s:3.3.0-stackable0.2.0
+
+RUN curl -L -O http://search.maven.org/remotecontent?filepath=org/apache/ivy/ivy/2.5.0/ivy-2.5.0.jar
+
+RUN java -jar ivy-2.5.0.jar -notransitive \
+-dependency org.apache.iceberg iceberg-spark-runtime-3.3_2.12 0.14.1 \
+-retrieve "/stackable/spark/jars/[artifact]-[revision](-[classifier]).[ext]"
+RUN java -jar ivy-2.5.0.jar -confs compile \
+-dependency org.apache.iceberg iceberg-spark-runtime-3.3_2.12 0.14.1 \
+-retrieve "/stackable/spark/jars/[artifact]-[revision](-[classifier]).[ext]"

demos/spark-k8s-anomaly-detection-taxi-data/create-spark-anomaly-detection-job.yaml

@@ -0,0 +1,185 @@
+---
+apiVersion: batch/v1
+kind: Job
+metadata:
+  name: create-spark-anomaly-detection-job
+spec:
+  template:
+    spec:
+      initContainers:
+        - name: wait-for-testdata
+          image: docker.stackable.tech/stackable/tools:0.2.0-stackable0.3.0
+          command: ["bash", "-c", "echo 'Waiting for job load-ny-taxi-data to finish' && kubectl wait --for=condition=complete --timeout=30m job/load-ny-taxi-data"]
+      containers:
+        - name: create-spark-anomaly-detection-job
+          image: docker.stackable.tech/stackable/tools:0.2.0-stackable0.3.0
+          command: ["bash", "-c", "echo 'Submitting Spark job' && kubectl apply -f /tmp/manifest/spark-ad-job.yaml"]
+          volumeMounts:
+            - name: manifest
+              mountPath: /tmp/manifest
+      volumes:
+        - name: manifest
+          configMap:
+            name: create-spark-ad-job-manifest
+      restartPolicy: OnFailure
+  backoffLimit: 50
+---
+apiVersion: v1
+kind: ConfigMap
+metadata:
+  name: create-spark-ad-job-manifest
+data:
+  spark-ad-job.yaml: |
+    ---
+    apiVersion: spark.stackable.tech/v1alpha1
+    kind: SparkApplication
+    metadata:
+      name: spark-ad
+    spec:
+      version: "1.0"
+      sparkImage: docker.stackable.tech/aken/pyspark-k8s-with-iceberg:0.1.0 #docker.stackable.tech/stackable/pyspark-k8s:3.3.0-stackable0.2.0
+      mode: cluster
+      mainApplicationFile: local:///spark-scripts/spark-ad.py
+      deps:
+        requirements:
+          - scikit-learn==0.24.2
+        #packages:
+        #  - org.apache.iceberg:iceberg-spark-runtime-3.3_2.12:0.14.1
+      volumes:
+        - name: cm-spark
+          configMap:
+            name: cm-spark
+      driver:
+        volumeMounts:
+          - name: cm-spark
+            mountPath: /spark-scripts
+      executor:
+        volumeMounts:
+          - name: cm-spark
+            mountPath: /spark-scripts
+      sparkConf:
+        spark.kubernetes.submission.waitAppCompletion: "false"
+        spark.kubernetes.driver.pod.name: "spark-ad-driver"
+        spark.kubernetes.executor.podNamePrefix: "spark-ad"
+        spark.kubernetes.driver.request.cores: "2"
+        spark.kubernetes.driver.limit.cores: "3"
+        spark.driver.cores: "3"
+        spark.driver.memory: "1g"
+        spark.driver.memoryOverheadFactor: "0.4"
+        spark.kubernetes.executor.request.cores: "2"
+        spark.kubernetes.executor.limit.cores: "3"
+        spark.executor.cores: "3"
+        spark.executor.memory: "4g"
+        spark.executor.memoryOverheadFactor: "0.4"
+        spark.executor.instances: "6"
+        spark.hadoop.fs.s3a.aws.credentials.provider: "org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider"
+        spark.hadoop.fs.s3a.endpoint: http://minio-trino:9000
+        spark.hadoop.fs.s3a.path.style.access: "true"
+        spark.hadoop.fs.s3a.access.key: demo
+        spark.hadoop.fs.s3a.secret.key: demodemo
+        spark.sql.extensions: org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions
+        spark.sql.catalog.prediction: org.apache.iceberg.spark.SparkCatalog
+        spark.sql.catalog.prediction.type: hive
+        spark.sql.catalog.prediction.uri: thrift://hive-iceberg:9083
+---
+apiVersion: v1
+kind: ConfigMap
+metadata:
+  name: cm-spark
+data:
+  spark-ad.py: |
+    from pyspark.sql import SparkSession
+    from pyspark.sql.functions import dayofweek, to_date, to_timestamp, date_format, year, hour, minute, month, when, dayofmonth, dayofweek
+    from pyspark.sql.functions import concat_ws, substring, concat, lpad, lit
+    from pyspark.sql.functions import round, sum, count, avg
+    from pyspark.sql.functions import lag
+    from pyspark.sql.window import Window
+    from pyspark.sql import functions, types
+    from sklearn.ensemble import IsolationForest
+    from sklearn.preprocessing import StandardScaler
+
+    spark = SparkSession.builder.appName("ny-tlc-anomaly-detection").getOrCreate()
+    spark.sql("CREATE SCHEMA IF NOT EXISTS prediction.ad LOCATION 's3a://prediction/anomaly-detection'")
+    spark.sql("CREATE TABLE IF NOT EXISTS prediction.ad.iforest (pickup_ts TIMESTAMP, pickup_minute_group VARCHAR(4), pickup_hour INT, pickup_year INT, pickup_month INT, pickup_dayofmonth INT, pickup_dayofweek INT, norides INT, total_bill DOUBLE, avg_bill DOUBLE, norides_lag INT, pred INT) USING iceberg")
+
+    input_df = spark.read.parquet("s3a://demo/ny-taxi-data/raw/")
+
+    df = input_df.select(
+        to_date(input_df.pickup_datetime).alias("day_date")
+        , year(input_df.pickup_datetime).alias('year')
+        , month(input_df.pickup_datetime).alias('month')
+        , dayofmonth(input_df.pickup_datetime).alias("dayofmonth")
+        , dayofweek(input_df.pickup_datetime).alias("dayofweek")
+        , hour(input_df.pickup_datetime).alias("hour")
+        , minute(input_df.pickup_datetime).alias("minute")
+        , input_df.driver_pay
+    )
+
+    df = df.withColumn("minute_group", when(df.minute < 30, '00').otherwise('30'))
+    df = df.withColumn("time_group",concat_ws(":", lpad(df.hour, 2, '0'), df.minute_group, lit('00')))
+    df = df.withColumn("ts",concat_ws(" ", df.day_date, df.time_group))
+
+    dfs = df.select(
+        to_timestamp(df.ts, "yyyy-MM-dd HH:mm:ss").alias("date_group")
+        , df.minute_group
+        , df.year
+        , df.hour
+        , df.month
+        , df.dayofmonth
+        , df.dayofweek
+        , df.driver_pay
+    ).groupby("date_group", "minute_group", "hour", "year", "month", "dayofmonth", "dayofweek").agg(functions.count('driver_pay').alias('no_rides'), functions.round(functions.sum('driver_pay'), 2).alias('total_bill'), functions.round(functions.avg('driver_pay'), 2).alias('avg_bill')).orderBy("date_group")
+
+    windowSpec  = Window.partitionBy("hour").orderBy("date_group")
+
+    dfs = dfs.withColumn("lag",lag("no_rides",2).over(windowSpec))
+    dfs = dfs.filter("lag IS NOT NULL")
+
+    scaler = StandardScaler()
+    classifier = IsolationForest(contamination=0.005, n_estimators=200, max_samples=0.7, random_state=42, n_jobs=-1)
+
+    df_model = dfs.select(dfs.minute_group, dfs.hour, dfs.year, dfs.month, dfs.dayofmonth, dfs.dayofweek, dfs.no_rides, dfs.total_bill, dfs.avg_bill, dfs.lag)
+
+    x_train = scaler.fit_transform(df_model.collect())
+    clf = classifier.fit(x_train)
+
+    SCL = spark.sparkContext.broadcast(scaler)
+    CLF = spark.sparkContext.broadcast(clf)
+
+    def predict_using_broadcasts(minute_group, hour, year, month, dayofmonth, dayofweek, no_rides, total_bill, avg_bill, lag):
+        prediction = 0
+        x_test = [[minute_group, hour, year, month, dayofmonth, dayofweek, no_rides, total_bill, avg_bill, lag]]
+        try:
+            x_test = SCL.value.transform(x_test)
+            prediction = CLF.value.predict(x_test)[0]
+        except ValueError:
+            import traceback
+            traceback.print_exc()
+            print('Cannot predict:', x_test)
+        return int(prediction)
+
+    udf_predict_using_broadcasts = functions.udf(predict_using_broadcasts, types.IntegerType())
+
+    df_pred = dfs.withColumn(
+        'prediction',
+        udf_predict_using_broadcasts('minute_group', 'hour', 'year', 'month', 'dayofmonth', 'dayofweek', 'no_rides', 'total_bill', 'avg_bill', 'lag')
+    )
+
+    # map to table columns
+    df_out = df_pred.select(
+        df_pred.date_group.alias("pickup_ts")
+        , df_pred.minute_group.alias("pickup_minute_group")
+        , df_pred.hour.alias("pickup_hour")
+        , df_pred.year.alias("pickup_year")
+        , df_pred.month.alias("pickup_month")
+        , df_pred.dayofmonth.alias("pickup_dayofmonth")
+        , df_pred.dayofweek.alias("pickup_dayofweek")
+        , df_pred.no_rides.alias("norides")
+        , df_pred.total_bill.alias("total_bill")
+        , df_pred.avg_bill.alias("avg_bill")
+        , df_pred.lag.alias("norides_lag")
+        , df_pred.prediction.alias("pred")
+    )
+
+    # write via iceberg
+    df_out.writeTo("prediction.ad.iforest").append()

demos/spark-k8s-anomaly-detection-taxi-data/load-test-data.yaml

@@ -0,0 +1,14 @@
+---
+apiVersion: batch/v1
+kind: Job
+metadata:
+  name: load-ny-taxi-data
+spec:
+  template:
+    spec:
+      containers:
+        - name: load-ny-taxi-data
+          image: "bitnami/minio:2022-debian-10"
+          command: ["bash", "-c", "cd /tmp && for month in 2020-09 2020-10 2020-11 2020-12; do curl -O https://repo.stackable.tech/repository/misc/ny-taxi-data/fhvhv_tripdata_$month.parquet && mc --insecure alias set minio http://minio-trino:9000/ demo demodemo && mc cp fhvhv_tripdata_$month.parquet minio/demo/ny-taxi-data/raw/ && mc mb --ignore-existing minio/prediction; done"]
+      restartPolicy: OnFailure
+  backoffLimit: 50

demos/spark-k8s-anomaly-detection-taxi-data/serviceaccount.yaml

@@ -0,0 +1,44 @@
+---
+apiVersion: rbac.authorization.k8s.io/v1
+kind: ClusterRoleBinding
+metadata:
+  name: demo-clusterrolebinding
+subjects:
+  - apiGroup: rbac.authorization.k8s.io
+    kind: Group
+    name: system:serviceaccounts
+roleRef:
+  kind: ClusterRole
+  name: demo-clusterrole
+  apiGroup: rbac.authorization.k8s.io
+---
+apiVersion: rbac.authorization.k8s.io/v1
+kind: ClusterRole
+metadata:
+  name: demo-clusterrole
+rules:
+  - apiGroups:
+      - ""
+    resources:
+      - pods
+    verbs:
+      - get
+      - list
+      - watch
+  - apiGroups:
+      - batch
+    resources:
+      - jobs
+    verbs:
+      - get
+      - list
+      - watch
+  - apiGroups:
+      - spark.stackable.tech
+    resources:
+      - sparkapplications
+    verbs:
+      - get
+      - list
+      - watch
+      - create

demos/spark-k8s-anomaly-detection-taxi-data/setup-superset.yaml

@@ -0,0 +1,76 @@
+---
+apiVersion: batch/v1
+kind: Job
+metadata:
+  name: setup-superset
+spec:
+  template:
+    spec:
+      containers:
+        - name: setup-superset
+          image: docker.stackable.tech/stackable/testing-tools:0.1.0-stackable0.1.0
+          # TODO update
+          command: ["bash", "-c", "curl -o superset-assets.zip https://raw.githubusercontent.com/stackabletech/stackablectl/142_spark_anomaly_detection/demos/spark-k8s-anomaly-detection-taxi-data/superset-assets.zip && python -u /tmp/script/script.py"]
+          volumeMounts:
+            - name: script
+              mountPath: /tmp/script
+      volumes:
+        - name: script
+          configMap:
+            name: setup-superset-script
+      restartPolicy: OnFailure
+  backoffLimit: 50
+---
+apiVersion: v1
+kind: ConfigMap
+metadata:
+  name: setup-superset-script
+data:
+  script.py: |
+    import logging
+    import requests
+
+    base_url = "http://superset-external:8088" # For local testing / developing replace it, afterwards change back to http://superset-external:8088
+    username = "admin"
+    password = "admin"
+
+    logging.basicConfig(level=logging.INFO)
+    logging.info("Starting setup of Superset")
+    logging.info("Getting access token from /api/v1/security/login")
+    session = requests.session()
+    access_token = session.post(f"{base_url}/api/v1/security/login", json={"username": username, "password": password, "provider": "db", "refresh": True}).json()['access_token']
+
+    logging.info("Getting csrf token from /api/v1/security/csrf_token")
+    csrf_token = session.get(f"{base_url}/api/v1/security/csrf_token", headers={"Authorization": f"Bearer {access_token}"}).json()["result"]
+
+    headers = {
+        "accept": "application/json",
+        "Authorization": f"Bearer {access_token}",
+        "X-CSRFToken": csrf_token,
+    }
+
+    #########################
+    # Export
+    #########################
+    #logging.info("Exporting all assets")
+    #result = session.get(f"{base_url}/api/v1/assets/export", headers=headers)
+    #assert result.status_code == 200
+    #with open("superset-assets.zip", "wb") as f:
+    #    f.write(result.content)
+
+    #########################
+    # Import
+    #########################
+    logging.info("Importing all assets")
+    files = {
+        "bundle": ("superset-assets.zip", open("superset-assets.zip", "rb")),
+    }
+    data = {
+        "passwords": '{"databases/Trino.yaml": "demo"}'
+    }
+    result = session.post(f"{base_url}/api/v1/assets/import", headers=headers, files=files, data=data)
+    print(result)
+    print(result.text)
+    assert result.status_code == 200
+
+    logging.info("Finished setup of Superset")