feat: Spark Streaming using Spark Operator (#516)

streaming/spark-streaming/examples/consumer/Dockerfile

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+# Dockerfile for Apache Spark with additional JARs downloaded at build time
+FROM apache/spark-py:v3.3.2
+WORKDIR /app
+
+# Use root to create a new user and configure permissions
+USER root
+
+# Install wget to download JAR files
+RUN apt-get update && apt-get install -y wget && \
+    rm -rf /var/lib/apt/lists/*
+
+# Create a new user 'spark-user' with UID 1001
+RUN groupadd -r spark-group && useradd -r -u 1001 -g spark-group spark-user
+
+RUN mkdir -p /home/spark-user/.ivy2/cache
+RUN mkdir -p /app/jars
+
+RUN cd /app/jars && \
+    wget -q "https://repo1.maven.org/maven2/com/amazonaws/aws-java-sdk-bundle/1.11.1026/aws-java-sdk-bundle-1.11.1026.jar" && \
+    wget -q "https://repo1.maven.org/maven2/commons-logging/commons-logging/1.1.3/commons-logging-1.1.3.jar" && \
+    wget -q "https://repo1.maven.org/maven2/org/apache/commons/commons-pool2/2.11.1/commons-pool2-2.11.1.jar" && \
+    wget -q "https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-aws/3.3.2/hadoop-aws-3.3.2.jar" && \
+    wget -q "https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-client-api/3.3.2/hadoop-client-api-3.3.2.jar" && \
+    wget -q "https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-client-runtime/3.3.2/hadoop-client-runtime-3.3.2.jar" && \
+    wget -q "https://repo1.maven.org/maven2/org/apache/iceberg/iceberg-spark-runtime-3.3_2.12/1.0.0/iceberg-spark-runtime-3.3_2.12-1.0.0.jar" && \
+    wget -q "https://repo1.maven.org/maven2/com/google/code/findbugs/jsr305/3.0.0/jsr305-3.0.0.jar" && \
+    wget -q "https://repo1.maven.org/maven2/org/apache/kafka/kafka-clients/2.8.1/kafka-clients-2.8.1.jar" && \
+    wget -q "https://repo1.maven.org/maven2/org/lz4/lz4-java/1.7.1/lz4-java-1.7.1.jar" && \
+    wget -q "https://repo1.maven.org/maven2/org/apache/parquet/parquet-avro/1.12.3/parquet-avro-1.12.3.jar" && \
+    wget -q "https://repo1.maven.org/maven2/org/scala-lang/scala-library/2.12.15/scala-library-2.12.15.jar" && \
+    wget -q "https://repo1.maven.org/maven2/org/slf4j/slf4j-api/1.7.30/slf4j-api-1.7.30.jar" && \
+    wget -q "https://repo1.maven.org/maven2/org/xerial/snappy/snappy-java/1.1.8.1/snappy-java-1.1.8.1.jar" && \
+    wget -q "https://repo1.maven.org/maven2/org/apache/spark/spark-sql-kafka-0-10_2.12/3.3.2/spark-sql-kafka-0-10_2.12-3.3.2.jar" && \
+    wget -q "https://repo1.maven.org/maven2/org/apache/spark/spark-tags_2.12/3.3.2/spark-tags_2.12-3.3.2.jar" && \
+    wget -q "https://repo1.maven.org/maven2/org/apache/spark/spark-token-provider-kafka-0-10_2.12/3.3.2/spark-token-provider-kafka-0-10_2.12-3.3.2.jar" && \
+    wget -q "https://repo1.maven.org/maven2/org/wildfly/openssl/wildfly-openssl/1.0.7.Final/wildfly-openssl-1.0.7.Final.jar"
+
+# Set the owner of the Ivy cache directory and /app to the new user
+RUN chown -R spark-user:spark-group /home/spark-user/
+RUN chown -R spark-user:spark-group /app/
+
+# Switch to the new user for running the application
+USER spark-user
+
+# Add the Spark application script to the container
+ADD app.py /app
+
+# Set the entry point for the container
+ENTRYPOINT ["/opt/entrypoint.sh"]

streaming/spark-streaming/examples/consumer/app.py

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+from pyspark.sql import SparkSession
+from pyspark.sql.types import StructType, StructField, StringType
+from pyspark.sql.functions import from_json
+import os
+
+# Variables
+s3_bucket_name = os.getenv("S3_BUCKET_NAME", "my-iceberg-data-bucket")
+kafka_address = os.getenv("KAFKA_ADDRESS", 'b-1.kafkademospark.mkjcj4.c12.kafka.us-west-2.amazonaws.com:9092,b-2.kafkademospark.mkjcj4.c12.kafka.us-west-2.amazonaws.com:9092')
+
+def create_spark_session():
+    spark = SparkSession.builder \
+        .appName("KafkaToIceberg") \
+        .config("spark.jars.packages", "org.apache.iceberg:iceberg-spark-runtime-3.3_2.12:1.0.0,org.apache.hadoop:hadoop-aws:3.3.2,org.apache.spark:spark-sql-kafka-0-10_2.12:3.3.2") \
+        .config("spark.jars.repositories", "https://repo1.maven.org/maven2/") \
+        .config("spark.sql.extensions", "org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions") \
+        .config("spark.sql.catalog.local", "org.apache.iceberg.spark.SparkCatalog") \
+        .config("spark.sql.catalog.local.type", "hadoop") \
+        .config("spark.sql.catalog.local.warehouse", f"s3a://{s3_bucket_name}/iceberg/warehouse/") \
+        .config("spark.hadoop.fs.s3a.impl", "org.apache.hadoop.fs.s3a.S3AFileSystem") \
+        .config("spark.hadoop.fs.s3a.aws.credentials.provider", "com.amazonaws.auth.DefaultAWSCredentialsProviderChain") \
+        .config("spark.sql.warehouse.dir", f"s3a://{s3_bucket_name}/iceberg/warehouse/") \
+        .config("spark.serializer", "org.apache.spark.serializer.KryoSerializer") \
+        .config("spark.kryo.registrationRequired", "false") \
+        .getOrCreate()
+    return spark
+
+def consume_and_write():
+    spark = create_spark_session()
+    # Debug spark DEBUG
+    spark.sparkContext.setLogLevel("ERROR")
+    # Create the table
+    spark.sql(f"""
+        CREATE TABLE IF NOT EXISTS local.my_table (
+            id STRING,
+            timestamp STRING,
+            alert_type STRING,
+            severity STRING,
+            description STRING
+        )
+        USING iceberg
+        LOCATION 's3a://{s3_bucket_name}/iceberg/warehouse/my_table'
+        TBLPROPERTIES (
+            'write.format.default'='parquet'  -- Explicitly specifying Parquet format
+        )
+    """)
+
+    # Read from Kafka
+    df = spark.readStream \
+        .format("kafka") \
+        .option("kafka.bootstrap.servers", kafka_address) \
+        .option("subscribe", "security-topic") \
+        .option("startingOffsets", "earliest") \
+        .option("failOnDataLoss", "false") \
+        .load()
+
+    # Define the schema for the JSON data
+    json_schema = StructType([
+        StructField("id", StringType(), True),
+        StructField("timestamp", StringType(), True),
+        StructField("alert_type", StringType(), True),
+        StructField("severity", StringType(), True),
+        StructField("description", StringType(), True)
+    ])
+
+    # Parse JSON and select the required columns
+    parsed_df = df.selectExpr("CAST(value AS STRING) as json") \
+                  .select(from_json("json", json_schema).alias("data")) \
+                  .select("data.id", "data.timestamp", "data.alert_type", "data.severity", "data.description")
+
+    # Write the stream to Iceberg using table name
+    query = parsed_df.writeStream \
+        .format("iceberg") \
+        .option("checkpointLocation", f"s3a://{s3_bucket_name}/iceberg/checkpoints/") \
+        .option("path", f"s3a://{s3_bucket_name}/iceberg/warehouse/my_table") \
+        .outputMode("append") \
+        .start()
+
+    query.awaitTermination()  # Wait for the stream to finish
+
+if __name__ == "__main__":
+    consume_and_write()

streaming/spark-streaming/examples/consumer/manifests/01_spark_application.yaml

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+# This script is used to run the spark-consumer application on EKS, 
+# users need to replace MY_BUCKET_NAME and MY_KAFKA_BROKERS_ADRESS to match your environment.
+---
+apiVersion: "sparkoperator.k8s.io/v1beta2"
+kind: SparkApplication
+metadata:
+  name: spark-consumer
+  namespace: spark-team-a
+spec:
+  type: Python
+  pythonVersion: "3"
+  mode: cluster
+  image: "public.ecr.aws/data-on-eks/consumer-spark-streaming-3.3.2-kafka:1" # You can build your own image using the Dockerfile in this folder
+  mainApplicationFile: "local:///app/app.py"
+  sparkVersion: "3.3.2"
+  deps:
+    jars:
+      - "local:///app/jars/commons-logging-1.1.3.jar"
+      - "local:///app/jars/commons-pool2-2.11.1.jar"
+      - "local:///app/jars/hadoop-client-api-3.3.2.jar"
+      - "local:///app/jars/hadoop-client-runtime-3.3.2.jar"
+      - "local:///app/jars/jsr305-3.0.0.jar"
+      - "local:///app/jars/kafka-clients-2.8.1.jar"
+      - "local:///app/jars/lz4-java-1.7.1.jar"
+      - "local:///app/jars/scala-library-2.12.15.jar"
+      - "local:///app/jars/slf4j-api-1.7.30.jar"
+      - "local:///app/jars/snappy-java-1.1.8.1.jar"
+      - "local:///app/jars/spark-sql-kafka-0-10_2.12-3.3.2.jar"
+      - "local:///app/jars/spark-tags_2.12-3.3.2.jar"
+      - "local:///app/jars/spark-token-provider-kafka-0-10_2.12-3.3.2.jar"
+      - "local:///app/jars/iceberg-spark-runtime-3.3_2.12-1.0.0.jar"
+      - "local:///app/jars/hadoop-aws-3.3.2.jar"
+      - "local:///app/jars/aws-java-sdk-bundle-1.11.1026.jar"
+      - "local:///app/jars/wildfly-openssl-1.0.7.Final.jar"
+      - "local:///app/jars/parquet-avro-1.12.3.jar"
+  sparkConf:
+    "spark.app.name": "KafkaToIceberg"
+    "spark.jars.repositories": "https://repo1.maven.org/maven2/"
+    "spark.sql.extensions": "org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions"
+    "spark.sql.catalog.local": "org.apache.iceberg.spark.SparkCatalog"
+    "spark.sql.catalog.local.type": "hadoop"
+    "spark.sql.catalog.local.warehouse": "s3a://__MY_BUCKET_NAME__/iceberg/warehouse/" # Replace bucket name with your S3 bucket name: s3_bucket_id_iceberg_bucket
+    "spark.hadoop.fs.s3a.impl": "org.apache.hadoop.fs.s3a.S3AFileSystem"
+    "spark.hadoop.fs.s3a.aws.credentials.provider": "com.amazonaws.auth.DefaultAWSCredentialsProviderChain"
+    "spark.sql.warehouse.dir": "s3a://__MY_BUCKET_NAME__/iceberg/warehouse/" # Replace bucket name with your S3 bucket name: s3_bucket_id_iceberg_bucket
+    "spark.metrics.conf.*.sink.prometheusServlet.class": "org.apache.spark.metrics.sink.PrometheusServlet"
+    "spark.metrics.conf.*.sink.prometheusServlet.path": "/metrics"
+    "spark.metrics.conf.master.sink.prometheusServlet.path": "/metrics/master"
+    "spark.metrics.conf.applications.sink.prometheusServlet.path": "/metrics/applications"
+    "spark.ui.prometheus.enabled": "true"
+    "spark.ui.prometheus.port": "4040"
+  restartPolicy:
+    type: OnFailure
+    onFailureRetries: 2
+    onFailureRetryInterval: 10
+    onSubmissionFailureRetries: 3
+    onSubmissionFailureRetryInterval: 20
+  dynamicAllocation:
+    enabled: true
+    initialExecutors: 3
+    minExecutors: 3
+    maxExecutors: 10
+  driver:
+    cores: 1
+    coreLimit: "1200m"
+    memory: "1024m"
+    labels:
+      version: "3.3.2"
+      app: spark
+    annotations:
+      prometheus.io/scrape: 'true'
+      prometheus.io/path: /metrics
+      prometheus.io/port: '4040'
+    serviceAccount: spark-team-a
+    nodeSelector:
+      NodeGroupType: "SparkComputeOptimized"
+    tolerations:
+      - key: "spark-compute-optimized"
+        operator: "Exists"
+        effect: "NoSchedule"
+    env:
+      - name: S3_BUCKET_NAME
+        value: "__MY_BUCKET_NAME__" # Replace with your S3 bucket name: s3_bucket_id_iceberg_bucket
+      - name: KAFKA_ADDRESS
+        value: "__MY_KAFKA_BROKERS_ADRESS__" # Replace with your Kafka brokers address: bootstrap_brokers
+        # value: "b-1.kafkademospark.mkjcj4.c12.kafka.us-west-2.amazonaws.com:9092,b-2.kafkademospark.mkjcj4.c12.kafka.us-west-2.amazonaws.com:9092"
+  executor:
+    cores: 2
+    memory: "1024m"
+    labels:
+      version: "3.3.2"
+      app: spark
+    annotations:
+      prometheus.io/scrape: 'true'
+      prometheus.io/path: /metrics
+      prometheus.io/port: '4040'
+    serviceAccount: spark-team-a
+    nodeSelector:
+      NodeGroupType: "SparkComputeOptimized"
+    tolerations:
+      - key: "spark-compute-optimized"
+        operator: "Exists"
+        effect: "NoSchedule"
+    env:
+      - name: S3_BUCKET_NAME
+        value: "__MY_BUCKET_NAME__" # Replace with your S3 bucket name: s3_bucket_id_iceberg_bucket
+      - name: KAFKA_ADDRESS
+        value: "__MY_KAFKA_BROKERS_ADRESS__" # Replace with your Kafka brokers address: bootstrap_brokers
+        # value: "b-1.kafkademospark.mkjcj4.c12.kafka.us-west-2.amazonaws.com:9092,b-2.kafkademospark.mkjcj4.c12.kafka.us-west-2.amazonaws.com:9092"

streaming/spark-streaming/examples/consumer/requirements.txt

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+py4j==0.10.9.5
+pyspark==3.3.2

streaming/spark-streaming/examples/docker-compose.yaml

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+version: '3'
+services:
+  zookeeper:
+    image: confluentinc/cp-zookeeper:latest
+    container_name: zookeeper
+    ports:
+      - "2181:2181"
+    environment:
+      ZOOKEEPER_CLIENT_PORT: 2181
+      ZOOKEEPER_TICK_TIME: 2000
+
+  kafka:
+    image: confluentinc/cp-kafka:latest
+    container_name: kafka
+    depends_on:
+      - zookeeper
+    ports:
+      - "9092:9092"
+      - "9093:9093"  # Added a new port for the external listener
+    environment:
+      KAFKA_BROKER_ID: 1
+      KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181
+      KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: PLAINTEXT:PLAINTEXT,PLAINTEXT_HOST:PLAINTEXT
+      KAFKA_LISTENERS: PLAINTEXT://0.0.0.0:9092,PLAINTEXT_HOST://0.0.0.0:9093  # Changed the PLAINTEXT_HOST to a different port
+      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://kafka:9092,PLAINTEXT_HOST://localhost:9093  # Changed the PLAINTEXT_HOST to advertise the new port
+      KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1
+      KAFKA_TRANSACTION_STATE_LOG_MIN_ISR: 1
+      KAFKA_TRANSACTION_STATE_LOG_REPLICATION_FACTOR: 1

streaming/spark-streaming/examples/producer/00_deployment.yaml

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+---
+# This is the producer deployment file, you can adjust the number of replicas to produce more data.
+# You will need to change __MY_AWS_REGION__, __MY_KAFKA_BROKERS__, and __MY_PRODUCER_ROLE_ARN__ to match your environment.
+apiVersion: v1
+kind: ServiceAccount
+metadata:
+  name: producer-sa
+  annotations:
+    eks.amazonaws.com/role-arn: __MY_PRODUCER_ROLE_ARN__ # Replace with your producer role ARN: producer_iam_role_arn
+---
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: producer-deployment
+spec:
+  replicas: 100  # Adjusted to match the required number of replicas
+  selector:
+    matchLabels:
+      app: producer
+  template:
+    metadata:
+      labels:
+        app: producer
+    spec:
+      serviceAccountName: producer-sa
+      containers:
+      - name: producer
+        image: public.ecr.aws/data-on-eks/producer-kafka:1
+        #image: public.ecr.aws/data-on-eks/producer-kafka:1
+        command: ["python", "app.py"]
+        env:
+          - name: RATE_PER_SECOND
+            value: "100000"
+          - name: NUM_OF_MESSAGES
+            value: "10000000"
+          - name: AWS_REGION
+            value: "__MY_AWS_REGION__" # Replace with your AWS region
+          - name: BOOTSTRAP_BROKERS
+            value: "__MY_KAFKA_BROKERS__" # Replace with your bootstrap brokers: bootstrap_brokers
+        resources:
+          limits:
+            cpu: "2"  # Increased CPU limit
+            memory: "4Gi"  # Increased memory limit
+          requests:
+            cpu: "1"  # Increased CPU request
+            memory: "2Gi"  # Increased memory request
+        volumeMounts:
+          - name: shared-volume
+            mountPath: /mnt
+      volumes:
+        - name: shared-volume
+          emptyDir: {}

streaming/spark-streaming/examples/producer/01_delete_topic.yaml

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+apiVersion: v1
+kind: ConfigMap
+metadata:
+  name: kafka-delete-topic-script
+  namespace: default
+data:
+  delete_topic.py: |
+    from kafka.admin import KafkaAdminClient
+
+    def delete_topic(bootstrap_servers, topic_name):
+        """Delete a Kafka topic."""
+        client = KafkaAdminClient(bootstrap_servers=bootstrap_servers)
+        try:
+            client.delete_topics([topic_name])
+            print(f"Topic {topic_name} deleted successfully.")
+        except Exception as e:
+            print(f"Failed to delete topic {topic_name}: {e}")
+
+    # Configuration
+    import os
+    bootstrap_servers = os.getenv('BOOTSTRAP_BROKERS', 'localhost:9092')  # Replace with your Kafka broker address
+    topic_name = os.getenv('TOPIC_NAME', 'security-topic')  # Replace with your topic name
+
+    # Delete Kafka topic
+    delete_topic(bootstrap_servers, topic_name)
+
+---
+apiVersion: v1
+kind: Pod
+metadata:
+  name: kafka-delete-topic-pod
+  namespace: default
+spec:
+  containers:
+  - name: delete-topic
+    image: public.ecr.aws/data-on-eks/producer-kafka:1 # Use an appropriate Python image
+    command: ["python", "/scripts/delete_topic.py"]
+    env:
+      - name: BOOTSTRAP_BROKERS
+        value: "__MY_KAFKA_BROKERS__"  # Replace with your Kafka broker address
+      - name: TOPIC_NAME
+        value: "security-topic"  # Replace with your topic name
+    volumeMounts:
+    - name: script-volume
+      mountPath: /scripts
+  restartPolicy: Never
+  volumes:
+  - name: script-volume
+    configMap:
+      name: kafka-delete-topic-script

streaming/spark-streaming/examples/producer/Dockerfile

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+# Use an official Python runtime as a parent image
+FROM python:3.9-slim
+
+# Set the working directory in the container
+WORKDIR /usr/src/app
+
+# Copy the local code to the container
+COPY . .
+
+# Install any needed packages specified in requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Run app.py when the container launches
+CMD ["python", "app.py"]