Spark Job 수행 / Orange Pi 5 Max Cluster 환경

Docs

Record

Spark를 활용해서 MinIO에 저장되어 있는 데이터 변환을 수행한다.

1. 실습 환경 구성

1.1. 전체 실습 환경

Spark를 통해서 MinIO에 저장되어 있는 데이터를 변환하는 환경은 다음과 같다.

MinIO : Data를 저장하는 Object Storage 역할을 수행한다. South Korea Weather Data를 저장한다.
- South Korea Weather Data : CSV, Parquet, Iceberg 3가지 Data Format으로 날짜별로 Partition되어 저장된다.
Spark Job : MinIO에 저장되어 있는 South Korea Weather Data의 평균 데이터를 계산하고 다시 MinIO에 저장한다.
Spark History Server : Spark Job의 실행 로그를 확인하기 위한 역할을 수행한다.
Volcano Scheduler : Spark Job 실행을 위한 Pod들을 대상으로 Gang Scheduling을 수행한다.
Trino : MinIO에 저장되어 있는 Data를 조회하는 역할을 수행한다.
Hive Metastore : Data의 Schema 정보를 관리하며, Trino에게 Schema 정보를 제공한다.
Dagster : Data Pipeline을 실행하여 MinIO에 South Korea Weather Data의 저장 형태를 CSV에서 Parquet으로, Parquet에서 Iceberg로 변환한다.
DBeaver : Trino에 접속하고 Query를 수행하기 위한 Client 역할을 수행한다.

전체 실슴 환경 구성은 다음의 링크를 참조한다.

Orange Pi 5 Max 기반 Kubernetes Cluster 구축 : https://ssup2.github.io/blog-software/docs/record/orangepi5-cluster-build/
Orange Pi 5 Max 기반 Kubernetes Data Platform 구축 : https://ssup2.github.io/blog-software/docs/record/kubernetes-data-platform-orangepi5-cluster/
Trino MinIO Query 수행 : https://ssup2.github.io/blog-software/docs/record/trino-minio-query-orangepi5-cluster/
Dagster Workflow Github : https://github.com/ssup2-playground/k8s-data-platform_dagster-workflows
Spark Job Github : https://github.com/ssup2-playground/k8s-data-platform_spark-jobs

1.2. Spark 설치

Java 11를 설치한다.

brew install openjdk@17
sudo ln -sfn /opt/homebrew/opt/openjdk@17/libexec/openjdk.jdk /Library/Java/JavaVirtualMachines/openjdk-17.jdk

echo 'export JAVA_HOME="/opt/homebrew/opt/openjdk@17"' >> ~/.zshrc
echo 'export PATH="$JAVA_HOME/bin:$PATH"' >> ~/.zshrc
export JAVA_HOME="/opt/homebrew/opt/openjdk@17"
export PATH="$JAVA_HOME/bin:$PATH"

Spark를 설치한다.

SPARK_VERSION="3.5.5"
HADOOP_VERSION="3"

curl -O "https://archive.apache.org/dist/spark/spark-${SPARK_VERSION}/spark-${SPARK_VERSION}-bin-hadoop${HADOOP_VERSION}.tgz"
tar -xvzf "spark-${SPARK_VERSION}-bin-hadoop${HADOOP_VERSION}.tgz"
mv "spark-${SPARK_VERSION}-bin-hadoop${HADOOP_VERSION}" ~/spark

echo 'export SPARK_HOME=~/spark' >> ~/.zshrc
echo 'export PATH="$SPARK_HOME/bin:$PATH"' >> ~/.zshrc
export SPARK_HOME=~/spark
export PATH="$SPARK_HOME/bin:$PATH"

1.3. Hive Metastore Table 생성

평균 날씨 데이터를 저장하는 Parquet Table을 생성한다.

CREATE TABLE hive.weather.southkorea_daily_average_parquet (
  branch_name VARCHAR,

  avg_temp DOUBLE,
  avg_rain DOUBLE,
  avg_snow DOUBLE,

  avg_cloud_cover_total     DOUBLE,
  avg_cloud_cover_lowmiddle DOUBLE,
  avg_cloud_lowest          DOUBLE,

  avg_humidity       DOUBLE,
  avg_wind_speed     DOUBLE,
  avg_pressure_local DOUBLE,
  avg_pressure_sea   DOUBLE,
  avg_pressure_vaper DOUBLE,
  avg_dew_point      DOUBLE,

  year  INT,
  month INT,
  day   INT
)
WITH (
	external_location = 's3a://weather/southkorea/daily-average-parquet',
	format = 'PARQUET',
	partitioned_by = ARRAY['year', 'month', 'day']
);

CALL hive.system.sync_partition_metadata('weather', 'southkorea_daily_average_parquet', 'ADD');

평균 날씨 데이터를 저장하는 Iceberg Parquet Table을 생성한다.

CREATE TABLE iceberg.weather.southkorea_daily_average_iceberg_parquet (
  branch_name VARCHAR,

  avg_temp DOUBLE,
  avg_rain DOUBLE,
  avg_snow DOUBLE,

  avg_cloud_cover_total     DOUBLE,
  avg_cloud_cover_lowmiddle DOUBLE,
  avg_cloud_lowest          DOUBLE,

  avg_humidity       DOUBLE,
  avg_wind_speed     DOUBLE,
  avg_pressure_local DOUBLE,
  avg_pressure_sea   DOUBLE,
  avg_pressure_vaper DOUBLE,
  avg_dew_point      DOUBLE,

  year  INT,
  month INT,
  day   INT
)
WITH (
	location = 's3a://weather/southkorea/daily-average-iceberg-parquet',
	format = 'PARQUET',
	partitioning = ARRAY['year', 'month', 'day']
);

2. Local 환경에서 실행

2.1. Spark Application Download

Spark Application을 Download 하고, Python 패키지를 설치한다.

git clone https://github.com/ssup2-playground/k8s-data-platform_spark-jobs.git
cd k8s-data-platform_spark-jobs
uv sync

2.2. Spark Master와 Worker 실행

Shell을 2개 실행하여 각각 Master와 Worker로 설정하여 Local Spark Cluster를 구성한다.

spark-class org.apache.spark.deploy.master.Master -h localhost
spark-class org.apache.spark.deploy.worker.Worker spark://localhost:7077

2.3. Spark Job 실행

구성한 Local Spark Cluster에 daily-parquet 데이터를 활용하여 평균 날씨 데이터를 계산하는 Spark Job을 실행한다. Package에 hadoop-aws와 aws-java-sdk-bundle을 추가하여 MinIO에 접근할 수 있도록 설정한다.

export PYTHONPATH=$(pwd)/src
spark-submit \
  --packages org.apache.hadoop:hadoop-aws:3.3.4,com.amazonaws:aws-java-sdk-bundle:1.12.262 \
  --master spark://localhost:7077 \
  --total-executor-cores 2 \
  --executor-memory 500m \
  src/jobs/weather_southkorea_daily_average_parquet.py \
  --date 20250601

Trino의 Partition 정보를 갱신하고, Query를 수행하여 평균 날씨 데이터를 확인한다.

CALL hive.system.sync_partition_metadata('weather', 'southkorea_daily_average_parquet', 'ADD');
SELECT * FROM hive.weather.southkorea_daily_average_parquet;

구성한 Local Spark Cluster에 daily-iceberg-parquet 데이터를 활용하여 평균 날씨 데이터를 계산하는 Spark Job을 실행한다. Package에 iceberg-spark3-runtime을 추가하여 Iceberg Table을 활용한다.

export PYTHONPATH=$(pwd)/src
spark-submit \
  --packages org.apache.hadoop:hadoop-aws:3.3.4,com.amazonaws:aws-java-sdk-bundle:1.12.262,org.apache.iceberg:iceberg-spark3-runtime:0.13.2 \
  --master spark://localhost:7077 \
  --total-executor-cores 2 \
  --executor-memory 500m \
  src/jobs/weather_southkorea_daily_average_iceberg_parquet.py \
  --date 20250601

Query를 수행하여 평균 날씨 데이터를 확인한다.

SELECT * FROM iceberg.weather.southkorea_daily_average_iceberg_parquet;

3. Kubernetes 환경에서 실행

3.1. Service Account 설정

Spark Job 실행을 위한 권한을 부여하기 위해서 Service Account를 설정한다.

---
apiVersion: v1
kind: ServiceAccount
metadata:
  name: spark
  namespace: spark
---
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
  name: spark-role
  namespace: spark
rules:
  - apiGroups: [""]
    resources: ["pods", "services", "endpoints", "configmaps", "persistentvolumeclaims"]
    verbs: ["create", "get", "list", "watch", "delete", "deletecollection"]
---
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  name: spark-rolebinding
  namespace: spark
subjects:
  - kind: ServiceAccount
    name: spark
    namespace: spark
roleRef:
  kind: Role
  name: spark-role
  apiGroup: rbac.authorization.k8s.io

3.2. Spark Job 실행

Kubernetes Cluster에 daily-parquet 데이터를 활용하여 평균 날씨 데이터를 계산하는 Spark Job을 실행한다. 주요 설정은 다음과 같다.

eventLog : Spark Job가 저장될 MinIO의 위치를 지정한다.
spark.ui.prometheus.enabled : Spark Job에서 Prometheus Metric을 노출시킨다.
spark.kubernetes.driver.annotation.prometheus.io : Prometheus Server가 Spark Job이 노출하는 Metric을 수집할 수 있도록 설정한다.

spark-submit \
  --master k8s://192.168.1.71:6443 \
  --deploy-mode cluster \
  --name weather-southkorea-daily-average-parquet \
  --driver-cores 1 \
  --driver-memory 1g \
  --executor-cores 1 \
  --executor-memory 1g \
  --conf spark.executor.instances=2 \
  --conf spark.kubernetes.namespace=spark \
  --conf spark.kubernetes.container.image=ghcr.io/ssup2-playground/k8s-data-platform_spark-jobs:0.1.8 \
  --conf spark.kubernetes.authenticate.driver.serviceAccountName=spark \
  --conf spark.pyspark.python=/app/.venv/bin/python3 \
  --conf spark.jars.packages=org.apache.hadoop:hadoop-aws:3.3.4,com.amazonaws:aws-java-sdk-bundle:1.12.262 \
  --conf spark.eventLog.enabled=true \
  --conf spark.eventLog.dir=s3a://spark/logs \
  --conf spark.ui.prometheus.enabled=true \
  --conf spark.kubernetes.driver.annotation.prometheus.io/scrape=true \
  --conf spark.kubernetes.driver.annotation.prometheus.io/path=/metrics/executors/prometheus \
  --conf spark.kubernetes.driver.annotation.prometheus.io/port=4040 \
  local:///app/jobs/weather_southkorea_daily_average_parquet.py \
  --date 20250601

Kubernetes Cluster에 daily-iceberg-parquet 데이터를 활용하여 평균 날씨 데이터를 계산하는 Spark Job을 실행한다.

spark-submit \
  --master k8s://192.168.1.71:6443 \
  --deploy-mode cluster \
  --name weather-southkorea-daily-average-iceberg-parquet \
  --driver-cores 1 \
  --driver-memory 1g \
  --executor-cores 1 \
  --executor-memory 1g \
  --conf spark.executor.instances=2 \
  --conf spark.kubernetes.namespace=spark \
  --conf spark.kubernetes.container.image=ghcr.io/ssup2-playground/k8s-data-platform_spark-jobs:0.1.8 \
  --conf spark.kubernetes.authenticate.driver.serviceAccountName=spark \
  --conf spark.pyspark.python=/app/.venv/bin/python3 \
  --conf spark.jars.packages=org.apache.hadoop:hadoop-aws:3.3.4,com.amazonaws:aws-java-sdk-bundle:1.12.262,org.apache.iceberg:iceberg-spark3-runtime:0.13.2 \
  --conf spark.eventLog.enabled=true \
  --conf spark.eventLog.dir=s3a://spark/logs \
  --conf spark.ui.prometheus.enabled=true \
  --conf spark.kubernetes.driver.annotation.prometheus.io/scrape=true \
  --conf spark.kubernetes.driver.annotation.prometheus.io/path=/metrics/executors/prometheus \
  --conf spark.kubernetes.driver.annotation.prometheus.io/port=4040 \
  local:///app/jobs/weather_southkorea_daily_average_iceberg_parquet.py \
  --date 20250601

Spark History Server를 확인하여 Spark Job의 실행 로그를 확인한다. [Figure 2]는 Spark History Server에서 Spark Job의 실행 로그를 확인하는 모습이다.

Prometheus에서 executors Metric을 확인한다. [Figure 3]는 Prometheus에서 executors Metric을 확인하는 모습이다.

3.4. Spark Operator를 이용한 Spark Job 실행

Spark Operator를 통해서 daily-parquet 데이터를 활용하여 평균 날씨 데이터를 계산하는 Spark Job을 실행한다.

apiVersion: "sparkoperator.k8s.io/v1beta2"
kind: SparkApplication
metadata:
  namespace: spark
  name:  weather-southkorea-daily-average-parquet
spec:
  type: Python
  mode: cluster
  image: "ghcr.io/ssup2-playground/k8s-data-platform_spark-jobs:0.1.8"
  sparkVersion: "3.5.5"
  imagePullPolicy: Always
  mainApplicationFile: "local:///app/jobs/weather_southkorea_daily_average_parquet.py"
  
  # Application arguments
  arguments:
    - "--date"
    - "20250601"
  
  # Spark configuration
  sparkConf:
    "spark.eventLog.enabled": "true"
    "spark.eventLog.dir": "s3a://spark/logs"
    "spark.ui.prometheus.enabled": "true"
    "spark.kubernetes.driver.annotation.prometheus.io/scrape": "true"
    "spark.kubernetes.driver.annotation.prometheus.io/path": "/metrics/executors/prometheus"
    "spark.kubernetes.driver.annotation.prometheus.io/port": "4040"

  # Spark dependencies
  deps:
    packages:
      - org.apache.hadoop:hadoop-aws:3.4.0
      - com.amazonaws:aws-java-sdk-bundle:1.12.262
  
  # Executor configuration
  executor:
    instances: 2
    cores: 1
    memory: "1g"
    serviceAccount: spark
  
  # Driver configuration
  driver:
    cores: 1
    memory: "1g"
    serviceAccount: spark
  
  # Restart policy
  restartPolicy:
    type: Never
  
  # TTL for automatic cleanup (1 hour after completion)
  timeToLiveSeconds: 300

Spark Operator를 통해서 daily-iceberg-parquet 데이터를 활용하여 평균 날씨 데이터를 계산하는 Spark Job을 실행한다.

apiVersion: "sparkoperator.k8s.io/v1beta2"
kind: SparkApplication
metadata:
  namespace: spark
  name:  weather-southkorea-daily-average-iceberg-parquet
spec:
  type: Python
  mode: cluster
  image: "ghcr.io/ssup2-playground/k8s-data-platform_spark-jobs:0.1.8"
  sparkVersion: "3.5.5"
  imagePullPolicy: Always
  mainApplicationFile: "local:///app/jobs/weather_southkorea_daily_average_iceberg_parquet.py"
  
  # Application arguments
  arguments:
    - "--date"
    - "20250601"
  
  # Spark configuration
  sparkConf:
    "spark.eventLog.enabled": "true"
    "spark.eventLog.dir": "s3a://spark/logs"
    "spark.ui.prometheus.enabled": "true"
    "spark.kubernetes.driver.annotation.prometheus.io/scrape": "true"
    "spark.kubernetes.driver.annotation.prometheus.io/path": "/metrics/executors/prometheus"
    "spark.kubernetes.driver.annotation.prometheus.io/port": "4040"

  # Spark dependencies
  deps:
    packages:
      - org.apache.hadoop:hadoop-aws:3.4.0
      - com.amazonaws:aws-java-sdk-bundle:1.12.262
      - org.apache.iceberg:iceberg-spark3-runtime:0.13.2

  # Executor configuration
  executor:
    instances: 2
    cores: 1
    memory: "1g"
    serviceAccount: spark
  
  # Driver configuration
  driver:
    cores: 1
    memory: "1g"
    serviceAccount: spark
  
  # Restart policy
  restartPolicy:
    type: Never
  
  # TTL for automatic cleanup (1 hour after completion)
  timeToLiveSeconds: 300

4. Kubernetes 환경에서 Volcano Scheduler와 함께 실행

4.1. Volcano Scheduler Queue 설정

Spark Job을 위한 Volcano Scheduler의 Queue를 설정한다.

apiVersion: scheduling.volcano.sh/v1beta1
kind: Queue
metadata:
  name: sparkqueue
spec:
  weight: 4
  reclaimable: false
  capability:
    cpu: 10
    memory: 20Gi

4.2. PodGroup 설정

PodGroup 파일을 생성하여 Spark Job Container Image의 /app/configs/volcano.yaml에 복사한다. 주요 설정은 다음과 같다.

queue : 사용할 Queue 이름을 지정한다. 위에서 생성한 Queue 이름을 지정한다.
minMember : 최소 실행 가능한 Pod 수를 지정한다. Driver Pod는 단독으로 동작하기 때문에 반드시 1로 설정한다.
minResources : 최소 실행 가능한 Pod의 자원을 지정한다. Driver Pod와 Executor Pod의 Resource의 총합을 지정한다. Volcano Scheduler는 minResources를 만큼 Resource가 할당 가능할때 Spark Job Pod를 Scheduling한다.

apiVersion: scheduling.volcano.sh/v1beta1
kind: PodGroup
spec:
  queue: sparkqueue
  minMember: 1
  minResources:
    cpu: "4"
    memory: "4Gi"

4.2. Spark Job 실행

Volcano Scheduler와 함께 daily-parquet 데이터를 활용하여 평균 날께 데이터를 계산하는 Spark Job을 실행한다. spark.kubernetes.scheduler.name에 volcano를 지정하고, spark.kubernetes.scheduler.volcano.podGroupTemplateFile에 /app/configs/volcano.yaml을 지정한다.

spark-submit \
  --master k8s://192.168.1.71:6443 \
  --deploy-mode cluster \
  --name weather-southkorea-daily-average-parquet \
  --driver-cores 1 \
  --driver-memory 1g \
  --executor-cores 1 \
  --executor-memory 1g \
  --conf spark.executor.instances=2 \
  --conf spark.kubernetes.namespace=spark \
  --conf spark.kubernetes.container.image=ghcr.io/ssup2-playground/k8s-data-platform_spark-jobs:0.1.8 \
  --conf spark.kubernetes.authenticate.driver.serviceAccountName=spark \
  --conf spark.pyspark.python=/app/.venv/bin/python3 \
  --conf spark.jars.packages=org.apache.hadoop:hadoop-aws:3.3.4,com.amazonaws:aws-java-sdk-bundle:1.12.262 \
  --conf spark.kubernetes.scheduler.name=volcano \
  --conf spark.kubernetes.scheduler.volcano.podGroupTemplateFile=/app/configs/volcano.yaml \
  --conf spark.eventLog.enabled=true \
  --conf spark.eventLog.dir=s3a://spark/logs \
  --conf spark.ui.prometheus.enabled=true \
  --conf spark.kubernetes.driver.annotation.prometheus.io/scrape=true \
  --conf spark.kubernetes.driver.annotation.prometheus.io/path=/metrics/executors/prometheus \
  --conf spark.kubernetes.driver.annotation.prometheus.io/port=4040 \
  local:///app/jobs/weather_southkorea_daily_average_parquet.py \
  --date 20250601

Kubernetes Cluster에 daily-iceberg-parquet 데이터를 활용하여 평균 날씨 데이터를 계산하는 Spark Job을 실행한다.

spark-submit \
  --master k8s://192.168.1.71:6443 \
  --deploy-mode cluster \
  --name weather-southkorea-daily-average-iceberg-parquet \
  --driver-cores 1 \
  --driver-memory 1g \
  --executor-cores 1 \
  --executor-memory 1g \
  --conf spark.executor.instances=2 \
  --conf spark.kubernetes.namespace=spark \
  --conf spark.kubernetes.container.image=ghcr.io/ssup2-playground/k8s-data-platform_spark-jobs:0.1.8 \
  --conf spark.kubernetes.authenticate.driver.serviceAccountName=spark \
  --conf spark.pyspark.python=/app/.venv/bin/python3 \
  --conf spark.jars.packages=org.apache.hadoop:hadoop-aws:3.3.4,com.amazonaws:aws-java-sdk-bundle:1.12.262,org.apache.iceberg:iceberg-spark3-runtime:0.13.2 \
  --conf spark.kubernetes.scheduler.name=volcano \
  --conf spark.kubernetes.scheduler.volcano.podGroupTemplateFile=/app/configs/volcano.yaml \
  --conf spark.eventLog.enabled=true \
  --conf spark.eventLog.dir=s3a://spark/logs \
  --conf spark.ui.prometheus.enabled=true \
  --conf spark.kubernetes.driver.annotation.prometheus.io/scrape=true \
  --conf spark.kubernetes.driver.annotation.prometheus.io/path=/metrics/executors/prometheus \
  --conf spark.kubernetes.driver.annotation.prometheus.io/port=4040 \
  local:///app/jobs/weather_southkorea_daily_average_iceberg_parquet.py \
  --date 20250601

5. 참고

Spark Local 환경 설정 : https://bluehorn07.github.io/2024/08/18/run-spark-on-local-2/
Volcano Scheduler 설정 : https://docs.aws.amazon.com/emr/latest/EMR-on-EKS-DevelopmentGuide/tutorial-volcano.html

Orange Pi 5 Max Cluster 구축 SSL Self-signed Certificate 생성