Janis Eccarius
d3feb65eb3
With SPARK_WORKER_CORES=1 and 2 workers the cluster has 2 total cores. LiveDashboard holds both — batch jobs wait indefinitely. 4 cores per worker gives 8 total, enough to run streaming + batch concurrently. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
47 lines
1.2 KiB
YAML
Executable File
47 lines
1.2 KiB
YAML
Executable File
apiVersion: apps/v1
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kind: Deployment
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metadata:
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name: spark-worker
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labels:
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app.kubernetes.io/name: spark-worker
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app.kubernetes.io/part-of: nowchess
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spec:
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replicas: 2
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selector:
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matchLabels:
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app.kubernetes.io/name: spark-worker
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template:
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metadata:
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labels:
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app.kubernetes.io/name: spark-worker
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app.kubernetes.io/part-of: nowchess
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spec:
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containers:
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- name: spark-worker
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image: apache/spark:4.0.3-scala2.13-java17-ubuntu
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command:
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- /opt/spark/bin/spark-class
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- org.apache.spark.deploy.worker.Worker
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- spark://spark-master:7077
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env:
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- name: SPARK_WORKER_CORES
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value: "4"
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- name: SPARK_WORKER_MEMORY
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value: "512m"
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ports:
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- name: webui
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containerPort: 8081
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resources:
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requests:
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cpu: 200m
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memory: 256Mi
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limits:
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cpu: "1"
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memory: 2Gi
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volumeMounts:
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- name: spark-events
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mountPath: /spark-events
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volumes:
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- name: spark-events
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emptyDir: {}
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