Spark on OpenShift: Pulse Observability

Spark on OpenShift: Pulse Observability provides deep insights into Spark workloads running on Kubernetes clusters.

It monitors key performance metrics, logs, and cluster-level events to help ensure stable, efficient, and observable Spark jobs.

This integration allows teams to troubleshoot issues quickly, optimize resource usage, and maintain high reliability in cloud-native environments.

Key Benefits

Key advantages of Spark on OpenShift-Pulse Observability:

• Enables real-time monitoring of Spark jobs running on OpenShift.

• Provides centralized visibility into logs, metrics, and events.

• Supports automatic alerting on job failures and performance issues.

• Integrates seamlessly with Kubernetes and OpenShift APIs.

• Helps in rapid root cause analysis using Pulse Logs.

• Scales effortlessly with distributed data workloads.

• Improves operational efficiency with a unified observability layer.

Architecture Components and Flow

• Spark Driver and Executor Pods run inside the OpenShift cluster with an embedded AdSparkListener capturing job-level events and metrics.

• Container logs from Spark pods are collected and forwarded for analysis and audit.

• Logs and events are pushed to the Pulse Node Agent for processing and observability.

• Metrics and logs are routed to storage systems like Victoria Metrics, forming the time-series data backend.

• PulseNode Agent collects Spark Graphite metrics and forward to Victoria Metrics for processing and analysis.

• The Pulse Manager Server and Pulse Operator together manage the deployment and lifecycle of all Pulse services within the Kubernetes environment.

• Pulse Logs Audit feeds data into the Pulse Manager for further correlation and visualization.

• Pulse Core Services, such as Dashplots, UI, Alerts, and Streaming, fetch data from backend databases to power dashboards and notifications.

• Pulse Manager Agent manages database configuration, deployment, and orchestration. It ensures consistent communication across distributed databases—MongoDB, VictoriaMetrics, NATS, and ElasticSearch—enabling end-to-end observability.