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Apache Pinot on Kubernetes

Apache Pinot onKubernetes

Deploy production-grade Apache Pinot on Kubernetes with operator-managed lifecycle, Helm-based provisioning, and cloud-native scaling for ultra-low-latency real-time analytics at any scale.

Real-Time
OLAP
Sub-Second
Queries
Horizontal
Scaling

Comprehensive Apache Pinot on Kubernetes Services

From cluster deployment to production monitoring, we provide end-to-end Apache Pinot on Kubernetes solutions for real-time analytics workloads.

Pinot Deployment on K8s
Controller, broker, server, and minion node deployment with StatefulSets and operator-managed lifecycle
  • Controller StatefulSet configuration
  • Broker Deployment with HPA
  • Server StatefulSet with persistent storage
  • Minion node setup for background tasks
Helm Chart Management
Chart customization, values tuning, and upgrade strategies for reproducible Pinot deployments
  • Helm chart customization and templating
  • Values file tuning for production
  • Chart version upgrade strategies
  • GitOps integration with ArgoCD / Flux
Real-Time & Offline Tables on K8s
Kafka-connected real-time ingestion, batch offline tables, and hybrid table configurations on Kubernetes
  • Real-time table with Kafka integration
  • Offline batch ingestion via K8s Jobs
  • Hybrid table lambda architecture
  • Schema and table config management
Storage & Persistence
Deep store, segment store, PVCs, StorageClasses, and tiered storage for cost-optimized Pinot data
  • PVC and StorageClass configuration
  • Deep store on S3 / GCS / Azure Blob
  • Tiered storage (hot/cold) setup
  • Volume expansion and snapshots
Monitoring on K8s
Pinot metrics, Prometheus, Grafana, and alerting on Kubernetes for full cluster observability
  • Prometheus ServiceMonitor setup
  • Grafana dashboard provisioning
  • Query latency and ingestion lag alerts
  • Alertmanager rules and routing
Backup & Disaster Recovery
Segment backup, deep store replication, and cross-region restore for Pinot data protection
  • Deep store backup and replication
  • Segment snapshot strategies
  • Cross-region DR configuration
  • Controller metadata backup

Why Run Apache Pinot on Kubernetes?

Cloud-native real-time analytics with declarative cluster management, elastic scaling, and infrastructure-as-code for consistent, repeatable Pinot deployments.

Cloud-Native OLAP

Run Apache Pinot as a first-class Kubernetes workload with declarative configuration, self-healing, and seamless integration with your cloud-native infrastructure and CI/CD pipelines.

Elastic Scaling

Scale broker nodes with HPA for query throughput, add server nodes for data capacity, and use VPA for right-sizing resources. Pinot's segment rebalance API redistributes data automatically.

Real-Time Ingestion

Ingest streaming data from Apache Kafka with sub-second latency. Kubernetes manages Pinot server pods that consume from Kafka topics and make data queryable in real time.

Rolling Upgrades

Upgrade Pinot versions with zero downtime using Kubernetes rolling update strategies. Controllers, brokers, servers, and minions are upgraded sequentially with health checks at each step.

Pinot on K8s Key Metrics

Query Latency(P99 at scale)
Sub-Second
Ingestion(Kafka streaming)
Real-Time
Scaling(HPA / VPA support)
Horizontal
Deep Store(Object storage)
S3 / GCS
Self-Healing(Pod restart & rebalance)
Yes
GitOps Ready(ArgoCD / Flux compatible)
Yes

Apache Pinot Architecture on Kubernetes

Pinot's distributed architecture maps naturally to Kubernetes primitives, with each node type deployed as the optimal workload resource for its role.

StatefulSet
Controller
Cluster metadata and orchestration
Manages cluster topology and state
Handles segment assignment
Orchestrates rebalance operations
Serves admin REST API
Deployed as StatefulSet with PVC
Leader election via ZooKeeper
Deployment / HPA
Broker
Query routing and fan-out
Accepts SQL queries from clients
Routes queries to correct servers
Merges partial results
Stateless — scales with HPA
Load-balanced via K8s Service
Query optimization and planning
StatefulSet
Server
Segment storage and query execution
Hosts table segments on local storage
Executes queries on segment data
Consumes real-time streams (Kafka)
Persistent volumes for segment data
Supports tiered storage (hot/cold)
Scales by adding replicas + rebalance
Deployment / Job
Minion
Background task execution
Executes segment merge tasks
Runs purge and retention jobs
Handles segment conversion
Offloads work from servers
Deployed as Deployment or K8s Jobs
Scales independently of data path

Our Apache Pinot on Kubernetes Implementation Process

A proven methodology for deploying production-ready Apache Pinot on Kubernetes with comprehensive testing and validation.

1

Assessment & Planning

Evaluate your analytics workload requirements, data volumes, query patterns, and Kubernetes environment. Select the right node sizing, storage backend, and cluster topology.

2

Cluster Deployment

Deploy Pinot via Helm charts with production-tuned values. Configure controller, broker, server, and minion nodes with appropriate resource requests, persistent volumes, and networking.

3

Table Setup & Validation

Create real-time and offline table schemas, configure Kafka stream ingestion, set up batch ingestion jobs, and validate query performance under production-like load.

4

Production & Operations

Go live with full monitoring, alerting, automated scaling, and runbooks. Provide team training on Pinot cluster management, day-2 operations, and 24/7 support.

Apache Pinot on Kubernetes — Frequently Asked Questions

Common questions about running Apache Pinot on Kubernetes in production environments.

Ready to Run Apache Pinot on Kubernetes?

Let our experts deploy and manage production Apache Pinot on Kubernetes with real-time ingestion, sub-second analytics, and elastic scaling for your OLAP workloads.