Real-Time Data Processing with Apache Storm/Flink on OpenMetal

Understanding Apache Storm vs Apache Flink

Before diving into deployment strategies, you need to understand which framework aligns with your specific requirements. Both frameworks excel at different aspects of stream processing.

Apache Storm: Low-Latency Stream Processing

Apache Storm excels in providing low latency and high throughput for real-time stream processing applications. The framework’s architecture revolves around two primary components: Spouts (data ingestion) and Bolts (data processing), connected through a Directed Acyclic Graph (DAG) structure.

Storm’s key strengths include:

• Impressively low latency for near real-time data processing
• Simple setup and configuration process
• Benchmark performance of over a million tuples processed per second per node
• Straightforward integration with existing queueing and database technologies

Apache Flink: Unified Stream and Batch Processing

Flink offers a more unified architecture that seamlessly integrates both batch and stream processing capabilities. Unlike frameworks that rely on micro-batching, Flink provides native streaming support with extremely low latency.

Flink’s distinguishing features include:

• Superior memory utilization compared to other frameworks
• Built-in support for complex event processing (CEP)
• Event-time processing and sophisticated late data handling
• SQL on both stream and batch data

Why Infrastructure Choice Matters for Stream Processing

Traditional virtualized cloud environments introduce several challenges for streaming workloads:

Performance Jitter: Multi-tenant environments create resource contention that can cause unpredictable latency spikes

Network Bottlenecks: Shared network infrastructure limits the constant message passing between processing nodes

Virtualization Overhead: Hypervisor layers add computational overhead that affects processing efficiency

Unpredictable Costs: Variable pricing models become expensive when stream volumes spike unexpectedly

These issues become particularly problematic for stream processing, where consistent performance and predictable latency are fundamental requirements.

OpenMetal’s Infrastructure Advantage for Stream Processing

Dedicated Bare Metal for Consistent Performance

Real-time processing with Storm and Flink requires consistent low latency, which is the main benefit of using OpenMetal’s dedicated bare metal servers. This helps avoid the performance jitters and resource contention found in typical multi-tenant virtualized environments.

The dedicated hardware approach eliminates the “noisy neighbor” problem entirely. When your Storm or Flink cluster needs to process a sudden spike in stream volume, you have guaranteed access to 100% of the server resources without competing with other tenants.

High-Performance Networking

The high throughput from the 20 Gbps internal network with unmetered east-west optimized private traffic is important for the constant message passing between nodes, which prevents network backpressure. This network architecture becomes particularly valuable when running distributed Storm topologies or Flink job graphs that require frequent inter-node communication.

For Storm deployments, this means your Spouts can reliably feed data to downstream Bolts without network congestion creating bottlenecks. For Flink, the pipelined execution model benefits from the high-bandwidth, low-latency network that enables efficient task chaining across the cluster.

Advanced Hardware Optimization Capabilities

Because we provide full hardware access, users can perform advanced optimizations like CPU pinning to assign specific threads to dedicated cores, reducing context-switching overhead and lowering processing latency. Unlike managed services, teams get complete control over the entire stack.

You can tune kernel parameters, install custom monitoring, and optimize for specific Storm/Flink configurations. This level of control becomes important when you need to squeeze maximum performance from your streaming infrastructure.

Our direct-attached Micron 7450 and 7500 MAX NVMe drives provide support for consistent low-latency I/O operations, which benefits both frameworks when handling checkpointing, state management, and local data processing.

Flexible Deployment Options

Customers can also combine bare metal dedicated servers with OpenStack-powered private clouds to eliminate the virtualization overhead for compute-intensive workloads, while having cloud flexibility for scaling, management, and customization.

This hybrid approach allows you to deploy your core Storm/Flink processing nodes on bare metal for maximum performance, while using the private cloud for supporting services like monitoring, log aggregation, and development environments.

Rapid Scaling for Dynamic Workloads

Real-time processing platforms often need to scale quickly during peak stream volume. New Cloud Cores deploy in 45 seconds and additional nodes can be added to clusters in 20 minutes, providing the agility that fits the dynamic nature of streaming workloads.

This rapid provisioning capability addresses one of the biggest challenges in stream processing: handling unexpected data volume spikes. Whether you’re processing financial transactions during market volatility or social media streams during viral events, you can scale your infrastructure to match demand without lengthy provisioning delays.

Storage and State Management

Distributed Storage for Stateful Processing

For stateful processing, Flink’s checkpoints and state can be stored on the underlying Ceph cluster using its S3-compatible API, which provides a durable and distributed backend for state management and recovery. Built-in distributed storage cluster supports both block and object storage in the same environment with triple replication ensuring fault tolerance for stateful streaming workloads.

This eliminates the need for external dependencies like Amazon S3 or HDFS for checkpoint storage. Storm/Flink can use persistent storage for checkpointing and state management without external dependencies, reducing complexity and potential points of failure.

High-Availability Architecture

The standard three-server Private Cloud Core is fitting for a high-availability setup, allowing Zookeeper and redundant master daemons to run on separate physical machines. This architecture ensures that your stream processing infrastructure can survive individual node failures without losing data or stopping processing.

For Storm deployments, you can run Nimbus (master daemon) instances across different physical servers, with Zookeeper coordination distributed across the cluster. Flink JobManagers can similarly be deployed in high-availability mode with proper failover capabilities.

Memory and Computing Performance

Our servers have high RAM-to-CPU ratios which support in-memory streaming processing well, with GPU clusters available if streaming workloads involve AI inference, and the control plane overhead is predictable.

Storm’s in-memory processing and Flink’s stateful stream processing benefit from this predictable memory performance. Large state sizes in Flink applications can be maintained entirely in memory, with spillover to the high-performance NVMe storage when needed.

GPU clusters become valuable when your streaming workloads involve machine learning inference, computer vision processing, or other compute-intensive operations that can benefit from parallel processing acceleration.

Security and Compliance

OpenMetal v4 servers support Intel TDX/SGX confidential computing, ensuring isolation even in multi-tenant or regulated environments where streaming often processes sensitive data from finance, healthcare, or IoT sources.

This capability becomes important for organizations processing sensitive streams like financial transactions, healthcare records, or personally identifiable information. The hardware-level isolation provides an additional security layer beyond traditional software-based protections.

Cost Predictability

Our fixed-cost model also means that billing is based on the hardware, so costs remain predictable even if data stream volume is volatile and experiences large spikes. Our 95th percentile pricing model helps streaming workloads avoid the per-GB egress tax of public clouds, with generous included bandwidth allowances and fair pricing above limits helping avoid the unpredictable egress costs that are common with streaming workloads on hyperscale clouds.

Traditional cloud pricing can create budget surprises when streaming volumes increase unexpectedly. OpenMetal’s pricing model eliminates these concerns, allowing you to focus on processing performance rather than cost management.

Deployment Architecture Examples

Apache Storm on OpenMetal

A typical Storm deployment on OpenMetal might include:

Bare Metal Storm Cluster:

• 3x bare metal servers for Nimbus masters and Zookeeper quorum
• 6-12x bare metal workers for Storm Supervisor nodes
• Dedicated high-memory nodes for complex aggregation bolts

Configuration Example:

# storm.yaml configuration for OpenMetal deployment
storm.zookeeper.servers:
  - "storm-master-01.internal"
  - "storm-master-02.internal" 
  - "storm-master-03.internal"

nimbus.seeds:
  - "storm-master-01.internal"
  - "storm-master-02.internal"

supervisor.slots.ports:
  - 6700
  - 6701
  - 6702
  - 6703

worker.childopts: "-Xmx4g -XX:+UseG1GC"
supervisor.childopts: "-Xmx1g"

Apache Flink on OpenMetal

A production Flink setup leverages the hybrid approach:

JobManager High Availability:

# Flink HA configuration
high-availability: zookeeper
high-availability.zookeeper.quorum: flink-master-01:2181,flink-master-02:2181,flink-master-03:2181
high-availability.storageDir: s3://openmetal-ceph-s3/flink-ha

TaskManager Optimization:

# TaskManager configuration for bare metal nodes
taskmanager.memory.process.size: 32gb
taskmanager.numberOfTaskSlots: 8
taskmanager.memory.managed.fraction: 0.4

Geographic Distribution and Edge Processing

Our Tier III data centers are strategically located in Ashburn, LA, Amsterdam, and Singapore for sub-30ms latency to major metros and provide global coverage for distributed streaming architectures closer to data sources and users.

This geographic distribution enables you to build distributed streaming architectures that process data closer to its source, reducing latency and improving user experience. You can deploy Storm or Flink clusters in multiple regions, with data replication and coordination across locations.

Operational Management

We allow for Day 2 operations, hardware management (failures, maintenance) is handled by OpenMetal, but OS and application stack remain under customer control. This balance reduces operational overhead while maintaining customization flexibility.

You maintain full control over:

• Storm/Flink version selection and configuration
• JVM tuning and garbage collection settings
• Custom monitoring and alerting setup
• Application deployment and lifecycle management

OpenMetal handles:

• Hardware replacement and maintenance
• Network infrastructure management
• Power and cooling systems
• Physical security

Monitoring and Observability

Root access allows installation of custom monitoring stacks (Prometheus, Grafana, etc.) tuned for streaming workload metrics. You can implement comprehensive observability including:

Storm-Specific Metrics:

# Custom Storm metrics collection
storm.topology.metrics.consumer.register:
  - class: "org.apache.storm.metric.LoggingMetricsConsumer"
  - class: "org.apache.storm.metric.MetricsConsumerBolt"

Flink Monitoring Setup:

# Flink metrics configuration
metrics.reporters: prom
metrics.reporter.prom.class: org.apache.flink.metrics.prometheus.PrometheusReporter
metrics.reporter.prom.port: 9249

Containerized Deployments

Our infrastructure also supports Kubernetes/OpenShift deployments on both VMs and bare metal, enabling containerized Storm/Flink deployments. This approach provides:

• Simplified application lifecycle management
• Resource isolation and allocation control
• Integration with cloud-native tooling
• Hybrid deployment flexibility

Validation and Support

We have validated Apache Storm for use on our cloud infrastructure, and can assist with building and deploying big data platforms and pipelines to save customer time and resources. This validation means you can deploy with confidence, knowing that the infrastructure has been tested with your workloads.

Our support extends beyond infrastructure to include architectural guidance for optimizing your specific streaming use cases on our platform.

Getting Started

Built on our open source-first approach, Storm and Flink run on OpenStack-powered private clouds where customers benefit from full API-driven control through Terraform, Ansible, and other tools without vendor lock-in.

To begin deploying Storm or Flink on OpenMetal:

1. Assessment: Evaluate your stream processing requirements and choose between Storm and Flink based on your specific needs
2. Architecture Design: Plan your cluster topology using our big data infrastructure guidance
3. Infrastructure Provisioning: Deploy your Private Cloud Core or bare metal servers
4. Framework Installation: Install and configure Storm or Flink using our validated configurations
5. Performance Tuning: Optimize for your specific workload characteristics

The combination of OpenMetal’s dedicated infrastructure, flexible deployment options, and predictable pricing creates an ideal foundation for production stream processing workloads. Whether you choose Storm for its simplicity and low latency or Flink for its unified processing capabilities, you get the performance consistency and operational control needed for demanding real-time applications.

Ready to deploy high-performance stream processing infrastructure? Explore our bare metal and hosted private cloud solutions, or learn more about big data infrastructure options for your organization.