Redpanda has quickly become a leading Kafka-compatible platform for event streaming. It’s simple to run, delivers massive throughput, and cuts latency down to milliseconds. But there’s a catch: Redpanda’s performance depends on how well the storage layer handles its nonstop commit logs and replication traffic. On standard cloud block storage, brokers get bogged down, recovery slows, and consumer lag increases.
Simplyblock solves this. With NVMe-over-TCP, zone-resilient volumes, and real-time scalability, simplyblock gives Redpanda the storage backbone it needs to handle streaming workloads at full speed.
Why Storage Defines Redpanda’s Throughput
Every event in Redpanda is written to a commit log. These writings are sequential and relentless. If the underlying disk can’t keep up, producers face backpressure, consumers fall behind, and replication becomes unstable. The result is laggy pipelines and slower data delivery.
By running on disaggregated NVMe-over-TCP storage, Redpanda gets low-latency writes and consistent throughput. This ensures that producers push events at full speed while consumers process data in real time — without the drag of storage bottlenecks.
Check out the Redpanda Documentation on Performance Tuning for insights on improving throughput and minimizing lag.
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Step 1: Faster Broker Recovery With High-Speed Storage
When a Redpanda broker goes down, it needs to replay logs to catch up. On slow disks, recovery can take minutes or even hours, depending on log size. That delay impacts availability and puts extra load on the cluster.
Simplyblock accelerates recovery by delivering NVMe-level storage performance across zones. Brokers restart quickly, logs replay faster, and the cluster returns to full strength without extended lag. For best practices on setting up recovery, refer to the Redpanda Recovery Documentation.
Step 2: Handling Spikes in Streaming Data Without Bottlenecks
Event streams don’t flow evenly. Traffic spikes during peak hours or flash events can overwhelm standard volumes, causing throttling and queue buildup. Once latency enters the system, it ripples through producers, brokers, and consumers.
With simplyblock, Redpanda volumes absorb spikes smoothly. Sub-millisecond latency and scalable throughput ensure event bursts are handled without slowdown. For teams running high-volume pipelines, simplyblock keeps streams stable and responsive — no matter how unpredictable the load.
To scale Redpanda seamlessly on Kubernetes for handling unpredictable workloads, refer to the Redpanda Kubernetes Scaling Guide.
Step 3: Scaling Log Volumes as Pipelines Grow
Streaming pipelines generate massive amounts of data, especially when long retention periods are required. A fixed storage plan often forces costly rebalancing or downtime when logs outgrow their space.
With simplyblock, Redpanda log volumes can expand instantly while brokers stay online:
sbctl volume resize –name rp-logs –size 400Gi
resize2fs /dev/simplyblock/rp-logs
No interruptions, no cluster restarts. This flexibility makes database workloads on Kubernetes and hybrid deployments easier to manage, especially as pipelines scale beyond initial projections.
Step 4: Keeping Brokers Stable Across Availability Zones
Brokers in distributed environments don’t always stay in one zone. Node failures, rescheduling, or scaling events often move workloads around. Traditional cloud block storage is zone-bound, so a volume can’t follow the broker. The result? Failed mounts and lost availability.
Simplyblock volumes are zone-independent. If a Redpanda broker shifts to another zone, its logs stay accessible without reattachment. This ensures cluster stability and supports multi-availability zone disaster recovery strategies by design.
Step 5: Double Protection With Storage-Level Replication
Redpanda’s replication model protects at the broker level, but adding storage-level redundancy ensures even stronger durability. With simplyblock, you can replicate log volumes across zones in real time:
sbctl volume replicate –volume-id=rp-logs –target-zone=us-east-b
This gives you two layers of resilience: broker-to-broker replication and block-level storage replication. Even in the event of a zone outage, Redpanda brokers can continue streaming without data loss — a key advantage for database performance optimization and disaster recovery planning.
Redpanda + Simplyblock – Built for Real-Time Streaming
Redpanda is engineered for speed, but storage often drags it down. Simplyblock eliminates that bottleneck with NVMe-over-TCP performance, live scaling, and zone-aware durability. The result is streaming pipelines that remain fast, brokers that recover quickly, and clusters that scale without disruption.
For businesses relying on event streaming, Redpanda + Simplyblock is the combination that keeps real-time data moving without compromise.
Questions and Answers
Redpanda delivers high-throughput streaming but often faces bottlenecks in persistence. Simplyblock provides NVMe storage with extremely low latency, ensuring Redpanda log segments are written at NVMe speed. This boosts throughput and durability while keeping in-memory performance uncompromised.
Yes, simplyblock’s database on Kubernetes use case demonstrates how persistent NVMe-backed volumes scale across nodes. For Redpanda clusters, this ensures consistent performance as brokers and partitions are added, avoiding storage bottlenecks during scaling.
Cloud-native block storage can add unpredictable latency and higher costs. With simplyblock’s AWS storage optimization, Redpanda gets faster persistence and lower cloud spend. This makes it more efficient than relying on services like Amazon EBS for log durability.
Redpanda runs best when paired with storage optimized for stateful workloads. Simplyblock integrates through its CSI driver and Kubernetes stateful workload support, enabling persistent, high-performance NVMe storage for Redpanda brokers without complex setup.
Yes. Redpanda relies on fast storage for log replication and recovery. With simplyblock’s database optimization, brokers resync quicker, reducing failover times and minimizing downtime for critical streaming applications.