Storage Latency
Terms related to simplyblock
Storage latency is the time it takes for a storage system to respond to a read or write request. In simpler terms, it’s the delay between asking for data and receiving it. While IOPS and throughput often steal the spotlight, latency is usually what determines how fast your app feels—or how sluggish it gets under pressure.
Whether you’re dealing with transactional databases, analytics engines, or persistent volumes in Kubernetes, latency affects response time, consistency, and user experience. Even small delays, measured in microseconds, can stack up and choke performance at scale.
How Storage Latency Impacts Modern Applications
Latency doesn’t just affect infrastructure—it directly hits your applications. When a write takes 2ms instead of 200μs, that’s a 10× slowdown on disk I/O. Multiply that across thousands of transactions per second, and you’ve got a bottleneck.
Databases like PostgreSQL and MySQL are especially sensitive to high tail latency. The same goes for distributed systems running in Kubernetes clusters, where every I/O delay slows down pods, autoscaling behavior, and API response times.
Storage latency also compounds in CI/CD pipelines, where fast read/write access is essential for build caching, logs, and package handling. In short, if you’re shipping fast, latency needs to be low and predictable.
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Storage Latency vs IOPS – Not the Same Thing
A lot of teams lump IOPS and latency together, but they measure different things. Understanding the difference is key to diagnosing slow workloads—especially when metrics look fine on the surface but apps are still lagging.
| Metric | What It Measures | Why It Matters |
|---|---|---|
| IOPS | Number of read/write operations per second | Measures throughput |
| Latency | Time it takes to complete a single operation | Measures responsiveness |
| Throughput | Volume of data transferred over time | Indicates total transfer capacity |
A system can have high IOPS and still feel slow if latency is unpredictable. IOPS is about volume. Latency is about speed. When apps stall, it’s usually latency—not IOPS—that’s to blame.
Why Low Latency Storage Matters in Kubernetes
Latency becomes even more critical in containerized environments. Kubernetes spreads workloads across nodes, zones, or even regions. If your PersistentVolume suffers from high latency, the pod slows down—even if compute resources are healthy. Low-latency storage ensures faster pod startup, better performance for stateful sets, and consistent behavior across availability zones. It also improves autoscaling response times during traffic spikes.
And when you’re using disaggregated storage, latency becomes even more important. In these setups, data isn’t sitting on the same node—it’s traveling across the network. That’s where technologies like NVMe over TCP help reduce delays and keep I/O responsive.
7 Causes of High Storage Latency
- Slow disks – HDDs or consumer-grade SSDs can’t keep up under load
- Network congestion – Especially in hybrid cloud or zone-spanning clusters
- Over-provisioned volumes – Too many apps sharing a single backend
- File system overhead – Especially in legacy setups with layered storage
- Snapshot sprawl – Old, unmanaged snapshots can affect write performance
- Improper caching – Poorly tuned or disabled cache policies add delay
- Poor replication logic – If replication isn’t async or optimized, writes wait
Understanding latency means looking beyond volume metrics—it’s often caused by things outside the core disk I/O path.
How to Measure and Monitor Storage Latency
You can’t fix what you can’t see. Monitoring storage latency should be part of every environment—especially if you’re running production databases or persistent volumes.
In Kubernetes, tools like kubectl, Prometheus and CSI metrics offer some visibility. For deeper insight, integrate with observability platforms that show per-volume latency, tail percentiles, and node-to-volume delays.
Amazon CloudWatch and tools like iostat or fio also help track read/write latency at the block level.
Set alerts not just for average latency, but for p99 values. Apps usually break under spikes, not averages.
How Simplyblock Reduces Latency Without Extra Tuning
Simplyblock is built to run high-performance, software-defined storage with consistent low latency—out of the box. It uses NVMe-over-TCP to deliver high throughput and microsecond-level latency, even across zones or clusters.
Because Simplyblock separates the control and data planes, it avoids bottlenecks caused by legacy storage architectures. CSI-native provisioning means every PersistentVolume is optimized from the start—no extra steps, no hand tuning.
Whether you’re running databases on Kubernetes, backing up multi-tenant workloads, or optimizing CI/CD pipelines, Simplyblock helps you hit latency targets without relying on expensive SAN setups or manual cache tuning.
Where Storage Latency Hits Hardest
Latency problems show up everywhere—but they hit hardest in:
- Stateful apps like PostgreSQL, MongoDB, and Redis
- Logging platforms with constant I/O
- Multi-zone or multi-cluster workloads
- High-throughput CI/CD pipelines
- Backup and disaster recovery setups that rely on quick snapshot and restore times
If you’re managing fast backups and disaster recovery, high latency can make restore time unacceptable—even if IOPS looks fine on paper.
And in database branching scenarios or dev environments where fast clones are needed, slow storage turns agile teams into blocked ones.
Latency Isn’t Optional Anymore
If your infrastructure feels slow but your IOPS are fine, latency is the real issue. And it’s not just about milliseconds—it’s about consistency. Predictable, low latency keeps your apps responsive and your teams moving.
You can scale IOPS. You can increase throughput. But you can’t cheat latency. You have to design for it—at the storage layer.
Questions and Answers
Storage latency directly affects how fast applications respond to user actions or process data. High latency can delay database queries, slow down analytics, or create lag in streaming services. In performance-sensitive systems, even microseconds matter—especially at scale.
NVMe over TCP reduces storage latency by using a streamlined command set and parallel queues over standard Ethernet. It eliminates the bottlenecks of older protocols like iSCSI and brings latency closer to that of local SSDs.
Key latency drivers include device type (HDD vs NVMe), network protocol, block size, and queue depth. Optimizing these—alongside using software-defined storage—can significantly lower response times for critical workloads.
Latency measures the time per operation, IOPS counts the number of operations per second, and throughput measures data volume transferred. Each metric reveals a different aspect of performance. Read more in our guide to IOPS, throughput, and latency.
Workloads that demand ultra-low latency—like financial systems or real-time analytics—benefit most from NVMe storage combined with modern protocols and high-speed networking. Simplyblock’s NVMe-over-TCP SDS is built for exactly this use case.