| Scenario | Block (KB) | Queue Depth | Latency (ms) | Estimated IOPS | Estimated MB/s |
|---|---|---|---|---|---|
| Random reads, transactional | 4 | 32 | 0.40 | 80,000 | 312.50 |
| Mixed workload, moderate blocks | 16 | 16 | 0.80 | 20,000 | 312.50 |
| Sequential transfer, large blocks | 128 | 8 | 1.50 | 5,333 | 666.63 |
- Throughput (MB/s) = Total Data (MB) ÷ Time (s)
- Measured IOPS = Operations ÷ Time (s)
- Throughput (MB/s) = IOPS × Block Size (KB) ÷ 1024
- Latency IOPS Estimate ≈ (Queue Depth × 1000) ÷ (Latency (ms) + Overhead (ms)) × Streams
- Effective Ceiling = min(Interface Limit, Device Limit) × Efficiency
- Final Throughput = min(Base Throughput, Effective Ceiling) when a ceiling is provided
- Enter block size that matches your workload (for example, 4 KB for random reads).
- For measured results, enter total data and elapsed time. Add operations if you also tracked request counts.
- For a latency-driven estimate, enter average latency and queue depth. Add overhead if you know protocol cost.
- Optionally enter interface/device limits and efficiency to clamp results to a realistic ceiling.
- Click Calculate. Use Download CSV or Download PDF to share results.
Throughput, IOPS, and latency relationships
Throughput expresses data moved per second, while IOPS counts operations per second. They connect through block size: MB/s = IOPS × KB ÷ 1024. Latency sets an upper bound because each request consumes time. A simple estimate is IOPS ≈ (Queue Depth × 1000) ÷ Latency(ms). Use this calculator to compare measured transfer rates with latency-based limits before tuning workloads. If results disagree, check caching, throttling, and controller scheduling effects during tests.
Block size effects on bandwidth and operations
Block size changes the balance between bandwidth and IOPS. Small blocks such as 4 KB are common for databases and metadata, but they demand high IOPS. Larger blocks like 64–256 KB increase MB/s at the same IOPS and fit sequential transfers. However, oversized blocks waste bandwidth when reads are partial. Use the read/write mix to split total throughput into directional rates for sizing caches and logs. Tests often report both values together.
Queue depth and concurrency planning
Queue depth represents outstanding requests and acts like pipeline fill. Higher depth can raise throughput until the device or interface saturates, but latency usually rises. For interactive services, target a depth that meets your latency budget at peak load. For batch jobs, deeper queues may be acceptable if tail latency is irrelevant. The streams input models multiple workers, RAID lanes, or parallel files increasing concurrency. Monitor 99th percentile latency, not only averages.
Interface and device limits in real systems
Real throughput is capped by the tightest limit in the path. An interface ceiling, controller firmware, filesystem overhead, encryption, or network hops can dominate. Enter device and interface limits to compute an effective ceiling, then apply an efficiency factor for protocol overhead. If your computed throughput exceeds the ceiling, the final value is clamped and utilization is shown. This highlights whether tuning should focus on software or hardware upgrades for future growth.
Validating results with test runs
Validate inputs with a short benchmark run before committing to capacity plans. Record total bytes transferred, elapsed time, average latency, and queue depth at steady state. Then compare measured IOPS and MB/s with the latency-based estimate; large gaps suggest caching, write coalescing, or throttling. Repeat with different block sizes to map workload sensitivity. Use the example table as a template for documenting assumptions and sharing results across teams in a change review.
What does the calculator output first?
It prioritizes measured throughput from data and time. If those are missing, it estimates throughput from IOPS and block size, or from latency and queue depth, then applies any bandwidth limits.
Why is block size important?
Throughput depends on bytes per operation. With the same IOPS, larger blocks raise MB/s, while small blocks emphasize transaction rates. Match block size to your application’s typical request size.
How do queue depth and latency relate to IOPS?
At steady state, IOPS can be approximated as queue depth divided by response time. Increasing depth raises concurrency, but latency usually rises once the device approaches saturation.
What should I enter for efficiency?
Efficiency represents protocol and software overhead. Start with 85–95% for well-tuned local storage and 60–85% for heavier stacks. Measure and adjust using real benchmark data.
Why are my measured and estimated results different?
Caching, write buffering, compression, throttling, and background maintenance can change observed performance. Also verify you measured steady state, used consistent units, and recorded average latency under the same load.
Can I use this for mixed read/write workloads?
Yes. Set the read percentage to split final throughput into read and write rates. For deeper analysis, measure read and write latencies separately because writes may have different buffering behavior.