Estimated Results
Results appear here after submission. The graph uses current values and projects scaling across disk counts.
Plotly Performance Projection
The chart shows estimated front-end IOPS and throughput as disk count increases while keeping the current workload profile unchanged.
Calculator Inputs
Example Data Table
These examples use the same equations as the calculator and demonstrate how workload mix, latency, and RAID policy change usable performance.
| Scenario | Disks | Read % | Mixed Latency (ms) | Queue Depth | Block Size (KB) | RAID Penalty | Estimated IOPS | Throughput (MB/s) |
|---|---|---|---|---|---|---|---|---|
| 4-disk SATA parity pool | 4 | 70 | 4.98 | 8 | 8 | 4 | 2,289.99 | 17.89 |
| 8-disk low-latency solid-state pool | 8 | 80 | 1.36 | 16 | 16 | 2 | 60,166.67 | 940.10 |
| 12-disk high-protection parity group | 12 | 60 | 4.10 | 12 | 32 | 6 | 7,112.20 | 222.26 |
Formula Used
1) Effective mixed latency
Effective Latency = (Read Fraction × Read Latency) + (Write Fraction × Write Latency)
2) Raw single-disk IOPS at queue depth 1
Raw Mixed IOPS Per Disk = 1000 ÷ Effective Latency(ms)
3) Queue-limited per-disk IOPS
Queue-Limited IOPS Per Disk = (1000 × Queue Depth) ÷ Effective Latency(ms)
4) Usable back-end per-disk IOPS
Usable Back-end IOPS Per Disk = Queue-Limited IOPS × Utilization × Efficiency × (1 - Overhead)
5) RAID cost factor
RAID Cost Factor = (Read Fraction × 1) + (Write Fraction × RAID Write Penalty)
6) Front-end array IOPS
Front-end Array IOPS = (Usable Back-end IOPS Per Disk × Disk Count) ÷ RAID Cost Factor
7) Throughput conversion
Throughput MB/s = (Front-end IOPS × Block Size KB) ÷ 1024
This engineering model is useful for sizing and comparison. Real performance also depends on cache policy, controller firmware, IO randomness, command batching, and host path saturation.
How to Use This Calculator
- Enter the total number of disks participating in the workload.
- Add realistic read and write latency figures from specifications or measurements.
- Set the read ratio to match your application profile. The write ratio is inferred.
- Choose queue depth to reflect the expected concurrency per disk or device path.
- Provide block size so the calculator can convert IOPS to throughput.
- Apply utilization, controller efficiency, and protocol overhead for safer capacity planning.
- Enter the RAID write penalty that matches your storage protection method.
- Press the calculate button to view front-end IOPS, back-end IOPS, throughput, and scaling behavior above the form.
FAQs
1. What does IOPS mean for a disk system?
IOPS means input and output operations per second. It describes how many read or write requests a disk system can complete in one second under a defined workload profile.
2. Why does queue depth increase estimated IOPS?
Higher queue depth allows more operations to stay in flight. That improves parallelism and reduces idle gaps, especially on flash storage and multi-channel controllers.
3. Why is write penalty important in RAID groups?
Parity-based RAID needs extra internal work for writes. The calculator reduces front-end IOPS by a RAID cost factor so user-visible performance is not overstated.
4. Should I design for 100% utilization?
Usually no. Running storage at full sustained utilization often increases latency and instability. A lower design target leaves room for bursts, rebuilds, and operational safety.
5. What block size should I enter?
Use the typical IO size of your application or benchmark. Small random blocks emphasize IOPS, while larger blocks increase throughput more quickly for the same IOPS.
6. Are the results exact vendor benchmark values?
No. These are engineering estimates built from latency, concurrency, overhead, and RAID behavior. Vendor tuning, caching, firmware, and workload skew can shift the real result.
7. Can this calculator compare hard disks and solid-state media?
Yes. Enter realistic latency, queue depth, and efficiency values for each technology. The model can compare workload capacity even when the physical media is different.
8. When should I trust throughput more than IOPS?
Use throughput when workloads move large sequential blocks. Use IOPS when workloads are random or small-block heavy. Many systems need both metrics reviewed together.