Troubleshooting DiskSpd Results: Common Pitfalls and FixesDiskSpd is a powerful, flexible command-line tool from Microsoft for measuring storage performance on Windows systems. It’s widely used by system administrators, storage engineers, and application developers to simulate real-world I/O patterns and measure throughput, latency, and CPU utilization. However, interpreting DiskSpd output and configuring tests correctly can be tricky. This article covers common pitfalls, how to detect them, and practical fixes to get reliable, meaningful results.
1) Understand what DiskSpd measures
DiskSpd reports a variety of metrics: IOPS (I/O operations per second), throughput (MB/s), average and percentile latencies, CPU time, and queue depths. It simulates workloads by controlling parameters like block size, read/write mix, concurrency, and access patterns. Before troubleshooting, ensure the test scenario matches the real workload you’re trying to model.
2) Pitfall: Misconfigured test parameters
Symptoms:
- Results that don’t reflect expected application behavior (e.g., unrealistically high IOPS or low latency).
- Inconsistent results across runs.
Causes:
- Incorrect block size — many applications use 4 KB or 8 KB I/O, while others use larger sizes (64 KB, 256 KB).
- Unrealistic read/write mix — e.g., testing 100% reads when your application is write-heavy.
- Too few or too many threads — insufficient concurrency underestimates parallelism; excessive concurrency may saturate CPU instead of storage.
- Sequential vs. random patterns mismatch.
Fixes:
- Match block size and read/write ratio to your application’s profile.
- Use multiple thread counts to find the storage system’s concurrency sweet spot.
- Use the -r (random) flag for random I/O, and a deterministic seed if you need repeatability.
- Use realistic file sizes and working set to avoid priming caches unintentionally.
3) Pitfall: Caching effects (OS or hardware)
Symptoms:
- Very high throughput/IOPS on small tests, which drop when test size increases.
- Latency numbers that look better than real-world observations.
Causes:
- Windows file-system cache or storage device caches (DRAM, NVRAM) masking true media performance.
- Testing on thin-provisioned virtual disks where host or hypervisor caching is involved.
Fixes:
- Use the -W flag to disable Windows write-back caching where appropriate.
- Use large test files and working sets exceeding cache sizes to measure underlying storage (e.g., test file > aggregate cache).
- Use the -Sh flags to bypass file-system cache for reads and writes when testing raw device performance.
- For NVMe, ensure that device-level caches are considered; some vendors provide tools/firmware switches to control cache behavior.
- On VMs, run DiskSpd both inside the VM and on the host to understand virtualization-layer caching.
4) Pitfall: Measuring with insufficient test duration
Symptoms:
- High variance between runs; results influenced by startup spikes or transient background activity.
Causes:
- Short tests dominated by ramp-up, caching, or transient OS activity.
Fixes:
- Increase test duration using the -d parameter (e.g., -d 60 for 60 seconds) to allow steady-state behavior.
- Add a warm-up phase before measurement (DiskSpd supports a warm-up using separate runs or by discarding initial seconds).
5) Pitfall: Background processes and system noise
Symptoms:
- Unexpected latency spikes or throughput variability.
- Different results on seemingly identical runs.
Causes:
- Antivirus scans, Windows Update, scheduled tasks, telemetry, backup agents, or other user-space processes interfering.
- Hypervisor host noise in virtual environments (other VMs competing for I/O).
Fixes:
- Run tests on an isolated system or maintenance window where background activity is minimized.
- Disable nonessential services (antivirus real-time scanning, indexing) during tests.
- On virtual hosts, ensure resource isolation or test on dedicated hardware when possible.
6) Pitfall: Incorrect alignment or file-system overhead
Symptoms:
- Lower-than-expected throughput and IOPS, especially with certain block sizes.
Causes:
- Misaligned partitions or files can cause extra read-modify-write cycles.
- File-system fragmentation or suboptimal allocation unit size.
Fixes:
- Ensure proper partition alignment to the underlying storage (typically 1 MB alignment for modern devices).
- Use appropriate cluster size when formatting (e.g., 64 KB for large-block workloads).
- Consider testing on raw volumes (with -Sh or opening the physical device) to eliminate file-system effects.
7) Pitfall: Mixing logical and physical metrics incorrectly
Symptoms:
- Confusion when reported MB/s or IOPS don’t match what device-level tools show.
Causes:
- DiskSpd reports logical host-side I/O; device-level compression/deduplication, RAID controller caching, or write coalescing can change on-device metrics.
- Thin provisioning and snapshotting in storage arrays may alter observable performance.
Fixes:
- Correlate DiskSpd results with device/vendor monitoring (SMART, controller stats) and host counters (Performance Monitor).
- When possible, disable deduplication/compression for test volumes or account for them in analysis.
- Use raw device tests to compare with logical file tests and understand differences.
8) Pitfall: Overlooking queue depth and concurrency interaction
Symptoms:
- IOPS plateau despite adding threads; latency increases rapidly.
Causes:
- Storage performs differently depending on queue depth; small random I/O benefits from deeper queues on SSDs.
- CPU can become the bottleneck before storage is saturated.
Fixes:
- Test different combinations of threads and outstanding I/Os (DiskSpd’s -o parameter controls outstanding I/Os).
- Use Performance Monitor to watch CPU, memory, and disk queue length during tests.
- For SSDs, increase queue depth to reveal true device parallelism; for HDDs, lower queue depth may be more representative.
9) Pitfall: Incorrect interpretation of latency metrics
Symptoms:
- Confusing average latency with tail latencies; decisions made using averages only.
Causes:
- Average latency masks outliers; high percentiles (95th, 99th) matter for user experience.
- DiskSpd reports multiple latency stats—mean, min, max, and percentiles if requested.
Fixes:
- Always examine percentile latencies (use -L to capture latency distribution).
- Focus on p95/p99 for latency-sensitive applications, not just the mean.
- Visualize latency distributions when possible to spot long tails.
10) Pitfall: Not validating test repeatability
Symptoms:
- Different teams get different results; inability to reproduce published numbers.
Causes:
- Non-deterministic seeds, background noise, variation in test setup.
Fixes:
- Use deterministic seeds and document all parameters (block size, threads, duration, file sizes, flags).
- Automate tests with scripts that set system state (disable services, set power plans) to ensure consistency.
- Run multiple iterations and report averages with standard deviation.
11) Useful DiskSpd flags and tips (quick reference)
- -b
— block size (e.g., -b4K) - -d
— duration (e.g., -d60) - -o
— outstanding I/Os per thread - -t
— number of threads - -r — random I/O
- -w
— write percentage (e.g., -w30 for 30% writes) - -Sh — disable system cache (useful for raw device tests)
- -W — disable Windows write cache (careful: performance and data safety implications)
- -L — capture latency distribution and percentiles
- -D — generate CSV output for analysis
12) Example command patterns
diskspd -b4K -d60 -o32 -t4 -r -w0 -L testfile.dat diskspd -b64K -d120 -o8 -t16 -w30 testfile.dat diskspd -b4K -d180 -o64 -t8 -r -Sh -L testfile_raw.dat 13) When results still look wrong: deeper diagnostics
- Run Windows Performance Recorder/Analyzer to capture system activity during tests.
- Cross-check with vendor tools (controller dashboards, smartctl, NVMe-cli) for device-side metrics.
- Test with another benchmarking tool (fio, Iometer) to compare behavior.
- If on virtual infrastructure, test on bare metal to eliminate hypervisor factors.
14) Summary checklist before trusting DiskSpd numbers
- Match test parameters to real workload (block size, read/write mix).
- Ensure test file size and duration exceed caches and reach steady state.
- Disable or account for caching layers.
- Minimize background noise and standardize system state.
- Vary threads and outstanding I/Os to find realistic operating points.
- Examine latency percentiles, not just averages.
- Document and automate tests for repeatability.
If you want, I can: generate ready-to-run DiskSpd scripts for a specific workload (database, VM host, web server), or analyze a DiskSpd CSV output you have and point out anomalies.
Leave a Reply