Etherealm ResearchStorage tools
ER StorProof
Etherealm Research StorProof
A storage testing and validation tool from Etherealm Research, using test files to check data integrity, read/write performance, and behavior under sustained load.
Not available for download- Brand
- Etherealm Research
- Target platforms
- macOS
- Windows
- Linux
- Android (CLI)
- Technologies
- Rust
- JSON
- C ABI
About the project
ER StorProof, short for Etherealm Research StorProof, is a storage testing and validation tool from Etherealm Research. It addresses three practical questions: can written data be read back intact, how does storage perform under different workloads, and do errors emerge during sustained operation?
Tests run in mounted directories with read and write access on SSDs, hard drives, USB drives, memory cards, and built-in storage. Entry points include a CLI, interactive terminal, and desktop workbench. Downloads are not yet available; available interfaces and features depend on the platform, filesystem, and device connection.
Write, then verify
Create test files in a selected directory, reopen them after writing, and compare every byte. Track error locations and completed ranges, or recheck retained data.
Performance and sustained load
Measure throughput, IOPS, and latency with sequential, random, and mixed workloads, then explore sustained behavior with repeated stress tests.
From one target to batch testing
The CLI, interactive terminal, and Workbench desktop share the same test logic, with local or remote nodes, test recipes, and structured results.
Write data, then read it back in full
Write-and-verify creates test files in a selected directory, fills the chosen range, synchronizes and reopens the files, then compares every byte against the generated test data. It records errors and completed coverage. Separate write-only tasks and re-verification of retained test data are also available. Resumable write-and-verify uses a separate progress record and fully rechecks committed data when resumed.
Tests operate only on their own numbered regular files. New files are created exclusively, refusing existing names; explicitly reusing test data for write benchmarks will change that data. There is no raw-device writing or formatting. Writing still consumes free space and adds wear, so check the target, test size, and space reserve before starting. Command-line write tests use available space when no size is specified; choose an explicit range for a first test.
Measure performance under defined conditions
Benchmarks cover sequential, random, and mixed reads and writes. Standard mode transfers a fixed amount of data; time-based mode repeatedly accesses a working set within a measurement window; write-based mode combines fixed-volume writes with repeated reads. Throughput, IOPS, latency, and speed charts reveal how performance changes with the workload.
Working-set size is not total write volume: preparation, warm-up, and repeated measurements also generate traffic. Compare results with consistent sizes, workloads, cache policies, and environments. Bypassing the operating system’s file cache does not establish that device or virtual-machine host caches were bypassed, and scores from different tools are not directly comparable. A completed benchmark is not an integrity pass.
Check data under sustained load
Burn-in combines file patterns such as sequences, random seeking, fixed bytes, and random data. The complete-cycle preset runs its required patterns in sequence. Verified soak initializes and verifies the working set, runs random requests with content comparison, then reopens the files for a full final check.
Factory file-cycle presets size test segments as a percentage of volume capacity and stop after a specified number of write/read cycles. Reaching that count does not mean every data pattern has been covered. Preview the range, cycles, and estimated traffic before starting. Cancellation and timeout are not normal completion, and burn-in cannot currently resume from a checkpoint.
From one directory to a batch workbench
Core provides the shared testing capabilities. For an individual check, choose a directory, task, and size in the terminal, preview the plan, and start. Automation can use JSON or programmatic interfaces. The same task keeps the same test logic and result semantics across these entry points.
Workbench brings target selection, recipes, preflight checks, start and stop controls, live monitoring, and results into a desktop workflow, with connections to remote test nodes. Recipes and target sets can be reused, with a new preflight required after changes. Node resources and volume constraints limit concurrency, and tasks on the same logical volume run serially. Automatic thermal control, formatting, and full production-line acceptance are not currently available.
Understand the result and its scope
Results distinguish a verified selected range, unverified writes, a completed benchmark, and a completed burn-in. Missing or truncated data within a known range, content errors, and cancellation cannot count as a pass. Automatic discovery verifies only the files present; checking the original full range requires an explicit size or a saved plan. Results can be exported and viewed offline. Supported device paths can also provide temperature, wear, or error information; missing data stays unknown. A pass does not prove full-drive capacity, power-loss persistence, or long-term reliability.
Core targets macOS, Windows, Linux, and Android; Workbench targets macOS, Windows 10+, and Ubuntu 24.04 desktops. Android currently requires a directory directly accessible through Termux or ADB, without a native mobile app or Android system document-picker integration. Architecture, filesystem, permissions, and device-health access must each be checked for the intended setup.
Research background
ETHEREALM RESEARCH
Storage testing tools: evolution and development background
Read about the questions and tradeoffs behind the test engine and its applications, from file verification and performance measurement to batch testing.
The article lists an update date of October 20, 2026, and uses the earlier development codename PXDT3. The current product name is ER StorProof; this is development background, not a download page or release announcement.
Read development background (Chinese) (opens in a new tab)