Get This Tool
Eatmydata.ai
Pricing
- Model
- Free
Summary
Every test suite that hits disk for package installs or database setup burns time on fsync calls that exist to protect data you were about to delete anyway. eatmydata strips those calls out.
eatmydata is an LD_PRELOAD library that intercepts and disables fsync, fdatasync, sync, and related calls at the process level — without modifying the application or the kernel. Drop it in front of any command and disk operations that normally wait for write confirmation return immediately. The win is real in CI: package manager installs and SQLite-backed test suites run measurably faster because they stop waiting on durability guarantees that only matter if the machine loses power mid-operation. The tool is available as a Debian package and as an open-source library you can compile yourself.
Bottom line: Use eatmydata to cut minutes off disposable CI jobs and throwaway container setups — never touch it on any process where the data needs to survive a crash.
Community Performance Report Card
No community ratings yet. Be the first to rate this tool!
Community Benchmarks Community
Sign in to submit a benchmarkNo community benchmarks yet. Be the first to share a real-world data point.
Pros
Sign in to edit- Process-scoped via LD_PRELOAD, so you apply acceleration to exactly one command without touching system-wide disk behavior — which means a misconfigured CI job cannot accidentally affect adjacent processes.
- Zero application modification required — any binary that calls fsync through glibc picks up the intercept automatically, so you do not need to patch your test runner or package manager.
- Available as a Debian package, so adding it to a CI base image requires one apt install line and no compile step — which means the setup cost does not eat the time savings.
- Open-source and self-hosted, so there is no external service dependency that can introduce latency, rate limits, or outages into your build pipeline.
- Works across SQLite-backed test suites and package manager operations — the two places where fsync overhead is most concentrated in a typical CI run — which means the acceleration applies precisely where CI time is lost.
Cons
Sign in to edit- Data corruption is silent and guaranteed on power loss or process crash: eatmydata suppresses the calls that protect write ordering, so any environment where data must survive an unexpected termination cannot use this tool at all — teams that discover this by accident lose database state with no recovery path.
- Scope is limited to processes that load glibc and respect LD_PRELOAD — statically linked binaries, containers with LD_PRELOAD restrictions, or setups that clear the environment before exec will silently ignore it, leaving teams to debug why the speedup did not appear.
- There is no monitoring, reporting, or verification layer: you cannot confirm which syscalls were intercepted or measure the actual impact without external profiling tools, so teams troubleshooting unexpected behavior in CI have no internal signal to start from.
- A team whose bottleneck is CPU-bound compilation or network-bound package downloads will see no benefit and will need to profile before reaching for this tool — teams that switch away typically do so after discovering the wall is not disk I/O.
Community Reviews
Sign in to write a reviewNo reviews yet. Be the first to share your experience.
About
- Platforms
- Linux
- API Available
- No
- Self-Hosted
- Yes
- Last Updated
- 2026-06-12T14:49:35.717Z
Best For
Who it's for
- Developers running tests or CI pipelines
- System administrators performing disposable chroot or container setups
- Users needing quick non-production disk operations
What it does well
- Speeding up package manager operations in CI or ephemeral environments
- Accelerating test suites that use databases like SQLite
- Faster builds or installs where power loss is not a concern
Discussion Community
Sign in to commentNo discussion yet. Sign in to start the conversation.
Compare Eatmydata.ai
Spotted incorrect or missing data? Join our community of contributors.
Sign Up to ContributeCommunity Notes & Tips Community
Sign in to contributeBe the first to contribute. General notes, observations, gotchas, and tips from people who use this tool day-to-day.
Frequently Asked Questions
- Is Eatmydata.ai free?
- Yes — Eatmydata.ai is fully free to use. There is no paid tier.
- Is Eatmydata.ai open source?
- Yes. Eatmydata.ai is open source.
- Can I self-host Eatmydata.ai?
- Yes. Eatmydata.ai supports self-hosting on your own infrastructure.
- What platforms does Eatmydata.ai support?
- Eatmydata.ai is available on: Linux.
Hours Saved & ROI Stories Community
Sign in to contributeBe the first to contribute. Concrete time/cost savings, with context. e.g. "Cut my code review backlog from 4h to 45m per week."
Curated lists that include this category
Disk writes that wait for confirmation are a correctness feature — one that costs real time when you are running a test suite or provisioning a container that will be destroyed in sixty seconds. eatmydata is an LD_PRELOAD utility: you prefix your command with it, and it intercepts fsync and related syscalls at the library level, returning success immediately instead of waiting for the kernel to flush to disk. No kernel patches, no application changes, no configuration files. The workflow is a single command prefix.
The differentiating feature is scope control through LD_PRELOAD. Because it operates at the process level, you apply it precisely — one apt-get, one test runner, one database seed script — without affecting the rest of the system. Debian packages it directly, so installation in a CI image is a single apt install line with no build step required.
eatmydata fits CI pipelines, ephemeral chroot setups, and any test environment where the disk state is thrown away at the end of the run. It does not fit anywhere else. Use it on a production database and a power loss or OOM event will corrupt data silently — the syscalls it suppresses are the ones that would have saved you. Teams that need this kind of acceleration in production, or need it across a whole system rather than a single process, are solving a different problem with different tools.