Files
zooqueen dd74d3e6d6 Replace AGPL MinIO fork with Apache-2.0 SeaweedFS fork; rebrand CLI weed->s3
hanzoai/s3 was an AGPL-3.0 MinIO fork whose network-copyleft blocks embedding
and reselling as a managed service. Replace it wholesale with a fork of
SeaweedFS (github.com/seaweedfs/seaweedfs, Apache-2.0, Go) which ships a
built-in S3 gateway + Filer.

- License: AGPL-3.0 -> Apache-2.0 (upstream LICENSE kept verbatim, incl.
  "Copyright 2025 Chris Lu"); NOTICE added crediting SeaweedFS. No AGPL/BUSL/
  SSPL/GPL or enterprise ("ee") files remain.
- Module: go.mod path set to github.com/hanzoai/s3 with a self-replace
  (replace github.com/seaweedfs/seaweedfs => ./) so the ~1800 internal Go files
  keep their upstream import paths with zero churn. Internal identifiers and
  import paths are intentionally NOT renamed.
- CLI rebrand: the binary is built as `s3` (was `weed`); root usage, per-command
  help/usage/flag strings, shell autocomplete program name, and the product name
  in CLI output now read "Hanzo S3" / `s3`. Users never see `weed`.
  (e.g. `s3 server`, `s3 filer`, `s3 master`, `s3 volume`, `s3 s3`, `s3 mount`.)
- Build: `make` (or `go build -o s3 ./weed`) builds the `s3` binary green;
  `cmd/weed-db` and `cmd/weed-sql` also build; `go vet ./weed/command/` clean.
- README/LLM.md rewritten for Hanzo S3; AGENTS.md/CLAUDE.md symlinks preserved.
- Dropped a 33MB prebuilt Mach-O `stress` binary upstream had committed; `.gitignore`
  updated to ignore the `s3` build output.
2026-06-18 23:41:25 -07:00
..

FUSE database load / durability / performance benchmark

Runs MySQL (InnoDB) and SQLite with their data files on a SeaweedFS FUSE mount, with ~1 GB datasets, and answers two questions:

  1. Durability — does any committed data get lost across normal shutdown, an unexpected kill -9, or a crash during active writes?
  2. Performance — how much slower is the FUSE mount than the local disk for the same workload (bulk load, OLTP commits, scans, raw fsync)?

Each row carries a CRC32 of an incompressible (so ~1 GB actually hits the volumes) deterministic payload; verification checks integrity_check/CHECK TABLE, exact row count, contiguous-id prefix, and a per-row CRC recompute.

Results (single node, macOS arm64 + macFUSE, weed 4.34, local NVMe, 2026-06-15)

Durability — 6/6 PASS, no committed data lost, no corruption

scenario what SQLite MySQL
A normal shutdown graceful DB stop, unmount, cluster stop, restart, verify PASS PASS
B unexpected (kill -9) kill -9 db+mount+cluster, restart, recover, verify PASS PASS
C crash during writes kill -9 mid-load, restart, recover, verify committed prefix PASS PASS

Scenario C confirmed correct recovery: the in-flight transaction rolled back, every fsync-committed row survived as a contiguous prefix (InnoDB redo recovery ~9 s).

Performance — FUSE vs host (same disk, same durability settings)

Raw filesystem:

metric host FUSE FUSE slower
seq write +fsync 2336 MB/s 369 MB/s 6.3x
seq read (warm) 3435 MB/s 1422 MB/s 2.4x
fsync latency 0.13 ms 1.18 ms 9x

SQLite (1 GB, journal=DELETE, synchronous=FULL):

metric host FUSE FUSE slower
bulk load 5.8 s (177 MB/s) 11.6 s (88 MB/s) 2.0x
OLTP 1-row txns 1987 tx/s 171 tx/s 11.6x
full scan (warm) 316 MB/s 427 MB/s ~equal (python-bound)

MySQL/InnoDB (1 GB, trx_commit=1, flush_method=fsync):

metric host FUSE FUSE slower
bulk load 24.4 s (42 MB/s) 32.9 s (31 MB/s) 1.35x
OLTP 1-row commits 10542 tx/s 1144 tx/s 9.2x
full scan 2419 MB/s 430 MB/s 5.6x

Takeaway: the cost is dominated by fsync/commit latency (0.13 → 1.18 ms, ~9x) because every durable commit uploads the chunk to the volume + persists the filer chunk-manifest over loopback. Small fsync'd transactions are ~9-12x slower; bulk loads (commits amortized) are 1.3-2x slower; warm/sequential reads are close to host. These are single-node loopback numbers — a real cluster (remote volumes, replication) adds network RTT + replica fsync per commit.

Durability caveat (test scope)

kill -9 terminates the processes while the OS keeps running, so everything that reached the OS page cache (all fsync'd data) survives — this faithfully tests process/daemon crashes and is what passed 6/6. It does not simulate true power loss (page cache lost). The FUSE mount uploads chunks without fsync=true (weed/mount/weedfs_write.go builds UploadOption with no Fsync), so the volume does not fsync .dat per upload and the filer leveldb likely does not sync per write; under a real power cut, recently-"committed" data that only reached the page cache could be lost. A VM hard-reset / power-loss test on real hardware is the follow-up to close that gap.

Requirements

  • weed on $PATH (or set WEED=/path/to/weed)
  • macFUSE (macOS) or libfuse (Linux)
  • python3, sqlite3
  • MySQL/MariaDB install; set MYSQL_BASE to its prefix (default macOS Homebrew /opt/homebrew/opt/mysql; must contain bin/mysqld, bin/mysql, bin/mysqladmin). For mysql_bench.py on a non-default install, also set MYSQL_BIN=/path/to/mysql.

Run

Runtime artifacts (cluster, mount, logs) go to $SEAWEED_BENCH_WORK (default /tmp/seaweedfs_fuse_db_bench), kept out of the repo.

cd test/benchmark/fuse_db
bash bin/run_sqlite.sh   > results/sqlite.log   2>&1   # SQLite durability suite
bash bin/run_mysql.sh    > results/mysql.log    2>&1   # MySQL durability suite
bash bin/compare.sh      > results/compare.log  2>&1   # FUSE vs host performance

The harness manages only its own processes (via pidfiles) on non-default ports (9555/9560/9565, mysqld 3308); it never runs pkill weed, so other SeaweedFS instances on the box are untouched.

Files

bin/lib.sh           cluster/mount/mysqld lifecycle helpers (start/stop/kill, clean state)
bin/run_sqlite.sh    SQLite 1GB load + A/B/C crash scenarios
bin/run_mysql.sh     MySQL 1GB load + A/B/C crash scenarios
bin/compare.sh       FUSE-vs-host driver
bin/sqlite_gen.py    deterministic incompressible SQLite loader (progress/ref files)
bin/sqlite_verify.py SQLite integrity + count + prefix + per-row CRC verifier
bin/sqlite_bench.py  SQLite load/OLTP/scan timing probe
bin/mysql_bench.py   MySQL load/OLTP/scan timing probe (via mysql CLI)
bin/fsbench.py       raw seq-write / fsync-latency / seq-read microbenchmark

The numbers above are the reference baseline; rerun the scripts to reproduce. Run output is written to results/*.log, which is gitignored (repo-wide *.log rule) -- not checked in.