A minimal Julia service that receives files over HTTP and hands them off to a pool of worker threads for processing. The HTTP endpoint does no real work: it spools each uploaded file to disk, pushes a lightweight reference onto a work queue, and responds immediately — staying free to accept the next upload.

The per-file "processing" runs each file through a small neural-network classifier that labels it known (a file type resembling the training set) or unknown, and logs the result. See "File classifier" below.

Architecture

        POST /upload (multipart)
                 │
                 ▼
        ┌─────────────────┐     spool bytes to disk
        │  HTTP handler    │────────────────────────► data/spool/<uuid>-<name>
        │  (Oxygen.jl)     │
        └────────┬─────────┘     enqueue reference (non-blocking)
                 │                        │
                 ▼                        ▼
          202 + job IDs           ┌───────────────┐
          (503 if full)           │  work queue   │  bounded, thread-safe
                                  │  (Channel-ish)│
                                  └───────┬───────┘
                                          │ dequeue
                          ┌───────────────┼───────────────┐
                          ▼               ▼               ▼
                      worker 1        worker 2   …    worker N   (Threads.@spawn)
                          │
              success ────┴──► data/done/<uuid>-<name>
              failure  ───────► data/failed/<uuid>-<name>

Key properties:

Fast intake: the queue only ever carries small references; file bytes live on disk, so memory stays flat regardless of file size.
Backpressure: the queue is bounded (default 1000). When full, uploads get 503 Service Unavailable instead of silently piling up.
Crash-resilient: files survive on disk. On startup, anything left in data/spool/ is re-enqueued (recovered = N in the log).
Graceful shutdown: SIGINT (Ctrl-C) and SIGTERM (systemd/Docker/k8s stop) both stop accepting uploads, drain the queue, wait for in-flight files to finish, then exit. (See "Shutdown" below for one cosmetic caveat on SIGTERM.)
Safe filenames: client-supplied names are sanitized and prefixed with a server-minted UUID before touching the filesystem (no path traversal).

The queue seam (→ RabbitMQ later)

The HTTP handler and workers only ever call enqueue!, dequeue!, and close! on a JobQueue (see src/queue.jl). Today that's an in-process ChannelQueue. To move to RabbitMQ (or any broker), implement a new JobQueue subtype with those three methods and swap the construction in run — no handler or worker code changes.

Running

# install deps (first time)
julia --project=. -e 'using Pkg; Pkg.instantiate()'

# start the server; -t sets the number of OS threads available to workers
julia --project=. -t auto bin/server.jl

Shutdown

Both SIGINT and SIGTERM trigger the same idempotent graceful drain (stop serving → close queue → wait for workers → exit):

SIGINT is caught as an InterruptException (we call Base.exit_on_sigint(false)), so shutdown is clean and quiet.
SIGTERM can't be intercepted directly — Julia blocks it on worker threads and handles it in its own runtime, so a user signal() handler never fires. Instead we hook the drain into an atexit handler, which Julia's SIGTERM path does run. Caveat: Julia prints its own signal 15: Terminated backtrace before atexit runs. It's harmless noise — the drain still completes right after it — but if you want a fully quiet stop under a process manager, configure it to send SIGINT instead (systemd: KillSignal=SIGINT; Docker: STOPSIGNAL SIGINT). Give the stop timeout enough headroom to drain in-flight work (systemd: TimeoutStopSec).

File classifier

Each file is scored by a fixed-structure neural network (Lux.jl) that answers a single binary question: is this file known (like the types in the training set) or unknown? It's novelty detection, not exact file-typing — it won't tell you "PDF", just "this looks like something I was trained on, or not".

Features: the first 16 bytes + last 16 bytes of the file, each scaled 0–255 → [0,1], giving a 32-dim input. Files under 32 bytes can't form that window and are classified unknown without touching the model.
Architecture: Dense(32→64,relu) → Dense(64→16,relu) → Dense(16→2), raw logits; decision is argmax (class 1 = known, class 2 = unknown).
Artifact: trained weights live in model/classifier.jld2 (committed), so the server just loads them at startup. Missing/unreadable ⇒ the server fails fast rather than run without classification.
Effect today: annotate-only. The class is logged (classification=known|unknown) but every file still moves to done/; the classifier can't misroute real files while it's unproven.

The architecture and byte→feature mapping are defined once in src/model.jl and shared by the trainer and the server, so they can't drift apart.

Training

Training is a separate, offline script — it never runs in the request path:

julia --project=. bin/train.jl <positives_dir> [negatives_dir]

positives_dir — every file in it (≥32 bytes) is a "known" example.
negatives_dir (optional) — a grab-bag of other real file types used as "unknown" examples. Negatives are generated ~1:1 with positives, split 50/50 between uniform-random byte vectors and grab-bag files. With no grab-bag dir, negatives are all random (weaker: the net may just learn "high entropy = unknown" rather than your actual types, so a grab-bag of real off-distribution files is recommended).

The script uses an 80/20 seeded split, reports validation accuracy, and writes model/classifier.jld2 (path overridable via FS_MODEL_PATH). A fixed seed (FS_TRAIN_SEED, default 42) drives negative generation, the split, and weight init, so the artifact is exactly regenerable from the same inputs.

Configuration (environment variables)

Variable	Default	Meaning
`FS_HOST`	`127.0.0.1`	Bind address
`FS_PORT`	`8080`	Port
`FS_WORKERS`	`nthreads()`	Number of worker tasks
`FS_QUEUE_CAPACITY`	`1000`	Max pending jobs before `503`
`FS_SPOOL_DIR`	`data/spool`	Incoming files (pending)
`FS_DONE_DIR`	`data/done`	Files after successful processing
`FS_FAILED_DIR`	`data/failed`	Files whose processing threw
`FS_MODEL_PATH`	`model/classifier.jld2`	Classifier artifact loaded at startup

To get real parallelism, start Julia with enough threads (-t N) to match FS_WORKERS. If FS_WORKERS exceeds available threads you'll get a warning and workers will share threads.

Usage

# health check
curl http://127.0.0.1:8080/health
# {"status":"ok"}

# upload one or more files (multipart/form-data)
curl -F "a=@report.pdf" -F "b=@data.csv" http://127.0.0.1:8080/upload
# 202 {"accepted":[{"id":"<uuid>","name":"report.pdf"}, ...]}

Each file in a request becomes its own job. Responses:

202 Accepted — all files spooled and queued (with per-file job IDs)
400 Bad Request — not multipart, or no files present
503 Service Unavailable — queue full, retry later
500 Internal Server Error — failed to write a file to disk

Layout

src/
  FileServer.jl   module + run() (startup, recovery, workers, serve, shutdown)
  config.jl       Config struct + env parsing
  job.jl          Job (the queue reference)
  queue.jl        JobQueue seam + in-process ChannelQueue
  spool.jl        filename sanitizing, spool/move, startup recovery
  model.jl        NN architecture + byte→feature mapping (shared with trainer)
  classify.jl     load artifact + classify a file at inference time
  worker.jl       worker loop + per-job processing (classify + move)
  server.jl       HTTP routes/handlers
bin/
  server.jl       entry point
  train.jl        offline training script → model/classifier.jld2
model/
  classifier.jld2 committed trained weights (loaded at startup)

README.md Unescape Escape

FileServer