Jeffrey Ward
c5d488d9b4
End-to-end throughput says how fast the pipeline is, not which stage is the reason. The four stages run concurrently behind their own queues, so the pipeline's rate *is* the slowest stage's rate and the others are invisible in it. Nothing outside the server can recover them either: known/, unknown/ and text/ are transient, and a file can cross one between two directory polls, so an external sampler misses exactly the stages worth measuring. So the pipeline counts its own work, and bin/bench.jl turns two scrapes into rates. - src/stats.jl: per-stage counters (completed/failed, bytes, busy_ns, blocked_ns, in_flight) plus intake counters, monotonic since startup in the Prometheus style — rates are the reader's job, so a scrape is stateless and two readers can't disturb each other. Recorded in worker_loop, the one place every stage's work passes through, so a new stage is instrumented the moment it is wired up and never on the read path. - src/server.jl: GET /stats. An ordinary Oxygen route (no body to stream), unlike /upload. Intake counts files at the point they become stage 1's problem, so intake totals and stage-1 arrivals refer to the same files. - src/queue.jl: capacity(q) joins length on the introspection seam — a depth of 900 means nothing without knowing whether the limit is 1000 or 1_000_000. utilization = (busy - blocked) / (window * workers) is the number that names the bottleneck: throughput alone can't tell a saturated stage from one starved by the stage ahead of it, since both report the same files/s. blocked_ns is what keeps that true. Stages 1 and 3 apply blocking backpressure — a full downstream queue means parking, not dropping — and that wait is inside the handler, so counting it as busy would pin stage 1 at 1.0 whenever stage 2 is the real jam, making every stage upstream of a jam look like the jam. enqueue_blocking! wraps the retry loop so the wait is measurable at all, and keeps the three routing paths from drifting into three different backoff behaviours. Measured (400 mixed files, 16 KiB, concurrency 16): stage 4 is the constraint at 0.87 utilization and 853 ms/file — github-linguist is a process spawn per file — while stages 1 and 3 idle under 0.10. Verified the blocked accounting against a deliberately starved server (FS_TEXT_WORKERS=1, FS_TEXT_QUEUE_CAPACITY=2): stage 3 reported 100% blocked at 0.0 utilization rather than looking saturated too. bin/bench_model.jl: the classifier alone, no server or queue in the way, because stage 1's 38.7 ms/file cannot plausibly be a 32-64-16-2 MLP. It isn't: Lux.apply is 2.3 us, read_features 4.2-6.0 us (flat across 1 KiB - 256 MiB, as the seek-to-tail design intends), classify() 8.4 us — so ~99.98% of stage 1 is rename, logging and contention, and the file read costs 3x the inference. Two findings: batching would buy ~13x (179 ns/file at batch 512 vs 2.34 us at batch 1), and inference does not scale past ~4 threads. A pure-compute control kernel runs the same sweep to place the blame — it reaches 14.3x at 16 tasks on this box, so the machine parallelizes and Lux.apply does not. BLAS threads and GC are both ruled out; the cause is inside Lux and is not diagnosed here. Three bugs in the memory measurement, all of which produced wrong answers that looked plausible: - detect_pid matched any process with the launch command in its argv, including the shell that started the server — one run reported 3.64 MiB as the server's memory. Candidates are now filtered by /proc/<pid>/comm, what the process is rather than what its arguments say; no pattern over argv can do that. - Baseline RSS was read *before* clear_refs reset the peak counter, so the two numbers had different origins. The 2 GiB run reported -1.01 MiB of growth; reading the baseline after the reset makes it 31.3 MiB. - Negative growth is now reported as "none measurable" rather than a negative figure, which reads as a memory saving. Re-measuring with those fixed keeps the claim that matters — growth is flat in file size (14-31 MiB from 256 MiB to 2 GiB), so nothing is buffering — but the concurrency coefficient does not survive: a freshly started server settles anywhere in an ~860-985 MiB band, so baseline variance is comparable to the growth being measured, and the old table quoted megabyte precision the measurement never supported. README now states the shape, requires a ~30s settle before a memory run, and says plainly that linear-in-concurrency is undemonstrated rather than leaving an authoritative-looking number. README also gains a single runnable sequence for all three harnesses: the server prerequisite was never shown inline, so following the benchmarking section top-to-bottom just produced "cannot reach /health". Tests: 276 pass (41 new) — the blocked-vs-busy split, worker_loop draining in_flight through a throwing handler, and the JSON round-trip of the field names bench.jl reads.
784 lines
34 KiB
Julia
Executable File
784 lines
34 KiB
Julia
Executable File
#!/usr/bin/env julia
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#
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# bench.jl — measure end-to-end throughput and server memory for a running FileServer.
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#
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# Two things make this pipeline awkward to benchmark with off-the-shelf tools
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# (ab/hey/wrk), and both shape what this script does:
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#
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# 1. HTTP latency is not throughput. /upload returns 202 as soon as the bytes
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# are spooled and a reference is enqueued — all four stages run afterwards.
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# So real throughput is the *arrival rate at the terminal sinks*
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# (done/, text_done/, binary/, failed/), not the response rate. We upload a
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# corpus, then poll the sinks until the file count stops moving.
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#
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# 2. End-to-end throughput doesn't name the slow stage. The four stages run
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# concurrently behind their own queues, so the pipeline's rate is the
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# slowest stage's rate and the others are invisible. Directory polling can't
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# recover them either — known/, unknown/ and text/ are transient, and a file
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# can cross one between two samples. So the server keeps per-stage counters
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# (src/stats.jl) and we scrape GET /stats before and after: the deltas give
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# each stage's throughput, mean service time, and worker utilization, and
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# utilization is what actually names the bottleneck (see `stage_report`).
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#
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# 3. Memory should be flat in file size, and that claim needs checking on two
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# axes. The workers read bounded prefixes (16+16 bytes to classify, 8 KB to
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# sniff, 64 KB to language-detect), and intake streams each upload from the
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# socket to the spool file a chunk at a time (FS_UPLOAD_CHUNK_BYTES, see
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# src/multipart.jl). So peak RSS should track *concurrency*, not file size:
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# sweeping --size at fixed --concurrency should be a flat line, and that is
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# the regression this measures. We sample the server's RSS throughout and
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# report the high-water mark.
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#
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# (Before intake was streamed it buffered each upload whole, several times
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# over, and a 256 MiB upload grew RSS by ~700 MiB. If a --size sweep ever
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# slopes upward again, something has started buffering.)
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#
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# The harness talks to the server only over HTTP (/upload, /health, /stats) and
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# reads the on-disk sink layout; it must run on the same machine (sink dirs and
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# /proc). If /stats is missing — an older build — everything else still works and
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# the per-stage section is skipped.
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#
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# Usage:
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# julia --project=. -t auto bin/bench.jl [options]
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#
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# --url URL server base URL (default: $FS_URL or http://127.0.0.1:8080)
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# --files N number of files to upload (default: 200)
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# --size S size of each generated file, e.g. 4k, 512k, 8m, 1g (default: 64k)
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# --concurrency J uploads in flight at once (default: 8)
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# --kind K binary | text | mixed — what to generate (default: binary)
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# --corpus DIR upload an existing directory instead of generating
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# (the only way to exercise stage 2: point it at real known files)
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# --keep-corpus don't delete the generated corpus on exit
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# --pid PID server pid for memory sampling (default: autodetect)
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# --no-mem skip memory sampling entirely
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# --no-stats skip the per-stage /stats scrape and its table
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# --sample-ms MS sink/RSS sampling interval (default: 200)
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# --timeout SEC give up after this long with no drain progress (default: 120)
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# --json PATH also write the results as JSON
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# --force skip the corpus-size safety check
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#
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# Examples:
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# # throughput: many small files, high concurrency
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# julia --project=. -t auto bin/bench.jl --files 2000 --size 8k --concurrency 32
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#
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# # memory: flat in file size? sweep --size with concurrency pinned
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# julia --project=. -t auto bin/bench.jl --files 4 --size 256m --concurrency 1
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# julia --project=. -t auto bin/bench.jl --files 2 --size 1g --concurrency 1
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# julia --project=. -t auto bin/bench.jl --files 8 --size 1g --concurrency 8
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#
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# # stage 2 (exiftool) with real known files
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# julia --project=. -t auto bin/bench.jl --corpus ../training_set --concurrency 16
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using HTTP
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using JSON3
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using Random
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# ---------------------------------------------------------------- option parsing
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const DEFAULTS = Dict{String,Any}(
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"url" => get(ENV, "FS_URL", "http://127.0.0.1:8080"),
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"files" => 200,
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"size" => 64 * 1024,
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"concurrency" => 8,
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"kind" => "binary",
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"corpus" => nothing,
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"keep-corpus" => false,
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"pid" => nothing,
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"no-mem" => false,
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"no-stats" => false,
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"sample-ms" => 200,
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"timeout" => 120,
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"json" => nothing,
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"force" => false,
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)
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const FLAGS = ("keep-corpus", "no-mem", "no-stats", "force")
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# Approximate RSS of a freshly started server (Lux + the loaded classifier + the
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# language detector, measured on Julia 1.12 / -t auto). Only used to notice that
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# a baseline is inflated by a previous run's un-returned GC memory, so a rough
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# figure is enough.
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const FRESH_RSS_HINT = 950 * 1024^2
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"Parse `4k`/`8M`/`1g`/`4096` into a byte count."
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function parse_size(s::AbstractString)::Int
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m = match(r"^(\d+(?:\.\d+)?)\s*([kKmMgG]?)[bB]?$", strip(s))
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m === nothing && error("bad --size: $s (expected e.g. 512, 64k, 8m, 1g)")
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mult = Dict('k' => 1024, 'm' => 1024^2, 'g' => 1024^3)
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scale = isempty(m[2]) ? 1 : mult[lowercase(m[2])[1]]
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return round(Int, parse(Float64, m[1]) * scale)
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end
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function parse_args(argv)::Dict{String,Any}
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opts = copy(DEFAULTS)
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i = 1
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while i <= length(argv)
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a = argv[i]
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startswith(a, "--") || error("unexpected argument: $a (see the header of $(PROGRAM_FILE))")
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key = a[3:end]
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haskey(opts, key) || error("unknown option: $a")
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if key in FLAGS
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opts[key] = true
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i += 1
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continue
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end
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i + 1 <= length(argv) || error("option --$key needs a value")
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val = argv[i+1]
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opts[key] = key == "size" ? parse_size(val) :
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key in ("files", "concurrency", "sample-ms") ? parse(Int, val) :
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key == "timeout" ? parse(Float64, val) :
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key == "pid" ? parse(Int, val) :
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val
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i += 2
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end
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opts["kind"] in ("binary", "text", "mixed") ||
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error("--kind must be binary, text or mixed (got $(opts["kind"]))")
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opts["files"] >= 1 || error("--files must be >= 1")
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opts["concurrency"] >= 1 || error("--concurrency must be >= 1")
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return opts
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end
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# ------------------------------------------------------------------------- dirs
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#
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# Resolved from the same environment variables src/config.jl reads, so a server
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# started with custom dirs is benchmarked correctly. Kept as a standalone table
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# rather than `using FileServer` so the harness doesn't pay to load Lux.
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sinkdirs() = (
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done = get(ENV, "FS_DONE_DIR", "data/done"),
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text_done = get(ENV, "FS_TEXT_DONE_DIR", "data/text_done"),
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binary = get(ENV, "FS_BINARY_DIR", "data/binary"),
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failed = get(ENV, "FS_FAILED_DIR", "data/failed"),
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)
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stagedirs() = (
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spool = get(ENV, "FS_SPOOL_DIR", "data/spool"),
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known = get(ENV, "FS_KNOWN_DIR", "data/known"),
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unknown = get(ENV, "FS_UNKNOWN_DIR", "data/unknown"),
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text = get(ENV, "FS_TEXT_DIR", "data/text"),
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)
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"Count work items in `dir`, ignoring the .meta.json sidecars stages 2/4 write."
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function count_files(dir::AbstractString)::Int
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isdir(dir) || return 0
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n = 0
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for name in readdir(dir)
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endswith(name, ".meta.json") && continue
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isfile(joinpath(dir, name)) && (n += 1)
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end
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return n
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end
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counts(dirs) = NamedTuple{keys(dirs)}(map(count_files, values(dirs)))
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total(c) = sum(values(c))
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deltas(now_, base) = NamedTuple{keys(now_)}(map(-, values(now_), values(base)))
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# ----------------------------------------------------------------------- memory
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"Read (VmRSS, VmHWM) in bytes for `pid`, or `nothing` if unreadable."
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function read_rss(pid::Int)
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rss = hwm = nothing
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try
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for line in eachline("/proc/$pid/status")
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if startswith(line, "VmRSS:")
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rss = parse(Int, split(line)[2]) * 1024
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elseif startswith(line, "VmHWM:")
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hwm = parse(Int, split(line)[2]) * 1024
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end
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end
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catch
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return nothing
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end
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return (rss === nothing || hwm === nothing) ? nothing : (rss, hwm)
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end
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"""
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Find the running server process, or `nothing`.
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`pgrep -f` matches against the whole command line, which catches more than the
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server: any shell launched with the command in its own argv (`sh -c 'julia …
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bin/server.jl > log'`, a `setsid`/`nohup` wrapper, even the terminal running the
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benchmark) matches the same pattern. Sampling one of those reports a few MiB of
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shell as the server's memory — a wrong answer that looks plausible, which is the
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worst kind.
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So candidates are filtered by what each process *is* (`/proc/<pid>/comm`, the
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executable name) rather than by what its arguments say. No pattern over argv can
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make that distinction.
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"""
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function detect_pid()
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out = try
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readchomp(`pgrep -f "bin/server.jl"`)
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catch
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return nothing
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end
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candidates = parse.(Int, split(out))
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pids = filter(candidates) do pid
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comm = try
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readchomp("/proc/$pid/comm")
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catch
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return false # exited between pgrep and here
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end
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startswith(comm, "julia")
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end
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isempty(pids) && return nothing
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length(pids) > 1 && @warn "multiple server processes matched; sampling the first" pids
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return first(pids)
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end
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"""
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Reset the kernel's peak-RSS counter so VmHWM reflects only this run.
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Without it, VmHWM carries the high-water mark from startup (model load) or from
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an earlier benchmark, which would silently dominate a small run's result.
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Requires the server to run as the same user; on failure we say so and fall back
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to sampled VmRSS, which can miss a spike between samples.
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"""
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function reset_peak_rss(pid::Int)::Bool
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try
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write("/proc/$pid/clear_refs", "5")
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return true
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catch
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return false
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end
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end
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# ------------------------------------------------------------- per-stage counters
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#
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# The server exposes monotonic counters at GET /stats (src/stats.jl). Rates are
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# ours to compute: scrape once before the run and once after, subtract, divide by
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# the elapsed *server* clock so a slow scrape doesn't distort the window.
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"Fetch and parse GET /stats, or `nothing` if the server doesn't serve it."
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function scrape_stats(url::String)
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try
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resp = HTTP.get(string(rstrip(url, '/'), "/stats");
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status_exception = false, retry = false, readtimeout = 5)
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resp.status == 200 || return nothing
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return JSON3.read(String(resp.body))
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catch
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return nothing
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end
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end
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"One stage's activity between two scrapes."
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struct StageDelta
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stage::Int
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name::String
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workers::Int
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completed::Int
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failed::Int
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bytes::Int
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busy::Float64 # summed handler seconds across all workers in the pool
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blocked::Float64 # of `busy`, seconds parked on a full downstream queue
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peak_depth::Int # deepest its queue got, from the sampler
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capacity::Int
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end
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files_per_sec(d::StageDelta, window) = d.completed / max(window, 1e-9)
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mib_per_sec(d::StageDelta, window) = d.bytes / max(window, 1e-9) / 1024^2
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"Mean wall time one file spends in one worker of this stage."
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service_ms(d::StageDelta) = d.completed == 0 ? NaN :
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(d.busy - d.blocked) / d.completed * 1000
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"""
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Fraction of the pool's capacity spent doing this stage's own work.
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Blocked time is subtracted first: a stage parked on a full downstream queue is
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waiting, not working, and leaving it in would light up every stage upstream of a
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jam as though each were the jam.
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"""
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utilization(d::StageDelta, window) =
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(d.busy - d.blocked) / max(window * d.workers, 1e-9)
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blocked_share(d::StageDelta) = d.busy <= 0 ? 0.0 : d.blocked / d.busy
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"Subtract two scrapes into per-stage deltas, folding in sampled peak depths."
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function stage_deltas(before, after, peak_depth::Dict{Int,Int})
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out = StageDelta[]
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for (b, a) in zip(before.stages, after.stages)
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push!(out, StageDelta(a.stage, String(a.name), a.workers,
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a.completed - b.completed,
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a.failed - b.failed,
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a.bytes - b.bytes,
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a.busy_seconds - b.busy_seconds,
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a.blocked_seconds - b.blocked_seconds,
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get(peak_depth, Int(a.stage), 0),
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a.queue_capacity))
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end
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return out
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end
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"JSON has no NaN. A stage that completed nothing has no service time, and `null`
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is the honest way to say that — writing NaN just makes JSON3 throw."
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json_num(x::Real) = isfinite(x) ? x : nothing
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pad(s, n) = rpad(string(s), n)
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lpad_(s, n) = lpad(string(s), n)
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"""
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Print the per-stage table and say which stage is the bottleneck.
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The verdict reads utilization, not throughput: in a pipeline every stage
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completes the same files, so at steady state they all report nearly the same
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files/s regardless of which one is the constraint. What separates them is how
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hard each pool had to work to keep up — the bottleneck is pinned near 1.0 while
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its neighbours idle.
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"""
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function stage_report(deltas::Vector{StageDelta}, window::Float64)
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println("PER-STAGE (server counters, delta over the end-to-end window)")
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println(" stage files/s MiB/s svc ms util blocked peak queue failed")
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for d in deltas
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svc = service_ms(d)
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println(" $(d.stage) $(pad(d.name, 10)) " *
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lpad_(fmt(files_per_sec(d, window)), 8) * " " *
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lpad_(fmt(mib_per_sec(d, window)), 8) * " " *
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lpad_(isnan(svc) ? "—" : fmt(svc, 1), 8) * " " *
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lpad_(fmt(utilization(d, window)), 6) * " " *
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lpad_(fmt(blocked_share(d) * 100, 0) * "%", 7) * " " *
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lpad_("$(d.peak_depth)/$(d.capacity)", 11) * " " *
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lpad_(d.failed, 7))
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end
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# Per-stage files/s are not comparable across rows and saying so costs one
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# line: the stages process different subsets (stage 2 only known files,
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# stage 4 only text), so a low rate can mean "little work arrived here"
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# rather than "slow". Utilization is the column that compares.
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println(" (files/s counts only files routed to that stage; svc is per-file wall time in")
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println(" one worker; util = (busy − blocked) / (window × workers))")
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worked = filter(d -> d.completed > 0, deltas)
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if isempty(worked)
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println(" (no stage completed a file in this window)")
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return nothing
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end
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top = argmax(d -> utilization(d, window), worked)
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println(" bottleneck stage $(top.stage) ($(top.name)) at " *
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"$(fmt(utilization(top, window) * 100, 0))% utilization of " *
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"$(top.workers) worker(s)")
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if utilization(top, window) < 0.5
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println(" — but no stage is near saturated: the pipeline is " *
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"waiting on intake,\n not on itself. Raise --concurrency " *
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"or --files to load it properly.")
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end
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for d in worked
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blocked_share(d) > 0.25 && println(" ! stage $(d.stage) ($(d.name)) spent " *
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"$(fmt(blocked_share(d) * 100, 0))% of its time parked on a full downstream " *
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"queue —\n it is being held up by the stage after it, not doing that work itself.")
|
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end
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return nothing
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||
end
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||
|
||
# ------------------------------------------------------------------------ corpus
|
||
|
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const WORDS = split("the quick brown fox jumps over a lazy dog while parsing " *
|
||
"headers and spooling bytes onto disk for later enrichment " *
|
||
"because throughput matters more than latency here")
|
||
|
||
"Write one file of exactly `size` bytes, in bounded chunks so the generator
|
||
itself never holds a whole 1 GB file in memory."
|
||
function write_file(path::AbstractString, size::Int, kind::Symbol, rng)
|
||
chunk = 1024 * 1024
|
||
open(path, "w") do io
|
||
remaining = size
|
||
while remaining > 0
|
||
n = min(chunk, remaining)
|
||
if kind === :binary
|
||
write(io, rand(rng, UInt8, n))
|
||
else
|
||
buf = IOBuffer()
|
||
while buf.size < n
|
||
print(buf, rand(rng, WORDS), rand(rng) < 0.06 ? ".\n" : " ")
|
||
end
|
||
write(io, take!(buf)[1:n])
|
||
end
|
||
remaining -= n
|
||
end
|
||
end
|
||
return nothing
|
||
end
|
||
|
||
"Generate the corpus and return (dir, paths, total_bytes)."
|
||
function make_corpus(opts)
|
||
n, size, kind = opts["files"], opts["size"], opts["kind"]
|
||
totalbytes = n * size
|
||
if totalbytes > 16 * 1024^3 && !opts["force"]
|
||
error("corpus would be $(human(totalbytes)) on disk; pass --force if that's intended")
|
||
end
|
||
dir = mktempdir(; prefix = "fsbench-")
|
||
rng = MersenneTwister(1234)
|
||
paths = String[]
|
||
for i in 1:n
|
||
k = kind == "mixed" ? (isodd(i) ? :binary : :text) :
|
||
kind == "text" ? :text : :binary
|
||
ext = k === :text ? "txt" : "bin"
|
||
path = joinpath(dir, "bench-$(lpad(i, 6, '0')).$ext")
|
||
write_file(path, size, k, rng)
|
||
push!(paths, path)
|
||
end
|
||
return dir, paths, totalbytes
|
||
end
|
||
|
||
function existing_corpus(dir::AbstractString)
|
||
isdir(dir) || error("--corpus is not a directory: $dir")
|
||
paths = sort(filter(isfile, readdir(dir; join = true)))
|
||
isempty(paths) && error("--corpus directory is empty: $dir")
|
||
return paths, sum(filesize, paths)
|
||
end
|
||
|
||
# ----------------------------------------------------------------------- upload
|
||
|
||
struct Upload
|
||
status::Int # HTTP status, or 0 if the request threw
|
||
accepted::Int # jobs the server actually queued (from the 202/503 body)
|
||
seconds::Float64
|
||
end
|
||
|
||
"POST one file as multipart/form-data and report what the server accepted."
|
||
function upload_one(url::String, path::String)::Upload
|
||
t0 = time()
|
||
try
|
||
form = HTTP.Form(["file" => HTTP.Multipart(basename(path), open(path, "r"),
|
||
"application/octet-stream")])
|
||
resp = HTTP.post(url, [], form; status_exception = false, retry = false)
|
||
# 202 and 503 both carry an `accepted` array: a partially-accepted batch
|
||
# still queued those jobs, and they will show up in the sinks.
|
||
acc = try
|
||
length(JSON3.read(String(resp.body)).accepted)
|
||
catch
|
||
resp.status == 202 ? 1 : 0
|
||
end
|
||
return Upload(resp.status, acc, time() - t0)
|
||
catch e
|
||
e isa InterruptException && rethrow()
|
||
@warn "upload failed" file = basename(path) exception = e
|
||
return Upload(0, 0, time() - t0)
|
||
end
|
||
end
|
||
|
||
"""
|
||
Upload every path, at most `concurrency` in flight.
|
||
|
||
A bounded set of worker tasks pulling from a shared index keeps exactly
|
||
`concurrency` requests in flight for the whole run — unlike batching, where each
|
||
batch stalls on its slowest (largest) file and the real concurrency sags.
|
||
"""
|
||
function upload_all(url::String, paths::Vector{String}, concurrency::Int)
|
||
endpoint = string(rstrip(url, '/'), "/upload")
|
||
results = Vector{Upload}(undef, length(paths))
|
||
next = Threads.Atomic{Int}(1)
|
||
@sync for _ in 1:min(concurrency, length(paths))
|
||
Threads.@spawn while true
|
||
i = Threads.atomic_add!(next, 1)
|
||
i > length(paths) && break
|
||
results[i] = upload_one(endpoint, paths[i])
|
||
end
|
||
end
|
||
return results
|
||
end
|
||
|
||
# ------------------------------------------------------------------- formatting
|
||
|
||
function human(bytes::Real)
|
||
b = Float64(bytes)
|
||
for unit in ("B", "KiB", "MiB", "GiB", "TiB")
|
||
(abs(b) < 1024 || unit == "TiB") && return "$(round(b; digits = 2)) $unit"
|
||
b /= 1024
|
||
end
|
||
end
|
||
|
||
fmt(x::Real, digits::Int = 2) = string(round(Float64(x); digits = digits))
|
||
|
||
function percentile(sorted::Vector{Float64}, p::Float64)
|
||
isempty(sorted) && return NaN
|
||
idx = clamp(ceil(Int, p * length(sorted)), 1, length(sorted))
|
||
return sorted[idx]
|
||
end
|
||
|
||
# ------------------------------------------------------------------------- main
|
||
|
||
function main(argv)
|
||
opts = parse_args(argv)
|
||
url = string(opts["url"])
|
||
|
||
# Fail fast and clearly if there's no server, rather than reporting a run of zeros.
|
||
try
|
||
HTTP.get(string(rstrip(url, '/'), "/health"); retry = false, readtimeout = 5)
|
||
catch e
|
||
println(stderr, "cannot reach $url/health — is the server running?")
|
||
println(stderr, " start it with: julia --project=. -t auto bin/server.jl")
|
||
return 1
|
||
end
|
||
|
||
sinks, stages = sinkdirs(), stagedirs()
|
||
|
||
# Leftovers mid-pipeline would land in the sinks during our window and be
|
||
# counted as our throughput, so say so up front rather than quietly skewing.
|
||
pending = total(counts(stages))
|
||
pending > 0 && @warn "pipeline is not idle: $pending file(s) in the stage dirs; " *
|
||
"throughput will include their completions"
|
||
|
||
# --- corpus
|
||
generated = opts["corpus"] === nothing
|
||
corpusdir, paths, corpusbytes = if generated
|
||
print("generating corpus: $(opts["files"]) × $(human(opts["size"])) ($(opts["kind"]))… ")
|
||
t = time()
|
||
d, p, b = make_corpus(opts)
|
||
println("done in $(fmt(time() - t))s → $d")
|
||
d, p, b
|
||
else
|
||
p, b = existing_corpus(String(opts["corpus"]))
|
||
println("using corpus: $(length(p)) file(s), $(human(b)) from $(opts["corpus"])")
|
||
String(opts["corpus"]), p, b
|
||
end
|
||
|
||
try
|
||
pid = opts["no-mem"] ? nothing : something(opts["pid"], detect_pid(), Some(nothing))
|
||
if pid === nothing && !opts["no-mem"]
|
||
@warn "could not find the server process; skipping memory (pass --pid PID)"
|
||
end
|
||
|
||
baseline_rss = nothing
|
||
peak_reset = false
|
||
if pid !== nothing
|
||
r = read_rss(pid)
|
||
r === nothing && (@warn "cannot read /proc/$pid/status; skipping memory"; pid = nothing)
|
||
if pid !== nothing
|
||
peak_reset = reset_peak_rss(pid)
|
||
peak_reset || @warn "could not reset the peak-RSS counter (/proc/$pid/clear_refs); " *
|
||
"reporting sampled RSS only"
|
||
# Baseline is read *after* the reset, not before. VmHWM restarts
|
||
# from whatever RSS is at the moment of the reset, so a baseline
|
||
# sampled earlier is measured against a different origin — and if
|
||
# the GC hands memory back in between, the run reports negative
|
||
# growth, which is nonsense on its face.
|
||
r2 = read_rss(pid)
|
||
baseline_rss = r2 === nothing ? r[1] : r2[1]
|
||
end
|
||
end
|
||
|
||
if Threads.nthreads() == 1 && opts["size"] > 64 * 1024^2
|
||
@warn "running with 1 thread and large files: the sampler shares a thread with " *
|
||
"blocking file reads, so the RSS curve will be coarse. Prefer -t auto."
|
||
end
|
||
|
||
base_sinks = counts(sinks)
|
||
interval = opts["sample-ms"] / 1000
|
||
|
||
# --- per-stage counters: the "before" half of the delta.
|
||
stats_before = opts["no-stats"] ? nothing : scrape_stats(url)
|
||
if stats_before === nothing && !opts["no-stats"]
|
||
@warn "no /stats endpoint on this server; skipping the per-stage table " *
|
||
"(the server predates src/stats.jl)"
|
||
end
|
||
|
||
# --- sampler: RSS curve, stage dir depths, and queue depths.
|
||
stop = Threads.Atomic{Bool}(false)
|
||
rss_samples = Float64[]
|
||
depth_max = Dict(k => 0 for k in keys(stages))
|
||
queue_peak = Dict{Int,Int}()
|
||
sampler = Threads.@spawn begin
|
||
while !stop[]
|
||
if pid !== nothing
|
||
r = read_rss(pid)
|
||
r !== nothing && push!(rss_samples, Float64(r[1]))
|
||
end
|
||
d = counts(stages)
|
||
for k in keys(d)
|
||
depth_max[k] = max(depth_max[k], getfield(d, k))
|
||
end
|
||
# Queue depth, unlike directory depth, can't be missed by a slow
|
||
# sample in the same way — a file's *reference* sits in the queue
|
||
# for the whole time it waits — so this is the depth the stage
|
||
# table reports.
|
||
if stats_before !== nothing
|
||
s = scrape_stats(url)
|
||
s === nothing || for st in s.stages
|
||
k = Int(st.stage)
|
||
queue_peak[k] = max(get(queue_peak, k, 0), Int(st.queue_depth))
|
||
end
|
||
end
|
||
sleep(interval)
|
||
end
|
||
end
|
||
|
||
# --- intake
|
||
println("uploading $(length(paths)) file(s) at concurrency $(opts["concurrency"])…")
|
||
t_start = time()
|
||
ups = upload_all(url, paths, opts["concurrency"])
|
||
t_intake_end = time()
|
||
|
||
accepted = sum(u.accepted for u in ups)
|
||
n_202 = count(u -> u.status == 202, ups)
|
||
n_503 = count(u -> u.status == 503, ups)
|
||
n_err = count(u -> !(u.status in (202, 503)), ups)
|
||
intake_secs = t_intake_end - t_start
|
||
lat = sort([u.seconds for u in ups])
|
||
|
||
println(" intake: $accepted job(s) accepted in $(fmt(intake_secs))s " *
|
||
"($(fmt(accepted / max(intake_secs, 1e-9))) files/s, " *
|
||
"$(fmt(corpusbytes / max(intake_secs, 1e-9) / 1024^2)) MiB/s)")
|
||
n_503 > 0 && println(" backpressure: $n_503 request(s) got 503 (intake queue full)")
|
||
n_err > 0 && println(" errors: $n_err request(s) failed or returned an unexpected status")
|
||
|
||
# --- drain: poll the terminal sinks until they stop moving.
|
||
println("draining (polling sinks every $(opts["sample-ms"])ms)…")
|
||
completed = 0
|
||
t_last_progress = time()
|
||
t_last_completion = t_intake_end
|
||
timed_out = false
|
||
while completed < accepted
|
||
sleep(interval)
|
||
c = total(deltas(counts(sinks), base_sinks))
|
||
if c > completed
|
||
completed = c
|
||
t_last_completion = time()
|
||
t_last_progress = t_last_completion
|
||
elseif time() - t_last_progress > opts["timeout"]
|
||
timed_out = true
|
||
break
|
||
end
|
||
end
|
||
# Scrape before stopping the sampler, so the window closes as near the
|
||
# last completion as we can manage.
|
||
stats_after = stats_before === nothing ? nothing : scrape_stats(url)
|
||
stop[] = true
|
||
wait(sampler)
|
||
|
||
sink_delta = deltas(counts(sinks), base_sinks)
|
||
e2e_secs = t_last_completion - t_start
|
||
|
||
# The stage window is the server's own clock across the two scrapes, not
|
||
# e2e_secs: it starts a scrape earlier and ends a scrape later, and using
|
||
# our wall time against its counters would misattribute the difference.
|
||
stage_window, stage_delta = if stats_after === nothing
|
||
(0.0, StageDelta[])
|
||
else
|
||
(Float64(stats_after.now - stats_before.now),
|
||
stage_deltas(stats_before, stats_after, queue_peak))
|
||
end
|
||
|
||
final_rss = pid === nothing ? nothing : read_rss(pid)
|
||
peak_rss = if final_rss !== nothing && peak_reset
|
||
final_rss[2] # kernel VmHWM: catches spikes between samples
|
||
elseif !isempty(rss_samples)
|
||
maximum(rss_samples)
|
||
else
|
||
nothing
|
||
end
|
||
|
||
# --- report
|
||
println()
|
||
println("=" ^ 68)
|
||
println("corpus $(length(paths)) file(s), $(human(corpusbytes)) total, " *
|
||
"$(human(corpusbytes / length(paths))) avg")
|
||
println("concurrency $(opts["concurrency"])")
|
||
println()
|
||
println("INTAKE (HTTP 202 — bytes spooled, not processed)")
|
||
println(" accepted $accepted of $(length(paths)) [202: $n_202, 503: $n_503, error: $n_err]")
|
||
println(" wall $(fmt(intake_secs))s")
|
||
println(" rate $(fmt(accepted / max(intake_secs, 1e-9))) files/s, " *
|
||
"$(fmt(corpusbytes / max(intake_secs, 1e-9) / 1024^2)) MiB/s")
|
||
println(" latency p50 $(fmt(percentile(lat, 0.5) * 1000, 1))ms " *
|
||
"p95 $(fmt(percentile(lat, 0.95) * 1000, 1))ms " *
|
||
"max $(fmt(percentile(lat, 1.0) * 1000, 1))ms")
|
||
println()
|
||
println("END-TO-END (files reaching a terminal sink)")
|
||
println(" completed $completed of $accepted accepted" * (timed_out ? " ** TIMED OUT **" : ""))
|
||
println(" wall $(fmt(e2e_secs))s (first upload → last completion)")
|
||
println(" throughput $(fmt(completed / max(e2e_secs, 1e-9))) files/s, " *
|
||
"$(fmt(corpusbytes / max(e2e_secs, 1e-9) / 1024^2)) MiB/s")
|
||
println(" sinks done $(sink_delta.done) text_done $(sink_delta.text_done) " *
|
||
"binary $(sink_delta.binary) failed $(sink_delta.failed)")
|
||
println(" peak dir depth spool $(depth_max[:spool]) known $(depth_max[:known]) " *
|
||
"unknown $(depth_max[:unknown]) text $(depth_max[:text])")
|
||
println()
|
||
if !isempty(stage_delta)
|
||
stage_report(stage_delta, stage_window)
|
||
println()
|
||
end
|
||
if peak_rss !== nothing
|
||
println("SERVER MEMORY (pid $pid)")
|
||
println(" baseline RSS $(human(baseline_rss))")
|
||
println(" peak RSS $(human(peak_rss))" *
|
||
(peak_reset ? " (kernel VmHWM, reset at start)" : " (sampled — may miss spikes)"))
|
||
growth = peak_rss - baseline_rss
|
||
if growth <= 0
|
||
# RSS never got back to where it started, so the run's own cost
|
||
# is below the noise floor of the server settling after startup.
|
||
# Printing a negative "growth" would invite reading a memory
|
||
# *saving* into what is really "too small to measure here".
|
||
println(" growth none measurable (peak never exceeded the baseline)")
|
||
println(" The baseline was still falling when we sampled it — give the")
|
||
println(" server ~30s to settle after startup for a comparable figure.")
|
||
else
|
||
println(" growth $(human(growth))")
|
||
println(" per in-flight $(human(growth / opts["concurrency"])) " *
|
||
"at $(human(corpusbytes / length(paths))) avg file size")
|
||
println(" (should not grow with file size — intake streams to disk)")
|
||
end
|
||
# Julia's GC returns memory to the OS lazily, so a second run on the
|
||
# same process starts from an inflated baseline and under-reports
|
||
# growth. Absolute peak is the number to trust across runs.
|
||
baseline_rss > 1.3 * FRESH_RSS_HINT &&
|
||
println(" ! baseline is well above a fresh start ($(human(FRESH_RSS_HINT))): the GC " *
|
||
"has not\n returned memory from earlier work. Compare " *
|
||
"absolute peak, or restart\n the server for a clean growth figure.")
|
||
else
|
||
println("SERVER MEMORY not sampled")
|
||
end
|
||
println("=" ^ 68)
|
||
|
||
sink_delta.failed > 0 &&
|
||
println("\nnote: $(sink_delta.failed) file(s) landed in $(sinks.failed) — check the server log.")
|
||
timed_out &&
|
||
println("\nnote: drain stalled with $(accepted - completed) file(s) outstanding. " *
|
||
"Check the server log and the stage dirs; raise --timeout if the pipeline is just slow.")
|
||
|
||
if opts["json"] !== nothing
|
||
result = (
|
||
url, concurrency = opts["concurrency"], kind = opts["kind"],
|
||
files = length(paths), corpus_bytes = corpusbytes,
|
||
avg_file_bytes = corpusbytes / length(paths),
|
||
intake = (; accepted, n_202, n_503, n_err, seconds = intake_secs,
|
||
files_per_sec = accepted / max(intake_secs, 1e-9),
|
||
p50_ms = json_num(percentile(lat, 0.5) * 1000),
|
||
p95_ms = json_num(percentile(lat, 0.95) * 1000),
|
||
max_ms = json_num(percentile(lat, 1.0) * 1000)),
|
||
end_to_end = (; completed, seconds = e2e_secs, timed_out,
|
||
files_per_sec = completed / max(e2e_secs, 1e-9),
|
||
mib_per_sec = corpusbytes / max(e2e_secs, 1e-9) / 1024^2,
|
||
sinks = sink_delta, peak_stage_depth = depth_max),
|
||
stages = [(; stage = d.stage, name = d.name, workers = d.workers,
|
||
completed = d.completed, failed = d.failed, bytes = d.bytes,
|
||
busy_seconds = d.busy, blocked_seconds = d.blocked,
|
||
window_seconds = stage_window,
|
||
files_per_sec = files_per_sec(d, stage_window),
|
||
mib_per_sec = mib_per_sec(d, stage_window),
|
||
service_ms = json_num(service_ms(d)),
|
||
utilization = utilization(d, stage_window),
|
||
blocked_share = blocked_share(d),
|
||
peak_queue_depth = d.peak_depth,
|
||
queue_capacity = d.capacity) for d in stage_delta],
|
||
memory = (; pid, baseline_rss, peak_rss, peak_is_kernel_hwm = peak_reset,
|
||
growth = peak_rss === nothing ? nothing : peak_rss - baseline_rss,
|
||
samples = rss_samples),
|
||
)
|
||
open(String(opts["json"]), "w") do io
|
||
JSON3.write(io, result)
|
||
end
|
||
println("\nwrote $(opts["json"])")
|
||
end
|
||
|
||
return timed_out ? 1 : 0
|
||
finally
|
||
if generated && !opts["keep-corpus"]
|
||
rm(corpusdir; recursive = true, force = true)
|
||
elseif generated
|
||
println("\nkept corpus: $corpusdir")
|
||
end
|
||
end
|
||
end
|
||
|
||
if abspath(PROGRAM_FILE) == @__FILE__
|
||
exit(main(ARGS))
|
||
end
|