BarFlow

A fast Python progress bar library with a C++ core. Cross-platform — first-class on Windows, macOS, and Linux. Built to beat tqdm, rich.progress, and alive-progress on cold import, per-iteration overhead, peak it/s, memory footprint, tail latency, multi-bar throughput, and first-frame latency — simultaneously.

import barflow

# Fastest: `for _ in progress:` runs at 160+ M it/s — faster than
# a bare `for _ in range(n): pass` because Py_None is immortal.
with barflow.Progress(total=n, desc="Crunching") as p:
    for _ in p:
        do_work()

# When you also need the iterated values:
for x in barflow.track(range(1_000_000), desc="Working"):
    ...

# Event-driven / manual:
with barflow.Progress(total=n, desc="Streaming") as p:
    for chunk in data:
        process(chunk)
        p.advance(len(chunk))

Benchmarks

Numbers below are from benchmarks/bench.py on Windows 11 / Python 3.13 / N = 20,000,000 iterations (5 runs per data point, best wall time for rate measurements, min CPU time for CPU measurements). Baseline bare for _ in range(n): pass is 145.75 M it/s (6.9 ns/iter, 140.6 ms of CPU). Raw output lives in benchmarks/bench_raw.md; methodology and platform notes are in benchmarks/results.md.

Headline

Axis	BarFlow	tqdm	rich	alive-progress
Cold import (ms)	1.21	72.27	74.96	30.12
Overhead, `for _ in p:` (ns/iter)	0.0	7.4	471.9	384.9
Overhead, `track(...)` (ns/iter)	3.0	7.4	471.9	384.9
Peak it/s, display off	160.8 M	70.2 M	2.1 M	2.6 M
Peak it/s, display on	101.8 M	19.6 M	2.1 M	2.1 M
Python heap peak (1 M iters)	486 B	298 KB	661 KB	3.4 MB
Tail latency p99.9 (ns)	100	200	2,200	2,200
First-frame latency (µs)	32	97	921	n/a
Multi-bar, 4 tasks (M it/s)	43.9 M	8.8 M	2.2 M	n/a
Metadata churn (M it/s)	29.6 M	6.6 M	2.0 M	1.9 M
Total CPU, display on (ms for 20 M)	188	953	9,297	9,391

BarFlow wins on every axis — 25–62× faster cold import, zero measurable overhead on its iteration fast path (faster than a bare for _ in range(n) because Py_None is immortal on 3.12+ and skips the store-cycle refcount work that range's small-int yields incur), 5.2× display-on throughput vs tqdm, ~50× vs rich / alive, 600×+ less Python heap peak, 2× tighter tail latency, 3× faster first-frame paint than tqdm, and ~50× less CPU than rich / alive over 20 M iterations. The sub-1.0 CPU/wall ratio reflects the decoupled render thread: it wakes on a 50 ms timeout, formats into a preallocated buffer, and spends most of its life parked on a condition variable, so the producer loop never pays for rendering inline.

Import startup (median, baseline-subtracted, 11 runs)

Library	Cold import (ms)	vs BarFlow
barflow	1.21	1×
alive	30.12	25×
tqdm	72.27	60×
rich	74.96	62×

Measured by timing python -c "from <lib> import ..." in a subprocess and subtracting a bare-interpreter baseline (python -c "pass"), so the number is just the work the library does at import time. BarFlow's module graph is deliberately lazy: themes, columns, style, spinners, hooks, and aio are all resolved on first attribute access via __getattr__, so the cold import only pays for the C extension load and an __init__.py that does nothing but expose Progress, Tracker, and track.

No-display hot path — pure per-tick overhead

Display is disabled (disable=True where the library supports it) so we measure the cost of advancing the counter, not rendering. ns/iter is over the bare for-loop baseline.

Variant	M it/s	ns/iter over baseline
barflow-iter	160.76	0.0
barflow-track	101.13	3.0
tqdm	70.23	7.4
barflow-tick	65.39	8.4
alive	2.55	384.9
rich	2.09	471.9

barflow-iter is for _ in p: — BarFlow's Progress type implements the iteration protocol directly, so FOR_ITER dispatches tp_iternext without the CPython vectorcall trampoline. The iternext body is three x86 instructions (load, fetch_add, return Py_None), and Py_None's immortal refcount on 3.12+ means the loop's STORE_FAST _ is free. Net result: below the bare for-loop baseline, because a range-driven loop still does refcount work on its cached small-int yields.
barflow-track is the for x in barflow.track(iterable): wrapper, used when you also need the yielded values.
barflow-tick is the manual Progress.tick() call from Python, which pays the full CPython vectorcall dispatch overhead per call. Use the iteration protocol above when you don't have a source iterable.

Display on — throughput with a live renderer

Comparator libraries write into an io.StringIO sink with force_terminal=True so no real console I/O is measured. BarFlow writes to its native Windows console path (no sink parameter), which makes the comparison conservatively worse for BarFlow.

Library	M it/s	vs BarFlow
barflow	101.76	1×
tqdm	19.57	5.20×
rich	2.11	48×
alive-progress	2.09	49×

BarFlow's render loop emits delta frames: each column's previously-rendered bytes are cached, and on the next frame the render thread emits \x1b[<n>C (cursor-right) over unchanged spans instead of re-writing the bytes. On a real TTY this cuts bytes-written per frame by roughly 60% for the default layout; the sink-based benchmark above does not exercise the delta path, so the number you see is the lower bound — real terminals get more.

Memory footprint

Library	tracemalloc peak	RSS import	RSS run
barflow	486 B	236 KB	192 KB
tqdm	298 KB	6.83 MB	992 KB
rich	661 KB	6.58 MB	1.77 MB
alive-progress	3.41 MB	1.64 MB	3.79 MB

tracemalloc peak is the high-water mark of the Python heap over a 1 M-iteration run (bench_memory.py). BarFlow's ~500 bytes is effectively one Progress object's shell — the counter, output buffer, render thread, and render scratch all live in C-owned storage that tracemalloc cannot see. Competitors allocate hundreds of KB to several MB of Python objects per run.

Tail latency (per-iter distribution, display on)

Library	p50	p90	p99	p99.9	max
barflow	100 ns	100 ns	100 ns	100 ns	7.80 µs
tqdm	100 ns	200 ns	200 ns	200 ns	28.00 µs
rich	500 ns	600 ns	800 ns	2.20 µs	153.20 µs
alive-progress	500 ns	600 ns	700 ns	2.20 µs	138.00 µs

Per-iter timestamps recorded with perf_counter_ns() across 100 K iterations; bench_tail_latency.py. BarFlow is the only library whose p99.9 does not diverge from its p50 — the render thread never spills work onto the producer, so there is no jitter source to create tail spikes.

First-frame latency (`enter` to first byte)

Library	median	min	p90
barflow	32 µs	28 µs	41 µs
tqdm	97 µs	93 µs	109 µs
rich	921 µs	845 µs	1.05 ms

BarFlow paints a synchronous first frame on Progress.__enter__ before the render thread takes over, eliminating the 50 ms "blank bar" window that would otherwise be visible for short-lived jobs. Measured by bench_first_frame.py.

Multi-bar throughput (4 concurrent tasks)

Library	wall time	aggregate
barflow	22.8 ms	43.9 M it/s
tqdm	114.1 ms	8.8 M it/s
rich	465.8 ms	2.2 M it/s
alive-progress	skipped (no clean multi-task API)

4 tasks × 250 K ticks each, driven round-robin from one thread (bench_multibar.py). BarFlow stays lock-free — every task has its own cache-line-padded counter, and the render thread walks the task vector under a mutex that the single-task hot path never touches. The multi-task update(task_id, n) does take that mutex to resolve the task pointer, but holds the GIL across it — skipping the per-call thread-state save/restore — whenever no CallbackColumn is present, which is the only case where the render thread needs the GIL while holding the lock. (bench_multibar.py numbers above are a pinned Python 3.13 run; the GIL-held path's speedup is larger on 3.14, where GIL save/restore costs more.)

Metadata churn (description updated every 1 K iters)

Library	wall time	it/s
barflow	33.8 ms	29.6 M
tqdm	152.4 ms	6.6 M
rich	514.1 ms	2.0 M
alive-progress	526.9 ms	1.9 M

1 M ticks, set_description called every 1000 ticks with a pre-generated 40-char string (bench_metadata_churn.py). BarFlow exposes set_description(str) and set_task_description(task_id, str) that briefly acquire the render mutex to swap the description; the lock-free tick hot path is unaffected.

CPU cost — render thread counted

time.process_time() sums user+system time across every thread of the process (Windows GetProcessTimes, Linux CLOCK_PROCESS_CPUTIME_ID, macOS task_info), so a background render thread cannot hide from this measurement.

Library	Mode	CPU ms (best of 5)	Extra ns/iter	CPU / wall
barflow	display-off	187.5	2.3	0.96
barflow	display-on	187.5	2.3	0.96
tqdm	display-off	265.6	6.2	0.93
tqdm	display-on	953.1	40.6	0.95
alive-progress	display-off	7,671.9	376.6	0.98
alive-progress	display-on	9,390.6	462.5	0.98
rich	display-off	9,437.5	464.8	0.96
rich	display-on	9,296.9	457.8	0.98

Two things stand out:

BarFlow's CPU cost is identical whether the display is on or off. Turning the bar on adds no measurable per-iter CPU because the render thread wakes on a 50 ms condition-variable timeout and spends the rest of its life parked. The producer loop sees the same hot path in both modes.
tqdm's CPU grows 3.6× when the display turns on (266 → 953 ms), because rendering runs inline on the producer thread. Rich and alive-progress sit near ~50× BarFlow's CPU cost in both modes — they pay hundreds of nanoseconds of dict/lock work per advance() call before any rendering happens.

Caveats / reproducibility

Numbers are from a single Windows 11 box; absolute values will differ on Linux / macOS but the ratios are stable in repeated runs. Re-run python benchmarks/bench.py --n 20000000 --runs 5 to reproduce the main table, and python benchmarks/bench_*.py for each extra axis (tail latency, memory, first-frame, multi-bar, metadata churn).
tqdm is run with mininterval=0.05 (matching BarFlow's default) rather than its out-of-box 0.10, so the comparison isolates per-render work from render frequency instead of giving tqdm a free 2× render-skip advantage.
time.process_time() resolution is ~15 ms on Windows, so the smallest CPU numbers (barflow: 187 ms) sit only ~12 ticks above noise floor. Differences against tqdm (5×) and rich/alive (~50×) are well outside that window.
Display-on throughput is measured against an io.StringIO sink, which skips Windows console latency. On a real TTY, BarFlow's delta-render (cursor-advance over unchanged column spans) gives it additional headroom that the StringIO harness cannot see.

Install

pip install barflow

Wheels are published for Windows (AMD64), Linux (x86_64, aarch64), and macOS (x86_64, arm64) for CPython 3.10 through 3.14, including the free-threaded cp313t / cp314t builds.

Features

Zero-overhead iteration. for _ in progress: runs at 160+ M it/s — below the bare for _ in range(n) baseline, because FOR_ITER dispatches directly to tp_iternext (no vectorcall trampoline) and Py_None is immortal on 3.12+ (no refcount work on STORE_FAST).
C++ hot path. tick, advance, and Tracker's iter-next are single std::atomic::fetch_add calls with no locks and no Python-level bookkeeping. Task counters are cache-line padded so the render thread's reads never false-share with producer writes.
Decoupled renderer. A background thread wakes on a 50 ms condition-variable timeout and formats into a preallocated buffer. The producer never blocks.
Delta-render. The render loop caches each column's previous bytes and emits \x1b[<n>C cursor-advance over unchanged spans instead of rewriting the frame. Roughly 60% fewer bytes written per frame on the default layout.
Synchronous first frame. Progress.__enter__ paints one frame inline before the render thread takes over, so short-lived jobs don't see the 50 ms blank-bar window.
Native console I/O on every OS. Windows: unconditional ENABLE_VIRTUAL_TERMINAL_PROCESSING, UTF-16 transcoded WriteConsoleW chunked at 32 KB, legacy-console fallback, no colorama dependency, and a reusable wscratch transcoding buffer so steady-state frames are zero-alloc. POSIX (macOS/Linux): native ANSI write(2) to fd 2 with TIOCGWINSZ width detection. Same rendering core behind a thin platform shim.
Multi-task + columns. 11 built-in column types (description/bar/percent/count/rate/elapsed/eta/spinner/text/postfix — all in C++ — plus a Python-rendered callback column), rich-style column API, themes, ANSI cursor stacking for nested bars. Progress.set_description(str) and set_task_description(task_id, str) expose metadata churn without touching the lock-free hot path.
tqdm-shaped ergonomics. reset(task_id, total) restarts a bar for reuse across phases; set_postfix(loss=…, acc=…) adds a live trailing annotation (the default layout carries it invisibly until set); unit="B", unit_scale=True, unit_divisor=1024 humanizes counts/rate for byte transfers (1.50M/4.10M, … B/s); smoothing=0.3 swaps the whole-run average rate for an EMA that tracks current speed; leave=False clears the bar on completion. Progress.total / .elapsed read task 0, and list(track(range(n))) preallocates via __length_hint__.
More tqdm/rich parity. barflow.wrap_file(fileobj, total=…) / wrapattr meter a stream's byte throughput (read/write) through a transparent proxy; initial= resumes a partially-done job (shown but excluded from the rate); set_visible(task_id, bool) hides/shows a task's row while it keeps counting; delay= suppresses the bar until it has run longer than N seconds; disable=None auto-disables when stderr is not a TTY (so piped/redirected runs stay clean).
Spinner DSL. Compositional factories (frame / scrolling / bouncing / alongside / sequential) compile to precomputed frame tables at __enter__.
print() interception. capture_output=True reroutes sys.stdout through write_above() so user prints appear above live bars without tearing.
asyncio. barflow.aio.atrack(aiter) wraps async iterables.
Tiny cold import. import barflow is ~1.2 ms (baseline-subtracted median) — 25–62× faster than the alternatives. All non-core submodules (themes, columns, spinners, style, hooks, aio) are lazy-loaded via PEP 562 __getattr__.
Sub-kilobyte Python heap. Peak tracemalloc usage across a 1 M-iteration run is ~500 bytes, vs 300 KB (tqdm), 660 KB (rich), and 3.4 MB (alive-progress).

Usage

import barflow
from barflow.columns import (
    SpinnerColumn, DescriptionColumn, BarColumn, PercentColumn,
    CountColumn, RateColumn, EtaColumn,
)

# Simplest form — when you need the iterated values
for x in barflow.track(range(1000), desc="task"):
    ...

# Fastest form — when you just need a counter
with barflow.Progress(total=1000, desc="task") as p:
    for _ in p:
        do_work()

# Custom columns
with barflow.Progress(
    SpinnerColumn(name="dots"), " ",
    DescriptionColumn(), " ",
    BarColumn(width=40, color="magenta"), " ",
    PercentColumn(), "  ",
    CountColumn(), " | ", EtaColumn(),
    total=1000, desc="build",
) as p:
    for _ in range(1000):
        p.tick()

# Named theme
with barflow.Progress(theme="classic", total=1000) as p:
    ...

# Multi-task
with barflow.Progress(theme="classic") as p:
    dl = p.add_task(total=100, desc="download")
    ex = p.add_task(total=100, desc="extract")
    for i in range(100):
        p.update(dl, 1)
        p.update(ex, 1)

# Live prints during a bar
with barflow.Progress(total=100, capture_output=True) as p:
    for i in range(100):
        if i % 10 == 0:
            print(f"checkpoint {i}")   # appears above the bar
        p.tick()

# Byte transfer — humanized count/rate, EMA-smoothed speed
with barflow.Progress(total=4_100 * 1024, desc="download.iso",
                      unit="B", unit_scale=True, unit_divisor=1024,
                      smoothing=0.3) as p:
    while not done:
        chunk = recv()
        p.advance(len(chunk))       # renders 1.50M/4.10M … 3.2M B/s

# Training loop — reuse one bar across epochs, live metrics
with barflow.Progress(total=steps, smoothing=0.4) as p:
    for epoch in range(epochs):
        p.reset(total=steps)                       # restart counter + timers
        p.set_description(f"epoch {epoch}")
        for step in range(steps):
            p.advance(1)
            p.set_postfix(loss=loss, acc=acc)      # … loss=0.031, acc=0.98

# asyncio
import asyncio, barflow.aio as aio
async def main():
    async for x in aio.atrack(some_async_iter(), total=1000):
        ...
asyncio.run(main())

Design

See docs/DESIGN.md for the full architecture: atomic hot path, background render thread, column pipeline, Windows console handling, and the benchmarks methodology.

Build from source

Requires Visual Studio 2022+ (Windows) or GCC/Clang + Python headers (POSIX) and Python ≥ 3.10.

# Windows
build.bat

# POSIX
python -m pip install -e .

License

MIT. See LICENSE.

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barflow 0.5.1

BarFlow

Benchmarks

Headline

Import startup (median, baseline-subtracted, 11 runs)

No-display hot path — pure per-tick overhead

Display on — throughput with a live renderer

Memory footprint

Tail latency (per-iter distribution, display on)

First-frame latency (__enter__ to first byte)

Multi-bar throughput (4 concurrent tasks)

Metadata churn (description updated every 1 K iters)

CPU cost — render thread counted

Caveats / reproducibility

Install

Features

Usage

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Build from source

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1 maintainer

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First-frame latency (`enter` to first byte)

Algorithm	Hash digest
SHA256	`6b367fdf93df46617c94c396492c11553ec1c7690428c3d4ea147ae50d7afd6f`
MD5	`083db22043978ff591ad200edd2d0a19`
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Algorithm	Hash digest
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MD5	`062ada04add55cf4fa5ddb712847d60c`
BLAKE2b-256	`2da069295162955d3387b330afe0cf1ba42f8021ca65f04065fd1c64dc881db0`

Algorithm	Hash digest
SHA256	`ed23f9e7da053560c5e91b7460b1844cd5b075b55b787d840f03c83128f1a18d`
MD5	`6f1d4390386d830d805a2bc80e641fb6`
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Algorithm	Hash digest
SHA256	`96ff773e426dd98f59094dea8b660222a9cd33a6355a4ee04978c83d2c807eba`
MD5	`3560a059c39f442df37a44caa1a69a05`
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Algorithm	Hash digest
SHA256	`6855f90ab891ca0901cc3100094860088b7a379e00867078003a9d6d145a91b5`
MD5	`81e89e3504d783519c6ed4ead4eb11e1`
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