stroblmeandClaude Opus 5
da528340a9
Measured with `make bench-engine` against a real Redis: 103.6 -> 164.4 messages a second on a five-node chain (p50 latency 2125 -> 1171 ms) and 34.8 -> 63.2 on a fan-out of twenty. Against the memory backend, which is what a pip install runs on, 262 -> 626. The two that bought most of it: - `StateBackend.record` puts a published value, its timestamp, its series and its version counter in one round trip. They were four calls building four pipelines, and a value crossing an edge pays them twice. A released rate-limit hold rides along instead of a DEL per port. - the readiness check reads a node's inputs and hands them to the node, rather than reading the triggering ones to count them and having the node read the same keys again a moment later. `apply_outputs` was a second copy of `_record_outputs` and is now the same code plus the event that distinguishes it. The rest, each small: - `_derive` builds a node-by-id map and a `consumes` index, so dispatching an item and publishing a value stop scanning every node in the installation. - `read_all` is memoised against the store revision — it sits on the publish path, so a dashboard slider was reading and validating every flow file per value. Same mechanism `_wiring` already uses. - the `message_value` source block is built once per node instead of per emission. - both timer threads ask the queue to promote only when something is actually due, which takes an idle engine from ~4 Redis round trips a second to one. - the shared httpx client is bounded (32 connections, one retry); its default pool is 100 with no per-host cap, so one slow endpoint could take it and every other sender node with it. - the MQTT and delay nodes no longer log a line per message at INFO. Robustness, in the same pass: - `MemoryWorkQueue._done` was a set nothing ever removed from — one entry per non-idempotent node per item, for the life of the process, in the default configuration. Capped, the way the Redis side expires its markers. - a saturated engine can claim from the due lane past the cascade limit. The capacity gate sits in front of the claim, so the due lane's priority — decided inside it — did not apply while every slot was held: a motor's stop was not behind the long nodes, it was unread. Only after a slot has genuinely failed to free for half a second, and briefly, so the backlog is not starved in turn. - `reclaim_stale` dispatches through that same gate. It could return sixty entries and push in-flight far past the limit the gate exists to hold. - a flow's nodes are stopped together rather than one after another. Each gets `NODE_STOP_TIMEOUT`, so a flow whose broker was unreachable took five seconds per node — long enough to outlast `REBUILD_WAIT` and 503 the deploy. - the worker pool and the HTTP client are closed on a thread, not on the event loop, and a run closes the state backend it built (on Redis, a client and a connection pool per run). - the five background tasks say something when they die. Each catches exceptions inside its loop, so one raised anywhere else left the engine serving with no metrics, no alerts or no artifact sweep, silently. `tests/flow/test_round_trips.py` counts the state operations one message costs — four, where it was about eleven — because none of the above would fail a behavioural test if it were undone. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01M6hPWS6YEbT1P8LxhhFb2T
Fluksio
Fluksio is a node-based automation software that brings trust and reliability to your flow. It just works and looks good. Get started by running
pip install fluksio
fluksio serve
and you're ready to go.
For data science
You can turn your existing data science project into a flow by decorating your functions with @node ...
# myresearch/train.py
import fluksio
from fluksio import Port, node
@node(
requires=["dataset", Port("lr", "float")],
provides=[Port("loss", "float", stream=True), Port("weights", "artifact")],
device="gpu", device_policy="prefer",
)
def fit(dataset, lr, epochs=25):
for epoch in range(epochs):
loss = step(...)
yield {"loss": loss} # published as it happens, kept as a series
return {"weights": fluksio.save_artifact("weights.pt")}
... and passing them to a Flow:
# myresearch/pipeline.py
from fluksio import Flow, Port
from myresearch.data import prepare
from myresearch.evaluate import evaluate
from myresearch.train import fit
train = Flow("train", nodes=[prepare, fit, evaluate],
inputs=[Port("lr", "float", initial=0.01)], outputs=["score"])
Fluksio will automatically infer the order of nodes based on the inputs and outputs you defined. When everything is set, you can launch your first run as follows:
fluksio run train --lr 0.05 --wait
Checkout our documentation for more infos.
Some other features
- Flows: typed messages between nodes, wired by name, edited on a canvas or declared in code. Every change is a commit in a git repository you own.
- Runs: an experiment and a CI-style job are the same entity. Parameters, seed, result, per-node timings, artifacts and the commit it ran at.
- Dashboards: charts and controls bound to the same messages the flows carry, with no separate metrics pipeline.
- Remote workers:
pip install fluksio-workeron the GPU box; it dials out over one websocket, so nothing there has to be reachable.
Fluksio can also be used for facility automation. Visit us on fluksio.com or go straight to our documentation.
License
Copyright (C) 2026 Melvin Strobl - GNU Affero General Public License v3.0 or later. Running a modified version over a network obliges you to offer its users the corresponding source (AGPL §13).