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A cached node keeps its curve, and any input can name a run's output
A cache hit still replays no emissions — those values were the story of an
execution that is not happening — but the run they were recorded in is now
written on the row (`run_node.cached_from`), and the metrics endpoints read the
series back from there. So a reused run answers `run.metrics("train.loss")`
with the same points the run that trained did, rather than looking like a run
that produced no numbers at all. Pointed at rather than copied: a sweep of 500
reusing one frozen node would otherwise duplicate its curve 500 times.

That needed the cross-flow restore fixed first. The cache key has no flow in
it while the stored outputs are named for the flow that produced them, so
`quick.prepare` getting a hit from `train` wrote `train.dataset` into `quick`'s
state and the next node was called without its argument. One rule now covers
both halves: `requalify` reads a name owned by one flow as the same name in
another, applied to the restored outputs, to the node id behind the pointer,
and to the series names on the way out. Reuse across flows is kept.

Also: `@run:<id>.<output>` and a bare `sha256:` digest resolve on every input,
not only artifacts. Chaining a run's json config into the next one from a shell
meant pasting the whole object inline, and the CLI could not even send the
spelling — `_coerce` died in `json.loads` before the engine saw it. Both
spellings are reserved on every input now, `str` included, and `_from_run`
returns whatever the run's result holds rather than only a reference.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Dp9L6gakMVro1K2C5zdtBE
2026-08-25 15:12:28 +02:00

4.9 KiB

Payload types

Every port declares a dtype, and every value that passes through it is checked against that declaration.

This is not decoration. It is what lets the dashboard editor offer you only the messages a gauge can actually draw, what lets the canvas refuse a binding before anything runs, and what lets a downstream node know the shape of what it is getting before the flow starts.

Everything on the wire is JSON. That is what lets the same value pass through the state backend, the work queue and the worker protocol unchanged.

The scalars

dtype Accepts
float any number — int or float, but not bool
int a whole number, not bool
str a string
bool exactly true or false

bool is an int subclass in Python and deliberately not a number here: a flag is not a measurement, and a switch bound to a temperature is a mistake worth catching.

NaN and infinity are refused, wherever they sit — including inside a json, record, series or list. JSON cannot spell either, so one that travelled would come back as a response nobody can parse and a row the database rejects, a long way from the node that made it. An empty subset or a division with no denominator is what usually produces one; publish None instead.

The structured ones

These are declared shapes rather than "some JSON", which is what makes a widget binding checkable.

record

Flat named scalars.

{"title": "Boiler", "body": "Pressure low", "severity": "warning"}

Nesting is deliberately out: a record that can contain a record is a schema language, and the shape stops being readable from the declaration alone.

Read by the Notification widget. It is also what an alert channel of kind dashboard writes.

list

Ordered items of one declared shape. The port also declares item:

item Meaning
unset record — what the agenda and forecast widgets read
float, int, str, bool a list of scalars
json anything

A list of lists, or a list of series, is refused. One declared level is the point.

series

Labelled lines of (timestamp, value) pairs — what a chart draws.

{
  "lines": [
    {"label": "living", "points": [[1717000000, 21.4], [1717000060, 21.5]]}
  ],
  "range": "-24h"
}

Keys beside lines are carried through untouched, which is how a querying chart puts the window and resolution it asked for on the request and reads them back off the answer. That is what stops an answer to a different question from overwriting the picture.

GET /runs/series/compare answers in this shape, which is why comparing three training curves is a widget binding rather than a screen of its own.

artifact

A reference to stored bytes.

{"digest": "sha256:…", "size": 4194304, "media_type": "application/octet-stream", "name": "weights.pt"}

Binary payloads — tensors, checkpoints, images — never travel as a message. The bytes go to a content-addressed store and the message carries this. A thirty-megabyte checkpoint never sits in the state backend, and the reference stays valid wherever the store is reachable from, including on another machine.

Node code produces one with fluksio.save_artifact and opens one with fluksio.load_artifact. See Writing node code.

json

Anything JSON-serializable. The escape hatch, and the right answer when a payload genuinely has no fixed shape.

Reach for it last. A json port tells the canvas, the widget picker and the next author nothing.

Naming a run's output

Any run parameter is also accepted as text, since nobody wants to paste an object into a shell. @run:<id>.<output> names what an earlier run produced — whatever its type, an artifact reference or a json config alike — and a bare sha256:… digest names content in the artifact store. Both resolve before the run starts, so the CLI, the run dialog and a python caller all mean the same thing by the same string.

Both spellings are reserved on every input, str included: an input that has to carry one of them literally is asking for a value this engine reads as a name.

What a widget will bind to

Widget Accepts
Gauge, Chart, Slider, Bar float, int
Switch bool
Agenda, Forecast list
Notification record
Value anything
Icon weather strings, booleans and numbers alike
Clock, Text nothing — they bind to no message

Enforced on the server as well as in the editor.

Type failures

A value that does not match its port's declaration raises on the node that published it, naming the port and what arrived. It does not get published, and it does not reach anything downstream — a wrong value stopping at its source is much easier to diagnose than one propagating.

See also