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
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.