Every question about a part -- what values exist, what attributes go with
one -- was answered by querying the source, and the source is the wrong
place to ask. It is a view over a transaction table, so the query is slow
(76s for one ILIKE against 6.9M rows), it describes only what was
transacted, and it cannot express intent: there is no way to say a part is
discontinued, or to name one that has not sold yet.
pf.dim_member holds the app's own list: one row per key value per group,
siblings in jsonb, keyed on (source_id, dim_group, key_value). Refresh is a
merge rather than a replace, so curation survives it -- members absent from
the source are marked source_seen = false, not deleted. Triggered from
Setup, next to Generate SQL, because it reads the whole source and the
answer only changes when the catalogue does.
A key can carry several attribute sets across history -- 11,290 parts
against 13,662 combinations on osm_skinny -- so the refresh takes the most
recent by the source's date column. That also fixes the sibling autofill,
which used to run a DISTINCT ... LIMIT 2 against the source and silently
fill nothing whenever a part came back ambiguous. A member row is one
definition by construction.
The client fetches each group's list once per source and does both
completion and autofill against it in memory, so neither costs a request.
Columns outside a group, or a group never refreshed, still fall back to the
version's values endpoint.
Run 01_schema.sql to create the table.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Brings in the /agg endpoint, col_meta.in_grain, the pf_gkey index and the
append/remove write model that replaces undo's full reload. On the live
2,574,287-row forecast this collapses to 32,411 rows at a
rep/customer/channel/season/month grain, with totals tying exactly
(857,792,111.91 either way) and pf_gkey unique across every group.
Three conflicts, all from work done on this branch after the grain branch
was cut:
- sql_generator exports: union of both sides, adding grainOf.
- Forecast.jsx fetchArrow: the grain branch factored the inline progress
reader into a helper; kept the helper, and the tag/note ledger functions
beside it, since the two were only textually adjacent.
- Forecast.jsx initViewer: took the grain branch's endpoint selection, but
dropped its loadPerspective() -- 99375bb replaced that lazy CDN loader
with a static inline import, so awaiting fetchArrow directly is correct
here.
Carried the segment labels into grain mode as well: /agg now joins pf.log
the way /data does. pf_logid is part of the grain, so the join adds no
rows. Without it the labels would have disappeared exactly when a source
declared a grain -- which is the mode that will actually be used.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The label on a baseline or reference load ("Open Orders", "Prior Year")
lived only on the pf.log row; the data stream was a straight dump of the
forecast table, so Perspective saw pf_logid and never the name.
The stream now joins pf.log and emits two columns rather than one, because
commingling them makes neither useful: pf_segment names the load a row came
from, and is '(adjustment)' for everything else; pf_note carries the free
text on scale/recode/clone and is null on loads. The operation routes stamp
the same two fields on the rows they push back incrementally.
The tag column those labels belong in is declared in 01_schema.sql but
predates some installs, so the backfill seeding it from note joins the
ALTER already there. Adjustment notes are free text, not labels, and are
left alone.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Forecast operations could only act on one clicked row at a time, and the
panel that drove them separated the numbers you were reading from the
inputs that changed them. This reworks both, and adds initiative tags so
a version's history can be read as a bridge.
Operations
- Accept `slices` (array) alongside the legacy single `slice`, with
apply_mode 'prorate' (one pool) or 'each' (independent per slice).
- buildWhereAny() ORs the slices into one predicate. A union of slices
cannot be flattened into per-column IN lists without over-selecting,
and the result is parenthesised so the appended exclude clause does not
bind wrong.
- resolveIncrs() now resolves each measure independently: target, percent
or change amount per measure, so a target on value and a percent on
units can be submitted together. Replaces the single global `mode`.
- target_basis chooses what a target measures against: only the rows an
operation can write, or everything the pivot shows for the slice.
Excluded iters are visible in the grid but immovable, so a target set
against the visible total previously overshot by their contribution.
Two latent bugs surfaced by the above, both pre-existing:
- A slice naming no filterable column reduced to TRUE and applied the
operation to the entire version. Now rejected on all three operations.
- Prorating across a pool that nets to ~zero multiplies each row's share
by an exploding factor, sending rows to extreme opposite values to hit
the target. Refused when the net falls below 1% of gross.
Tags and the bridge
- pf.log gains a nullable `tag`, written by a follow-up UPDATE rather
than through the generated SQL: those templates are stored per source
in pf.sql, so a {{tag}} token would strand any source that had not
re-run "Generate SQL".
- Tag is editable after the fact in the change log, with completion from
tags already used on the source. PATCH branches on whether a field was
sent, so a tag can be cleared as well as set.
- BridgeView renders the walk from baseline to current as a waterfall,
one step per tag, scoped to the selection, the pivot's filters, or the
whole version. Computed from the loaded Perspective table so the
figures always reconcile with what is on screen; overlapping slices are
deduped by pf_id to match the OR semantics operations use.
- Colour is a polarity job, so it uses the validated diverging pair
(blue/red, CVD dE 21.6) with neutral anchors, not categorical hues.
Every bar is directly labelled and a table view is available.
Panel
- Extracted to OperationPanel; the scale form is one continuous ledger:
baseline, each adjustment, current, then New value / Change / % change
as three interchangeable editable rows. Typing in any one derives the
others, which removes the target/delta/percent mode toggle entirely.
- Dockable bottom, right, or floating (drag to move, grip to resize), and
closable via header, Esc, or the toolbar. Placement persists.
- Controls no longer stretch to the dock width, and text contrast now
clears WCAG AA against white throughout.
Also: the status bar names the physical table writes land in, with live
row counts; and the pivot's expand depth is re-applied when the tab
regains focus, since Perspective rebuilds its view on redraw and a
ROLLUP view with no depth set renders fully expanded.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01W1TQiBYZbbWWkMNoCtUd8M
Ship rows pre-aggregated to the grain the pivot displays instead of raw
forecast rows. This is Path B from pf_perspective_options.md: it keeps
Perspective's native WASM engine — so expand/collapse/depth/sort/filter
all still work — and fixes load time by cutting rows, not transport.
Measured on pf.fc_osm_stack_20 at pending_rep x customer x smon:
534,902 -> 6,154 rows (~87x), pf_gkey unique across all 6,154, and both
measures reconcile exactly to the raw totals.
The grain is static: flagged once per source in Setup and baked into the
stored pf.sql templates, so load and operations agree by construction.
Sources with no flagged column keep the previous raw-row behaviour, so
this is backward compatible.
- pf.col_meta gains in_grain; grainOf() in lib/sql_generator.js is the
single definition of the grain and is reused by routes/log.js.
- New get_agg template + GET /api/versions/:id/agg, generated only when a
grain is defined. Regenerating drops templates no longer produced, so
clearing the grain falls back to /data.
- scale/recode/clone now aggregate their own new rows to grain before
returning. Because pf_logid is part of pf_gkey those keys are always
new, so table.update() appends and the view re-sums — the Excel
pivot-cache pattern, no bucket recomputation.
- Undo reports pf_gkeys (RETURNING cannot take DISTINCT, so the delete
feeds a CTE that reduces to distinct keys); the client removes those
index values and the view re-sums.
- pf_gkey is concat_ws(chr(31), COALESCE(col::text, chr(30)), ...).
The separator and NULL sentinel are load-bearing: plain concat_ws skips
NULLs, so ('a',NULL) and (NULL,'a') would collide and silently merge two
groups into one indexed row.
- Forecast.jsx reads col_meta first to pick /agg vs /data; the Arrow
streaming logic is extracted to fetchArrow() since both share it.
- Setup.jsx gains a grain checkbox and shows the resulting grain.
- 01_schema.sql: move the col_meta ALTERs after its CREATE TABLE — they
referenced the table before it existed on a fresh install.
All six generated statements verified to plan against the real forecast
table; the in_grain column has been added to the dev database.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
- col_meta gets dim_period_col field: maps a dimension column to its pf.dim_period counterpart (e.g. year -> cal_year, month -> cal_month)
- When the date column is is_key of a dim_group and any sibling dimension has dim_period_col set, baseline and reference SQL JOIN pf.dim_period on the shifted date instead of copying raw source values
- No dim_period config = identical SQL to before (fully backwards compatible)
- Setup UI: period col input in col_meta editor, enabled for dimension columns with a dim_group set
- Schema migration applied: dim_period_col text null on pf.col_meta
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- col_meta: add dim_group field to group related columns (dimension hierarchies, date-adjacent columns); is_key now enabled for date role to mark group parent
- sources.js: upsert includes dim_group
- Setup.jsx: group column in col_meta editor, key checkbox enabled for date role
- gen_dim_period.sql: create and populate pf.dim_period with calendar and fiscal period cuts (monthly grain, 2018-2035)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Forecast falls back to a saved per-source layout when no version-local
layout is cached, so new versions of a source open with a sensible pivot
without each user reconfiguring it.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
