feat(gfql/cypher): Earley → LALR(1) parser (~70× faster parse) with machine-checked grammar invariants

CHANGELOG.md

@@ -20,6 +20,9 @@ This project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.htm
 - **GFQL Cypher `count(*)` short-circuit — O(1) instead of O(N) materialize**: a lone `RETURN count(*)` over a single node or edge pattern (`MATCH (n) RETURN count(*)`, `MATCH ()-[r]->() RETURN count(*)`) previously materialized the entire matched frame and ran a constant-key `group_by` just to count its rows. The lowering now emits a new `count_table` row op that reads the scanned table's height directly (or, when the pattern applies a filter, sums the boolean alias-mask column) in a single reduction — no full-frame copy, no group_by. Applies only to the provably-equivalent shapes: exactly one non-DISTINCT `count(*)`, no group keys / post-aggregate exprs / row-level `WHERE` / `UNWIND` / paging / multi-relationship binding, and either a pure node scan (`relationship_count == 0`) or a single relationship counted on its edge alias — every other shape (node-alias-over-relationship, multi-hop paths, `count(DISTINCT …)`, grouped counts) falls through to the general aggregate path unchanged. Engine-polymorphic across pandas/cuDF/polars/polars-gpu; differential parity verified on all four engines (`count_all_nodes`/`count_all_edges` cypher conformance cases + a `count_table` row-op subject case, chain and DAG surfaces). No change to any result value — only the execution path.
 - **GFQL `engine='polars'`/`'polars-gpu'` can now run off-engine analytic `call()`s (umap/hypergraph/compute_cugraph/…) — `call_mode='auto'` (default)**: a GFQL `call()` that invokes a Plottable-method analytic with **no native polars implementation** (and never will — `umap`, `hypergraph`, `compute_cugraph`, `compute_igraph`, `layout_*`, `collapse`, `get_topological_levels`, …) previously raised `NotImplementedError` under a polars engine, forcing users off polars for the whole pipeline. It now runs as a **mode-gated, warned modality switch**: `call_mode='auto'` (default) bridges the graph off-engine (`polars`→pandas, `polars-gpu`→cuDF **on-device**), runs the analytic, and coerces the result back to polars **losslessly via Arrow**, warning once per (process, function). `call_mode='strict'` keeps the honest `NotImplementedError` decline (for benchmark integrity / a hard memory ceiling). `polars-gpu` is **GPU-or-error**: it bridges to cuDF and declines rather than silently dropping a GPU analytic to host pandas. This is **deliberately narrower than CHAIN traversal/filter/row ops**, which stay parity-or-NIE (a bridge there would hide a missing impl and cheat a benchmark) — the split is mechanical (`is_row_pipeline_call`), and the chain and DAG surfaces bridge consistently. Configurable via `graphistry.compute.gfql.lazy.set_call_mode('auto'|'strict')` (Python) or `GFQL_POLARS_CALL_MODE` (env), resolving Python override > env > default('auto'), read live. Verified on dgx: `compute_cugraph` PageRank is byte-parity with the pandas oracle under both `polars` and `polars-gpu`.
 
+### Changed
+- **GFQL Cypher parser: Earley → LALR(1) (~70× faster parse, same AST)**: `parse_cypher` now uses a single LALR(1) parser (contextual lexer) instead of Earley (dynamic lexer) — ~0.25ms vs ~17ms per query on representative queries. The WHERE grammar was unified to one `where_clause: "WHERE" expr` rule (the former `where_predicates | expr` dual rule was a genuine reduce/reduce ambiguity that forced Earley), and the structured flat-AND predicate form is recovered by a post-parse lift that re-parses the WHERE body with a LALR sub-parser (`start="where_predicate_chain"`). The full WHERE language is preserved — OR/XOR/NOT, parentheses, arithmetic, IN, pattern predicates — nothing is restricted to a subset. The grammar was then **purified to (near-)unambiguous**: WHERE binds to its preceding clause *in the grammar* (bundled into `match_clause`; the WITH..WHERE attachment ambiguity is eliminated, not tie-broken), and name-rooted dot chains derive only via `qualified_name` (the `property_access` redundancy is gone) — dropping the redundant `label_predicate_expr` rule (`(n:Admin)` parses via `grouped_expr`), and excluding a top-level `IN` from list-literal elements (`[x IN xs ...` is comprehension syntax; allowing a bare `IN` list element made it overlap). Net: the LALR conflict profile goes from 8 shift/reduce to **ZERO conflicts** — the grammar is now **provably unambiguous LALR(1)** and builds under Lark's `strict=True` (a build-time proof, machine-checked in CI as zero conflicts, plus a strict-mode build test where the optional `interegular` dep is present). Every input has a single derivation. **Machine-checked invariants** (`test_grammar_invariants.py`): (1) **zero LALR conflicts** (dependency-free, always-on in CI) plus a `strict=True` build test (skipped where the optional `interegular` dep is absent) — a grammar edit that introduces any ambiguity fails CI; (2) semantic ambiguity is ZERO — every corpus query has exactly one Earley derivation and it transforms to a single AST, no exceptions; (3) a rule-coverage gate (`test_every_grammar_rule_is_exercised_by_the_corpus`) forces every new grammar rule through the invariants; (4) differential tests run the production pipeline under both parsers (byte-identical ASTs, identical rejections). **Full-repo differentials** (1,850+ scraped queries, LALR-vs-Earley and old-vs-new production): **zero AST divergences**, and a small set of deliberate language fixes — accept-by-accident shapes with ill-defined semantics, now honest syntax errors and pinned as tests (`DELIBERATE_LANGUAGE_FIXES`): `RETURN DISTINCT` with no items (Earley re-lexed DISTINCT as a variable name), double WHERE (the old positional attachment kept *both* predicates in different AST fields), double post-WITH WHERE, WHERE after UNWIND, WHERE before MATCH in a graph constructor, and the invalid "list of IN-booleans" (`[x IN xs, y]`; use `[(x IN xs), y]` for a genuine bool list). The lift stays an internal optimization: only a flat `AND` chain over present columns lifts to `filter_dict`; parens / OR / XOR / NOT and any absent-column case stay on `where_rows` (a correctness boundary, since `where_rows` treats an absent property as null while `filter_dict` would raise). Full cypher suite (1,681 tests) passes. Downstream execution (`filter_dict` vs `where_rows` routing) is unchanged. A pre-existing `<>`-over-null 3VL divergence between the two execution paths (surfaced by #1653's metamorphic test) is tracked separately in #1683.
+
 ### Fixed
 - **CI: `compute_networkx` HITS test was scipy-version flaky**: `test_compute_networkx_hits_outputs_hubs_and_authorities` used a pure directed 3-cycle (`a→b→c→a`), whose adjacency is a permutation matrix (all singular values equal). networkx HITS computes scores via scipy `svds(k=1)`, which then returns an arbitrary vector from that fully-degenerate singular space; when its components sum to ~0, HITS's `h /= h.sum()` normalization blows up to `inf`/`nan`. Which vector `svds` returns is scipy/LAPACK-version dependent, so the test passed on the pinned scipy but failed on the `nx-upper-scipy` CI profile's newer scipy — a latent flake, not a code regression. Switched to a graph with a well-defined hub/authority structure (`a→b`, `a→c`, `b→c`), whose dominant singular vector is unique (Perron-Frobenius) and therefore version-stable. Test-only change.
 - **GFQL `materialize_nodes()` on an edges-only graph under `engine='polars'`**: crashed with `AttributeError: 'Series' object has no attribute 'drop_duplicates'` — the node-id derivation used pandas-only `.drop_duplicates()` / `.reset_index()` / `.rename(columns=)` on a polars Series/DataFrame. Now polars-aware (`.unique(maintain_order=True)`, `.select().rename({...})`), matching the pandas oracle. Surfaced by any `let()` DAG on an edges-only graph under Polars (the DAG pre-materializes nodes). pandas/cuDF paths unchanged.

graphistry/compute/gfql/cypher/ast.py

@@ -192,11 +192,12 @@ class WhereClause:
     populated by the parser; consumers MUST handle all three:
 
     - **Structured path**: ``predicates`` populated, ``expr_tree is None``.
-      ``predicates`` carries either ``WherePredicate`` entries (pure AND of
-      comparable / has-labels predicates routed via the ``where_predicates``
-      grammar rule, or AND-joined bare label predicates lifted by
-      ``generic_where_clause`` via label narrowing) or a single
-      ``WherePatternPredicate`` (pattern-only WHERE: ``WHERE (n)-[]->(m)``).
+      ``predicates`` carries either ``WherePredicate`` entries (a flat top-level
+      AND of simple predicates -- cmp / IS NULL / CONTAINS / STARTS/ENDS WITH /
+      has-labels -- lifted post-parse from the generic ``expr_tree`` by
+      ``generic_where_clause`` via ``_lift_label_only_and_spine`` /
+      ``_lift_and_spine_predicates``) or a single ``WherePatternPredicate``
+      (pattern-only WHERE: ``WHERE (n)-[]->(m)``).
     - **Tree path**: ``predicates == ()``, ``expr_tree`` populated.  Fires
       when ``generic_where_clause`` cannot lift to structured predicates
       (OR / XOR / NOT / parenthesized boolean / non-label atoms); consumers