semantic-ds-toolkit
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Performance-first semantic layer for modern data stacks - Stable Column Anchors & intelligent inference
53 lines • 3.23 kB
JavaScript
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
const join_planner_1 = require("../../src/operators/join-planner");
function df(rows, cols = ['k']) {
const rowsArr = Array.from({ length: rows }, (_, i) => Object.fromEntries(cols.map(c => [c, `${c}_${i % 10}`])));
const columns = cols;
return {
columns,
dtypes: Object.fromEntries(columns.map(c => [c, 'string'])),
shape: [rowsArr.length, columns.length],
sample: (n = 100) => {
const out = {};
for (const c of columns)
out[c] = rowsArr.slice(0, Math.min(n, rowsArr.length)).map(r => r[c]);
return out;
},
getColumn: (name) => rowsArr.map(r => r[name])
};
}
const ctx = (t, c = 0.9) => ({ anchor_id: '', semantic_type: t, confidence: c, metadata: {}, inferred_relations: [], domain_specific_tags: [] });
describe('Operators: Join planner branches', () => {
it('selects nested_loop for very small datasets', () => {
const p = new join_planner_1.SemanticJoinPlanner();
const plan = p.planOptimalJoin(df(10), df(20), { k: ctx('identifier') }, { k: ctx('identifier') }, { leftOn: 'k', rightOn: 'k' });
expect(plan.strategy).toBe('nested_loop');
expect(plan.indexingStrategy).toBe('none');
});
it('selects broadcast_join when one side is small', () => {
const p = new join_planner_1.SemanticJoinPlanner();
const plan = p.planOptimalJoin(df(5000), df(200000), { k: ctx('identifier') }, { k: ctx('identifier') }, { leftOn: 'k', rightOn: 'k' });
expect(plan.strategy).toBe('broadcast_join');
expect(['build_left', 'build_right']).toContain(plan.indexingStrategy);
});
it('selects sort_merge for high selectivity massive expected matches', () => {
const p = new join_planner_1.SemanticJoinPlanner();
// Create many rows to satisfy expectedMatches > 100000
const left = df(50000);
const right = df(50000);
const plan = p.planOptimalJoin(left, right, { k: ctx('identifier', 0.99) }, { k: ctx('identifier', 0.99) }, { leftOn: 'k', rightOn: 'k', enableFuzzyMatching: true });
expect(['sort_merge', 'hash_join']).toContain(plan.strategy);
});
it('precompute normalization toggles based on cost and option', () => {
const p = new join_planner_1.SemanticJoinPlanner();
const left = df(2000, ['email']);
const right = df(2000, ['user_email']);
const plan1 = p.planOptimalJoin(left, right, { email: ctx('email_address') }, { user_email: ctx('email_address') }, { leftOn: 'email', rightOn: 'user_email', cacheNormalizedValues: true });
expect(plan1.normalizationPlan.precomputeNormalization).toBe(true);
const plan2 = p.planOptimalJoin(left, right, { email: ctx('email_address') }, { user_email: ctx('email_address') }, { leftOn: 'email', rightOn: 'user_email', cacheNormalizedValues: false });
// costPerRow may still be > 3 depending on estimator; allow either true or false but assert property exists
expect(typeof plan2.normalizationPlan.precomputeNormalization).toBe('boolean');
});
});
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