Build custom queries, apply multi-dimensional filters, and run cross-dataset analysis
on your Juicebox people-intelligence data. Use this workflow when you need to go beyond
standard search — comparing candidate pools across roles, analyzing skill density by
geography, or identifying talent trends over time. This is the secondary workflow;
for basic search and enrichment, see juicebox-core-workflow-a.
const query = await client.analysis.query({
dataset: 'candidates',
filters: [
{ field: 'skills', operator: 'contains_any', value: ['TypeScript', 'Rust', 'Go'] },
{ field: 'experience_years', operator: 'gte', value: 5 },
{ field: 'location.country', operator: 'eq', value: 'US' },
],
sort: { field: 'relevance_score', order: 'desc' },
limit: 100,
});
console.log(`Found ${query.total} candidates matching filters`);
query.results.forEach(c =>
console.log(` ${c.name} — ${c.title} (${c.relevance_score}/100)`)
);
const comparison = await client.analysis.compare({
datasets: ['candidates_q1_2026', 'candidates_q4_2025'],
group_by: 'primary_skill',
metrics: ['count', 'avg_experience', 'avg_salary_estimate'],
});
comparison.groups.forEach(g =>
console.log(`${g.skill}: Q1=${g.datasets[0].count} vs Q4=${g.datasets[1].count} (${g.delta > 0 ? '+' : ''}${g.delta}%)`)
);
const density = await client.analysis.aggregate({
dataset: 'candidates',
group_by: 'location.metro_area',
metric: 'skill_density',
skill_filter: ['ML Engineering', 'Data Science'],
top_n: 10,
});
density.regions.forEach(r =>
console.log(`${r.metro}: ${r.candidate_count} candidates, density=${r.density_score}`)
);
const exportJob = await client.analysis.export({
query_id: query.id,
format: 'csv',
fields: ['name', 'email', 'primary_skill', 'experience_years', 'location'],
});
console.log(`Export ready: ${exportJob.download_url} (${exportJob.row_count} rows)`);
| Issue | Cause | Fix |
|---|---|---|
400 Invalid filter |
Unsupported operator for field type | Check field schema with client.schema.fields() |
404 Dataset not found |
Stale dataset ID or typo | List datasets with client.datasets.list() |
408 Query timeout |
Too many filters on large dataset | Add limit or narrow date range |
429 Rate limited |
Exceeded analysis quota | Implement backoff; check plan limits |
| Partial comparison data | One dataset has sparse coverage | Expected — use include_nulls: true for completeness |
A successful workflow produces filtered candidate lists with relevance scores, cross-dataset comparison tables showing talent market shifts, and regional skill-density rankings. Results can be exported as CSV for downstream reporting.
See juicebox-sdk-patterns for authentication and query builder helpers.