mobiniqow
a464e550c9
Full-stack implementation of the Cyberyan full-stack trial task. Backend (Django 5 / DRF) - Normalised PostgreSQL model (Profile, Company, School, Skill, Experience, Education) as the source of truth, with GIN + trigram indexes. - Self-healing CSV reader: the supplied export carries an injected watermark column, missing columns, rows split by broken quoting and a duplicated header. A signature-scored Needleman-Wunsch alignment reconstructs 284 clean records out of 336 physical rows; only 10 rows parse correctly without it. - Pluggable search layer (Strategy): Elasticsearch by default, PostgreSQL full-text search as a fallback selected by a single environment variable. - Keyword search with per-field boosts, fuzziness and phrase boosting, six faceted filters (OR within a filter, AND across filters), an experience range, four sort orders, highlighting and facet aggregations. - JWT auth with rotation + blacklist, three roles, PII masking, scoped rate limits, a single error envelope, OpenAPI schema and health probes. Frontend (React 18 / TypeScript / Vite) - Debounced search with type-ahead, faceted filter panel, result cards, profile drawer, URL-synced search state, automatic token refresh. - Highlight snippets rendered without dangerouslySetInnerHTML. Infrastructure - Hardened multi-stage images (non-root, read-only rootfs, healthchecks). - docker-compose for the full stack plus dev and production overlays. - ELK: structured JSON logs shipped asynchronously to Logstash, routed per event type, with a composable index template and a Kibana setup script. Quality - 61 backend tests (85% coverage), ruff/black/bandit/mypy, ESLint + tsc, GitHub Actions running lint, tests and both image builds. - Persian documentation: README plus architecture, search, API, data, security and operations guides. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01GnFo6fNaHCTAkJbfcdkqtG
47 lines
1.5 KiB
Python
47 lines
1.5 KiB
Python
"""
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Contact-detail masking.
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The dataset contains real personal data (e-mail addresses, mobile numbers,
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street addresses). Exposing it to every authenticated user would be both a
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privacy and a compliance problem, so contact details are masked unless the
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caller holds a role that is explicitly allowed to see them
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(:pyattr:`apps.accounts.models.User.can_view_pii`).
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Masking happens in the serialisation layer - the raw values never leave the
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database for an unprivileged caller, and the search index stores them in a
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non-searchable field so a keyword query can never be used to confirm an e-mail
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address by brute force.
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"""
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from __future__ import annotations
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import re
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_EMAIL_RE = re.compile(r"^([^@]+)@(.+)$")
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def mask_email(address: str) -> str:
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"""``joseph.holland@garver.com`` -> ``jo************@garver.com``."""
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match = _EMAIL_RE.match(address or "")
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if not match:
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return "***"
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local, domain = match.groups()
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keep = min(2, max(len(local) - 1, 0))
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return f"{local[:keep]}{'*' * max(len(local) - keep, 1)}@{domain}"
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def mask_phone(number: str) -> str:
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"""``+19402058928`` -> ``+1940*****28``."""
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digits = (number or "").strip()
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if len(digits) < 6:
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return "***"
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return f"{digits[:5]}{'*' * (len(digits) - 7)}{digits[-2:]}"
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def mask_emails(emails: list[dict[str, str]]) -> list[dict[str, str]]:
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return [{**item, "address": mask_email(item.get("address", ""))} for item in emails or []]
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def mask_phones(numbers: list[str]) -> list[str]:
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return [mask_phone(number) for number in numbers or []]
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