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
75 lines
2.3 KiB
Python
75 lines
2.3 KiB
Python
"""Operational endpoints used by Docker/Kubernetes probes and by humans."""
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from __future__ import annotations
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from typing import Any
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from django.conf import settings
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from django.db import connection
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from drf_spectacular.utils import extend_schema
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from rest_framework import status
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from rest_framework.permissions import AllowAny
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from rest_framework.request import Request
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from rest_framework.response import Response
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from rest_framework.views import APIView
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from apps.search.backends import get_search_backend
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class LivenessView(APIView):
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"""Cheap check: the process is up and can answer HTTP."""
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permission_classes = [AllowAny]
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authentication_classes: list = []
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throttle_classes: list = []
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@extend_schema(tags=["health"], responses={200: dict})
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def get(self, request: Request) -> Response: # noqa: ARG002
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return Response({"status": "ok", "service": settings.SERVICE_NAME})
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class ReadinessView(APIView):
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"""
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Deep check: verifies every dependency the API needs to serve traffic.
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Returns 503 when any dependency is down so an orchestrator stops routing
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traffic to this instance.
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"""
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permission_classes = [AllowAny]
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authentication_classes: list = []
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throttle_classes: list = []
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@extend_schema(tags=["health"], responses={200: dict, 503: dict})
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def get(self, request: Request) -> Response: # noqa: ARG002
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checks: dict[str, Any] = {
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"database": _check(_database),
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"search": _check(_search),
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}
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healthy = all(c["healthy"] for c in checks.values())
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return Response(
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{"status": "ok" if healthy else "degraded", "checks": checks},
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status=status.HTTP_200_OK if healthy else status.HTTP_503_SERVICE_UNAVAILABLE,
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)
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def _check(fn) -> dict[str, Any]: # noqa: ANN001
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try:
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detail = fn()
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except Exception as exc: # noqa: BLE001 - health checks must never raise
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return {"healthy": False, "detail": str(exc)[:200]}
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return {"healthy": True, "detail": detail}
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def _database() -> str:
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with connection.cursor() as cursor:
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cursor.execute("SELECT 1")
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cursor.fetchone()
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return "reachable"
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def _search() -> str:
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backend = get_search_backend()
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backend.ping()
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return f"{backend.name} reachable"
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