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
41 lines
1.2 KiB
Python
41 lines
1.2 KiB
Python
"""
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Gunicorn configuration.
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Sized for a container: worker count follows the CPU quota, requests are
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recycled to bound memory growth, and access logs are emitted as JSON so they
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land in Elasticsearch in the same shape as the application logs.
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"""
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import multiprocessing
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import os
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bind = os.getenv("GUNICORN_BIND", "0.0.0.0:8000")
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workers = int(os.getenv("GUNICORN_WORKERS", str(min(multiprocessing.cpu_count() * 2 + 1, 8))))
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threads = int(os.getenv("GUNICORN_THREADS", "2"))
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worker_class = "gthread"
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timeout = int(os.getenv("GUNICORN_TIMEOUT", "45"))
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graceful_timeout = 30
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keepalive = 5
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# Recycle workers to keep any slow leak from growing without bound.
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max_requests = 1000
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max_requests_jitter = 100
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preload_app = False
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forwarded_allow_ips = os.getenv("GUNICORN_FORWARDED_ALLOW_IPS", "*")
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# Refuse absurd request lines/headers early - cheap protection against a class
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# of denial-of-service probes.
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limit_request_line = 8190
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limit_request_fields = 100
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limit_request_field_size = 8190
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accesslog = "-"
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errorlog = "-"
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loglevel = os.getenv("GUNICORN_LOG_LEVEL", "info")
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access_log_format = (
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'{"level":"INFO","logger":"gunicorn.access","message":"%(r)s",'
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'"http_status":%(s)s,"duration_ms":%(D)s,"client_ip":"%(h)s",'
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'"request_id":"%({x-request-id}i)s"}'
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)
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