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
34 lines
1.1 KiB
Python
34 lines
1.1 KiB
Python
"""
|
|
Enable the PostgreSQL extensions the search fallback relies on.
|
|
|
|
``pg_trgm`` powers ``TrigramSimilarity`` (typo tolerant name matching) and the
|
|
GIN trigram index below makes ``ILIKE '%term%'`` on skill names fast enough for
|
|
the type-ahead endpoint.
|
|
"""
|
|
|
|
from django.contrib.postgres.operations import TrigramExtension, UnaccentExtension
|
|
from django.db import migrations
|
|
|
|
|
|
class Migration(migrations.Migration):
|
|
dependencies = [("profiles", "0001_initial")]
|
|
|
|
operations = [
|
|
TrigramExtension(),
|
|
UnaccentExtension(),
|
|
migrations.RunSQL(
|
|
sql=(
|
|
"CREATE INDEX IF NOT EXISTS profile_full_name_trgm "
|
|
"ON profiles_profile USING gin (full_name gin_trgm_ops);"
|
|
),
|
|
reverse_sql="DROP INDEX IF EXISTS profile_full_name_trgm;",
|
|
),
|
|
migrations.RunSQL(
|
|
sql=(
|
|
"CREATE INDEX IF NOT EXISTS skill_name_trgm "
|
|
"ON profiles_skill USING gin (name gin_trgm_ops);"
|
|
),
|
|
reverse_sql="DROP INDEX IF EXISTS skill_name_trgm;",
|
|
),
|
|
]
|