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feat: split ClickHouse into dual configurable databases (ja4_logs / ja4_processing)

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Architecture:
- ja4_logs: raw log ingestion (http_logs_raw, http_logs, mv_http_logs)
- ja4_processing: analytics, aggregation, ML, dictionaries, audit

Configuration (env vars):
- CLICKHOUSE_DB_LOGS (default: ja4_logs)
- CLICKHOUSE_DB_PROCESSING (default: ja4_processing)

Changes:
- SQL migrations (10 files): all mabase_prod refs → ja4_logs or ja4_processing
  with correct cross-database references (MVs, views, dicts)
- deploy_schema.sh: substitutes DB names from env vars at deploy time
- Python shared settings: added CLICKHOUSE_DB_LOGS + CLICKHOUSE_DB_PROCESSING
- Dashboard routes (19 files): replaced ~80 hardcoded mabase_prod refs
  with settings.CLICKHOUSE_DB_LOGS / settings.CLICKHOUSE_DB_PROCESSING
- Bot-detector: DB → CLICKHOUSE_DB_PROCESSING, fetch_rules.py configurable
- Correlator: DSN example updated to ja4_logs
- Docker-compose + .env files: new env vars with defaults
- All documentation updated (14 markdown files)

All tests pass: sentinel 10/10, correlator 67.1%, bot-detector 11, dashboard 20, ja4_common 18

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
This commit is contained in:
toto
2026-04-07 19:10:35 +02:00
parent b6391afbeb
commit 9f3e0621e5
46 changed files with 638 additions and 549 deletions
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6
services/dashboard/.github/copilot-instructions.md vendored
View File

@ -4,7 +4,7 @@
This is a **SOC (Security Operations Center) dashboard** for visualizing bot detections from an upstream `bot_detector_ai` service. It is a **single-service, full-stack app**: the FastAPI backend serves the built React frontend as static files *and* exposes a REST API, all on port 8000. There is no separate frontend server in production and **no authentication**.
**Data source:** ClickHouse database (`mabase_prod`), primarily the `ml_detected_anomalies` table and the `view_dashboard_entities` view.
**Data source:** ClickHouse database (`ja4_processing`), primarily the `ml_detected_anomalies` table and the `view_dashboard_entities` view.
```
dashboard/
@ -95,7 +95,7 @@ curl "http://localhost:8000/api/detections?page=1&page_size=5" | jq '.items | le
|---|---|---|
| `CLICKHOUSE_HOST` | `clickhouse` | ClickHouse hostname |
| `CLICKHOUSE_PORT` | `8123` | ClickHouse HTTP port (set in code) |
| `CLICKHOUSE_DB` | `mabase_prod` | Database name |
| `CLICKHOUSE_DB` | `ja4_processing` | Database name |
| `CLICKHOUSE_USER` | `admin` | |
| `CLICKHOUSE_PASSWORD` | `` | |
| `API_HOST` | `0.0.0.0` | Uvicorn bind host |
@ -111,4 +111,4 @@ curl "http://localhost:8000/api/detections?page=1&page_size=5" | jq '.items | le
| `ml_detected_anomalies` | Primary source for detections, metrics, variability, analysis |
| `view_dashboard_entities` | User agents, client headers, paths, query params (entities routes) |
| `classifications` | SOC analyst classifications (created by `analysis.py`) |
| `mabase_prod.audit_logs` | Audit trail (optional — missing table is handled silently) |
| `ja4_processing.audit_logs` | Audit trail (optional — missing table is handled silently) |