From 600c165b5585faa14b99bd3447d7d7bff2d0e45d Mon Sep 17 00:00:00 2001 From: TerrifiedBug Date: Sun, 26 Apr 2026 16:46:25 +0100 Subject: [PATCH] chore: migrate to vectorflow.sh urls and self-hosted docs MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - pulse.terrifiedbug.com → pulse.vectorflow.sh (telemetry) - terrifiedbug.gitbook.io/vectorflow → vectorflow.sh/docs (docs links) - demo.terrifiedbug.com → demo.vectorflow.sh (README hero) - replace docs/public/ with stub README pointing at the vectorflow.sh repo (canonical docs source is now github.com/TerrifiedBug/vectorflow.sh) - update telemetry-sender test fixture for the new URL --- README.md | 6 +- docs/public/.gitbook.yaml | 5 - docs/public/.gitbook/gitbook-skill.md | 64 - docs/public/README.md | 48 +- docs/public/SUMMARY.md | 50 - docs/public/getting-started/deploy-agents.md | 208 --- docs/public/getting-started/deploy-server.md | 363 ----- docs/public/getting-started/first-pipeline.md | 150 -- docs/public/getting-started/quick-start.md | 97 -- docs/public/operations/architecture.md | 186 --- docs/public/operations/authentication.md | 291 ---- docs/public/operations/backup-restore.md | 464 ------ docs/public/operations/configuration.md | 190 --- docs/public/operations/gitops.md | 158 -- docs/public/operations/outbound-webhooks.md | 154 -- docs/public/operations/scim.md | 215 --- docs/public/operations/security.md | 186 --- docs/public/operations/service-accounts.md | 142 -- docs/public/operations/telemetry.md | 53 - 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Stop hand-editing YAML. Build observability pipelines with drag-and-drop
and deploy them across your fleet from a single dashboard. -[Documentation](https://terrifiedbug.gitbook.io/vectorflow) · [Quick start](#quick-start) · [Deployment](#deployment) · [Features](#features) · [Configuration](#configuration) · [Development](#development) +[Documentation](https://vectorflow.sh/docs) · [Quick start](#quick-start) · [Deployment](#deployment) · [Features](#features) · [Configuration](#configuration) · [Development](#development) -> 🌐 **[Try the live demo →](https://demo.terrifiedbug.com)** +> 🌐 **[Try the live demo →](https://demo.vectorflow.sh)** diff --git a/docs/public/.gitbook.yaml b/docs/public/.gitbook.yaml deleted file mode 100644 index 6acdd6ab..00000000 --- a/docs/public/.gitbook.yaml +++ /dev/null @@ -1,5 +0,0 @@ -root: ./ - -structure: - readme: README.md - summary: SUMMARY.md diff --git a/docs/public/.gitbook/gitbook-skill.md b/docs/public/.gitbook/gitbook-skill.md deleted file mode 100644 index ddc02e01..00000000 --- a/docs/public/.gitbook/gitbook-skill.md +++ /dev/null @@ -1,64 +0,0 @@ -# GitBook Documentation Editing Skill - -This guide enables AI tools to write GitBook-compatible markdown. - -## Key Formatting Elements - -GitBook extends standard markdown with custom blocks: - -### Tabs -{% tabs %} -{% tab title="Docker" %} -Content for Docker tab -{% endtab %} -{% tab title="Standalone" %} -Content for Standalone tab -{% endtab %} -{% endtabs %} - -### Hints / Callouts -{% hint style="info" %} -Informational callout -{% endhint %} - -{% hint style="warning" %} -Warning callout -{% endhint %} - -{% hint style="danger" %} -Danger callout -{% endhint %} - -{% hint style="success" %} -Success callout -{% endhint %} - -### Steppers -{% stepper %} -{% step %} -### Step Title -Step content -{% endstep %} -{% endstepper %} - -### Expandable Content -
-Click to expand -Hidden content here -
- -## Configuration - -- **.gitbook.yaml** -- Space configuration (root directory, readme/summary paths) -- **SUMMARY.md** -- Table of contents defining sidebar navigation -- **/.gitbook/vars.yaml** -- Space-level reusable variables - -## Writing Guidelines - -- Use hierarchical headings (H1-H3) -- Keep paragraphs short, use bullet points -- Include code snippets and practical examples -- Minimize jargon -- Use tabs for platform-specific content (Docker vs Standalone, Linux vs macOS) -- Use hints for important callouts -- Use steppers for sequential procedures diff --git a/docs/public/README.md b/docs/public/README.md index 654c6eb3..b3c08cd3 100644 --- a/docs/public/README.md +++ b/docs/public/README.md @@ -1,47 +1,11 @@ -# Introduction +# Public docs moved -VectorFlow is a visual pipeline management platform for [Vector](https://vector.dev), the high-performance observability data pipeline. It gives teams a web-based UI for designing, deploying, and monitoring Vector pipelines across an entire fleet of nodes -- no hand-edited TOML required. +The VectorFlow user-facing documentation is now at: -![VectorFlow Dashboard](screenshots/dashboard.png) +**** -## Why VectorFlow? +The Fumadocs source for that site lives in the standalone repo: -Observability pipelines are critical infrastructure, yet managing them usually means juggling config files across dozens of servers. VectorFlow replaces that workflow with a centralized control plane: +**** -- **Visual Pipeline Editor** -- Drag-and-drop sources, transforms, and sinks onto a canvas. The editor generates valid Vector configuration automatically. -- **Fleet Management** -- Enroll agents on every node and push pipeline updates from a single dashboard. See which agents are online, what they are running, and roll back instantly. -- **Multi-Environment Support** -- Maintain separate development, staging, and production environments. Promote pipelines through your deployment lifecycle with confidence. -- **VRL Snippet Testing** -- Write and test Vector Remap Language transforms interactively before they reach production. -- **Real-Time Metrics** -- Monitor throughput, error rates, and pipeline health at a glance. -- **Alerting** -- Define alert rules so you know the moment a pipeline degrades. - -## Technology - -| Layer | Technology | -|-------|------------| -| Frontend | Next.js 16, React 19, TypeScript, Tailwind CSS | -| Flow Editor | React Flow (@xyflow/react) | -| Code Editor | Monaco Editor with VRL syntax | -| API | tRPC (type-safe RPC) | -| Authentication | NextAuth (credentials + OIDC) | -| Database | PostgreSQL, Prisma ORM | -| Agent | Go (zero external dependencies) | -| Data Engine | Vector (vector.dev) | - -{% hint style="info" %} -**Get started in 5 minutes.** Follow the [Quick Start](getting-started/quick-start.md) guide to spin up VectorFlow with Docker and build your first pipeline. -{% endhint %} - -## Quick Links - -| | | -|---|---| -| **Getting Started** | [Quick Start](getting-started/quick-start.md) -- Install and run VectorFlow | -| **Deploy** | [Server](getting-started/deploy-server.md) -- Set up the VectorFlow server | -| | [Agents](getting-started/deploy-agents.md) -- Enroll agents on your nodes | -| **Learn** | [Pipeline Editor](user-guide/pipeline-editor.md) -- Build pipelines visually | -| | [Fleet Management](user-guide/fleet.md) -- Manage your agent fleet | -| **Operate** | [Architecture](operations/architecture.md) -- Understand how it all fits together | -| | [Configuration](operations/configuration.md) -- Environment variables and settings | -| **Reference** | [API](reference/api.md) -- Full API documentation | -| | [Pipeline YAML](reference/pipeline-yaml.md) -- Pipeline configuration format | +To propose a docs change, open a PR against `vectorflow.sh` instead of editing this directory. diff --git a/docs/public/SUMMARY.md b/docs/public/SUMMARY.md deleted file mode 100644 index 579591b6..00000000 --- a/docs/public/SUMMARY.md +++ /dev/null @@ -1,50 +0,0 @@ -# Summary - -* [Introduction](README.md) - -## Getting Started - -* [Quick Start](getting-started/quick-start.md) -* [Deploy the Server](getting-started/deploy-server.md) -* [Deploy Agents](getting-started/deploy-agents.md) -* [Your First Pipeline](getting-started/first-pipeline.md) - -## User Guide - -* [Dashboard](user-guide/dashboard.md) -* [Pipelines](user-guide/pipelines.md) -* [Pipeline Editor](user-guide/pipeline-editor.md) -* [VRL Snippets](user-guide/vrl-snippets.md) -* [Environments](user-guide/environments.md) -* [Fleet Management](user-guide/fleet.md) -* [Alerts](user-guide/alerts.md) -* [Templates](user-guide/templates.md) -* [Shared Components](user-guide/shared-components.md) -* [AI Suggestions](user-guide/ai-suggestions.md) -* [Cost Optimization](user-guide/cost-optimization.md) -* [Anomaly Detection](user-guide/anomaly-detection.md) -* [Pipeline Dependencies](user-guide/pipeline-dependencies.md) -* [Migration Toolkit](user-guide/migration-toolkit.md) - -## Operations - -* [Architecture](operations/architecture.md) -* [Configuration](operations/configuration.md) -* [Authentication](operations/authentication.md) -* [SCIM Provisioning](operations/scim.md) -* [Service Accounts](operations/service-accounts.md) -* [Backup & Restore](operations/backup-restore.md) -* [GitOps](operations/gitops.md) -* [Outbound Webhooks](operations/outbound-webhooks.md) -* [Security](operations/security.md) -* [Telemetry](operations/telemetry.md) -* [Upgrading](operations/upgrading.md) - -## Reference - -* [API Reference](reference/api.md) -* [Agent Reference](reference/agent.md) -* [Agent Architecture](reference/agent-architecture.md) -* [Agent Troubleshooting](reference/agent-troubleshooting.md) -* [Database Schema](reference/database.md) -* [Pipeline YAML](reference/pipeline-yaml.md) diff --git a/docs/public/getting-started/deploy-agents.md b/docs/public/getting-started/deploy-agents.md deleted file mode 100644 index 12ca8706..00000000 --- a/docs/public/getting-started/deploy-agents.md +++ /dev/null @@ -1,208 +0,0 @@ -# Deploy Agents - -The VectorFlow agent (`vf-agent`) is a lightweight Go binary that runs on each node in your fleet. It manages Vector processes, pulls pipeline configurations from the server, and reports health via heartbeats. - -{% hint style="info" %} -**Pull-based architecture** -- agents poll the server for config updates. No inbound ports are required on your fleet nodes. The agent initiates all connections, making it firewall-friendly and easy to deploy behind NAT. -{% endhint %} - -## How it works - -1. The agent starts and **enrolls** with the VectorFlow server using a one-time enrollment token -2. After enrollment, the server issues a persistent **node token** stored on disk -3. The agent **polls** the server every 15 seconds (configurable) for pipeline configuration changes -4. When a new config is received, the agent writes it to disk and starts or restarts the Vector process -5. The agent sends **heartbeats** with host metrics (CPU, memory, disk, network) so the server can track fleet health - -Each pipeline runs as an isolated Vector process -- a crash in one pipeline does not affect others. - -## Enrollment flow - -{% stepper %} -{% step %} -### Generate an enrollment token - -In the VectorFlow UI, navigate to the **Fleet** page. Select the environment you want to enroll the agent into, then click **Add Node**. - -VectorFlow generates a one-time enrollment token. Copy it -- you will need it in the next step. - -{% hint style="warning" %} -Enrollment tokens are single-use. Each agent needs its own token. Generate a new one for each node you want to enroll. -{% endhint %} -{% endstep %} - -{% step %} -### Deploy the agent - -Choose one of the deployment methods below and start the agent with the enrollment token. -{% endstep %} - -{% step %} -### Verify enrollment - -Once the agent starts, it enrolls with the server and appears on the **Fleet** page with an **Online** status. You can now deploy pipelines to this node. - -If the agent does not appear, check its logs for enrollment errors (`docker compose logs -f` or `journalctl -u vf-agent -f`). -{% endstep %} -{% endstepper %} - -## Deployment methods - -{% tabs %} -{% tab title="Docker" %} -The simplest approach -- ideal for containerized environments. - -```bash -mkdir -p vectorflow-agent && cd vectorflow-agent - -curl -sSfL -o docker-compose.yml \ - https://raw.githubusercontent.com/TerrifiedBug/vectorflow/main/docker/agent/docker-compose.yml -``` - -Create a `.env` file with your server URL and enrollment token: - -```bash -cat > .env << 'EOF' -VF_URL=https://your-vectorflow-server:3000 -VF_TOKEN=paste-enrollment-token-here -EOF -``` - -Start the agent: - -```bash -docker compose up -d -``` - -The Docker image bundles Vector, so no additional dependencies are needed. Two named volumes persist agent state and Vector data across restarts: - -| Volume | Mount point | Contents | -|--------|-------------|----------| -| `vectorflow-agent-data` | `/var/lib/vf-agent` | Node token, pipeline configs | -| `vectorflow-vector-data` | `/var/lib/vector` | Vector checkpoints and buffer data | - -The agent container uses `network_mode: host` so Vector can bind to local ports (e.g., for syslog or socket sources). - -{% hint style="warning" %} -After the first successful enrollment, the agent persists a node token to disk. The `VF_TOKEN` enrollment token is no longer needed. You can remove it from your `.env` file, but leaving it has no effect. -{% endhint %} -{% endtab %} - -{% tab title="Install script" %} -The install script downloads the agent binary, installs Vector if needed, and configures a systemd service -- all in one command. - -```bash -curl -sSfL https://raw.githubusercontent.com/TerrifiedBug/vectorflow/main/agent/install.sh | \ - sudo bash -s -- --url https://vectorflow.example.com --token -``` - -**Managing the service:** - -```bash -systemctl status vf-agent # Check status -journalctl -u vf-agent -f # Follow logs -sudo systemctl restart vf-agent # Restart -``` - -**Upgrading:** - -```bash -# Upgrade to the latest release -curl -sSfL https://raw.githubusercontent.com/TerrifiedBug/vectorflow/main/agent/install.sh | sudo bash - -# Install a specific version -curl -sSfL https://raw.githubusercontent.com/TerrifiedBug/vectorflow/main/agent/install.sh | \ - sudo bash -s -- --version v0.3.0 -``` - -Existing configuration at `/etc/vectorflow/agent.env` is preserved during upgrades. - -**Uninstalling:** - -```bash -sudo systemctl stop vf-agent -sudo systemctl disable vf-agent -sudo rm /etc/systemd/system/vf-agent.service -sudo systemctl daemon-reload -sudo rm /usr/local/bin/vf-agent -sudo rm -rf /var/lib/vf-agent /etc/vectorflow -``` -{% endtab %} - -{% tab title="Standalone binary" %} -Download the binary from the [Releases](https://github.com/TerrifiedBug/vectorflow/releases) page and run it directly. Useful for testing or environments without systemd. - -```bash -# Download the latest release for your platform -curl -sSfL -o vf-agent \ - https://github.com/TerrifiedBug/vectorflow/releases/latest/download/vf-agent-linux-amd64 -chmod +x vf-agent -``` - -Run with environment variables: - -```bash -VF_URL=https://your-vectorflow-server:3000 \ -VF_TOKEN=paste-enrollment-token-here \ -./vf-agent -``` - -Or with a manual systemd unit: - -```ini -# /etc/systemd/system/vf-agent.service -[Unit] -Description=VectorFlow Agent -After=network-online.target -Wants=network-online.target - -[Service] -Type=simple -EnvironmentFile=/etc/vectorflow/agent.env -ExecStart=/usr/local/bin/vf-agent -Restart=on-failure -RestartSec=5 - -# Security hardening -NoNewPrivileges=true -ProtectSystem=strict -ReadWritePaths=/var/lib/vf-agent - -[Install] -WantedBy=multi-user.target -``` - -Make sure Vector is installed and available in `PATH`, or set the `VF_VECTOR_BIN` variable to point to the binary. -{% endtab %} -{% endtabs %} - -## Environment variables - -| Variable | Required | Default | Description | -|----------|----------|---------|-------------| -| `VF_URL` | Yes | -- | VectorFlow server URL (e.g., `https://vectorflow.example.com:3000`) | -| `VF_TOKEN` | First run | -- | One-time enrollment token from the UI. Not needed after successful enrollment. | -| `VF_DATA_DIR` | No | `/var/lib/vf-agent` | Directory for agent state (node token, pipeline configs). Mapped to a Docker volume by default. | -| `VF_VECTOR_BIN` | No | `vector` | Path to the Vector binary. Set automatically in the Docker image. | -| `VF_POLL_INTERVAL` | No | `15s` | How often the agent polls for config changes. Uses Go duration format (`15s`, `1m`, `30s`). | -| `VF_LOG_LEVEL` | No | `info` | Log verbosity: `debug`, `info`, `warn`, or `error`. | - -## Troubleshooting - -**Agent does not appear in Fleet** - -- Verify `VF_URL` is correct and reachable from the agent host -- Check that the enrollment token has not already been used -- Look at agent logs for `enrollment failed` errors - -**Agent shows as "Unhealthy"** - -- The server marks a node unhealthy after 3 missed heartbeats (default: 45 seconds) -- Check network connectivity between the agent and server -- Verify the agent process is running (`systemctl status vf-agent` or `docker ps`) - -**Pipeline not starting on agent** - -- Confirm the pipeline is deployed to the correct environment -- Check agent logs for Vector process errors -- Verify Vector is installed and accessible at the path in `VF_VECTOR_BIN` diff --git a/docs/public/getting-started/deploy-server.md b/docs/public/getting-started/deploy-server.md deleted file mode 100644 index 9e62cca5..00000000 --- a/docs/public/getting-started/deploy-server.md +++ /dev/null @@ -1,363 +0,0 @@ -# Deploy the Server - -The VectorFlow server is a Next.js application backed by PostgreSQL. This page covers deployment options, environment variables, persistent storage, and production hardening. - -{% tabs %} -{% tab title="Docker (recommended)" %} -## Docker Compose - -The quickest path to production. The provided `docker-compose.yml` starts both the VectorFlow server and PostgreSQL. - -### 1. Download the Compose file - -```bash -mkdir -p vectorflow && cd vectorflow - -curl -sSfL -o docker-compose.yml \ - https://raw.githubusercontent.com/TerrifiedBug/vectorflow/main/docker/server/docker-compose.yml -``` - -### 2. Create your `.env` file - -```bash -cat > .env << 'EOF' -POSTGRES_PASSWORD= -NEXTAUTH_SECRET= -# NEXTAUTH_URL=https://vectorflow.example.com -EOF -``` - -Generate secrets with `openssl rand -base64 32`. - -### 3. Start the stack - -```bash -docker compose up -d -``` - -The entrypoint automatically runs database migrations on every start, so upgrades are handled by pulling a new image and restarting. - -### Compose file breakdown - -```yaml -services: - postgres: - image: postgres:17-alpine - environment: - POSTGRES_DB: vectorflow - POSTGRES_USER: vectorflow - POSTGRES_PASSWORD: ${POSTGRES_PASSWORD} - volumes: - - pgdata:/var/lib/postgresql/data - healthcheck: - test: ["CMD-SHELL", "pg_isready -U vectorflow"] - restart: unless-stopped - - vectorflow: - image: ghcr.io/terrifiedbug/vectorflow-server:${VF_VERSION:-latest} - depends_on: - postgres: - condition: service_healthy - ports: - - "3000:3000" - environment: - DATABASE_URL: postgresql://vectorflow:${POSTGRES_PASSWORD}@postgres:5432/vectorflow - NEXTAUTH_SECRET: ${NEXTAUTH_SECRET} - NEXTAUTH_URL: ${NEXTAUTH_URL} - volumes: - - vfdata:/app/.vectorflow - - backups:/backups - restart: unless-stopped - -volumes: - pgdata: - vfdata: - backups: -``` - -### Persistent volumes - -| Volume | Mount point | Contents | -|--------|-------------|----------| -| `vectorflow-pgdata` | `/var/lib/postgresql/data` | PostgreSQL database files | -| `vectorflow-data` | `/app/.vectorflow` | Application state, system Vector config | -| `vectorflow-backups` | `/backups` | Database backup snapshots | - -{% hint style="warning" %} -Never delete the `vectorflow-pgdata` volume without a backup. All pipeline definitions, environments, users, and audit history live in PostgreSQL. -{% endhint %} - -### Pinning a version - -By default the Compose file pulls `latest`. To pin a specific release: - -```bash -VF_VERSION=v0.3.0 docker compose up -d -``` - -Or set `VF_VERSION` in your `.env` file. - -### Networking - -The server listens on port 3000. The PostgreSQL port is not exposed by default -- only the VectorFlow container can reach it over the internal Docker network. If you need direct database access for debugging: - -```yaml -# Uncomment in docker-compose.yml -ports: - - "127.0.0.1:5432:5432" -``` -{% endtab %} - -{% tab title="Standalone" %} -## Standalone deployment - -Run VectorFlow directly on a Linux host without Docker. This approach gives you full control over the process manager, database, and networking. - -### Prerequisites - -- **Node.js 22+** and **pnpm** -- **PostgreSQL 17** (running and accessible) -- **Vector 0.44.0+** binary (for pipeline validation and VRL testing) - -### 1. Download the release - -Download the latest release archive from the [Releases](https://github.com/TerrifiedBug/vectorflow/releases) page and extract it. - -```bash -curl -sSfL -o vectorflow.tar.gz \ - https://github.com/TerrifiedBug/vectorflow/releases/latest/download/vectorflow-server.tar.gz -mkdir -p /opt/vectorflow && tar xzf vectorflow.tar.gz -C /opt/vectorflow -``` - -### 2. Set up PostgreSQL - -Create a database and user: - -```sql -CREATE USER vectorflow WITH PASSWORD 'your-strong-password'; -CREATE DATABASE vectorflow OWNER vectorflow; -``` - -### 3. Configure environment - -Create an environment file at `/etc/vectorflow/server.env`: - -```bash -DATABASE_URL=postgresql://vectorflow:your-strong-password@localhost:5432/vectorflow -NEXTAUTH_SECRET=generate-a-random-32-char-string -NEXTAUTH_URL=https://vectorflow.example.com -PORT=3000 -NODE_ENV=production -``` - -### 4. Run database migrations - -```bash -cd /opt/vectorflow -npx prisma migrate deploy -``` - -### 5. Start the server - -```bash -node server.js -``` - -### Systemd service - -For production, run VectorFlow as a systemd service: - -```ini -# /etc/systemd/system/vectorflow.service -[Unit] -Description=VectorFlow Server -After=network-online.target postgresql.service -Wants=network-online.target - -[Service] -Type=simple -User=vectorflow -Group=vectorflow -WorkingDirectory=/opt/vectorflow -EnvironmentFile=/etc/vectorflow/server.env -ExecStart=/usr/bin/node server.js -Restart=on-failure -RestartSec=5 - -# Security hardening -NoNewPrivileges=true -ProtectSystem=strict -ProtectHome=true -ReadWritePaths=/opt/vectorflow/.vectorflow /backups - -[Install] -WantedBy=multi-user.target -``` - -Enable and start: - -```bash -sudo systemctl daemon-reload -sudo systemctl enable --now vectorflow -``` -{% endtab %} -{% endtabs %} - -## Environment variables - -{% hint style="warning" %} -Always use strong, random values for `NEXTAUTH_SECRET` and `POSTGRES_PASSWORD`. These protect session data, encrypted secrets (TOTP, certificates), and your database. -{% endhint %} - -| Variable | Required | Default | Description | -|----------|----------|---------|-------------| -| `DATABASE_URL` | Yes | -- | PostgreSQL connection string (e.g., `postgresql://user:pass@host:5432/vectorflow`) | -| `NEXTAUTH_SECRET` | Yes | -- | Session encryption key. Must be 32+ characters. Generate with `openssl rand -base64 32` | -| `NEXTAUTH_URL` | No | -- | Canonical server URL (e.g., `https://vectorflow.example.com`). When unset, inferred from the `Host` header | -| `REDIS_URL` | No | -- | Redis connection string for HA mode (e.g., `redis://redis:6379`). Enables leader election, cross-instance SSE broadcast, and metric distribution. When unset, VectorFlow runs as a single instance with no behavioral change | -| `PORT` | No | `3000` | HTTP listen port | -| `NODE_ENV` | No | `production` | Set automatically in Docker. Use `production` for standalone deployments | - -When using the Docker Compose setup, the following variables go in your `.env` file and are interpolated into the Compose file: - -| Variable | Required | Default | Description | -|----------|----------|---------|-------------| -| `POSTGRES_PASSWORD` | Yes | -- | Password for the PostgreSQL `vectorflow` user | -| `VF_VERSION` | No | `latest` | Docker image tag to pull | - -## Production considerations - -### Reverse proxy - -In production, place VectorFlow behind a reverse proxy for TLS termination. - -
-Nginx example - -```nginx -server { - listen 443 ssl http2; - server_name vectorflow.example.com; - - ssl_certificate /etc/ssl/certs/vectorflow.crt; - ssl_certificate_key /etc/ssl/private/vectorflow.key; - - location / { - proxy_pass http://127.0.0.1:3000; - proxy_set_header Host $host; - proxy_set_header X-Real-IP $remote_addr; - proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; - proxy_set_header X-Forwarded-Proto $scheme; - - # SSE streaming support (agent push channel) - proxy_http_version 1.1; - proxy_buffering off; - proxy_cache off; - } -} -``` -
- -
-Caddy example - -``` -vectorflow.example.com { - reverse_proxy localhost:3000 -} -``` - -Caddy handles TLS certificates automatically via Let's Encrypt. -
- -When using a reverse proxy, set `NEXTAUTH_URL` to your public URL (e.g., `https://vectorflow.example.com`). - -### TLS - -- **Agents communicate with the server over HTTPS.** Always terminate TLS in production. -- If you cannot use a reverse proxy, consider a TLS-terminating load balancer. - -### Database tuning - -For deployments managing more than 50 agents, consider tuning PostgreSQL: - -- Increase `shared_buffers` to 25% of available RAM -- Set `work_mem` to 64 MB -- Enable `pg_stat_statements` for query monitoring -- Schedule regular `VACUUM ANALYZE` runs - -### Resource requirements - -| Scale | CPU | RAM | Disk | -|-------|-----|-----|------| -| Small (1-10 agents) | 1 core | 1 GB | 10 GB | -| Medium (10-50 agents) | 2 cores | 2 GB | 25 GB | -| Large (50+ agents) | 4 cores | 4 GB | 50 GB+ | - -These are minimums. The server is lightweight -- most resources go to PostgreSQL. - -## High Availability - -VectorFlow supports running two or more instances behind a load balancer for high availability. When `REDIS_URL` is set, instances coordinate through Redis to provide: - -- **Leader election** — only one instance runs singleton services (backup scheduler, alert evaluator, metric aggregation), with automatic failover if the leader goes down -- **Cross-instance SSE broadcast** — all SSE connections receive all fleet events regardless of which instance the client is connected to -- **Metric distribution** — heartbeat metrics processed by any instance are visible on all instances -- **Agent push relay** — configuration pushes reach agents connected to any instance - -When `REDIS_URL` is not set, VectorFlow runs in single-instance mode with no behavioral change. - -### Prerequisites - -- **Redis 7+** (included in the HA Compose file) -- All instances must share the same `DATABASE_URL`, `NEXTAUTH_SECRET`, and `REDIS_URL` - -### Quick start - -Download the HA Compose file and nginx config: - -```bash -mkdir -p vectorflow && cd vectorflow - -curl -sSfL -o docker-compose.ha.yml \ - https://raw.githubusercontent.com/TerrifiedBug/vectorflow/main/docker/server/docker-compose.ha.yml - -curl -sSfL -o nginx-ha.conf \ - https://raw.githubusercontent.com/TerrifiedBug/vectorflow/main/docker/server/nginx-ha.conf -``` - -Create your `.env` file: - -```bash -cat > .env << 'EOF' -POSTGRES_PASSWORD= -NEXTAUTH_SECRET= -NEXTAUTH_URL=http://localhost:3000 -EOF -``` - -{% hint style="info" %} -`REDIS_URL` is set automatically inside the Compose file — you do not need to add it to `.env`. -{% endhint %} - -Start the stack: - -```bash -docker compose -f docker-compose.ha.yml up -d -``` - -This starts five services: PostgreSQL, Redis, two VectorFlow instances (`vf1` and `vf2`), and an nginx reverse proxy on port 3000. - -### How it works - -On startup, instances compete for a Redis-based leader lock. The winner becomes the **leader** and runs singleton services (backup scheduler, alert evaluator, metric aggregation). The other instance runs as a **follower**, serving HTTP traffic and API requests but not running singletons. If the leader goes down, a follower automatically takes over. - -Both instances sit behind nginx, which round-robins HTTP requests across them. SSE connections stay open to whichever instance the client connects to, and Redis pub/sub ensures all instances broadcast the same events. - -{% hint style="warning" %} -`NEXTAUTH_SECRET` must be identical across all instances — sessions signed by one instance must be verifiable by the other. The HA Compose file handles this automatically by sharing the same environment variable. -{% endhint %} - -### Scaling beyond two instances - -To add more instances, duplicate a `vf` service block in `docker-compose.ha.yml` and add the new service name to the nginx `upstream` block in `nginx-ha.conf`. Leader election and Redis coordination scale to any number of instances. diff --git a/docs/public/getting-started/first-pipeline.md b/docs/public/getting-started/first-pipeline.md deleted file mode 100644 index 4756992c..00000000 --- a/docs/public/getting-started/first-pipeline.md +++ /dev/null @@ -1,150 +0,0 @@ -# Your First Pipeline - -This walkthrough guides you through creating a simple pipeline that generates demo log events, transforms them with VRL (Vector Remap Language), and outputs the result to the console. By the end, you will have a working pipeline deployed to your fleet. - -## Prerequisites - -- A running VectorFlow server ([Quick Start](quick-start.md)) -- At least one enrolled agent ([Deploy Agents](deploy-agents.md)) - -## Build the pipeline - -{% stepper %} -{% step %} -### Create a new pipeline - -Navigate to **Pipelines** in the sidebar and click **New Pipeline**. -{% endstep %} - -{% step %} -### Name and configure - -Give the pipeline a name (e.g., `demo-pipeline`) and select the **environment** where your agent is enrolled. Click **Create**. - -You are now in the pipeline editor -- a drag-and-drop canvas with three panels: - -- **Left** -- Component palette with all available sources, transforms, and sinks -- **Center** -- Canvas where you build the pipeline graph -- **Right** -- Detail panel for configuring the selected node - -![Pipeline Editor](../screenshots/pipeline-editor.png) -{% endstep %} - -{% step %} -### Add a source - -In the component palette on the left, find **Demo Logs** under the **Testing** category (or type "demo" in the search box). - -Drag it onto the canvas. A green source node appears. Click on it to open the detail panel on the right, and set the **format** to `json`. This generates fake JSON log events every second. -{% endstep %} - -{% step %} -### Add a transform - -Search for **Remap (VRL)** in the component palette and drag it onto the canvas to the right of your source node. - -Click the Remap node to open the detail panel. In the **VRL Source** editor, write a simple transformation: - -```coffeescript -.message = "processed: " + string!(.message) -.processed_at = now() -``` - -This prepends "processed: " to each log message and adds a timestamp field. -{% endstep %} - -{% step %} -### Add a sink - -Search for **Console** in the component palette and drag it onto the canvas to the right of your transform node. - -Click the Console node and set the **encoding** codec to `json`. The console sink prints events to Vector's stdout, which the agent captures and forwards to VectorFlow for viewing. -{% endstep %} - -{% step %} -### Connect the nodes - -Draw connections between your components: - -1. Hover over the **output port** (small circle on the right edge) of the Demo Logs source node -2. Click and drag a line to the **input port** (small circle on the left edge) of the Remap transform node -3. Release to create the connection -4. Repeat: connect the Remap output to the Console sink input - -Your pipeline graph should now show: **Demo Logs** -> **Remap (VRL)** -> **Console** - -{% hint style="info" %} -VectorFlow validates connection compatibility in real time. You cannot connect a metrics-only source to a logs-only sink, for example. Invalid connections are rejected automatically. -{% endhint %} -{% endstep %} - -{% step %} -### Name your components - -Each node has a **Name** field in the detail panel. By default, new components are named after their type (e.g., "Demo Logs"). You can rename them to something more descriptive like "Demo Source", "Add Timestamp", and "Debug Output". - -Renaming a component only requires saving the pipeline -- it does not require a redeploy. The backend component ID is auto-generated and shown as a read-only field below the name. -{% endstep %} - -{% step %} -### Validate the pipeline - -Click the **Validate** button (checkmark icon) in the toolbar at the top of the editor. - -VectorFlow generates the Vector YAML configuration from your graph and sends it to Vector for validation. If everything is correct, you see a green "Pipeline is valid!" toast notification. - -If validation fails, the error message tells you exactly which component has an issue. Fix the configuration and validate again. -{% endstep %} - -{% step %} -### Save the pipeline - -Click the **Save** button in the toolbar (or press `Cmd+S` / `Ctrl+S`). This persists your pipeline graph to the database but does not deploy it yet. -{% endstep %} - -{% step %} -### Deploy - -Click the **Deploy** button in the toolbar. The deploy dialog opens and shows: - -- The **target environment** and how many agents are enrolled -- A **validation check** (the pipeline must be valid to deploy) -- A **YAML diff** comparing the new config against the previously deployed version (if any) -- A **Deployment Reason** field -- describe what changed and why - -Enter a deployment reason (e.g., "Initial demo pipeline"), then click **Publish to Agents**. - -VectorFlow publishes the pipeline configuration. Agents pick up the new config on their next poll cycle (default: 15 seconds). -{% endstep %} - -{% step %} -### Verify - -Navigate to the **Fleet** page. You should see your agent with an **Online** status. The pipeline status shows as **Running** once Vector picks up the configuration. - -Back in the pipeline editor, the toolbar shows a green **Deployed** badge. If you enabled metrics, you can see live event rates on the canvas edges. -{% endstep %} -{% endstepper %} - -{% hint style="success" %} -**Congratulations!** You have built, validated, and deployed your first VectorFlow pipeline. Demo log events are flowing through your Remap transform and printing to the console. -{% endhint %} - -## What just happened - -Under the hood, VectorFlow: - -1. Converted your visual graph into a Vector YAML configuration -2. Validated the config using Vector's built-in `--config-yaml validate` command -3. Created an immutable **version snapshot** with your changelog entry -4. Published the config to all agents in the target environment -5. Each agent pulled the new config, wrote it to disk, and started a new Vector process - -## Next steps - -Now that you have the basics, explore more of VectorFlow: - -- [Pipeline Editor](../user-guide/pipeline-editor.md) -- keyboard shortcuts, import/export, templates, and metrics overlay -- [VRL Snippets](../user-guide/vrl-snippets.md) -- save and reuse VRL patterns across pipelines -- [Environments](../user-guide/environments.md) -- organize your fleet into staging, production, and other environments -- [Fleet Management](../user-guide/fleet.md) -- monitor agent health, view logs, and manage nodes diff --git a/docs/public/getting-started/quick-start.md b/docs/public/getting-started/quick-start.md deleted file mode 100644 index 3af40050..00000000 --- a/docs/public/getting-started/quick-start.md +++ /dev/null @@ -1,97 +0,0 @@ -# Quick Start - -Get VectorFlow running in under 5 minutes. This guide walks you through starting the server with Docker Compose and completing the initial setup. - -## Prerequisites - -- [Docker](https://docs.docker.com/get-docker/) and [Docker Compose](https://docs.docker.com/compose/install/) (v2+) -- A machine with at least 1 CPU core and 1 GB RAM - -{% stepper %} -{% step %} -### Download and configure - -Create a directory for VectorFlow, download the Docker Compose file, and create an environment file with your secrets. - -```bash -mkdir vectorflow && cd vectorflow - -# Download the server compose file -curl -sSfL -o docker-compose.yml \ - https://raw.githubusercontent.com/TerrifiedBug/vectorflow/main/docker/server/docker-compose.yml -``` - -Create a `.env` file next to `docker-compose.yml`: - -```bash -cat > .env << 'EOF' -# Database password — use a random 32+ character string -POSTGRES_PASSWORD=changeme - -# Session & encryption key — generate with: openssl rand -base64 32 -NEXTAUTH_SECRET=changeme -EOF -``` - -{% hint style="warning" %} -Replace both `changeme` values with strong, random strings before starting. You can generate them with: - -```bash -openssl rand -base64 32 -``` -{% endhint %} - -`NEXTAUTH_URL` is optional. When omitted, VectorFlow infers the URL from the incoming `Host` header. Set it explicitly if you place VectorFlow behind a reverse proxy (e.g., `https://vectorflow.example.com`). -{% endstep %} - -{% step %} -### Start VectorFlow - -```bash -docker compose up -d -``` - -Docker pulls the VectorFlow server and PostgreSQL images, runs database migrations automatically, and starts both services. You can follow the logs with: - -```bash -docker compose logs -f vectorflow -``` - -Wait until you see `Starting VectorFlow...` in the output. -{% endstep %} - -{% step %} -### Complete the setup wizard - -Open your browser and navigate to [http://localhost:3000](http://localhost:3000). - -The setup wizard walks you through creating your first admin account. Enter a username, email, and password, then click **Create Account**. - -Once logged in, you land on the VectorFlow dashboard. -{% endstep %} -{% endstepper %} - -{% hint style="success" %} -**You're ready!** VectorFlow is running and your admin account is set up. Time to deploy some agents and build your first pipeline. -{% endhint %} - -## What's in the stack - -The Docker Compose file starts two containers: - -| Container | Image | Purpose | -|-----------|-------|---------| -| `vectorflow-server` | `ghcr.io/terrifiedbug/vectorflow-server` | Next.js application, tRPC API, pipeline validation | -| `vectorflow-postgres` | `postgres:17-alpine` | Database for pipelines, environments, users, and audit logs | - -Three named volumes persist data across restarts: - -- **`vectorflow-pgdata`** -- PostgreSQL data -- **`vectorflow-data`** -- VectorFlow application state -- **`vectorflow-backups`** -- Database backups - -## Next steps - -- [Deploy Agents](deploy-agents.md) -- enroll your first fleet node -- [Your First Pipeline](first-pipeline.md) -- build and deploy a pipeline in the visual editor -- [Deploy the Server](deploy-server.md) -- production deployment options, TLS, and environment variables diff --git a/docs/public/operations/architecture.md b/docs/public/operations/architecture.md deleted file mode 100644 index 45caafac..00000000 --- a/docs/public/operations/architecture.md +++ /dev/null @@ -1,186 +0,0 @@ -# Architecture - -VectorFlow uses a **hub-and-spoke architecture** where a central server manages configuration and state while lightweight agents run on each node to execute Vector pipelines. - -## System overview - -``` - ┌─────────────────────────┐ - │ Browser (React) │ - │ Pipeline Editor, Fleet │ - │ Dashboard, Settings │ - └───────────┬─────────────┘ - │ HTTPS - ┌───────────▼─────────────┐ - │ VectorFlow Server │ - │ (Next.js + tRPC) │ - │ │ - │ ┌──────────────────┐ │ - │ │ PostgreSQL │ │ - │ │ (all state) │ │ - │ └──────────────────┘ │ - └───┬──────────┬──────┬───┘ - │ │ │ - ┌─────────▼──┐ ┌────▼───┐ ┌▼──────────┐ - │ Agent A │ │Agent B │ │ Agent N │ - │ (Go) │ │ (Go) │ │ (Go) │ - │ ┌───────┐ │ │┌─────┐ │ │ ┌───────┐ │ - │ │Vector │ │ ││Vec. │ │ │ │Vector │ │ - │ └───────┘ │ │└─────┘ │ │ └───────┘ │ - └────────────┘ └────────┘ └───────────┘ -``` - -## Components - -### Server - -The VectorFlow server is a **Next.js** application that provides the web UI, REST API, and all management logic. It is the single source of truth for pipeline definitions, environment configuration, user accounts, and audit history. - -Key responsibilities: -- Serve the browser-based pipeline editor and dashboard -- Store pipeline graphs, configurations, and deployment versions -- Generate Vector configuration files (YAML/TOML) from visual pipeline graphs -- Manage user authentication, teams, and role-based access -- Accept agent heartbeats and store fleet metrics -- Evaluate alert rules and fire webhook notifications - -### Agent - -The VectorFlow **agent** is a lightweight Go binary that runs on each node where you want to execute Vector pipelines. Agents are stateless -- all configuration comes from the server. - -Key responsibilities: -- Enroll with the server using a one-time enrollment token -- Poll the server for configuration changes and pending actions -- Start, stop, and reload Vector processes on the local node -- Report metrics, pipeline status, and logs back to the server -- Self-update when a new agent version is available - -### Database - -VectorFlow uses **PostgreSQL** as its sole data store. All state lives in the database: - -- Pipeline definitions and version history -- Environment, team, and user configuration -- Encrypted secrets and certificates -- Agent node registrations and metrics -- Audit log entries -- System settings (OIDC, backup schedule, fleet tuning) - -The schema is managed by Prisma ORM, and migrations run automatically on server startup. - -### Vector - -[Vector](https://vector.dev) is the high-performance data router that does the actual work of collecting, transforming, and shipping observability data. VectorFlow does not replace Vector -- it provides a management layer on top of it. - -Each agent manages one or more Vector processes on its node. When a pipeline is deployed, the agent receives a generated Vector configuration file, writes it to disk, and starts or reloads the Vector process. - -## Data flow - -### Pipeline lifecycle - -A pipeline moves through these stages from creation to execution: - -``` -Editor (browser) - │ User builds pipeline graph visually - ▼ -Server (tRPC mutation) - │ Pipeline graph saved to PostgreSQL - ▼ -Deploy preview - │ Server generates Vector YAML from graph - │ Resolves secrets and certificates - │ Validates configuration - ▼ -Deploy to agents - │ Creates a PipelineVersion snapshot - │ Sends config to each agent via heartbeat actions - ▼ -Agent receives config - │ Writes YAML to disk - │ Starts or reloads Vector process - ▼ -Vector runs pipeline - │ Data flows from sources → transforms → sinks - │ Agent reports metrics back via heartbeat - ▼ -Dashboard - Events processed, errors, throughput visible in UI -``` - -### Metrics collection - -Agents report metrics to the server on every heartbeat cycle (default: every 15 seconds): - -- **Node metrics** -- CPU, memory, disk, and network usage -- **Pipeline status** -- Events in/out, errors, bytes processed per component -- **Logs** -- Pipeline log output -- **Event samples** -- Sample events for schema discovery - -The server stores these in PostgreSQL and evaluates alert rules against configured thresholds on each heartbeat. - -## Agent communication - -### Pull-based polling - -Agents use a **pull-based** communication model. The agent initiates all connections -- the server never connects to agents. This design was chosen for three reasons: - -1. **Security** -- Agents can run behind firewalls and NATs without exposing any ports. Only outbound HTTPS is required. -2. **Simplicity** -- No need for service discovery, message brokers, or persistent connections. -3. **Scalability** -- The server handles agents as stateless HTTP clients. No per-agent connection state to manage. - -### Protocol - -Agents communicate via three REST endpoints: - -| Endpoint | Method | Purpose | -|----------|--------|---------| -| `/api/agent/enroll` | POST | One-time enrollment. Agent sends enrollment token, receives a persistent node token. | -| `/api/agent/heartbeat` | POST | Periodic check-in. Agent sends metrics and status, receives pending actions (deploy, undeploy, update). | -| `/api/agent/config` | POST | Fetch the generated Vector configuration for a specific pipeline. | - -### Heartbeat cycle - -On each heartbeat, the agent sends: -- Current agent version -- Node resource metrics (CPU, memory, disk) -- Status of each running pipeline (events processed, errors) -- Pipeline logs since last heartbeat - -The server responds with any **pending actions**: -- Deploy a new pipeline version -- Undeploy a pipeline -- Self-update to a new agent version - -### Enrollment - -When an agent starts for the first time, it sends the enrollment token (provided via `VF_TOKEN`) to the server. The server validates the token, registers the node in the target environment, and returns a persistent **node token**. The agent stores this token locally and uses it for all future heartbeat requests. - -``` -Agent Server - │ │ - │── POST /api/agent/enroll ────────▶│ - │ { enrollmentToken } │ - │ │ Validate token - │ │ Create node record - │◀── { nodeToken, nodeId } ────────│ - │ │ - │ (stores node token to disk) │ - │ │ - │── POST /api/agent/heartbeat ────▶│ - │ { nodeToken, metrics, ... } │ - │◀── { pendingActions: [...] } ────│ - │ │ -``` - -## Security model - -VectorFlow's architecture is designed with defense in depth: - -- **Agent-initiated connections only** -- The server never opens connections to agent nodes. Agents poll the server over HTTPS, so they work behind firewalls without exposing any inbound ports. -- **Encrypted secrets** -- Sensitive values (API keys, passwords, certificates) are encrypted with AES-256-GCM before storage. They are only decrypted at deploy time when generating Vector configuration. -- **Token-based agent auth** -- Each agent has a unique node token issued during enrollment. Tokens are stored with restricted file permissions (`0600`) on the agent host. -- **Role-based access control** -- Users are assigned roles (Viewer, Editor, Admin) per team. Super Admins have platform-wide access. -- **Audit logging** -- Every mutation is logged with the user, IP address, timestamp, and a diff of changed fields. - -For a detailed security guide, see [Security](security.md). diff --git a/docs/public/operations/authentication.md b/docs/public/operations/authentication.md deleted file mode 100644 index 716edf7c..00000000 --- a/docs/public/operations/authentication.md +++ /dev/null @@ -1,291 +0,0 @@ -# Authentication - -VectorFlow supports multiple authentication methods: local credentials, OIDC/SSO, and two-factor authentication (2FA). This page covers initial setup, login methods, user management, and role-based access control. - -![Login](../screenshots/login.png) - -## Initial setup - -When VectorFlow starts for the first time with an empty database, it redirects to a setup wizard. The wizard creates the first admin account and the first team. - -{% stepper %} -{% step %} -### Create admin account -Enter your name, email address, and a password (minimum 8 characters). -{% endstep %} -{% step %} -### Name your team -Choose a name for your first team (e.g., "Platform Engineering"). Teams organize environments and pipelines. -{% endstep %} -{% step %} -### Start using VectorFlow -After setup completes, you are redirected to the login page. Sign in with the credentials you just created. -{% endstep %} -{% endstepper %} - -The first user is automatically a **Super Admin** with full platform access. - -## Credentials authentication - -By default, users log in with an email address and password. Passwords are hashed with bcrypt before storage. - -When a Super Admin creates a new user, VectorFlow generates a random temporary password. The new user must change their password on first login. - -## OIDC / SSO - -VectorFlow supports any OpenID Connect provider (Okta, Entra ID, Keycloak, Google Workspace, Auth0, etc.). SSO is configured from the Settings page by a Super Admin. - -{% stepper %} -{% step %} -### Register VectorFlow with your identity provider -Create an OAuth2/OIDC application in your identity provider. Use the following redirect URI: - -``` -https://your-vectorflow-url/api/auth/callback/oidc -``` -{% endstep %} -{% step %} -### Open Settings in VectorFlow -Navigate to **Settings > Authentication** (Super Admin required). -{% endstep %} -{% step %} -### Enter provider details -Fill in the OIDC configuration: - -| Field | Description | Example | -|-------|-------------|---------| -| Issuer URL | OIDC discovery endpoint | `https://accounts.google.com` | -| Client ID | OAuth2 client ID from your provider | `abc123.apps.googleusercontent.com` | -| Client Secret | OAuth2 client secret | `GOCSPX-xxxxxxxxxxxx` | -| Display Name | Button label on the login page | `Sign in with Okta` | -| Token Auth Method | How the client authenticates to the token endpoint | `client_secret_post` (default) or `client_secret_basic` | - -{% endstep %} -{% step %} -### Test the connection -Click **Test Connection** to verify VectorFlow can reach your provider's discovery endpoint. The test fetches `/.well-known/openid-configuration` and validates that required fields are present. -{% endstep %} -{% step %} -### Save and verify -Save the settings. An SSO button will appear on the login page. Test the flow by logging in with an SSO account. -{% endstep %} -{% endstepper %} - -{% hint style="info" %} -OIDC settings are stored encrypted in the database. The client secret is encrypted with AES-256-GCM before storage. -{% endhint %} - -### Group mapping - -VectorFlow can automatically assign users to teams based on their identity provider group memberships. Group mappings are configured from **Settings > Team & Role Mapping** and are **shared between OIDC and SCIM** — the same mapping table drives both protocols. - -Group sync is **off by default** and must be explicitly enabled. When enabled, VectorFlow operates in one of two modes depending on whether SCIM is active: - -{% tabs %} -{% tab title="OIDC-only mode (SCIM disabled)" %} -Groups are read from the OIDC token on each login. Team memberships are **reconciled** — users are added to mapped teams **and removed** from teams they no longer belong to (based on the groups present in the token). Changes to group mappings in **Settings > Team & Role Mapping** take effect on the user's next login. -{% endtab %} -{% tab title="SCIM + OIDC mode (SCIM enabled)" %} -SCIM is the **primary lifecycle manager** for group memberships. Your IdP pushes group membership changes (create, update, remove) via SCIM, and VectorFlow tracks them internally. OIDC login acts as a **real-time refresh**, using the union of SCIM group data and token groups to reconcile team memberships. Changes to group mappings in **Settings > Team & Role Mapping** take effect immediately for all SCIM-managed users. -{% endtab %} -{% endtabs %} - -{% hint style="info" %} -**Manual assignments are preserved.** Team memberships assigned manually in the VectorFlow UI are never modified by automated group sync. If you want group sync to fully manage a user's membership on a team, remove the manual assignment first. -{% endhint %} - -{% hint style="info" %} -**Highest role wins.** When a user belongs to multiple IdP groups that map to the same VectorFlow team, the highest role is used (Admin > Editor > Viewer). -{% endhint %} - -{% stepper %} -{% step %} -### Enable group sync -Toggle **Enable Group Sync** on. This tells VectorFlow to process group-to-team mappings. When enabled, OIDC logins read group claims from the token, and SCIM group pushes use the same mapping table to assign team memberships. -{% endstep %} -{% step %} -### Configure scope and claim - -| Field | Default | Description | -|-------|---------|-------------| -| Groups Scope | `groups` | Extra OIDC scope to request from the provider. Leave empty if your provider includes groups automatically (e.g., Azure AD, Cognito). | -| Groups Claim | `groups` | Token claim containing group names (e.g., `groups`, `cognito:groups`, `roles`). | - -{% hint style="info" %} -**Scope** is what you ask the provider for in the authorization URL. **Claim** is where you read the groups from in the returned token. These are usually the same value (e.g., `groups` for Keycloak, Pocket ID, Authentik), but some providers differ — for example, AWS Cognito uses no extra scope but returns groups under the `cognito:groups` claim. -{% endhint %} -{% endstep %} -{% step %} -### Add group mappings -Map identity provider groups to VectorFlow teams with specific roles. These mappings apply to both OIDC sign-ins and SCIM group pushes — you only need to configure them once. - -| Column | Description | -|--------|-------------| -| Group Name | The group name as it appears in the OIDC token or SCIM Group displayName | -| Team(s) | One or more VectorFlow teams to assign the user to. Click the team selector to open a dropdown with checkboxes — select multiple teams to map a single group to several teams with the same role. When multiple teams are selected, the field shows a count (e.g., "3 teams") that you can click to view or modify the selection. | -| Role | The role to assign: Viewer, Editor, or Admin | -{% endstep %} -{% step %} -### Set defaults -Configure a **Default Team** and **Default Role** as a fallback. If a user logs in and has no group matches (no IdP groups map to any VectorFlow team), they are assigned to the default team with the default role. -{% endstep %} -{% endstepper %} - -{% hint style="warning" %} -Changing group sync settings takes effect immediately — the OIDC provider configuration is rebuilt without requiring a server restart. In SCIM+OIDC mode, mapping changes are applied to all SCIM-managed users at save time. In OIDC-only mode, changes take effect on each user's next login. -{% endhint %} - -## SCIM provisioning - -VectorFlow supports SCIM 2.0 for automated user provisioning and deprovisioning from your identity provider. When SCIM is enabled, your IdP can automatically create, update, and deactivate VectorFlow user accounts. - -SCIM is configured from **Settings > SCIM** by a Super Admin. You will need to generate a bearer token and enter it along with the SCIM base URL (`{your-vectorflow-url}/api/scim/v2`) into your IdP's SCIM configuration. - -{% hint style="info" %} -SCIM group provisioning does **not** create teams automatically. Instead, SCIM groups are resolved through the shared [group mapping table](#group-mapping) to assign users to existing teams. Make sure your group mappings are configured before enabling SCIM group pushes. -{% endhint %} - -{% hint style="info" %} -SCIM works best alongside OIDC/SSO. Users created via SCIM should authenticate through your identity provider rather than with local credentials. See the [SCIM Provisioning](scim.md) page for detailed setup instructions and IdP-specific guides. -{% endhint %} - -## Two-factor authentication (2FA) - -VectorFlow supports TOTP-based two-factor authentication compatible with any authenticator app (Google Authenticator, Authy, 1Password, etc.). - -### Enabling 2FA - -Users can enable 2FA from their **Profile** page. Teams can also require 2FA for all members -- when enabled, users who have not set up 2FA are redirected to the enrollment page on their next login. - -{% stepper %} -{% step %} -### Start 2FA setup -Navigate to your **Profile** page and click **Enable Two-Factor Authentication**, or you will be redirected automatically if your team requires it. -{% endstep %} -{% step %} -### Scan the QR code -Scan the displayed QR code with your authenticator app. Alternatively, enter the secret key manually. -{% endstep %} -{% step %} -### Save backup codes -VectorFlow generates **10 single-use backup codes**. Save these in a secure location. Each code can be used once to log in if you lose access to your authenticator app. -{% endstep %} -{% step %} -### Verify -Enter the 6-digit code from your authenticator app to confirm setup. 2FA is now active on your account. -{% endstep %} -{% endstepper %} - -### Logging in with 2FA - -After entering your email and password, you are prompted for a 6-digit verification code. Enter the code from your authenticator app, or use one of your backup codes. - -{% hint style="info" %} -2FA is only available for local (credentials) accounts. OIDC/SSO users should configure MFA through their identity provider. -{% endhint %} - -## User management - -Super Admins can manage users from the **Settings > Users** page. - -### Creating users - -Super Admins can create new local user accounts. VectorFlow generates a random temporary password that must be shared with the user securely. The user is required to change their password on first login. - -When creating a user, you can optionally assign them to a team with a specific role immediately. - -{% hint style="info" %} -When SCIM provisioning or OIDC group sync is enabled, a banner appears in the Add Member dialog: *"SSO users are managed by your identity provider. Only local users can be added manually."* SSO-managed users should be provisioned through your IdP rather than created here. -{% endhint %} - -### Managing users - -| Action | Description | -|--------|-------------| -| **Assign to team** | Add a user to a team with a specified role (Viewer, Editor, or Admin) | -| **Remove from team** | Remove a user's membership from a specific team | -| **Reset password** | Generate a new temporary password (local accounts only) | -| **Lock account** | Prevent the user from logging in. Locked users see an error on the login page | -| **Unlock account** | Restore login access for a locked account | -| **Toggle Super Admin** | Grant or revoke platform-wide Super Admin privileges | -| **Delete user** | Permanently remove the user and all their data | - -{% hint style="warning" %} -You cannot delete your own account, remove your own Super Admin status, or lock your own account. -{% endhint %} - -## Roles and permissions - -VectorFlow uses a hierarchical role system. Roles are assigned **per team**, so a user can be an Admin in one team and a Viewer in another. - -| Role | Permissions | -|------|-------------| -| **Viewer** | View pipelines, fleet status, dashboards, and audit logs. Cannot make changes. | -| **Editor** | Everything a Viewer can do, plus: create/edit/delete pipelines, manage secrets and certificates, deploy pipelines, manage alerts. | -| **Admin** | Everything an Editor can do, plus: manage environments, manage team members and roles, configure team settings (e.g., require 2FA). | -| **Super Admin** | Platform-wide access. Can manage all teams, configure system settings (OIDC, fleet, backups), create and delete users. Super Admin is a flag on the user, not a team role. | - -### Role hierarchy - -``` -Viewer < Editor < Admin < Super Admin - (0) (1) (2) (bypass) -``` - -Higher roles inherit all permissions from lower roles. Super Admin bypasses all team-level access checks. - -## SSO-only mode - -To enforce SSO-only authentication and hide the local login form, set the environment variable: - -| Variable | Default | Description | -|----------|---------|-------------| -| `VF_DISABLE_LOCAL_AUTH` | `false` | When `true`, hides the email/password form and only shows the SSO button on the login page | - -When SSO-only mode is active, the login page shows a simplified layout: - -- A centered card with a shield icon -- The heading **"Sign in"** -- The message *"Use your organization's single sign-on to access your account."* -- A single button: **"Sign in with [Provider Name]"** (using the display name configured in OIDC settings) - -The email/password form and all local authentication options are hidden. - -{% hint style="warning" %} -Before enabling SSO-only mode, ensure your OIDC provider is correctly configured and tested. The only way to re-enable local login is to change the environment variable and restart the server. -{% endhint %} - -{% hint style="info" %} -SSO-only mode acts as a breakglass mechanism -- if SSO breaks, an administrator can re-enable local login by setting `VF_DISABLE_LOCAL_AUTH=false` and restarting the container. -{% endhint %} - -## SSO-managed roles - -When users authenticate via OIDC or are provisioned via SCIM, their team roles are managed by the identity provider. Admins cannot change roles for SSO-managed users in the VectorFlow UI -- the role dropdown is locked with a tooltip indicating the role is managed by the identity provider. - -Role updates happen: - -- **On login** -- OIDC group claims are reconciled against team mappings. The user is added to newly matched teams, removed from teams they no longer match, and roles are updated to reflect the highest mapped role. -- **Via SCIM** -- When SCIM group membership changes are pushed, team memberships are reconciled immediately based on team mappings. Removals cascade correctly — if a user is removed from their only group mapping for a team, they are removed from that team. - -{% hint style="info" %} -Manual team assignments (made in the UI) are not affected by SSO-managed role sync. Only memberships created by group sync are subject to reconciliation. -{% endhint %} - -## Users without team assignments - -When a user authenticates successfully (via SSO or local credentials) but is not a member of any team, VectorFlow displays a full-page message instead of the normal dashboard: - -- A **"No Team Assigned"** heading with a shield icon -- A message: *"Your account is active but you haven't been assigned to a team yet. Contact your administrator to get access."* -- The user's identity (name or email) -- A **Sign Out** button - -This typically occurs when: -- A new SSO user logs in before group mappings or manual team assignments are configured -- A SCIM-provisioned user has not been assigned to any group that maps to a VectorFlow team -- An admin removes a user from all teams - -{% hint style="info" %} -The user can still access the password change dialog if their account requires it. Once an admin assigns them to a team (or group sync places them on one), the full dashboard becomes available on next login or page refresh. -{% endhint %} diff --git a/docs/public/operations/backup-restore.md b/docs/public/operations/backup-restore.md deleted file mode 100644 index e21f3949..00000000 --- a/docs/public/operations/backup-restore.md +++ /dev/null @@ -1,464 +0,0 @@ -# Backup & Restore - -VectorFlow includes built-in database backup and restore functionality. Backups capture the entire PostgreSQL database, including all pipelines, environments, users, secrets, audit history, and system settings. - -## What gets backed up - -Backups are full PostgreSQL dumps in compressed custom format (`pg_dump --format=custom`). Everything stored in the database is included: - -- Pipeline definitions and version history -- Environments, teams, and user accounts -- Encrypted secrets and certificates -- Agent node registrations -- Alert rules and webhook configurations -- Audit log entries -- System settings (OIDC, fleet, backup schedule) - -{% hint style="info" %} -Backups do **not** include the Vector data directory (`/var/lib/vector/`) on agent nodes. Vector's internal state (e.g., file checkpoints, disk buffers) is managed by each agent independently. -{% endhint %} - -## Integrity verification - -Every backup includes a SHA256 checksum computed after the database dump completes. Checksums are stored in the VectorFlow database alongside backup metadata. - -When you restore a backup, VectorFlow automatically verifies the checksum before applying it: - -- **Checksum matches** -- Restore proceeds normally -- **Checksum mismatch** -- Restore is blocked with an error message indicating the file may be corrupt - -{% hint style="info" %} -Backups created before this feature was added (legacy backups) do not have stored checksums. VectorFlow skips checksum verification for these backups and proceeds with the restore. -{% endhint %} - -## Remote Storage (S3) - -VectorFlow can store backups in any S3-compatible storage service, including AWS S3, MinIO, DigitalOcean Spaces, and Backblaze B2. - -### Configuring S3 storage - -1. Navigate to **Settings > Backups** -2. Toggle the storage backend from **Local** to **S3** -3. Fill in the required fields: - - **Bucket** -- the S3 bucket name - - **Region** -- the AWS region (e.g., `us-east-1`) - - **Access Key ID** -- IAM access key with S3 permissions - - **Secret Access Key** -- corresponding secret key (stored encrypted) -4. Optional fields: - - **Prefix** -- key prefix for organizing backups (e.g., `backups/vectorflow`) - - **Endpoint URL** -- custom endpoint for MinIO or other S3-compatible services -5. Click **Test Connection** to verify bucket access and write permissions -6. Click **Save Storage Settings** - -### How it works - -- When S3 is configured, backups are uploaded to the S3 bucket immediately after creation. The local dump file is deleted after a successful upload to prevent disk exhaustion. -- Each backup's storage location is recorded in the database (`s3://bucket/key` for S3, local path for disk). -- Restoring from an S3-stored backup downloads the file temporarily, runs `pg_restore`, then deletes the temporary file. -- The backup table shows a cloud icon for S3-stored backups and a disk icon for local backups. -- Switching from S3 back to Local keeps your S3 credentials saved -- you can switch back without re-entering them. - -### Required S3 permissions - -The IAM user or role needs the following permissions on the target bucket: - -- `s3:HeadBucket` (connection test) -- `s3:PutObject` (upload backups) -- `s3:GetObject` (download for restore) -- `s3:DeleteObject` (delete backups, cleanup test objects) -- `s3:HeadObject` (check if backup exists) - -### MinIO and S3-compatible services - -For self-hosted S3-compatible services like MinIO, set the **Endpoint URL** field to the service address (e.g., `https://minio.internal:9000`). VectorFlow automatically enables path-style addressing when a custom endpoint is set. - -## Automatic backups - -VectorFlow can run backups on a cron schedule with automatic retention cleanup. - -### Configuring the schedule - -Navigate to **Settings > Backup** (Super Admin required) to configure: - -| Setting | Default | Description | -|---------|---------|-------------| -| Enabled | Off | Toggle automatic backups on or off | -| Cron Schedule | `0 2 * * *` | Standard cron expression. Default runs at 2:00 AM daily | -| Retention Count | 7 | Number of backups to keep. Older backups are automatically deleted | - -**Common cron schedules:** - -| Schedule | Cron Expression | -|----------|----------------| -| Every day at 2:00 AM | `0 2 * * *` | -| Every 6 hours | `0 */6 * * *` | -| Every day at midnight | `0 0 * * *` | -| Every Sunday at 3:00 AM | `0 3 * * 0` | -| Every weekday at 1:00 AM | `0 1 * * 1-5` | - -After each scheduled backup completes, VectorFlow automatically runs retention cleanup to delete the oldest backups beyond the configured retention count. - -### Monitoring backup health - -The backup settings page shows the status of the most recent backup attempt: - -- **Success** -- The last backup completed without errors. The timestamp and file size are displayed. -- **Failed** -- A red error banner appears at the top of the page showing the error message from the failed backup attempt (e.g., `pg_dump` timeout, disk full, permission denied). The banner persists until the next successful backup. - -Failed backups are also visible in the backup list with a red **Failed** status badge and their error details. You can delete failed backup entries to clean them up. Download and Restore actions are only available for successful backups. - -{% hint style="warning" %} -If automatic backups are enabled but consistently failing, the error banner provides the diagnostic message needed to troubleshoot. Common causes include insufficient disk space in `VF_BACKUP_DIR`, PostgreSQL connection issues, or `pg_dump` not being available in the container. -{% endhint %} - -### Orphan cleanup - -VectorFlow automatically detects and handles orphaned backup entries: - -- **Files without database records** -- If a `.dump` file exists in the backup directory but has no matching database entry, it is automatically deleted during the next scheduled cleanup cycle. -- **Database records without files** -- If a backup record points to a file that no longer exists (locally or in S3), the record is marked as **Orphaned** in the backup list. Orphaned entries remain visible so operators can see what happened, and can be manually deleted. - -Orphan cleanup runs alongside retention cleanup after each scheduled backup. No manual configuration is needed. - -## Manual backup - -You can trigger a backup at any time from the **Settings > Backup** page by clicking **Create Backup**. The backup runs immediately and appears in the backup list when complete. - -The backup list is database-backed and shows all backups — both scheduled and manual — with their type, status, size, and duration. Backups persist across page refreshes and server restarts. - -Each backup generates two files: -- `vectorflow-.dump` -- The compressed PostgreSQL dump -- `vectorflow-.meta.json` -- Metadata (VectorFlow version, migration count, PostgreSQL version, file size) - -{% hint style="info" %} -When upgrading from a version before v1.2, any existing backup files in `VF_BACKUP_DIR` are automatically imported into the database on first startup. No manual action is required — your existing backups will appear in the list automatically. -{% endhint %} - -## Downloading backups - -Super Admins can download backup `.dump` files directly from the **Settings > Backup** page. - -Each row in the backup list includes a **Download** button. Clicking it streams the compressed dump file to your browser. Downloaded files can be used for: - -- Offline archival storage -- Restoring on a different VectorFlow instance via the CLI `pg_restore` procedure -- Disaster recovery from a separate machine - -{% hint style="info" %} -The download button is only visible to Super Admins. The download streams the file directly from the server's backup directory — no temporary copies are created. -{% endhint %} - -## Backup storage - -Backups are stored on the server's local filesystem in the directory configured by the `VF_BACKUP_DIR` environment variable (default: `/backups`). - -In the Docker Compose setup, this directory is mounted as a Docker volume: - -```yaml -volumes: - - backups:/backups -``` - -{% hint style="warning" %} -For production deployments, consider mounting `VF_BACKUP_DIR` to a location that is backed up by your infrastructure-level backup system (e.g., an NFS share, or a directory included in your host backup schedule). -{% endhint %} - -## Restore procedure - -Restoring from a backup replaces the entire database with the contents of the backup file. - -{% hint style="danger" %} -**Restoring a backup overwrites all current data.** All pipelines, users, secrets, and settings will be replaced with the state from the backup. VectorFlow shows a preview of the backup contents and requires a typed confirmation before proceeding. A safety backup is created automatically before restoring. -{% endhint %} - -### Restore from the UI - -{% stepper %} -{% step %} -### Navigate to Settings > Backup -Open the backup management page (Super Admin required). -{% endstep %} -{% step %} -### Click Restore on a backup -Find the backup you want to restore in the list and click the **Restore** button. A preview dialog opens showing the backup's metadata. -{% endstep %} -{% step %} -### Review the preview -The preview shows: -- **VectorFlow version** and **migration level** from when the backup was created -- **PostgreSQL version** used for the dump -- **Backup size** and **creation date** -- **Tables present** in the dump file - -This information helps you verify you are restoring the correct backup. -{% endstep %} -{% step %} -### Confirm the restore -Click **Continue to Confirmation**, then type `RESTORE` in the confirmation field and click **Restore Database**. VectorFlow will: -1. Validate version compatibility (blocks if the backup has more migrations than the current version) -2. Verify the backup file checksum -3. Create a safety backup of the current database -4. Run `pg_restore --clean --if-exists` to replace the database -{% endstep %} -{% step %} -### Restart the application -After restore completes, the dialog shows a success message. Restart the application for all changes to take full effect. If running in Docker, restart the container. Database migrations run automatically on startup. -{% endstep %} -{% endstepper %} - -### Manual restore (CLI) - -If you cannot access the UI, you can restore directly using `pg_restore`: - -```bash -# Stop the VectorFlow server first -docker compose stop vectorflow - -# Restore the backup -docker compose exec postgres pg_restore \ - --clean --if-exists \ - -U vectorflow -d vectorflow \ - /backups/vectorflow-2025-01-15T02-00-00-000Z.dump - -# Restart the server (migrations run automatically) -docker compose start vectorflow -``` - -## Version compatibility - -VectorFlow tracks the number of database migrations in each backup's metadata. When restoring: - -- **Same version or older backup → newer server**: Works. Migrations run automatically on startup to bring the schema up to date. -- **Newer backup → older server**: Blocked. If the backup contains more migrations than the current server version, the restore is rejected. Upgrade VectorFlow first, then restore. - -## Recommended backup strategy - -1. **Enable automatic daily backups** with a retention count of at least 7. -2. **Mount the backup directory** to storage that is included in your infrastructure backup system. -3. **Test restores periodically** in a staging environment to verify your backups are valid. -4. **Create a manual backup** before upgrading VectorFlow or making major configuration changes. -5. **Monitor backup status** on the Settings page. Failed backups are logged with error details. -6. **Check server logs for disk space warnings.** Before each backup, VectorFlow checks available disk space in `VF_BACKUP_DIR` and logs a warning if it drops below the configured threshold (default: 500 MB). Configure the threshold with the `VF_BACKUP_DISK_WARN_MB` environment variable. - -## Recovery targets (RTO/RPO) - -Recovery Point Objective (RPO) defines the maximum acceptable data loss. Recovery Time Objective (RTO) defines the maximum acceptable downtime during recovery. - -### Default targets - -| Metric | Default Target | Basis | -|--------|---------------|-------| -| **RPO** (max data loss) | 24 hours | Default daily backup schedule (`0 2 * * *`) | -| **RTO** (time to recover) | < 15 minutes | pg_restore of < 1 GB database + container startup | - -{% hint style="info" %} -These defaults assume the standard daily backup schedule and a database under 1 GB. Adjust targets based on your backup frequency and database size using the framework below. -{% endhint %} - -### Calculating your targets - -**RPO formula:** - -``` -RPO = backup_interval + backup_duration + transfer_time -``` - -- **backup_interval** — time between scheduled backups (e.g., 24h for daily, 6h for `0 */6 * * *`) -- **backup_duration** — time to complete `pg_dump` (typically seconds for < 1 GB) -- **transfer_time** — S3 upload time (0 for local storage) - -Data created after the last successful backup and before a failure is at risk. - -**RTO formula:** - -``` -RTO = download_time + restore_time + app_restart + smoke_test -``` - -- **download_time** — time to retrieve backup from S3 (0 for local storage) -- **restore_time** — `pg_restore` duration (see estimates below) -- **app_restart** — VectorFlow startup + automatic migration run (~30s typical) -- **smoke_test** — manual verification of application health (~2-5 min) - -### Size-based RTO estimates - -| Database Size | Local Restore | S3 Restore (100 Mbps) | -|--------------|---------------|----------------------| -| 100 MB | ~1 min | ~2 min | -| 500 MB | ~3 min | ~5 min | -| 1 GB | ~5 min | ~8 min | -| 5 GB | ~15 min | ~25 min | - -{% hint style="info" %} -These estimates include `pg_restore` time and application restart. Actual times depend on disk speed, CPU, and network throughput. Run `scripts/dr-verify.sh` to benchmark your environment. -{% endhint %} - -### Reducing RPO - -Increase backup frequency by changing the cron schedule: - -| Schedule | Cron Expression | RPO | -|----------|----------------|-----| -| Every 24 hours (default) | `0 2 * * *` | 24h | -| Every 12 hours | `0 2,14 * * *` | 12h | -| Every 6 hours | `0 */6 * * *` | 6h | -| Every hour | `0 * * * *` | 1h | - -{% hint style="warning" %} -More frequent backups increase storage usage and I/O load. Adjust the retention count accordingly and monitor disk space warnings in the server logs. -{% endhint %} - -### Reducing RTO - -- **Keep local backup copies** alongside S3 to eliminate download time -- **Use faster storage** (SSD) for the backup directory -- **Pre-provision a standby PostgreSQL** instance to eliminate container startup time -- **Automate the runbook** using `scripts/dr-verify.sh` as a starting point - -## Disaster recovery runbook - -Step-by-step procedure for recovering VectorFlow from a backup after a database failure, corruption, or full system loss. - -{% stepper %} -{% step %} -### Assess the situation - -Determine the failure type: -- **Database corruption** — PostgreSQL data is damaged but the server host is intact -- **Hardware failure** — the database host is lost; VectorFlow server may still be running -- **Full system loss** — both VectorFlow and PostgreSQL are unavailable - -This determines whether you restore in-place or provision new infrastructure. -{% endstep %} - -{% step %} -### Identify the most recent good backup - -Check the backup list in **Settings > Backup** (if the UI is accessible) or list files in the backup directory: - -```bash -ls -lt /backups/*.dump | head -5 -``` - -For S3-stored backups, list objects in the bucket: - -```bash -aws s3 ls s3://your-bucket/your-prefix/ --recursive | sort -r | head -5 -``` - -Verify the backup checksum before proceeding: - -```bash -sha256sum /backups/vectorflow-2026-01-15T02-00-00-000Z.dump -``` - -Compare against the checksum in the corresponding `.meta.json` file or the `BackupRecord` in the database. -{% endstep %} - -{% step %} -### Provision PostgreSQL - -If the existing PostgreSQL instance is recoverable, skip to the next step. - -For a new instance, use the same image as production: - -```bash -docker run -d \ - --name vectorflow-postgres \ - -e POSTGRES_DB=vectorflow \ - -e POSTGRES_USER=vectorflow \ - -e POSTGRES_PASSWORD= \ - -v pgdata:/var/lib/postgresql/data \ - timescale/timescaledb:latest-pg16 -``` - -Wait for it to be ready: - -```bash -docker exec vectorflow-postgres pg_isready -U vectorflow -``` -{% endstep %} - -{% step %} -### Restore the backup - -Stop the VectorFlow server first to prevent writes during restore: - -```bash -docker compose stop vectorflow -``` - -Run `pg_restore`: - -```bash -docker compose exec postgres pg_restore \ - --clean --if-exists \ - -U vectorflow -d vectorflow \ - /backups/vectorflow-2026-01-15T02-00-00-000Z.dump -``` - -For S3-stored backups, download first: - -```bash -aws s3 cp s3://your-bucket/your-prefix/vectorflow-2026-01-15T02-00-00-000Z.dump /tmp/ -docker cp /tmp/vectorflow-2026-01-15T02-00-00-000Z.dump vectorflow-postgres:/tmp/ -docker exec -e PGPASSWORD= vectorflow-postgres \ - pg_restore --clean --if-exists \ - -U vectorflow -d vectorflow /tmp/vectorflow-2026-01-15T02-00-00-000Z.dump -``` -{% endstep %} - -{% step %} -### Verify the restore - -Run the automated DR verification script: - -```bash -./scripts/dr-verify.sh /backups/vectorflow-2026-01-15T02-00-00-000Z.dump -``` - -Or verify manually: - -```bash -docker compose exec postgres psql -U vectorflow -d vectorflow -c "SELECT count(*) FROM \"User\";" -docker compose exec postgres psql -U vectorflow -d vectorflow -c "SELECT count(*) FROM \"Pipeline\";" -docker compose exec postgres psql -U vectorflow -d vectorflow -c "SELECT count(*) FROM \"_prisma_migrations\" WHERE finished_at IS NOT NULL;" -``` -{% endstep %} - -{% step %} -### Restart VectorFlow - -```bash -docker compose start vectorflow -``` - -Database migrations run automatically on startup. Check the logs for migration output: - -```bash -docker compose logs vectorflow --tail=50 -``` -{% endstep %} - -{% step %} -### Validate application health - -1. **Login** — verify authentication works -2. **Pipeline list** — confirm pipelines are visible and match expected state -3. **Agent connectivity** — check the Fleet page for connected agents -4. **Settings** — verify system settings, backup schedule, and S3 configuration - -{% endstep %} - -{% step %} -### Post-incident review - -- Was the RPO exceeded? If data loss was unacceptable, increase backup frequency. -- Was the RTO exceeded? If recovery took too long, consider keeping local backup copies or pre-provisioning standby infrastructure. -- Update this runbook with any lessons learned. -{% endstep %} -{% endstepper %} - -{% hint style="info" %} -**Automated DR verification:** Run `scripts/dr-verify.sh` periodically (or via CI) to confirm your backups remain restorable without waiting for a real incident. See the [recommended backup strategy](#recommended-backup-strategy) section above. -{% endhint %} diff --git a/docs/public/operations/configuration.md b/docs/public/operations/configuration.md deleted file mode 100644 index 963930e1..00000000 --- a/docs/public/operations/configuration.md +++ /dev/null @@ -1,190 +0,0 @@ -# Configuration - -VectorFlow is configured through environment variables (for the server and agents) and through the Settings page in the UI (for fleet tuning, OIDC, and backups). - -## Server environment variables - -### Required - -{% hint style="warning" %} -These variables must be set before the server can start. Without them, the application will fail to launch. -{% endhint %} - -| Variable | Description | Example | -|----------|-------------|---------| -| `DATABASE_URL` | PostgreSQL connection string | `postgresql://vectorflow:pass@localhost:5432/vectorflow` | -| `NEXTAUTH_SECRET` | Session encryption key (min 32 characters) | Output of `openssl rand -base64 32` | - -{% hint style="danger" %} -`NEXTAUTH_SECRET` is used to encrypt sessions, TOTP secrets, stored credentials, and all sensitive values in the database. Use a strong, random value and keep it safe. If you lose this key, all encrypted data becomes unrecoverable. -{% endhint %} - -### Optional - -| Variable | Default | Description | -|----------|---------|-------------| -| `NEXTAUTH_URL` | *(inferred from Host header)* | Canonical server URL. Set this when running behind a reverse proxy (e.g., `https://vectorflow.example.com`) | -| `PORT` | `3000` | HTTP listen port | -| `NODE_ENV` | `production` | Set automatically in Docker. Use `production` for standalone deployments | -| `VF_BACKUP_DIR` | `/backups` | Directory for database backup files | -| `VF_BACKUP_DISK_WARN_MB` | `500` | Disk space warning threshold in MB. Before each backup, available space in `VF_BACKUP_DIR` is checked. If below this value, a warning is logged | - -> S3 remote storage is configured through the Settings UI, not environment variables. See [Remote Storage (S3)](backup-restore.md#remote-storage-s3). -| `VF_DISABLE_LOCAL_AUTH` | `false` | Hide the local login form and enforce SSO-only authentication. Requires OIDC to be configured | -| `REDIS_URL` | *(none — single-instance mode)* | Redis connection string for HA mode (e.g., `redis://redis:6379`). Enables leader election, cross-instance SSE broadcast, and metric distribution. When unset, VectorFlow runs as a single instance with no behavioral change | -| `VF_LOG_LEVEL` | `info` | Server log verbosity: `debug`, `info`, `warn`, `error`. Set to `debug` for verbose logging of OIDC authentication, SCIM provisioning, agent enrollment, and group sync operations | - -### Docker Compose variables - -When using the Docker Compose setup, these variables go in your `.env` file and are interpolated into the Compose file: - -| Variable | Required | Default | Description | -|----------|----------|---------|-------------| -| `POSTGRES_PASSWORD` | Yes | -- | Password for the PostgreSQL `vectorflow` user | -| `VF_VERSION` | No | `latest` | Docker image tag to pull | - -## Agent environment variables - -| Variable | Required | Default | Description | -|----------|----------|---------|-------------| -| `VF_URL` | Yes | -- | VectorFlow server URL (e.g., `https://vectorflow.example.com`) | -| `VF_TOKEN` | First run only | -- | Enrollment token from the environment detail page. Only needed for initial registration | -| `VF_DATA_DIR` | No | `/var/lib/vf-agent` | Data directory for configs, tokens, and certificates | -| `VF_VECTOR_BIN` | No | `vector` | Path to the Vector binary | -| `VF_POLL_INTERVAL` | No | `15s` | How often the agent polls the server for changes | -| `VF_LOG_LEVEL` | No | `info` | Logging verbosity: `debug`, `info`, `warn`, `error` | -| `VF_AGENT_USER` | Docker only | — | Run the agent as this user instead of root. The entrypoint creates the user if needed. | - -## Database connection - -VectorFlow requires PostgreSQL 17 or later. The connection is configured via `DATABASE_URL`. - -**Connection string format:** - -``` -postgresql://[user]:[password]@[host]:[port]/[database]?[options] -``` - -**Common options:** - -| Option | Description | -|--------|-------------| -| `sslmode=require` | Enforce TLS for the database connection | -| `connection_limit=10` | Limit the Prisma connection pool size | - -## Example `.env` file - -### Server (Docker Compose) - -```bash -# Required -POSTGRES_PASSWORD=my-strong-database-password -NEXTAUTH_SECRET=Kj8mN2pQ4rT6vX9zA1cE3fG5hI7jL0nO2qR4sU6wY8 - -# Optional -NEXTAUTH_URL=https://vectorflow.example.com -VF_VERSION=latest -``` - -### Agent - -```bash -# Required -VF_URL=https://vectorflow.example.com - -# Only for first enrollment -VF_TOKEN=env_abc123_enrollment_token - -# Optional -VF_DATA_DIR=/var/lib/vf-agent -VF_VECTOR_BIN=/usr/bin/vector -VF_POLL_INTERVAL=15s -VF_LOG_LEVEL=info -``` - -## System settings (UI) - -The following settings are configured through the **Settings** page in the VectorFlow UI. These values are stored in the database and take effect immediately. - -### Settings navigation - -The Settings page has its own dedicated sidebar navigation, separate from the main application sidebar. When you click **Settings** in the main navigation, the sidebar transitions to show the settings menu organized into four sections: - -| Section | Pages | Visibility | -|---------|-------|------------| -| **System** | Fleet, Backup | Super Admin only | -| **Security** | Authentication, SCIM | Super Admin only | -| **Organization** | Team, Users, Service Accounts | Team: Admin+, Users: Super Admin, Service Accounts: Admin+ | -| **Operations** | Audit | Admin+ | - -Click the back arrow at the top of the settings sidebar to return to the main navigation. The transition between the main sidebar and settings sidebar is animated for a smooth experience. - -{% hint style="info" %} -Team admins see a subset of the settings pages (Team, Service Accounts, Audit). Super admins see all settings pages. Viewers and editors do not have access to the Settings page. -{% endhint %} - -### Fleet settings - -| Setting | Default | Range | Description | -|---------|---------|-------|-------------| -| Poll Interval | 15,000 ms | 1,000--300,000 | How frequently agents check in with the server | -| Unhealthy Threshold | 3 | 1--100 | Number of missed heartbeats before an agent is marked **Unreachable** | -| Metrics Retention | 7 days | 1--365 | How long node and pipeline metrics are kept | -| Logs Retention | 3 days | 1--30 | How long pipeline logs are kept | - -### Backup settings - -| Setting | Default | Description | -|---------|---------|-------------| -| Enabled | Off | Toggle automatic scheduled backups | -| Cron Schedule | `0 2 * * *` | Cron expression for backup timing (default: 2:00 AM daily) | -| Retention Count | 7 | Number of backups to keep before deleting the oldest | - -For more details, see [Backup & Restore](backup-restore.md). - -### OIDC / SSO settings - -OIDC is configured in the Settings page under the **Authentication** tab. See [Authentication](authentication.md) for full setup instructions. - -## Prometheus metrics - -The `/api/metrics` endpoint exposes metrics in Prometheus exposition format. It requires a service account API token with the `metrics.read` permission. - -Create a service account in **Settings > Service Accounts** with `metrics.read` permission, then configure your Prometheus scrape config: - -```yaml -scrape_configs: - - job_name: vectorflow - scheme: https - metrics_path: /api/metrics - bearer_token: "vf_your_service_account_key" - static_configs: - - targets: ["vectorflow.example.com"] -``` - -## Ports reference - -| Service | Default Port | Description | -|---------|-------------|-------------| -| VectorFlow Server | 3000 | Web UI and API | -| PostgreSQL | 5432 | Database (not exposed externally in Docker) | -| Vector API | 8686 | Vector GraphQL API (per node, managed by agent) | - -## File paths - -### Server - -| Path | Description | -|------|-------------| -| `/app/.vectorflow/` | Server data directory (Docker volume mount) | -| `/backups/` | Database backup storage (Docker volume mount) | - -### Agent - -| Path | Description | -|------|-------------| -| `/var/lib/vf-agent/` | Agent data directory (default) | -| `/var/lib/vf-agent/node-token` | Persistent authentication token (mode `0600`) | -| `/var/lib/vf-agent/pipelines/` | Pipeline configuration files | -| `/var/lib/vf-agent/certs/` | Deployed TLS certificates | -| `/var/lib/vector/` | Vector data directory | diff --git a/docs/public/operations/gitops.md b/docs/public/operations/gitops.md deleted file mode 100644 index 32fdf0d9..00000000 --- a/docs/public/operations/gitops.md +++ /dev/null @@ -1,158 +0,0 @@ -# GitOps (Pipeline-as-Code) - -VectorFlow supports **pipeline-as-code** workflows where pipeline configurations are stored in a Git repository and kept in sync between VectorFlow and your version control system. - -## Supported Git Providers - -VectorFlow supports the following Git hosting providers: - -| Provider | Webhook Verification | API Operations | -|----------|---------------------|----------------| -| **GitHub** | HMAC-SHA256 (`X-Hub-Signature-256`) | Contents API, Pulls API | -| **GitLab** | Shared secret (`X-Gitlab-Token`) | Repository Files API, Merge Requests API | -| **Bitbucket** | HMAC-SHA256 (`X-Hub-Signature`) | Source API, Pullrequests API | - -The provider is **auto-detected** from the repository URL domain (e.g., `github.com`, `gitlab.com`, `bitbucket.org`). For self-hosted instances (e.g., `gitlab.internal.corp`), you can explicitly set the provider in the Git Integration settings. - -## Modes - -Each environment can operate in one of four GitOps modes: - -| Mode | Direction | Description | -|------|-----------|-------------| -| **Off** | -- | Git integration is disabled (default). | -| **Push Only** | VectorFlow -> Git | Pipeline YAML is committed to the repo whenever you deploy or delete a pipeline. The repo serves as an audit trail. | -| **Bi-directional** | VectorFlow <-> Git | In addition to push, a webhook from your Git provider triggers VectorFlow to import changed YAML files automatically. | -| **Promotion** | VectorFlow -> Git -> VectorFlow | Promoting a pipeline creates a pull request (or merge request). Merging it automatically deploys the promoted config. | - -## Setting up Push Only - -Push-only mode commits pipeline YAML files to your Git repository every time you deploy or delete a pipeline. - -{% stepper %} -{% step %} -### Configure Git Integration -On the environment detail page, fill in the **Git Integration** card: -- **Repository URL** -- HTTPS URL of the target repo (e.g., `https://github.com/org/pipeline-configs.git`) -- **Branch** -- The branch to push to (default: `main`) -- **Access Token** -- A personal access token with write access -- **Git Provider** -- Leave as "Auto-detect" for hosted providers, or explicitly select for self-hosted instances -{% endstep %} -{% step %} -### Set GitOps Mode to Push Only -In the **GitOps Mode** dropdown, select **Push Only**. -{% endstep %} -{% step %} -### Save -Click **Save**. You can verify connectivity with **Test Connection** before saving. -{% endstep %} -{% endstepper %} - -From this point forward, every pipeline deploy writes the generated YAML to `{environment-name}/{pipeline-name}.yaml` in the configured repository, and every pipeline deletion removes the file. - -{% hint style="info" %} -Git sync is a post-deploy side effect. If the Git push fails, the pipeline deploy still succeeds -- VectorFlow automatically queues the failed sync for retry (up to 3 attempts with exponential backoff). You can monitor sync status in the **Git Sync Status** section on the environment page. -{% endhint %} - -## Setting up Bi-directional GitOps - -Bi-directional mode adds a webhook so that pushes to the Git repository automatically import or update pipelines in VectorFlow. - -{% stepper %} -{% step %} -### Configure Git Integration -On the environment detail page, fill in the **Repository URL**, **Branch**, and **Access Token** in the Git Integration card. -{% endstep %} -{% step %} -### Set GitOps Mode to Bi-directional -Select **Bi-directional** from the **GitOps Mode** dropdown and click **Save**. VectorFlow auto-generates a webhook secret. -{% endstep %} -{% step %} -### Copy the webhook details -After saving, the card shows: -- **Webhook URL** -- The endpoint your Git provider should send push events to. -- **Webhook Secret** -- The secret for signature verification. -{% endstep %} -{% step %} -### Create a Webhook in your Git provider -{% endstep %} -{% endstepper %} - -{% tabs %} -{% tab title="GitHub" %} -In your GitHub repository, go to **Settings > Webhooks > Add webhook**: -- **Payload URL** -- Paste the Webhook URL from VectorFlow. -- **Content type** -- Select `application/json`. -- **Secret** -- Paste the Webhook Secret from VectorFlow. -- **Events** -- Select **Just the push event** (and **Pull requests** if using Promotion mode). -{% endtab %} -{% tab title="GitLab" %} -In your GitLab project, go to **Settings > Webhooks > Add new webhook**: -- **URL** -- Paste the Webhook URL from VectorFlow. -- **Secret token** -- Paste the Webhook Secret from VectorFlow. -- **Trigger** -- Check **Push events** (and **Merge request events** if using Promotion mode). -{% endtab %} -{% tab title="Bitbucket" %} -In your Bitbucket repository, go to **Repository settings > Webhooks > Add webhook**: -- **URL** -- Paste the Webhook URL from VectorFlow. -- **Secret** -- Paste the Webhook Secret from VectorFlow. -- **Triggers** -- Select **Repository push** (and **Pull request merged/declined** if using Promotion mode). -{% endtab %} -{% endtabs %} - -## How the import works - -When a push event arrives: - -1. VectorFlow verifies the webhook signature using the appropriate method for the Git provider. -2. It checks that the push targets the configured branch. -3. For each added or modified `.yaml` / `.yml` file in the push, it fetches the file content via the provider's API. -4. The pipeline name is derived from the filename (e.g., `production/my-pipeline.yaml` becomes `my-pipeline`). -5. If a pipeline with a matching `gitPath` or name already exists in the environment, its graph is replaced. Otherwise, a new pipeline is created. -6. If the environment has **Require Deploy Approval** enabled, imported pipelines are saved as drafts with a pending deploy request instead of being deployed immediately. - -{% hint style="warning" %} -Bi-directional mode means the Git repository is the source of truth. Any manual edits made in the VectorFlow UI may be overwritten on the next push to the repository. The pipeline editor shows a banner to remind users of this. -{% endhint %} - -## Pipeline Name / Filename Decoupling - -VectorFlow uses a stable `gitPath` field to track the file path in Git for each pipeline. This means: - -- **Renaming a pipeline** in VectorFlow does not change its filename in Git. The original path is preserved. -- **First sync** automatically assigns a `gitPath` based on the environment and pipeline name slugs. -- **Webhook imports** match files by `gitPath` first, then by name as a fallback. - -This prevents broken sync when pipelines are renamed after initial setup. - -## Sync Status and Retries - -The **Git Sync Status** section on the environment detail page shows: - -- **Health badge** -- Green (healthy), yellow (pending retries), or red (failed). -- **Last successful sync** timestamp. -- **Recent sync jobs** with status, attempt count, and per-job retry buttons. -- **Import errors** from webhook events (YAML parse failures, invalid filenames, etc.). - -Failed sync operations are automatically retried up to 3 times with exponential backoff (30 seconds, 2 minutes, 10 minutes). After all retries are exhausted, a `git_sync_failed` alert is fired (if subscribed). You can also manually retry failed jobs from the UI. - -The environment list page shows a warning badge when an environment has unresolved sync failures. - -## File layout - -VectorFlow expects pipeline YAML files to follow the standard Vector configuration format: - -``` -repo-root/ - environment-name/ - pipeline-a.yaml - pipeline-b.yaml - other-environment/ - pipeline-c.yaml -``` - -The directory name should match the slugified environment name. Files must have a `.yaml` or `.yml` extension. - -## Disabling GitOps - -To disable GitOps, set the mode back to **Off** and click **Save**. The webhook secret is cleared, and incoming webhook requests will be rejected. diff --git a/docs/public/operations/outbound-webhooks.md b/docs/public/operations/outbound-webhooks.md deleted file mode 100644 index d4d96737..00000000 --- a/docs/public/operations/outbound-webhooks.md +++ /dev/null @@ -1,154 +0,0 @@ -# Outbound Webhooks - -VectorFlow can send HMAC-signed HTTP notifications to external systems when key events occur. Use outbound webhooks to integrate with incident management tools, CI/CD pipelines, custom dashboards, or any service that accepts HTTP callbacks. - -## Overview - -Each **webhook endpoint** is a URL that receives POST requests when one or more subscribed events fire. Requests carry Standard-Webhooks-compliant signature headers so receivers can verify authenticity. - -Key properties of each endpoint: - -- **Name** — A descriptive label shown in the management UI. -- **URL** — The HTTPS endpoint that receives event payloads. -- **Event types** — One or more event types that trigger delivery. -- **Signing secret** — Optional HMAC key. When set, every request includes a `webhook-signature` header. -- **Enabled / Disabled** — Endpoints can be temporarily disabled without deleting them. - -## Supported events - -| Event | When it fires | -|-------|---------------| -| `deploy_completed` | A pipeline deployment completed successfully | -| `deploy_rejected` | A deployment request was rejected | -| `deploy_cancelled` | A pending deployment was cancelled | -| `pipeline_crashed` | A running pipeline process exited unexpectedly | -| `node_unreachable` | A fleet node stopped sending heartbeats | -| `node_joined` | A new fleet node enrolled | -| `node_left` | A fleet node was removed | -| `promotion_completed` | A pipeline was promoted to another environment | - -## Creating a webhook endpoint - -{% stepper %} -{% step %} -### Open Webhook Settings -Navigate to **Settings → Outbound Webhooks**. -{% endstep %} -{% step %} -### Click New Endpoint -Click the **New Endpoint** button in the top-right corner. -{% endstep %} -{% step %} -### Fill in the form -- **Name** — A descriptive label (e.g., "PagerDuty Pipeline Alerts"). -- **Endpoint URL** — The HTTPS URL that will receive events. -- **Signing secret** — Optional. If provided, every request is signed and the secret is shown once — copy it before closing the dialog. -- **Event types** — Select one or more events this endpoint should receive. -{% endstep %} -{% step %} -### Create -Click **Create**. If you provided a signing secret, the dialog shows it once — copy it to a secure location. -{% endstep %} -{% endstepper %} - -{% hint style="warning" %} -The signing secret is displayed once at creation time and cannot be retrieved afterwards. Store it securely in your receiving application's configuration. -{% endhint %} - -## Payload format - -All webhook deliveries use the same envelope format: - -```json -{ - "type": "deploy_completed", - "timestamp": "2026-03-27T12:00:00.000Z", - "data": { - // Event-specific fields - } -} -``` - -| Field | Description | -|-------|-------------| -| `type` | The `AlertMetric` value that triggered this delivery | -| `timestamp` | ISO-8601 UTC timestamp of the event | -| `data` | Event-specific payload fields | - -## Verifying signatures - -When a signing secret is configured, every request includes three headers: - -| Header | Description | -|--------|-------------| -| `webhook-id` | Unique UUID for this delivery | -| `webhook-timestamp` | Unix timestamp (integer seconds) | -| `webhook-signature` | `v1,{base64(HMAC-SHA256)}` | - -To verify a request, compute: - -``` -signing_string = "{webhook-id}.{webhook-timestamp}.{raw_request_body}" -expected_sig = base64( HMAC-SHA256(signing_string, secret) ) -``` - -The received signature header is `v1,{expected_sig}`. Compare the value after the `v1,` prefix. - -{% hint style="info" %} -VectorFlow follows the [Standard Webhooks](https://www.standardwebhooks.com/) specification. Libraries are available for most languages. -{% endhint %} - -## Delivery and retry - -VectorFlow attempts delivery immediately when an event fires. If the request fails, it retries with exponential backoff: - -| Attempt | Delay | -|---------|-------| -| 1 | Immediate | -| 2 | 30 seconds | -| 3 | 5 minutes | -| 4 | 30 minutes | -| 5+ | 2 hours | - -**Permanent failures** (HTTP 4xx excluding 429, DNS errors, connection refused) are moved to **dead-letter** immediately and are not retried. - -**Transient failures** (HTTP 5xx, HTTP 429, timeouts) are retried up to the schedule above. - -## Delivery history - -Each endpoint row in the settings UI can be expanded to show recent deliveries: - -- **Event type** — Which event triggered the delivery. -- **Status** — `success`, `failed`, `dead_letter`, or `pending`. -- **HTTP status** — The HTTP status code returned by the receiver. -- **Attempt** — Which retry attempt this represents. -- **Requested / Completed** — Relative timestamps. - -## Test delivery - -To send a test delivery to an endpoint without waiting for a real event, click the **Play** button (▶) in the endpoint row. The test payload is: - -```json -{ - "type": "test", - "timestamp": "2026-03-27T12:00:00.000Z", - "data": { - "message": "Test delivery from VectorFlow", - "endpointId": "..." - } -} -``` - -The UI shows a success or failure notification. Check delivery history for the HTTP status code and any error details. - -## Managing endpoints - -| Action | How | -|--------|-----| -| **Enable / Disable** | Click the toggle icon in the endpoint row | -| **Edit** | Click the pencil icon to update name, URL, events, or rotate the secret | -| **Delete** | Click the trash icon — all delivery history is also deleted | - -{% hint style="info" %} -Disabling an endpoint stops deliveries immediately without deleting the endpoint or its history. Re-enable it when ready to receive events again. -{% endhint %} diff --git a/docs/public/operations/scim.md b/docs/public/operations/scim.md deleted file mode 100644 index fac80161..00000000 --- a/docs/public/operations/scim.md +++ /dev/null @@ -1,215 +0,0 @@ -# SCIM Provisioning - -VectorFlow supports SCIM 2.0 (System for Cross-domain Identity Management) for automated user provisioning and deprovisioning from your identity provider. When SCIM is enabled, your IdP can automatically create, update, and deactivate user accounts in VectorFlow. - -## Overview - -SCIM provisioning automates the user lifecycle: - -| Action | What happens in VectorFlow | -|--------|---------------------------| -| **Create user** | A new VectorFlow user account is created with a random password (SSO users authenticate via their IdP, not local credentials) | -| **Update user** | User attributes (name, email) are updated | -| **Deactivate user** | The user account is locked, preventing login | -| **Delete user** | The user account is locked (not deleted, to preserve audit history) | - -SCIM group membership is tracked internally — VectorFlow knows exactly which IdP groups each user belongs to. When your IdP pushes group membership changes, VectorFlow reconciles team memberships based on the configured [group mapping table](authentication.md#group-mapping), adding users to mapped teams and removing them when they no longer qualify. - -## Setup - -{% stepper %} -{% step %} -### Enable SCIM in VectorFlow - -Navigate to **Settings > Auth** (Super Admin required). Toggle **Enable SCIM** on. -{% endstep %} -{% step %} -### Generate a bearer token - -Click **Generate Token**. A bearer token is displayed once -- copy it and store it securely. This token authenticates your IdP's SCIM requests to VectorFlow. - -{% hint style="warning" %} -The token is shown only once. If you lose it, generate a new one. The previous token is immediately invalidated. -{% endhint %} -{% endstep %} -{% step %} -### Copy the SCIM base URL - -The SCIM base URL is displayed on the settings page: - -``` -https://your-vectorflow-url/api/scim/v2 -``` -{% endstep %} -{% step %} -### Configure your identity provider - -Enter the SCIM base URL and bearer token into your IdP's SCIM provisioning settings. See the IdP-specific instructions below. -{% endstep %} -{% step %} -### Test and assign - -Test the SCIM connection from your IdP, then assign users and groups to the VectorFlow application in your IdP. -{% endstep %} -{% endstepper %} - -## Group lifecycle and reconciliation - -SCIM group membership is tracked internally — VectorFlow maintains a record of exactly which IdP groups each user belongs to via SCIM. When group membership changes, VectorFlow reconciles team memberships using the shared [group mapping table](authentication.md#group-mapping). - -### How SCIM Group operations work - -| Operation | What happens | -|-----------|-------------| -| **POST /Groups** | Creates the group and processes initial members. Each member's team memberships are reconciled against the mapping table. | -| **PATCH add members** | Adds users to the group and reconciles their team memberships — users gain access to mapped teams with the configured role. | -| **PATCH remove members** | Removes users from the group and reconciles — if a user no longer belongs to any group that maps to a given team, their membership on that team is removed. | -| **PUT /Groups** | Full member sync. VectorFlow compares the provided member list against the current membership, adds missing members, removes absent members, and reconciles all affected users. | -| **DELETE /Groups** | Deletes the group, cascading to all group membership records. All affected users' team memberships are reconciled (memberships that were only justified by the deleted group are removed). | -| **PATCH displayName** | Updates the group name. If the new name matches a different mapping, team memberships are reconciled accordingly for all group members. | - -### Role assignment - -When SCIM pushes group membership changes, VectorFlow assigns roles using the same team mappings configured for OIDC: - -1. If **Team Mappings** are configured in **Settings > Team & Role Mapping**, the mapping's role is used -2. If a user is in multiple groups that map to the same team, the **highest role wins** (Admin > Editor > Viewer) -3. If no mapping matches, the **Default Role** is used -4. If no default role is set, `VIEWER` is assigned - -This ensures consistent role assignment regardless of whether sync happens via SCIM push or OIDC login. - -{% hint style="info" %} -**Manual assignments are preserved.** Team memberships assigned manually in the VectorFlow UI are never modified by SCIM group sync. Only memberships created by group sync are subject to reconciliation. -{% endhint %} - -## IdP-specific instructions - -{% tabs %} -{% tab title="Okta" %} -1. In your Okta admin console, open the VectorFlow application (or create a new SAML/OIDC app) -2. Go to the **Provisioning** tab and click **Configure API Integration** -3. Check **Enable API Integration** -4. Set **SCIM connector base URL** to your VectorFlow SCIM URL (e.g., `https://vectorflow.example.com/api/scim/v2`) -5. Set **API Token** to the bearer token generated in VectorFlow -6. Click **Test API Credentials** to verify the connection -7. Save the integration -8. Under **Provisioning > To App**, enable: - - Create Users - - Update User Attributes - - Deactivate Users -9. Go to the **Assignments** tab and assign users or groups -{% endtab %} -{% tab title="Entra ID (Azure AD)" %} -1. In the Azure portal, navigate to **Enterprise Applications** and select your VectorFlow application -2. Go to **Provisioning** and set the mode to **Automatic** -3. Under **Admin Credentials**: - - **Tenant URL**: Your VectorFlow SCIM URL (e.g., `https://vectorflow.example.com/api/scim/v2`) - - **Secret Token**: The bearer token generated in VectorFlow -4. Click **Test Connection** to verify -5. Configure **Attribute Mappings** as needed (the defaults usually work) -6. Set **Provisioning Status** to **On** -7. Save and assign users/groups to the application -{% endtab %} -{% tab title="OneLogin" %} -1. In the OneLogin admin console, open your VectorFlow application -2. Go to **Configuration** -3. Set **SCIM Base URL** to your VectorFlow SCIM URL -4. Set **SCIM Bearer Token** to the token generated in VectorFlow -5. Under **Provisioning**, enable the desired actions -6. Assign users via the **Users** tab -{% endtab %} -{% tab title="Other IdPs" %} -Any SCIM 2.0 compatible identity provider can integrate with VectorFlow. Configure: - -- **Base URL**: `https://your-vectorflow-url/api/scim/v2` -- **Authentication**: Bearer token (HTTP header) -- **Supported resources**: Users, Groups -{% endtab %} -{% endtabs %} - -## Supported endpoints - -| Method | Path | Description | -|--------|------|-------------| -| `GET` | `/api/scim/v2/Users` | List users (supports `filter`, `startIndex`, `count`) | -| `POST` | `/api/scim/v2/Users` | Create a user | -| `GET` | `/api/scim/v2/Users/:id` | Get a user | -| `PUT` | `/api/scim/v2/Users/:id` | Replace a user | -| `PATCH` | `/api/scim/v2/Users/:id` | Partial update (commonly used for deactivation) | -| `DELETE` | `/api/scim/v2/Users/:id` | Deactivate a user (locks the account) | -| `GET` | `/api/scim/v2/Groups` | List groups | -| `POST` | `/api/scim/v2/Groups` | Create a group and process initial members | -| `GET` | `/api/scim/v2/Groups/:id` | Get a group | -| `PATCH` | `/api/scim/v2/Groups/:id` | Update group membership (add/remove members, rename) | -| `PUT` | `/api/scim/v2/Groups/:id` | Replace group (full member sync) | -| `DELETE` | `/api/scim/v2/Groups/:id` | Delete group and cascade membership removal | - -### Group identity fields - -Group responses include both the VectorFlow-assigned `id` and the IdP-provided `externalId`. The `externalId` is set when the IdP includes it in the initial POST or subsequent PUT/PATCH requests. - -{% hint style="info" %} -The `externalId` field is critical for IdP sync stability. Without it, some identity providers cannot match remote groups to local groups during reconciliation, causing unnecessary delete-and-recreate cycles. If your IdP performs full group syncs, ensure it sends `externalId` in group creation requests. -{% endhint %} - -### Filtering - -The Users endpoint supports basic SCIM filtering: - -``` -GET /api/scim/v2/Users?filter=userName eq "john@example.com" -GET /api/scim/v2/Users?filter=externalId eq "abc123" -``` - -The Groups endpoint supports: - -``` -GET /api/scim/v2/Groups?filter=displayName eq "Platform Team" -``` - -## Security - -- The SCIM bearer token is encrypted with AES-256-GCM before storage (same encryption used for OIDC client secrets) -- The token is shown only once when generated; VectorFlow does not store the plaintext -- SCIM endpoints require a valid bearer token on every request -- Disabling SCIM clears the stored token -- All SCIM operations are recorded in the audit log under the **ScimUser** entity type - -### Audit logging - -SCIM user operations (create, update, deactivate, delete) are logged with the `ScimUser` entity type to distinguish them from manual user operations. On the **Audit** page, you can filter by: - -- **ScimUser** -- shows only SCIM user provisioning events -- **ScimGroup** -- shows only SCIM group operations -- **SCIM (All)** -- a combined filter that shows all SCIM-related activity (both user and group operations) in a single view - -This makes it easy to audit all identity provider-driven changes for compliance purposes. - -{% hint style="info" %} -SCIM provisioning works best alongside OIDC/SSO. Users created via SCIM receive a random password and should authenticate through your identity provider, not with local credentials. -{% endhint %} - -## Troubleshooting - -| Issue | Solution | -|-------|----------| -| IdP test connection fails | Verify the SCIM base URL is reachable from your IdP. Check that the bearer token is correct and SCIM is enabled in VectorFlow settings. | -| Users not being created | Check that "Create Users" is enabled in your IdP's provisioning settings. Review the IdP provisioning logs for error details. | -| Users not being deactivated | Check that "Deactivate Users" is enabled in your IdP. VectorFlow locks the account (sets `lockedAt`) rather than deleting it. | -| Group membership not syncing | SCIM Groups are mapped to VectorFlow Teams via the shared group mapping table in **Settings > Team & Role Mapping**. Ensure groups are assigned to the VectorFlow application in your IdP and that corresponding mappings exist. Without a matching mapping, group membership is tracked but no team assignment is created. | -| Token expired/invalid | Generate a new token from **Settings > Auth** and update it in your IdP. The previous token is invalidated immediately. | - -### SCIM sync returns HTML error - -If your IdP reports an error like `invalid character '<' looking for beginning of value`, the SCIM base URL may be incorrect. Ensure it is set to: - -``` -https://your-vectorflow-url/api/scim/v2 -``` - -VectorFlow exposes a `ServiceProviderConfig` endpoint at `/api/scim/v2/ServiceProviderConfig` that your IdP can use to verify connectivity. - -### Roles not updating via SCIM - -Ensure that **Team Mappings** are configured in **Settings > Team & Role Mapping**. Without team mappings, all SCIM-provisioned members default to the **VIEWER** role. If a user belongs to multiple groups that map to the same team, the highest role wins (Admin > Editor > Viewer). diff --git a/docs/public/operations/security.md b/docs/public/operations/security.md deleted file mode 100644 index 32ea47be..00000000 --- a/docs/public/operations/security.md +++ /dev/null @@ -1,186 +0,0 @@ -# Security - -This page covers VectorFlow's security architecture: how secrets are managed, how data is encrypted, and recommended hardening practices for production deployments. - -## Secret management - -VectorFlow provides a built-in secret store for each environment. Secrets hold sensitive values -- API keys, database passwords, authentication tokens -- that pipelines need at runtime but should not be stored in plain text in pipeline configurations. - -### Creating secrets - -Secrets are created on the **environment detail page** under **Secrets & Certificates**. Each secret has a name and a value. Secret names must start with a letter or number and can contain letters, numbers, hyphens, and underscores. - -Secrets are scoped to a single environment. The same secret name can hold different values in different environments (e.g., a `DB_PASSWORD` secret with a test value in dev and a production value in prod). - -### How secrets are stored - -When you create or update a secret, the value is encrypted with **AES-256-GCM** before being written to the database. The plaintext value is never stored. Only the encrypted ciphertext is persisted. - -### How secrets are resolved - -When a pipeline is deployed, VectorFlow generates the Vector configuration file. During generation, it scans the configuration for **secret references** and converts them into environment variable placeholders. The actual secret values are delivered separately to the agent and injected as environment variables into the Vector process. - -Secret references use the syntax: - -``` -SECRET[secret-name] -``` - -For example, a Kafka sink node configured with: - -```yaml -sasl: - username: my-user - password: SECRET[KAFKA_PASSWORD] -``` - -At deploy time, VectorFlow converts this to: - -```yaml -sasl: - username: "my-user" - password: "${VF_SECRET_KAFKA_PASSWORD}" -``` - -The actual decrypted value of `KAFKA_PASSWORD` is delivered to the agent in a separate `secrets` dictionary alongside the YAML. The agent sets `VF_SECRET_KAFKA_PASSWORD` as an environment variable when starting the Vector process, and Vector's built-in environment variable interpolation resolves the placeholder at runtime. - -{% hint style="info" %} -Secret values never appear in pipeline YAML -- not in the database, not in config files on disk, and not in the YAML sent to agents. Only environment variable placeholders are embedded in the configuration. The actual values are delivered separately in the config response and injected as process environment variables. -{% endhint %} - -#### Secret name normalization - -Secret names are converted to valid environment variable names using these rules: - -- Prefixed with `VF_SECRET_` -- All non-alphanumeric characters replaced with underscores -- Converted to uppercase - -For example, `my-api.key` becomes `VF_SECRET_MY_API_KEY`. If two secrets normalize to the same environment variable name (e.g., `db-password` and `db_password` both become `VF_SECRET_DB_PASSWORD`), VectorFlow logs a warning and the last match in alphabetical order takes precedence. - -### Sensitive fields - -Pipeline component fields that contain credentials -- passwords, API keys, tokens -- are restricted to **secret references only** in the pipeline editor. You cannot type a plaintext value into a sensitive field. Instead, the editor presents a secret picker that lets you select an existing secret or create a new one inline. - -Fields are treated as sensitive when: - -- The Vector component schema marks them as `sensitive: true` -- The field name matches a pattern like `password`, `secret`, `token`, or `api_key` - -### Certificate references - -TLS certificates work the same way, using the `CERT[name]` syntax. When a pipeline references a certificate, VectorFlow decrypts the certificate data and deploys it as a file on the agent node: - -```yaml -tls: - crt_file: CERT[my-tls-cert] -``` - -The agent receives the certificate file and writes it to `/var/lib/vf-agent/certs/`. - -## Encryption - -### At rest - -VectorFlow encrypts sensitive data before storing it in PostgreSQL: - -| Data | Algorithm | Key derivation | -|------|-----------|---------------| -| Secrets (user-created) | AES-256-GCM | SHA-256 hash of `NEXTAUTH_SECRET` | -| Certificates | AES-256-GCM | SHA-256 hash of `NEXTAUTH_SECRET` | -| OIDC client secret | AES-256-GCM | SHA-256 hash of `NEXTAUTH_SECRET` | -| Sensitive node config fields | AES-256-GCM | SHA-256 hash of `NEXTAUTH_SECRET` | -| Git access tokens (audit trail) | AES-256-GCM | SHA-256 hash of `NEXTAUTH_SECRET` | -| User passwords | bcrypt (cost 12) | Built-in salt | -| TOTP secrets | AES-256-GCM | SHA-256 hash of `NEXTAUTH_SECRET` | -| 2FA backup codes | SHA-256 hash | -- | -| Webhook signing | HMAC-SHA256 | Per-webhook secret | - -{% hint style="danger" %} -`NEXTAUTH_SECRET` is the master encryption key for all sensitive data. If this value is changed or lost, all encrypted data (secrets, certificates, OIDC config) becomes permanently unrecoverable. Back up this value securely. -{% endhint %} - -### Sensitive field auto-encryption - -Pipeline node configurations may contain sensitive fields (passwords, API keys, tokens). VectorFlow automatically detects and encrypts these fields when saving a pipeline, based on: - -1. Fields marked as `sensitive: true` in the Vector component schema -2. Field names matching patterns like `password`, `secret`, `token`, or `api_key` - -These fields are encrypted before database storage and decrypted only when generating the Vector configuration for deployment. - -### In transit - -- **Browser to server** -- HTTPS (TLS termination via reverse proxy or load balancer) -- **Agent to server** -- HTTPS over the same endpoint. Agents authenticate with a bearer token issued during enrollment. -- **Server to database** -- Configurable via `sslmode` in the `DATABASE_URL` connection string - -## Network security - -### Agent connections - -Agents initiate all connections to the server. The server never connects outbound to agents. This means: - -- Agents can run behind firewalls and NATs -- No inbound ports need to be opened on agent nodes -- Only outbound HTTPS (port 443) to the VectorFlow server is required - -### Reverse proxy - -In production, place VectorFlow behind a reverse proxy (Nginx, Caddy, Traefik) for TLS termination. See [Deploy the Server](../getting-started/deploy-server.md) for example configurations. - -### Agent authentication - -Each agent authenticates using a **node token** -- a unique bearer token issued during enrollment. The token is stored at `/var/lib/vf-agent/node-token` with file permissions `0600` (readable only by the owner). - -Enrollment tokens (used for initial registration) can be regenerated or revoked from the environment detail page. - -## Audit logging - -Every mutation in VectorFlow is logged to an audit trail. Audit entries include: - -- **Who** -- The authenticated user -- **What** -- The action performed and entity affected -- **When** -- Timestamp -- **Where** -- Client IP address -- **Changes** -- A diff of the fields that were modified - -Sensitive fields (passwords, tokens, secrets) are automatically redacted in audit log entries. - -View the audit log from the **Audit** page in the sidebar. The audit log supports filtering by entity type, including dedicated filters for SCIM operations: - -- **ScimUser** -- SCIM user provisioning events (create, update, deactivate) -- **ScimGroup** -- SCIM group operations (create, update members, delete) -- **SCIM (All)** -- Combined filter showing all SCIM activity in one view - -These filters make it straightforward to review all identity provider-driven changes for compliance audits. - -## Security hardening checklist - -Use this checklist to harden your VectorFlow deployment for production: - -{% hint style="warning" %} -Complete all items before exposing VectorFlow to untrusted networks. -{% endhint %} - -- [ ] **Generate a strong `NEXTAUTH_SECRET`** -- Use `openssl rand -base64 32` to generate a random 32+ character secret. Never use default or weak values. - -- [ ] **Generate a strong `POSTGRES_PASSWORD`** -- Use a random, high-entropy password for the database. - -- [ ] **Enable TLS/HTTPS** -- Place VectorFlow behind a reverse proxy with TLS termination. All agent communication should use HTTPS. - -- [ ] **Enable 2FA for all users** -- Use team-level "Require 2FA" settings to enforce two-factor authentication for all team members. - -- [ ] **Use OIDC/SSO** -- Integrate with your organization's identity provider for centralized authentication and MFA. - -- [ ] **Restrict network access** -- Limit access to the VectorFlow server to trusted networks. Use firewall rules or network policies. - -- [ ] **Enable database TLS** -- Add `sslmode=require` to your `DATABASE_URL` to encrypt the connection between VectorFlow and PostgreSQL. - -- [ ] **Regular backups** -- Enable automatic daily backups and verify restores periodically. See [Backup & Restore](backup-restore.md). - -- [ ] **Keep software updated** -- Regularly update VectorFlow server and agents to get security patches. See [Upgrading](upgrading.md). - -- [ ] **Review audit logs** -- Periodically review the audit log for unexpected actions or unauthorized access attempts. - -- [ ] **Lock unused accounts** -- Lock user accounts that are no longer active instead of leaving them accessible. diff --git a/docs/public/operations/service-accounts.md b/docs/public/operations/service-accounts.md deleted file mode 100644 index 4749cf05..00000000 --- a/docs/public/operations/service-accounts.md +++ /dev/null @@ -1,142 +0,0 @@ -# Service Accounts - -Service accounts provide API keys for programmatic access to VectorFlow's REST API. Unlike user sessions (cookie-based), service account keys use Bearer token authentication and are scoped to a single environment with granular permissions. - ---- - -## Overview - -| Feature | Details | -|---------|---------| -| Authentication | `Authorization: Bearer vf_live_...` | -| Scope | One environment per service account | -| Permissions | Granular per-resource (read/manage) | -| Key format | `vf_live_<48 hex chars>` | -| Storage | SHA-256 hashed (raw key shown once at creation) | -| Expiration | Optional (30, 60, 90 days, or never) | - -{% hint style="warning" %} -API keys are shown **only once** at creation time. If you lose a key, revoke the service account and create a new one. -{% endhint %} - ---- - -## Creating a Service Account - -{% stepper %} -{% step %} -### Navigate to Settings -Go to **Settings** and click **Service Accounts & API Keys**. -{% endstep %} - -{% step %} -### Click Create Service Account -Fill in the following fields: - -| Field | Required | Description | -|-------|----------|-------------| -| Name | Yes | A descriptive name (e.g., "CI/CD Deployer") | -| Description | No | Optional context about the account's purpose | -| Environment | Yes | The environment this key can access | -| Expiration | No | Auto-expire after 30, 60, or 90 days (default: never) | -| Permissions | Yes | At least one permission must be selected | -{% endstep %} - -{% step %} -### Copy Your API Key -After creation, a modal displays the raw API key. **Copy it immediately** -- it cannot be retrieved again. -{% endstep %} -{% endstepper %} - ---- - -## Permissions - -Permissions control what the service account can do via the REST API. - -| Permission | Description | -|-----------|-------------| -| `pipelines.read` | List and view pipeline details, versions | -| `pipelines.deploy` | Deploy, undeploy, and rollback pipelines | -| `nodes.read` | List and view node details | -| `nodes.manage` | Toggle node maintenance mode | -| `secrets.read` | List secret names (values are never exposed) | -| `secrets.manage` | Create, update, and delete secrets | -| `alerts.read` | List alert rules | -| `alerts.manage` | Create alert rules | -| `audit.read` | Read audit log events | - -{% hint style="info" %} -Permissions are enforced per-request. A `403 Forbidden` response indicates the service account lacks the required permission. -{% endhint %} - ---- - -## Managing Service Accounts - -### Revoking - -Revoking a service account **immediately disables** the API key. The service account record is preserved for audit purposes. - -1. Navigate to **Settings > Service Accounts** -2. Click the **ban icon** next to the account -3. Confirm revocation - -### Deleting - -Deleting permanently removes the service account and its audit trail association. - -1. Navigate to **Settings > Service Accounts** -2. Click the **trash icon** next to the account -3. Confirm deletion - -{% hint style="danger" %} -Deletion is irreversible. If you only need to disable access temporarily, use **Revoke** instead. -{% endhint %} - ---- - -## Using the API Key - -Include the key in the `Authorization` header of your HTTP requests: - -```bash -curl -s https://vectorflow.example.com/api/v1/pipelines \ - -H "Authorization: Bearer vf_live_abc123..." -``` - -All REST API endpoints are under `/api/v1/`. See the [API Reference](../reference/api.md) for the complete endpoint list. - ---- - -## Security Best Practices - -- **Least privilege**: Only grant the permissions the service account needs -- **Set expiration**: Use 30-90 day expiration for CI/CD keys and rotate regularly -- **Store securely**: Keep API keys in a secrets manager (not in source code) -- **Monitor usage**: Check the "Last Used" column to detect unused accounts -- **Revoke promptly**: Revoke keys when they are no longer needed or if compromised -- **One per integration**: Create separate service accounts for each CI/CD pipeline or integration - ---- - -## Key Rotation - -VectorFlow does not support in-place key rotation. To rotate a key: - -{% stepper %} -{% step %} -### Create a new service account -Use the same name (with a version suffix) and permissions. -{% endstep %} - -{% step %} -### Update your integration -Replace the old key with the new one in your CI/CD pipeline or integration. -{% endstep %} - -{% step %} -### Revoke the old account -Once the new key is confirmed working, revoke the old service account. -{% endstep %} -{% endstepper %} diff --git a/docs/public/operations/telemetry.md b/docs/public/operations/telemetry.md deleted file mode 100644 index fa8fc4f1..00000000 --- a/docs/public/operations/telemetry.md +++ /dev/null @@ -1,53 +0,0 @@ -# Telemetry - -VectorFlow can send anonymous, aggregate usage stats to a centralised receiver to help the maintainer understand how it's used and prioritise improvements. This is **opt-in** — VectorFlow does not send any data unless you explicitly turn it on. - -## What is collected - -When telemetry is enabled, VectorFlow sends a single JSON payload **once per day** to `https://pulse.terrifiedbug.com/api/v1/ping`. The payload contains: - -| Field | Source | Why | -|---|---|---| -| `schema_version` | hardcoded `1` | lets the receiver evolve without breaking old clients | -| `instance_id` | random ULID generated when you first opt in | a stable identifier for your installation. Anonymous — not linked to any user, organisation, or hostname. | -| `instance_created_at` | timestamp of your first opt-in | cohort tracking — lets us see retention over time | -| `sent_at` | current timestamp | when the heartbeat was sent | -| `vf_version` | `process.env.VF_VERSION` (or `"unknown"`) | which version of VectorFlow you're running | -| `agent_count` | row count of the `VectorNode` table (fleet agents managed by VectorFlow) | scale signal — how many fleet agents are managed | -| `pipeline_count` | derived from the `Pipeline` table — `active` = `isDraft: false` with `deployedAt` set, `paused` = `isDraft: false` with `deployedAt` null, `draft` = `isDraft: true` | `{ active, paused, draft }` — scale and lifecycle signal | -| `auth_method` | derived from OIDC issuer setting | `"credentials"` or `"oidc"` — informs which auth path matters | -| `deployment_mode` | `process.env.VF_DEPLOYMENT_MODE` (or `"unknown"`) | `"docker"`, `"helm"`, `"bare"`, or `"unknown"` | - -### What is NOT collected - -- Hostnames, IP addresses (the receiver's IP-based country lookup is server-side only — your IP is never stored, only a hash for rate limiting) -- Pipeline names, configurations, or VRL code -- User emails, names, or identifiers -- Source/sink endpoint URLs (Kafka brokers, S3 buckets, syslog servers, etc.) -- Any data flowing through your pipelines - -## Example payload - -```json -{ - "schema_version": 1, - "instance_id": "01HX0000000000000000000000", - "instance_created_at": "2026-04-25T10:00:00.000Z", - "sent_at": "2026-04-26T03:42:00.000Z", - "vf_version": "1.4.2", - "agent_count": 5, - "pipeline_count": { "active": 12, "paused": 2, "draft": 3 }, - "auth_method": "credentials", - "deployment_mode": "docker" -} -``` - -## How to opt out - -In VectorFlow, go to **Settings → Telemetry** and turn the toggle off. Telemetry stops immediately. Your instance ID is preserved in case you re-enable later (so you appear as the same anonymous instance, not a new one). - -You can also choose "No thanks" during initial setup if you want telemetry off from day one. - -## Receiver source - -The telemetry receiver is private. Its data-handling code is documented in its own `PRIVACY.md`. No telemetry data is shared per-instance publicly — only aggregate counts on a community stats dashboard. diff --git a/docs/public/operations/upgrading.md b/docs/public/operations/upgrading.md deleted file mode 100644 index 7213ba38..00000000 --- a/docs/public/operations/upgrading.md +++ /dev/null @@ -1,237 +0,0 @@ -# Upgrading - -VectorFlow is designed for zero-downtime upgrades. The server handles database migrations automatically on startup, and agents can self-update without manual intervention. - -## Pre-upgrade checklist - -Before upgrading, complete these steps: - -- [ ] **Create a database backup** -- Navigate to Settings > Backup and click **Create Backup**, or verify that a recent automatic backup exists. See [Backup & Restore](backup-restore.md). -- [ ] **Review release notes** -- Check the [Releases](https://github.com/TerrifiedBug/vectorflow/releases) page for breaking changes, required actions, or migration notes. -- [ ] **Verify agent compatibility** -- Server and agent versions should be kept in sync. The server is backward-compatible with older agents, but newer agents may require a newer server. - -## Version checking - -VectorFlow automatically checks for new releases every 24 hours by querying the GitHub Releases API. When a new version is available, a notification appears on the Settings page showing: - -- Current server version -- Latest available version -- Link to the release notes - -You can force a version check from **Settings** by clicking **Check for Updates**. - -## Server upgrade - -The server upgrade process is the same regardless of how you deployed: replace the binary or image, restart, and migrations run automatically. - -{% tabs %} -{% tab title="Docker" %} -### Docker upgrade - -{% stepper %} -{% step %} -### Pull the new image -```bash -docker compose pull vectorflow -``` - -Or pin a specific version in your `.env` file: -```bash -VF_VERSION=v0.4.0 -``` -{% endstep %} -{% step %} -### Restart the server -```bash -docker compose up -d -``` - -The entrypoint runs `prisma migrate deploy` automatically. Database schema changes are applied before the application starts. -{% endstep %} -{% step %} -### Verify -Check the logs to confirm the server started successfully: -```bash -docker compose logs -f vectorflow -``` - -Look for the migration output and the "Ready" message. -{% endstep %} -{% endstepper %} - -{% endtab %} -{% tab title="Standalone" %} -### Standalone upgrade - -{% stepper %} -{% step %} -### Download the new release -```bash -curl -sSfL -o vectorflow.tar.gz \ - https://github.com/TerrifiedBug/vectorflow/releases/latest/download/vectorflow-server.tar.gz -``` -{% endstep %} -{% step %} -### Stop the server -```bash -sudo systemctl stop vectorflow -``` -{% endstep %} -{% step %} -### Extract the new release -```bash -tar xzf vectorflow.tar.gz -C /opt/vectorflow -``` -{% endstep %} -{% step %} -### Run migrations -```bash -cd /opt/vectorflow -npx prisma migrate deploy -``` -{% endstep %} -{% step %} -### Start the server -```bash -sudo systemctl start vectorflow -``` -{% endstep %} -{% step %} -### Verify -```bash -sudo systemctl status vectorflow -journalctl -u vectorflow -f -``` -{% endstep %} -{% endstepper %} - -{% endtab %} -{% endtabs %} - -## Agent upgrade - -### Automatic self-update - -Agents can update themselves automatically. When the server detects that a newer agent version is available, it includes a **self-update action** in the heartbeat response. The agent then: - -1. Downloads the new binary from the release URL -2. Computes a SHA-256 checksum and verifies it against the expected value -3. Writes the new binary to a temporary file alongside the current executable -4. Atomically replaces the current binary (`rename`) -5. Re-executes the process (`syscall.Exec`) with the same arguments and environment - -The update is seamless -- running Vector pipelines are not interrupted during the agent binary swap. After re-exec, the agent resumes its heartbeat loop with the new version. - -{% hint style="info" %} -Self-update requires the agent binary to be writable by the process. If the agent runs as a restricted user, ensure it has write permission to its own executable path. -{% endhint %} - -### Manual agent update - -If automatic updates are not suitable for your environment, you can update agents manually: - -{% tabs %} -{% tab title="Docker" %} -```bash -# Pull the new agent image -docker compose pull vf-agent - -# Restart the agent -docker compose up -d vf-agent -``` -{% endtab %} -{% tab title="Standalone" %} -```bash -# Download the new binary -curl -sSfL -o /usr/local/bin/vf-agent \ - https://github.com/TerrifiedBug/vectorflow/releases/latest/download/vf-agent-linux-amd64 - -# Make it executable -chmod +x /usr/local/bin/vf-agent - -# Restart the agent -sudo systemctl restart vf-agent -``` -{% endtab %} -{% endtabs %} - -## Database migrations - -VectorFlow uses Prisma ORM for database schema management. Migrations are: - -- **Automatically applied** on server startup in the Docker image (the entrypoint runs `prisma migrate deploy`) -- **Forward-only** -- there is no automatic rollback of migrations -- **Non-destructive** where possible -- VectorFlow avoids dropping columns or tables in migrations - -If a migration fails, the server will not start. Check the logs for the specific error and resolve it before restarting. - -## Rollback - -### Server rollback - -If an upgrade causes issues, you can roll back to the previous version: - -{% tabs %} -{% tab title="Docker" %} -Pin the previous version in your `.env` file and restart: - -```bash -# Set the previous version -VF_VERSION=v0.3.0 - -# Restart with the old image -docker compose up -d -``` - -{% hint style="warning" %} -Rolling back the server after a database migration has run may cause errors if the application code expects the old schema. If migrations were applied, restore from a pre-upgrade backup instead. -{% endhint %} -{% endtab %} -{% tab title="Standalone" %} -Replace the application files with the previous release archive: - -```bash -sudo systemctl stop vectorflow -tar xzf vectorflow-v0.3.0.tar.gz -C /opt/vectorflow -sudo systemctl start vectorflow -``` - -If database migrations were applied, restore from a backup: - -```bash -# Stop the server -sudo systemctl stop vectorflow - -# Restore the pre-upgrade backup -pg_restore --clean --if-exists \ - -U vectorflow -d vectorflow \ - /backups/vectorflow-pre-upgrade.dump - -# Start the old version -sudo systemctl start vectorflow -``` -{% endtab %} -{% endtabs %} - -### Agent rollback - -For Docker-based agents, pin the previous image tag. For standalone agents, replace the binary with the previous version: - -```bash -# Download the specific previous version -curl -sSfL -o /usr/local/bin/vf-agent \ - https://github.com/TerrifiedBug/vectorflow/releases/download/v0.3.0/vf-agent-linux-amd64 - -chmod +x /usr/local/bin/vf-agent -sudo systemctl restart vf-agent -``` - -## Version compatibility - -| Server Version | Minimum Agent Version | Notes | -|---------------|----------------------|-------| -| Current | Current - 2 minor versions | Agents within 2 minor versions of the server are fully supported | - -{% hint style="info" %} -The server is generally backward-compatible with older agents. Older agents may not support newer features (e.g., new pipeline actions), but they will continue to run existing pipelines without issues. It is recommended to keep agents updated to match the server version. -{% endhint %} diff --git a/docs/public/reference/agent-architecture.md b/docs/public/reference/agent-architecture.md deleted file mode 100644 index c3ac4db4..00000000 --- a/docs/public/reference/agent-architecture.md +++ /dev/null @@ -1,266 +0,0 @@ -# Go Agent: Architecture & Internals - -This document describes how the `vf-agent` binary is structured internally. It is aimed at operators who need a deep understanding of the agent's runtime behavior, and contributors working on the agent codebase. - -For user-facing installation and configuration, see [Agent Reference](./agent.md). - ---- - -## Component map - -```mermaid -graph TD - A[main.go] --> B[agent.Agent] - - B --> C[client.Client\nHTTP to VF server] - B --> D[poller\nConfig diff engine] - B --> E[supervisor.Supervisor\nVector process manager] - B --> F[push.Client\nSSE push channel] - B --> G[tapper.Manager\nLive event tapping] - B --> H[selfmetrics.Metrics\nAgent self-observability] - - D -- GET /api/agent/config --> C - C -- POST /api/agent/heartbeat --> C - - E --> E1[Vector process\npipeline-a] - E --> E2[Vector process\npipeline-b] - E --> E3[Vector process\npipeline-n] - - E1 -- scrape :8688/metrics --> I[metrics.ScrapePrometheus] - E2 -- scrape :8690/metrics --> I - I --> B - - E1 -- tap via API :8689 --> G - G -- POST /api/agent/tap-events --> C - - F -- SSE stream --> J[VF server push endpoint] - J -- config_changed\nsample_request\naction --> B - - H -- :9090/metrics --> K[Prometheus scraper] -``` - ---- - -## Package responsibilities - -| Package | Path | Responsibility | -|---------|------|----------------| -| `agent` | `internal/agent/` | Top-level orchestrator: enrollment, main event loop, push message routing, log flushing, sample collection | -| `client` | `internal/client/` | Typed HTTP client for all VF server endpoints (enroll, config, heartbeat, logs, samples, tap-events) | -| `config` | `internal/config/` | Environment variable parsing and validation | -| `supervisor` | `internal/supervisor/` | Spawns, monitors, and restarts Vector child processes; allocates ports; writes sidecar configs | -| `metrics` | `internal/metrics/` | Scrapes Vector's Prometheus endpoint and parses component/host/pipeline metrics | -| `selfmetrics` | `internal/selfmetrics/` | Tracks agent-internal health counters (poll errors, reconnects, heartbeat errors) and exposes them as a Prometheus endpoint | -| `push` | `internal/push/` | Maintains a persistent SSE connection to the server push endpoint with automatic reconnection and fallback URL support | -| `tapper` | `internal/tapper/` | Spawns `vector tap` subprocesses for live event inspection; streams results to the server | -| `sampler` | `internal/sampler/` | Runs `vector tap` for one-shot event sampling; extracts field schema from sampled events | -| `logbuf` | `internal/logbuf/` | Fixed-size ring buffer (500 lines) capturing stdout/stderr from each Vector process | - ---- - -## Main event loop - -The agent's `Run()` method executes a single goroutine select loop. This keeps the critical state machine single-threaded and eliminates the need for locks on most fields. - -``` -┌─────────────────────────────────────────────────────────────┐ -│ Run() select loop │ -│ │ -│ ┌──────────────┐ ┌──────────────┐ ┌───────────────────┐ │ -│ │ ticker.C │ │ pushCh │ │ immediateHeartbeat│ │ -│ │ (poll │ │ (SSE push │ │ Ch │ │ -│ │ interval) │ │ messages) │ │ (debounced 1s) │ │ -│ └──────┬───────┘ └──────┬───────┘ └────────┬──────────┘ │ -│ │ │ │ │ -│ ▼ ▼ ▼ │ -│ pollAndApply() handlePushMessage() sendHeartbeat() │ -│ sendHeartbeat() │ -└─────────────────────────────────────────────────────────────┘ -``` - -The `immediateHeartbeatCh` debounces rapid push notifications — multiple `config_changed` events within one second collapse into a single heartbeat with the latest state. - ---- - -## Process lifecycle in detail - -### Startup sequence - -``` -main() - │ - ├─ config.Load() # parse all VF_* env vars - ├─ agent.New(cfg) # construct all subsystems - │ ├─ client.New(url) - │ ├─ supervisor.New(vectorBin) - │ ├─ tapper.New(vectorBin) - │ └─ selfmetrics.New(pipelinesRunningFn) - │ - └─ agent.Run() - ├─ loadOrEnroll() # load node-token from disk or enroll with server - ├─ (if VF_METRICS_PORT > 0) go metrics.Serve(port) - ├─ pollAndApply() # first poll immediately - ├─ push.Client.Connect() → go routine - ├─ runLogFlusher() → go routine - └─ sendHeartbeat() # first heartbeat -``` - -### Pipeline start - -``` -pollAndApply() - └─ poller.Poll() - └─ returns ActionStart for new pipelines - │ - └─ supervisor.Start(pipelineID, configPath, ...) - ├─ allocates metricsPort = basePort + (portSeq*2 - 1) - ├─ allocates apiPort = basePort + (portSeq*2) - ├─ writeSidecarConfig() → .vf-metrics.yaml - ├─ exec.Command(vectorBin, --config, configPath, - │ --config, sidecarPath) - └─ go monitor(info) # goroutine watches for exit -``` - -### Crash recovery - -``` -monitor(info) goroutine - ├─ time.Sleep(2s) # startup grace period → sets status RUNNING - ├─ cmd.Wait() # blocks until process exits - └─ if err != nil (crash): - info.Status = "CRASHED" - info.restarts++ - backoff = min(1s << (restarts-1), 60s) - go func() { - time.Sleep(backoff) - startProcess(...) # restart with same ports - }() -``` - -Backoff sequence: 1s → 2s → 4s → 8s → 16s → 32s → 60s (capped). - ---- - -## Port allocation - -Each pipeline gets two consecutive TCP ports on `127.0.0.1`, allocated by a sequential counter starting at 8688: - -| Pipeline | Prometheus metrics port | Vector API port | -|----------|------------------------|-----------------| -| First | 8688 | 8689 | -| Second | 8690 | 8691 | -| Third | 8692 | 8693 | -| N-th | 8688 + (2N-2) | 8688 + (2N-1) | - -The counter is **never reset** within a process lifetime. If a pipeline restarts (crash recovery or config change), it reuses the same ports it was originally allocated. If a pipeline is removed and a new one added, the new pipeline receives the next available ports in the sequence. - -{% hint style="info" %} -Ports are allocated from a monotonically increasing sequence, never recycled. On a long-running agent with many pipeline additions/removals, the port sequence drifts upward. This is intentional — reusing ports too soon after a process exit can cause bind failures if the old process hasn't fully released the socket. -{% endhint %} - ---- - -## Push vs. poll - -The agent uses **both** SSE push and periodic polling, working together: - -| Mechanism | Trigger | Purpose | -|-----------|---------|---------| -| Poll | Every `VF_POLL_INTERVAL` (default 15s) | Baseline reliability — ensures config is always eventually consistent | -| Push (SSE) | Server sends `config_changed` | Near-instant reaction to deploys (~200ms vs. up to 15s) | - -**Push does not replace poll.** Push messages only trigger an immediate poll cycle — the full config is always fetched from the config endpoint, never embedded in push messages. This keeps the push channel lightweight and the agent logic simple. - -**Push fallback**: If the primary push URL fails 3 times consecutively with short-lived connections, the agent switches to the derived fallback URL (`/api/agent/push`). This handles cases where the server provides a WebSocket proxy URL that becomes unavailable. - -**Push reconnection**: On any connection drop, the push client reconnects with exponential backoff (1s → 30s max). Each reconnection increments `vf_agent_push_reconnects_total`. - ---- - -## SSE push message types - -| Type | Payload | Action | -|------|---------|--------| -| `config_changed` | `pipelineId`, `reason` | Triggers an immediate `pollAndApply()` + debounced heartbeat | -| `sample_request` | `requestId`, `pipelineId`, `componentKeys`, `limit` | Launches `vector tap` for each component; results sent to `/api/agent/samples` | -| `tap_start` | `requestId`, `pipelineId`, `componentId` | Starts a long-running `vector tap` subprocess | -| `tap_stop` | `requestId` | Cancels a running tap subprocess | -| `action` | `action`, `targetVersion`, `downloadUrl`, `checksum` | Handles `self_update` or `restart` | -| `poll_interval` | `intervalMs` | Immediately resets the poll ticker | - ---- - -## Metrics sidecar - -Every pipeline launched by the supervisor gets a second Vector config file (`.vf-metrics.yaml`) merged in alongside the user's config. The sidecar adds three components: - -```yaml -sources: - vf_internal_metrics: # Vector's own internal metrics - type: internal_metrics - vf_host_metrics: # CPU, memory, disk, network from the host - type: host_metrics - -sinks: - vf_metrics_exporter: # Prometheus exporter for the agent to scrape - type: prometheus_exporter - inputs: ["vf_internal_metrics", "vf_host_metrics"] - address: "127.0.0.1:" - -api: - enabled: true - address: "127.0.0.1:" # Vector API for tap and sampling -``` - -The `vf_` prefix on component IDs is how the scraper distinguishes sidecar metrics from user pipeline metrics when computing pipeline totals. - ---- - -## Agent self-metrics - -As of v0.6.0, the agent exports its own health metrics at `:9090/metrics` (configurable via `VF_METRICS_PORT`): - -| Metric | Type | Description | -|--------|------|-------------| -| `vf_agent_poll_errors_total` | counter | Failed poll requests to the VF server | -| `vf_agent_poll_duration_seconds` | gauge | Duration of the most recent poll cycle | -| `vf_agent_push_reconnects_total` | counter | SSE push channel reconnection events | -| `vf_agent_push_connected` | gauge | 1 if connected, 0 if disconnected | -| `vf_agent_heartbeat_errors_total` | counter | Failed heartbeat sends | -| `vf_agent_heartbeat_duration_seconds` | gauge | Duration of the most recent heartbeat send | -| `vf_agent_pipelines_running` | gauge | Number of pipelines currently RUNNING | -| `vf_agent_uptime_seconds` | gauge | Agent process uptime | - -A subset of these (poll errors, reconnects, push connected, pipelines running, uptime) is embedded in every heartbeat payload as `agentHealth` for server-side visibility without requiring a Prometheus scrape. - ---- - -## Configuration reference - -All configuration is via environment variables. There are no config files. - -| Variable | Default | Description | -|----------|---------|-------------| -| `VF_URL` | *(required)* | VectorFlow server URL | -| `VF_TOKEN` | *(required first run)* | Enrollment token (not needed after node-token is written to disk) | -| `VF_DATA_DIR` | `/var/lib/vf-agent` | Storage for node-token, pipeline configs, and cert files | -| `VF_VECTOR_BIN` | `vector` | Path to the Vector binary | -| `VF_POLL_INTERVAL` | `5s` | Poll cycle duration (Go duration syntax: `5s`, `1m`, `30s`) | -| `VF_LOG_FLUSH_INTERVAL` | `2s` | How often to flush pipeline log buffers to the server | -| `VF_LOG_LEVEL` | `info` | Agent log verbosity: `debug`, `info`, `warn`, `error` | -| `VF_NODE_LABELS` | *(none)* | Comma-separated `key=value` labels (e.g., `region=us-east-1,tier=prod`) | -| `VF_METRICS_PORT` | `9090` | Port for agent self-metrics Prometheus endpoint; set to `0` to disable | - ---- - -## Concurrency model - -The agent deliberately minimises shared mutable state: - -- **Main goroutine** owns the event loop, `pollAndApply()`, and `sendHeartbeat()`. No locks needed for most fields. -- **`supervisor.Supervisor`** uses a single `sync.Mutex` protecting its `processes` map and port counter. -- **`sampleResults`** is the only field in `Agent` that is written from goroutines spawned by `processSampleRequests`. Protected by `Agent.mu`. -- **`selfmetrics.Metrics`** uses `sync/atomic` for all counters and the push-connected gauge. Mutex only for the float64 duration gauges (atomic float64 requires wider-than-pointer-width CAS on 32-bit platforms). -- **`push.Client`** uses a single mutex to protect the context cancel function. - -**Race detector**: All packages are tested with `go test -race ./...`. diff --git a/docs/public/reference/agent-troubleshooting.md b/docs/public/reference/agent-troubleshooting.md deleted file mode 100644 index b4d101c1..00000000 --- a/docs/public/reference/agent-troubleshooting.md +++ /dev/null @@ -1,302 +0,0 @@ -# Agent Troubleshooting - -This guide covers the most common operational issues with `vf-agent`. For a reference of all configuration options, see [Agent Reference](./agent.md). For an explanation of how the agent works internally, see [Agent Architecture](./agent-architecture.md). - ---- - -## 1. Agent not enrolling - -The agent enrolls once on first startup. If enrollment fails, it exits immediately with a non-zero status. - -### Diagnosis checklist - -```bash -# Check the error message -journalctl -u vf-agent -n 50 --no-pager - -# Or if running manually: -vf-agent 2>&1 | head -20 -``` - -### Common causes - -| Error message | Cause | Fix | -|---------------|-------|-----| -| `config error: VF_URL is required` | `VF_URL` env var not set | Set `VF_URL` to your server URL (e.g., `https://vectorflow.example.com`) | -| `enrollment failed: ... connection refused` | Server unreachable | Verify the server is running and `VF_URL` is correct including the scheme (`https://`) | -| `enrollment failed: (status 401)` | Invalid or expired enrollment token | Generate a new token in VectorFlow → Settings → Tokens | -| `enrollment failed: (status 403)` | Token already used or maximum node count reached | Generate a new token or check your node limit | -| `no node token found and VF_TOKEN is not set` | Re-enrollment needed but token not provided | Delete `/node-token` and restart with `VF_TOKEN` set, **or** re-set `VF_TOKEN` and restart | -| `enrollment failed: ... certificate verify failed` | TLS certificate issue | Check that your server's TLS certificate is valid; use `curl -v $VF_URL` to diagnose | - -### Network connectivity test - -```bash -# Verify the agent can reach the server -curl -v "$VF_URL/api/health" - -# If using a proxy, ensure HTTP_PROXY / HTTPS_PROXY is set correctly -``` - -### Re-enrollment - -To force re-enrollment (for example, after replacing a node): - -```bash -rm /var/lib/vf-agent/node-token -VF_TOKEN=vf_enroll_newtoken... vf-agent -``` - ---- - -## 2. Pipeline stuck in STARTING - -After a pipeline is deployed, it shows **STARTING** in the fleet UI and never transitions to **RUNNING**. - -### How STARTING works - -The supervisor marks a pipeline RUNNING 2 seconds after the Vector process starts (a fixed startup grace period). If the process crashes within those 2 seconds, the status remains STARTING until the crash recovery goroutine restarts it. - -### Diagnosis - -```bash -# Enable debug logging to see Vector startup details -VF_LOG_LEVEL=debug vf-agent - -# Check if Vector is actually running -ps aux | grep vector - -# Check agent stderr for crash messages -journalctl -u vf-agent -n 100 --no-pager | grep -E "CRASH|error|failed" -``` - -### Common causes - -| Symptom in logs | Cause | Fix | -|-----------------|-------|-----| -| `exec: "vector": executable file not found` | Vector not installed or not on PATH | Install Vector or set `VF_VECTOR_BIN=/path/to/vector` | -| Vector process exits immediately with config errors | Pipeline YAML is invalid | Check pipeline config in VectorFlow editor; look at Vector log output for `Configuration error:` | -| `address already in use` | Port conflict on 8688+ | Check if another Vector process or service is using ports starting at 8688; restart agent to get new port assignments | -| `failed to write sidecar config` | `VF_DATA_DIR` permissions | Ensure agent has write access to `VF_DATA_DIR/pipelines/` | -| Pipeline starts then keeps crashing (CRASHED) | Runtime error in pipeline (e.g., source unreachable) | View pipeline logs in the VectorFlow UI or agent stdout/stderr | - -### Port conflict resolution - -The agent allocates ports sequentially starting at 8688. If another process is using those ports: - -```bash -# Check what's using ports in the 8688+ range -ss -tlnp | grep 868 -lsof -i :8688-8750 -``` - -Restart the agent to get new port assignments from the next available sequence position. - -### Crash recovery backoff - -If a pipeline crashes repeatedly, the agent enters exponential backoff (1s → 2s → 4s → ... → 60s cap). A pipeline stuck in repeated crash/restart cycles will show alternating CRASHED/STARTING in the UI. To force a clean restart, stop and redeploy the pipeline from the VectorFlow UI. - ---- - -## 3. Metrics not appearing - -Pipeline metrics (events in/out, bytes, errors) show as zeros or are absent in the fleet dashboard. - -### Architecture context - -The agent scrapes Vector's Prometheus endpoint on `127.0.0.1:` during each heartbeat. If this scrape fails or returns no data, heartbeats will contain zero metrics. - -### Diagnosis - -```bash -# Find the metrics port for a running pipeline (look in agent debug logs) -VF_LOG_LEVEL=debug vf-agent 2>&1 | grep "metrics" - -# Try scraping manually (first pipeline uses port 8688) -curl -s http://127.0.0.1:8688/metrics | head -30 - -# Check if Vector is listening on that port -ss -tlnp | grep 8688 -``` - -### Common causes - -| Symptom | Cause | Fix | -|---------|-------|-----| -| Curl returns connection refused | Sidecar config write failed, or Vector API not started | Check `VF_DATA_DIR/pipelines/` for `.vf-metrics.yaml` files; check permissions | -| Curl returns data but dashboard shows zeros | Metrics haven't accumulated yet | Wait for the first poll cycle; Vector metrics start at 0 and only appear after events flow through | -| Host metrics missing but pipeline metrics present | `host_metrics` source failing | Check that the agent has access to `/proc`, `/sys`, `/dev/disk` (common in restricted containers) | -| Sidecar config exists but Vector rejects it | Component ID collision (`vf_internal_metrics` already defined) | Rename conflicting components in your pipeline config | -| Agent shows no metrics port | Pipeline in STARTING/CRASHED state | Metrics are only scraped for RUNNING pipelines | - -### Agent self-metrics not appearing - -If the agent's own metrics endpoint (`VF_METRICS_PORT`, default 9090) is not reachable: - -```bash -# Check if the agent is listening on 9090 -curl http://127.0.0.1:9090/metrics - -# Disable with VF_METRICS_PORT=0 if port conflicts exist -``` - ---- - -## 4. High memory usage - -The agent or its Vector child processes are consuming unexpectedly high memory. - -### Agent memory - -The agent itself is lightweight (typically 10–30 MB RSS). If it's growing: - -- **Log buffer growth**: Each pipeline gets a 500-line ring buffer (`internal/logbuf`). This is bounded and won't grow unboundedly. If many pipelines are running, the total buffer is `500 lines × N pipelines`. -- **Sample results**: If the server is sending many sample requests and the agent can't deliver results fast enough, `sampleResults` can accumulate. This is bounded by rate of sampling goroutines completing. -- **Memory leak**: If agent memory grows without bound, enable debug logging to check for unusual activity and file an issue. - -```bash -# Monitor agent memory over time -while true; do ps -o pid,rss,vsz,comm -p $(pgrep vf-agent); sleep 5; done -``` - -### Vector process memory - -High memory in Vector child processes is almost always caused by pipeline configuration: - -| Cause | Symptom | Fix | -|-------|---------|-----| -| Buffered events (slow sink) | Memory grows over time | Check sink health; add backpressure or drop policies | -| `global.data_dir` defaults | Vector writes to disk buffer | Set `data_dir` explicitly to a volume with sufficient space | -| Many concurrent pipelines | Linear memory growth with pipeline count | Each Vector process is independent; reduce active pipelines or use larger nodes | -| `host_metrics` scraping everything | High baseline memory | Restrict `host_metrics` collectors in the sidecar if not needed | - -```bash -# Check memory per Vector process -ps aux --sort=-%mem | grep vector -``` - ---- - -## 5. Push connection drops - -The SSE push connection drops frequently, causing delayed config updates. - -### Understanding push reconnection - -The push client maintains a persistent SSE connection to the server. On any drop, it reconnects with exponential backoff (1s → 30s max). Each reconnection increments `vf_agent_push_reconnects_total` and is logged at WARN level. - -Push drops are non-critical — the polling fallback ensures config updates still arrive within one poll interval. - -### Diagnosis - -```bash -# Watch for push reconnection warnings in real time -journalctl -u vf-agent -f | grep "push:" - -# Check the self-metrics endpoint for reconnect count -curl http://127.0.0.1:9090/metrics | grep reconnect -# vf_agent_push_reconnects_total 42 - -# Check push connected status -curl http://127.0.0.1:9090/metrics | grep connected -# vf_agent_push_connected 1 -``` - -### Common causes - -| Cause | Symptom | Fix | -|-------|---------|-----| -| Reverse proxy timeout | Connection drops every N minutes | Increase proxy idle/read timeout; set `proxy_read_timeout 3600` in nginx or equivalent | -| Load balancer session affinity | Connection works then switches servers | Enable sticky sessions (same-server routing) for the push endpoint | -| TLS handshake failures | Rapid reconnect loop with TLS errors | Check certificate validity and that the agent trusts the server CA | -| Network interruptions | Sporadic drops correlated with network issues | Normal; push reconnects automatically — monitor `vf_agent_push_reconnects_total` | -| Large push payload | Scanner buffer overflow (>256KB) | Reduce sample payload size; this is typically a server-side configuration issue | - -### Proxy configuration examples - -**nginx:** -```nginx -location /api/agent/push { - proxy_pass http://vectorflow:3000; - proxy_http_version 1.1; - proxy_set_header Connection ''; # disable keep-alive timeout - proxy_read_timeout 3600s; - proxy_buffering off; - chunked_transfer_encoding on; -} -``` - -**Caddy:** -```caddy -reverse_proxy /api/agent/push vectorflow:3000 { - flush_interval -1 # immediate flush (required for SSE) - transport http { - read_timeout 0 # no timeout - } -} -``` - -**Traefik:** Set `ReadTimeout` on the backend service, or use a `Middleware` with `InFlightReq` to avoid idle connection limits. - ---- - -## General diagnostics - -### Enable debug logging - -Debug logging shows every HTTP request, poll result, and pipeline event: - -```bash -VF_LOG_LEVEL=debug vf-agent -``` - -Key things to look for: -- `http request` / `http response` lines show the server communication -- `poll complete` shows how many pipeline actions were taken -- `heartbeat sent` confirms successful delivery -- `push: connected` / `push: connection lost` shows SSE state - -### Check agent self-metrics - -The `/metrics` endpoint exposes counters that summarize agent health over its lifetime: - -```bash -curl -s http://127.0.0.1:9090/metrics -``` - -Key counters: -- `vf_agent_poll_errors_total`: Non-zero means the agent can't reach the server -- `vf_agent_push_reconnects_total`: High value means frequent push drops -- `vf_agent_heartbeat_errors_total`: Non-zero means heartbeats failing (usually same root cause as poll errors) -- `vf_agent_pipelines_running`: Should match the number of deployed pipelines on this node - -### Fleet UI status mapping - -| Fleet UI status | Agent status string | Meaning | -|-----------------|---------------------|---------| -| Running | `RUNNING` | Vector process is live and healthy | -| Starting | `STARTING` | Vector process started; 2s startup grace period | -| Stopped | `STOPPED` | Pipeline cleanly stopped (undeploy) | -| Crashed | `CRASHED` | Vector process exited unexpectedly; restart pending | -| Unreachable | *(no heartbeat)* | Agent is not sending heartbeats; agent likely down | - -### Collect a support bundle - -When filing a bug report or support request, include: - -```bash -# Agent version -vf-agent --version - -# Agent logs (last 200 lines) -journalctl -u vf-agent -n 200 --no-pager - -# Self-metrics snapshot -curl -s http://127.0.0.1:9090/metrics - -# System info -uname -a -vector --version -free -h -df -h /var/lib/vf-agent -``` diff --git a/docs/public/reference/agent.md b/docs/public/reference/agent.md deleted file mode 100644 index 2bbdd410..00000000 --- a/docs/public/reference/agent.md +++ /dev/null @@ -1,423 +0,0 @@ -# Agent Reference - -The VectorFlow agent is a lightweight Go binary that runs on each node where you want to execute Vector pipelines. It has zero external dependencies -- a single binary is all you need. The agent communicates with the VectorFlow server to receive pipeline configurations, report status and metrics, and apply updates. - -See also: [Agent Architecture](./agent-architecture.md) — internal component map, port allocation, and concurrency model. [Agent Troubleshooting](./agent-troubleshooting.md) — step-by-step fixes for the most common operational issues. - -## Overview - -- **Single binary**: No runtime dependencies, no package managers. Download and run. -- **Zero config files**: All configuration is via environment variables. -- **Process-per-pipeline**: Each deployed pipeline runs as a separate Vector child process, providing isolation and independent lifecycle management. -- **Stateless**: The agent stores only its node token on disk. All pipeline configuration comes from the server on every poll. - ---- - -## Lifecycle - -The agent follows a predictable lifecycle from first startup to steady-state operation: - -``` -┌──────────┐ ┌──────────┐ ┌──────────────┐ ┌────────────┐ -│ Start │───▶│ Enroll │───▶│ Poll + Run │───▶│ Heartbeat │ -│ │ │ │ │ │ │ │ -│ Load env │ │ Send │ │ Fetch config │ │ Report │ -│ vars, │ │ hostname │ │ Start/stop │ │ pipeline │ -│ detect │ │ + token │ │ pipelines │ │ status, │ -│ Vector │ │ to server│ │ │ │ metrics, │ -└──────────┘ └──────────┘ └──────────────┘ │ logs │ - │ ▲ └─────┬──────┘ - │ │ │ - │ └──────────────────┘ - │ (every poll interval) - │ - ┌─────▼──────┐ - │ Save node │ - │ token to │ - │ disk │ - └────────────┘ -``` - -### Enrollment - -On first startup, the agent enrolls with the server using the enrollment token (`VF_TOKEN`). The server responds with a **node token** -- a unique credential for this specific agent instance. The node token is saved to `/node-token` and reused on subsequent starts. After enrollment, the `VF_TOKEN` is no longer needed. - -The enrollment request includes the agent's hostname, OS/architecture, agent version, and Vector version. - -### Polling - -After enrollment, the agent enters a poll loop. On each tick (default: every 15 seconds), it: - -1. **Fetches configuration** from the server (`GET /api/agent/config`) -2. **Compares** the received pipeline configs against locally known state (by checksum) -3. **Takes action**: starts new pipelines, restarts pipelines with changed configs, stops removed pipelines -4. **Reconciles** orphaned config files on disk from previous runs -5. **Processes** any pending sample requests or server-initiated actions (e.g., self-update) - -### Heartbeat - -After each poll, the agent sends a heartbeat (`POST /api/agent/heartbeat`) that includes: - -- Status of each running pipeline (RUNNING, STARTING, STOPPED, CRASHED) -- Per-pipeline metrics scraped from Vector's Prometheus endpoint (events in/out, bytes, errors) -- Per-component metrics for the visual editor node overlays -- Host system metrics (CPU, memory, disk, network) -- Recent stdout/stderr log lines from each pipeline process -- Agent and Vector version information -- Node labels (optional key-value metadata for selective deployment) - ---- - -## Environment variables - -| Variable | Required | Default | Description | -|----------|----------|---------|-------------| -| `VF_URL` | Yes | -- | VectorFlow server URL (e.g., `https://vectorflow.example.com`) | -| `VF_TOKEN` | On first run | -- | Enrollment token from the VectorFlow UI. Not needed after initial enrollment. | -| `VF_DATA_DIR` | No | `/var/lib/vf-agent` | Directory for node token, pipeline configs, and certificate files | -| `VF_VECTOR_BIN` | No | `vector` | Path to the Vector binary. Use if Vector is not on the system `PATH`. | -| `VF_POLL_INTERVAL` | No | `5s` | How often to poll the server for config changes. Accepts Go duration syntax (e.g., `10s`, `1m`). | -| `VF_LOG_FLUSH_INTERVAL` | No | `2s` | How often to flush pipeline log buffers to the server. | -| `VF_LOG_LEVEL` | No | `info` | Agent log level: `debug`, `info`, `warn`, `error` | -| `VF_NODE_LABELS` | No | -- | Comma-separated key=value pairs reported to the server on each heartbeat (e.g., `region=us-east-1,tier=production`). Labels set via the UI take precedence over agent-reported values. Used for selective pipeline deployment. | -| `VF_METRICS_PORT` | No | `9090` | Port for the agent's own Prometheus metrics endpoint (`/metrics`). Set to `0` to disable. | - -{% hint style="warning" %} -`VF_URL` is the only strictly required variable. However, `VF_TOKEN` must be set on the first run for enrollment. After the agent writes its node token to disk, `VF_TOKEN` can be removed. -{% endhint %} - ---- - -## CLI flags - -The agent accepts the following flags: - -| Flag | Description | -|------|-------------| -| `--version`, `-v` | Print the agent version and exit | -| `--help`, `-h` | Show usage help including the environment variable reference | -| `--channel ` | Set the update channel at first enrollment: `stable` (default) or `dev`. Only effective on initial startup — the channel cannot be changed afterward without re-installing the agent. Dev channel agents receive pre-release binaries and are tracked separately in the fleet UI. | - -All runtime configuration is via environment variables -- there are no flags for server URL, token, etc. - ---- - -## Agent communication protocol - -The agent communicates with the VectorFlow server over three HTTP endpoints. All requests use JSON. Authenticated requests include the node token as a Bearer token. - -### `POST /api/agent/enroll` - -Called once on first startup. No authentication required (the enrollment token is in the request body). - -**Request:** -```json -{ - "token": "vf_enroll_abc123...", - "hostname": "web-server-01", - "os": "linux/amd64", - "agentVersion": "0.5.0", - "vectorVersion": "vector 0.41.1 (x86_64-unknown-linux-gnu)" -} -``` - -**Response:** -```json -{ - "nodeId": "clxyz789", - "nodeToken": "vfn_abc123...", - "environmentId": "clxyz456", - "environmentName": "Production" -} -``` - -### `GET /api/agent/config` - -Called on every poll cycle. Returns all deployed pipeline configurations for this node's environment. - -**Headers:** `Authorization: Bearer ` - -**Response:** -```json -{ - "pipelines": [ - { - "pipelineId": "clxyz001", - "pipelineName": "syslog-to-s3", - "version": 3, - "configYaml": "sources:\n syslog_in:\n type: syslog\n ...", - "checksum": "sha256:abc123...", - "logLevel": "info", - "secrets": { - "VF_SECRET_AWS_KEY": "AKIAIOSFODNN7EXAMPLE" - }, - "certFiles": [ - { - "name": "ca-cert", - "filename": "ca.pem", - "data": "" - } - ] - } - ], - "pollIntervalMs": 15000, - "secretBackend": "BUILTIN", - "sampleRequests": [], - "pendingAction": null -} -``` - -Key fields: -- **`configYaml`**: The generated Vector YAML. Secret references are converted to environment variable placeholders (e.g., `${VF_SECRET_AWS_KEY}`) rather than containing decrypted values. -- **`secrets`**: Pre-resolved secret values keyed by their `VF_SECRET_` environment variable name. The agent injects these as environment variables into the Vector process, where Vector's interpolation resolves the placeholders. -- **`checksum`**: Includes both the YAML and the secrets dictionary, so rotating a secret triggers a pipeline restart even if the YAML itself hasn't changed. -- **`certFiles`**: Certificate data written to `/certs/` before starting the pipeline. -- **`pendingAction`**: Server-initiated action (currently only `self_update`). - -{% hint style="info" %} -When a pipeline has a **node selector** configured (via the deploy dialog), the config response only includes pipelines whose selector labels match this node's labels. A pipeline with no node selector deploys to all nodes. -{% endhint %} - -{% hint style="info" %} -When a node is in **maintenance mode**, the config response returns an empty `pipelines` array. The agent stops all running pipelines but continues sending heartbeats. See [Fleet Management](../user-guide/fleet.md#maintenance-mode) for details. -{% endhint %} - -### `POST /api/agent/heartbeat` - -Called after every poll. Sends status and metrics for all managed pipelines. - -**Headers:** `Authorization: Bearer `, `Content-Type: application/json` - -Key fields: -- **`labels`** (optional): Key-value pairs describing this node. Labels set via the `VF_LABELS` environment variable are reported here. The server merges them with any labels set through the UI, with UI-set labels taking precedence. - -**Request:** -```json -{ - "pipelines": [ - { - "pipelineId": "clxyz001", - "version": 3, - "status": "RUNNING", - "pid": 12345, - "uptimeSeconds": 3600, - "eventsIn": 150000, - "eventsOut": 148500, - "bytesIn": 75000000, - "bytesOut": 72000000, - "errorsTotal": 12, - "componentMetrics": [ - { - "componentId": "syslog_in", - "componentKind": "source", - "receivedEvents": 150000, - "sentEvents": 150000, - "receivedBytes": 75000000 - } - ], - "recentLogs": ["2025-01-15T10:30:00Z INFO vector: Pipeline running"] - } - ], - "hostMetrics": { - "memoryTotalBytes": 8589934592, - "memoryUsedBytes": 4294967296, - "cpuSecondsTotal": 12345.67, - "loadAvg1": 1.5 - }, - "agentVersion": "0.5.0", - "vectorVersion": "vector 0.41.1", - "deploymentMode": "STANDALONE", - "labels": { - "region": "us-east-1", - "tier": "production" - } -} -``` - ---- - -## Process supervision - -The agent manages Vector processes with full lifecycle control: - -- **Start**: Spawns `vector --config .yaml --config .yaml.vf-metrics.yaml`. The second config file is a sidecar that adds internal metrics, host metrics, a Prometheus exporter, and the Vector API. -- **Stop**: Sends `SIGTERM`, waits up to 30 seconds for graceful shutdown, then sends `SIGKILL` if needed. -- **Restart**: Stops the running process then starts a new one with the updated config. -- **Crash recovery**: If a Vector process exits unexpectedly, the agent automatically restarts it with exponential backoff (1s, 2s, 4s, ... up to 60s). - -### Environment injection - -Each Vector process receives: -- `VECTOR_LOG=` -- controls Vector's log verbosity -- All resolved secrets as environment variables with `VF_SECRET_` prefix (e.g., `VF_SECRET_AWS_KEY=value`) - -Pipeline YAML references secrets as `${VF_SECRET_NAME}` placeholders. Vector's built-in environment variable interpolation resolves these at startup, so secret values never appear in configuration files on disk. - -### Metrics sidecar - -The agent automatically generates a sidecar config for each pipeline that adds: -- `vf_internal_metrics` source (Vector internal metrics) -- `vf_host_metrics` source (host system metrics) -- `vf_metrics_exporter` sink (Prometheus exporter on a dynamic port) -- Vector API enabled on `127.0.0.1:` - -The agent scrapes the Prometheus endpoint on each heartbeat to collect per-component and host metrics. - ---- - -## Auto-update mechanism - -Standalone agents (not Docker) support in-place binary updates: - -1. An admin triggers an update from the VectorFlow UI, specifying a target version and download URL -2. The server stores a `pendingAction` of type `self_update` on the node -3. On the next poll, the agent receives the pending action -4. The agent downloads the new binary to a temp file next to the current executable -5. The SHA-256 checksum is verified against the expected value -6. The temp file is atomically renamed over the current executable -7. The agent re-executes itself via `syscall.Exec`, replacing the process in-place - -{% hint style="info" %} -Docker agents ignore `self_update` actions. Update Docker agents by pulling a new image version instead. -{% endhint %} - -### Update channels - -Agents operate on one of two update channels: - -| Channel | Version prefix | Description | -|---------|---------------|-------------| -| **stable** | `v1.2.3` | Production-ready releases. This is the default. | -| **dev** | `dev-abc1234` | Pre-release builds for testing. Set via `--channel dev` at first startup. | - -The fleet UI displays the channel for each node. When triggering updates, VectorFlow targets the correct channel — stable agents receive the latest stable release, and dev agents receive the latest dev build. An agent's channel is determined by its version string prefix and cannot be changed without re-installing. - -{% hint style="info" %} -Dev channel agents are intended for testing new agent features before rolling them out to production. They report a separate "latest version" in the fleet UI so you can track dev and stable fleets independently. -{% endhint %} - ---- - -## Deployment mode detection - -The agent automatically detects whether it is running inside a container: - -- Checks for `/.dockerenv` -- Inspects `/proc/1/cgroup` for `docker`, `containerd`, or `kubepods` entries - -The detected mode (`STANDALONE` or `DOCKER`) is reported in every heartbeat and displayed in the fleet UI. - ---- - -## Running as Non-Root - -By default the agent runs as root, which gives Vector access to the Docker socket and host filesystem. For environments that require non-root execution, both Docker and binary deployments support running as a dedicated user. - -### Docker - -Set the `VF_AGENT_USER` environment variable: - -```yaml -services: - vf-agent: - image: ghcr.io/terrifiedbug/vectorflow-agent:latest - environment: - - VF_AGENT_USER=vfagent - volumes: - - agent-data:/var/lib/vf-agent - - vector-data:/var/lib/vector -``` - -The entrypoint creates the user (if needed), sets ownership on data directories, and runs the agent as that user. - -### Binary (systemd) - -Use the `--user` flag during installation: - -```bash -curl -sSfL .../install.sh | sudo bash -s -- \ - --url https://vf.example.com \ - --token abc123 \ - --user vfagent -``` - -Or run the installer interactively (from a terminal) and select non-root when prompted. - -The installer creates a system user, sets directory ownership, and configures the systemd unit with `User=` and `Group=` directives. - -### Granting Permissions - -When running as non-root, pipelines that access privileged resources will fail with permission errors. Grant the agent user access as needed: - -| Resource | Command | -|----------|---------| -| Docker socket (`docker_logs` source) | `sudo usermod -aG docker vfagent` | -| Host log files (`file` source) | Ensure the user has read access to the paths | -| Network ports below 1024 | Use `setcap` or a reverse proxy | - -Permission errors are displayed on the pipeline card in the dashboard. - -### Viewing the Running User - -The fleet node detail page shows "Running As" when the agent reports its user. - ---- - -## Data directory layout - -``` -/var/lib/vf-agent/ # VF_DATA_DIR - node-token # Persisted node credential (0600) - pipelines/ - .yaml # Pipeline config from server (0600) - .yaml.vf-metrics.yaml # Auto-generated metrics sidecar - certs/ - ca.pem # Certificate files (0600) - server.crt - server.key -``` - ---- - -## Troubleshooting - -### Agent won't enroll - -| Symptom | Cause | Fix | -|---------|-------|-----| -| `config error: VF_URL is required` | `VF_URL` not set | Set the `VF_URL` environment variable | -| `enrollment failed: ... connection refused` | Server unreachable | Verify `VF_URL` is correct and the server is running | -| `enrollment failed: (status 401)` | Invalid enrollment token | Generate a new enrollment token in the VectorFlow UI | -| `enrollment failed: (status 403)` | Token already used or revoked | Generate a new enrollment token | -| `no node token found at ... and VF_TOKEN is not set` | First run without `VF_TOKEN` | Set `VF_TOKEN` to the enrollment token from the UI | - -### Agent shows offline - -| Symptom | Cause | Fix | -|---------|-------|-----| -| Node shows "Unreachable" in fleet UI | Agent not sending heartbeats | Check agent process is running, check network connectivity to server | -| Heartbeat errors in agent logs | Network issue or server down | Check `VF_URL`, firewalls, and server health | -| Agent enrolled but no heartbeats | Node token was revoked | Re-enroll by deleting `/node-token` and restarting with a new `VF_TOKEN` | - -### Pipeline won't start - -| Symptom | Cause | Fix | -|---------|-------|-----| -| `start vector for pipeline ...: exec: "vector": executable file not found` | Vector binary not on PATH | Install Vector or set `VF_VECTOR_BIN` to the full path | -| Pipeline status shows CRASHED | Vector config error or runtime crash | Check the pipeline logs in the VectorFlow UI or agent stderr | -| Pipeline stuck in STARTING | Vector process started but may have issues | Check agent logs at `debug` level (`VF_LOG_LEVEL=debug`) | - -### Diagnostic logging - -Enable debug logging to see all HTTP requests, poll results, and pipeline actions: - -```bash -VF_LOG_LEVEL=debug vf-agent -``` - -This logs: -- Every HTTP request and response status to the server -- Poll results including the number of pipeline actions taken -- Pipeline start/stop/restart events with PIDs -- Heartbeat payloads including pipeline and metrics data -- Certificate file writes and sample request processing diff --git a/docs/public/reference/api.md b/docs/public/reference/api.md deleted file mode 100644 index ba92e082..00000000 --- a/docs/public/reference/api.md +++ /dev/null @@ -1,1322 +0,0 @@ -# API Reference - -VectorFlow exposes its API via [tRPC](https://trpc.io/) -- a type-safe RPC framework built on HTTP. All API calls go through a single endpoint at `/api/trpc`, rather than traditional REST paths. This page documents every router, its procedures, and how to call them programmatically. - -## Calling convention - -tRPC uses a URL-based calling convention where the procedure name is encoded in the path: - -``` -# Query (read operation) — HTTP GET -GET /api/trpc/.?input= - -# Mutation (write operation) — HTTP POST -POST /api/trpc/. -Content-Type: application/json - -{"json": } -``` - -Responses are JSON-wrapped: - -```json -{ - "result": { - "data": { - "json": { ... } - } - } -} -``` - -VectorFlow uses [SuperJSON](https://github.com/blitz-js/superjson) as its serialization transformer, which means Date objects and BigInts are automatically serialized and deserialized. When calling the API with raw HTTP, you receive SuperJSON-encoded output -- dates appear as ISO strings with type annotations. - -### Example: list pipelines - -{% tabs %} -{% tab title="curl" %} -```bash -# Query — GET with URL-encoded JSON input -curl -s 'https://vectorflow.example.com/api/trpc/pipeline.list?input=%7B%22json%22%3A%7B%22environmentId%22%3A%22clxyz123%22%7D%7D' \ - -H 'Cookie: authjs.session-token=' -``` -{% endtab %} -{% tab title="fetch" %} -```typescript -const input = encodeURIComponent( - JSON.stringify({ json: { environmentId: "clxyz123" } }) -); - -const res = await fetch( - `https://vectorflow.example.com/api/trpc/pipeline.list?input=${input}`, - { - headers: { Cookie: `authjs.session-token=${sessionToken}` }, - } -); - -const { result } = await res.json(); -const pipelines = result.data.json; -``` -{% endtab %} -{% endtabs %} - -### Example: create a pipeline - -{% tabs %} -{% tab title="curl" %} -```bash -# Mutation — POST with JSON body -curl -s -X POST 'https://vectorflow.example.com/api/trpc/pipeline.create' \ - -H 'Content-Type: application/json' \ - -H 'Cookie: authjs.session-token=' \ - -d '{"json": {"name": "syslog-to-s3", "environmentId": "clxyz123"}}' -``` -{% endtab %} -{% tab title="fetch" %} -```typescript -const res = await fetch( - "https://vectorflow.example.com/api/trpc/pipeline.create", - { - method: "POST", - headers: { - "Content-Type": "application/json", - Cookie: `authjs.session-token=${sessionToken}`, - }, - body: JSON.stringify({ - json: { name: "syslog-to-s3", environmentId: "clxyz123" }, - }), - } -); - -const { result } = await res.json(); -const pipeline = result.data.json; -``` -{% endtab %} -{% endtabs %} - ---- - -## Authentication - -All API procedures (except the agent enrollment endpoint) require an authenticated session. - -VectorFlow supports two authentication methods: - -1. **Session cookies** -- Used by the web UI. When you sign in, an `authjs.session-token` cookie is set. Include this cookie in tRPC API requests. -2. **Service account API keys** -- Used for programmatic access via the REST API (`/api/v1/*`). Pass the key as a Bearer token in the `Authorization` header. - -{% hint style="info" %} -For CI/CD and automation, create a [Service Account](../operations/service-accounts.md) to get a dedicated API key with scoped permissions. -{% endhint %} - -### Roles - -Every procedure enforces a minimum role. VectorFlow has three roles, in ascending order of privilege: - -| Role | Level | Description | -|------|-------|-------------| -| `VIEWER` | 0 | Read-only access to pipelines, fleet, metrics, and logs | -| `EDITOR` | 1 | Create, update, deploy, and delete pipelines, secrets, and alerts | -| `ADMIN` | 2 | Manage environments, teams, members, enrollment tokens, and agent revocation | - -Some procedures require **Super Admin** access -- this is a server-wide flag on the user account, separate from team roles. - ---- - -## Router index - -| Router | Prefix | Description | -|--------|--------|-------------| -| `pipeline` | `pipeline.*` | Pipeline CRUD, graph saving, versioning, deployment status, metrics, logs, event sampling | -| `deploy` | `deploy.*` | Deploy preview, deploy to agents, undeploy | -| `fleet` | `fleet.*` | Fleet node management, node logs, node metrics, agent updates | -| `environment` | `environment.*` | Environment CRUD, enrollment tokens | -| `alert` | `alert.*` | Alert rules, webhooks, alert events | -| `template` | `template.*` | Pipeline template management | -| `secret` | `secret.*` | Encrypted secret management | -| `certificate` | `certificate.*` | TLS certificate management | -| `dashboard` | `dashboard.*` | Dashboard statistics and chart data | -| `team` | `team.*` | Team management, member roles | -| `user` | `user.*` | User profile, password changes, TOTP setup | -| `audit` | `audit.*` | Audit log queries | -| `vrl` | `vrl.*` | VRL expression testing | -| `vrlSnippet` | `vrlSnippet.*` | VRL snippet library | -| `admin` | `admin.*` | User management, super admin operations | -| `settings` | `settings.*` | System settings — OIDC, fleet, backup, SCIM (Super Admin) | -| `metrics` | `metrics.*` | Pipeline and component metrics, live rates | -| `validator` | `validator.*` | Pipeline config validation | -| `serviceAccount` | `serviceAccount.*` | Service account API key management | -| `userPreference` | `userPreference.*` | Per-user UI preferences | -| `sharedComponent` | `sharedComponent.*` | Reusable pipeline components shared across pipelines | -| `ai` | `ai.*` | AI assistant conversations and suggestions | -| `pipelineGroup` | `pipelineGroup.*` | Pipeline folder organization | -| `pipelineDependency` | `pipelineDependency.*` | Inter-pipeline dependency graph | -| `promotion` | `promotion.*` | Cross-environment pipeline promotion with approval workflow | -| `stagedRollout` | `stagedRollout.*` | Canary and staged pipeline deployments | -| `nodeGroup` | `nodeGroup.*` | Node grouping with label-based criteria and health stats | -| `webhookEndpoint` | `webhookEndpoint.*` | Outbound webhook endpoint management and delivery history | -| `gitSync` | `gitSync.*` | GitOps sync status, jobs, and error tracking | -| `migration` | `migration.*` | Config migration from Fluentd to Vector | -| `analytics` | `analytics.*` | Cost analytics, per-pipeline breakdown, CSV export | -| `costRecommendation` | `costRecommendation.*` | Cost optimization recommendations and analysis | -| `anomaly` | `anomaly.*` | Anomaly detection events, acknowledgement, dismissal | -| `filterPreset` | `filterPreset.*` | Saved filter presets for pipeline and fleet views | - ---- - -## Pipeline router - -Manage pipeline definitions, graphs, versions, and runtime data. - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `pipeline.list` | query | VIEWER | `{ environmentId: string }` | List all pipelines in an environment | -| `pipeline.get` | query | VIEWER | `{ id: string }` | Get a pipeline with its nodes, edges, and config change status | -| `pipeline.create` | mutation | EDITOR | `{ name: string, description?: string, environmentId: string }` | Create a new draft pipeline | -| `pipeline.update` | mutation | EDITOR | `{ id: string, name?: string, description?: string \| null }` | Update pipeline name or description | -| `pipeline.delete` | mutation | EDITOR | `{ id: string }` | Delete a pipeline (undeploys first if deployed) | -| `pipeline.clone` | mutation | EDITOR | `{ pipelineId: string }` | Clone a pipeline within the same environment | -| `pipeline.promote` | mutation | EDITOR | `{ pipelineId: string, targetEnvironmentId: string, name?: string }` | Copy a pipeline to a different environment (strips secrets) | -| `pipeline.saveGraph` | mutation | EDITOR | `{ pipelineId: string, nodes: Node[], edges: Edge[], globalConfig?: object }` | Save the visual pipeline graph | -| `pipeline.versions` | query | VIEWER | `{ pipelineId: string }` | List all deployed versions of a pipeline | -| `pipeline.getVersion` | query | VIEWER | `{ versionId: string }` | Get a specific version with its config YAML | -| `pipeline.createVersion` | mutation | EDITOR | `{ pipelineId: string, configYaml: string, changelog?: string }` | Create a new pipeline version | -| `pipeline.rollback` | mutation | EDITOR | `{ pipelineId: string, targetVersionId: string }` | Roll back to a previous version | -| `pipeline.deploymentStatus` | query | VIEWER | `{ pipelineId: string }` | Get per-node deployment status for a pipeline | -| `pipeline.metrics` | query | VIEWER | `{ pipelineId: string, hours?: number }` | Get pipeline metrics (events, bytes, errors) over time | -| `pipeline.logs` | query | VIEWER | `{ pipelineId: string, cursor?: string, limit?: number, levels?: LogLevel[], nodeId?: string, since?: Date }` | Paginated pipeline logs | -| `pipeline.requestSamples` | mutation | EDITOR | `{ pipelineId: string, componentKeys: string[], limit?: number }` | Request live event samples from running components | -| `pipeline.sampleResult` | query | VIEWER | `{ requestId: string }` | Poll for event sample results | -| `pipeline.eventSchemas` | query | VIEWER | `{ pipelineId: string }` | Get discovered event schemas per component | - -
-Pipeline name validation - -Pipeline names must match the pattern `^[a-zA-Z0-9][a-zA-Z0-9 _-]*$` and be between 1 and 100 characters long. The name must start with a letter or number and may contain letters, numbers, spaces, hyphens, and underscores. -
- -
-Node schema - -Each node in the `saveGraph` input: - -| Field | Type | Description | -|-------|------|-------------| -| `id` | `string?` | Optional ID (auto-generated if omitted) | -| `componentKey` | `string` | Auto-generated unique identifier within the pipeline (e.g., `syslog_k7xMp2nQ`). Must match `^[a-zA-Z_][a-zA-Z0-9_]*$` | -| `displayName` | `string?` | Optional human-readable name for the component (e.g., "Syslog Source") | -| `componentType` | `string` | Vector component type (e.g., `syslog`, `remap`, `aws_s3`) | -| `kind` | `"SOURCE" \| "TRANSFORM" \| "SINK"` | Component category | -| `config` | `object` | Component configuration fields | -| `positionX` | `number` | X coordinate in the visual editor | -| `positionY` | `number` | Y coordinate in the visual editor | -| `disabled` | `boolean` | Whether the node is excluded from the generated config | -
- ---- - -## Deploy router - -Preview and execute pipeline deployments. - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `deploy.preview` | query | VIEWER | `{ pipelineId: string }` | Generate and validate the YAML config, return diff against deployed version | -| `deploy.agent` | mutation | EDITOR | `{ pipelineId: string, changelog: string }` | Deploy a pipeline -- validates config, creates a version, marks as deployed | -| `deploy.undeploy` | mutation | EDITOR | `{ pipelineId: string }` | Undeploy a pipeline (agents stop it on next poll) | -| `deploy.environmentInfo` | query | VIEWER | `{ pipelineId: string }` | Get the environment and node list for a pipeline | - ---- - -## Fleet router - -Manage agent nodes and view their status, logs, and metrics. - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `fleet.list` | query | VIEWER | `{ environmentId: string }` | List all nodes in an environment | -| `fleet.get` | query | VIEWER | `{ id: string }` | Get a node with its pipeline statuses | -| `fleet.create` | mutation | EDITOR | `{ name: string, host: string, apiPort?: number, environmentId: string }` | Register a node manually | -| `fleet.update` | mutation | EDITOR | `{ id: string, name?: string }` | Update node name | -| `fleet.delete` | mutation | EDITOR | `{ id: string }` | Delete a node | -| `fleet.revokeNode` | mutation | ADMIN | `{ id: string }` | Revoke a node's token (prevents further communication) | -| `fleet.nodeLogs` | query | VIEWER | `{ nodeId: string, cursor?: string, limit?: number, levels?: LogLevel[], pipelineId?: string }` | Paginated logs for a node | -| `fleet.nodeMetrics` | query | VIEWER | `{ nodeId: string, hours?: number }` | System metrics for a node (CPU, memory, disk, network) | -| `fleet.triggerAgentUpdate` | mutation | ADMIN | `{ nodeId: string, targetVersion: string, downloadUrl: string, checksum: string }` | Trigger a self-update on a standalone agent | -| `fleet.listWithPipelineStatus` | query | VIEWER | `{ environmentId: string }` | List nodes with per-pipeline deployment status | - ---- - -## Environment router - -Manage environments and enrollment tokens. - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `environment.list` | query | VIEWER | `{ teamId: string }` | List environments for a team | -| `environment.get` | query | VIEWER | `{ id: string }` | Get environment details including node count | -| `environment.create` | mutation | EDITOR | `{ name: string, teamId: string }` | Create a new environment | -| `environment.update` | mutation | EDITOR | `{ id: string, name?: string, secretBackend?: string, secretBackendConfig?: any }` | Update environment name or secret backend | -| `environment.delete` | mutation | ADMIN | `{ id: string }` | Delete an environment and all its pipelines and nodes | -| `environment.generateEnrollmentToken` | mutation | ADMIN | `{ environmentId: string }` | Generate a new enrollment token for agent enrollment | -| `environment.revokeEnrollmentToken` | mutation | ADMIN | `{ environmentId: string }` | Revoke the enrollment token | - -
-Secret backend options - -The `secretBackend` field accepts one of: - -| Value | Description | -|-------|-------------| -| `BUILTIN` | Secrets encrypted in the VectorFlow database (default) | -| `VAULT` | HashiCorp Vault | -| `AWS_SM` | AWS Secrets Manager | -| `EXEC` | External command execution | -
- ---- - -## Alert router - -Manage alert rules, webhook destinations, and view alert events. - -### Alert rules - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `alert.listRules` | query | VIEWER | `{ environmentId: string }` | List alert rules for an environment | -| `alert.createRule` | mutation | EDITOR | `{ name: string, environmentId: string, pipelineId?: string, metric: AlertMetric, condition: AlertCondition, threshold: number, durationSeconds?: number, teamId: string }` | Create an alert rule | -| `alert.updateRule` | mutation | EDITOR | `{ id: string, name?: string, enabled?: boolean, threshold?: number, durationSeconds?: number }` | Update an alert rule | -| `alert.deleteRule` | mutation | EDITOR | `{ id: string }` | Delete an alert rule | - -
-AlertMetric values - -| Value | Description | -|-------|-------------| -| `node_unreachable` | Node has not sent a heartbeat | -| `cpu_usage` | Host CPU utilization percentage | -| `memory_usage` | Host memory utilization percentage | -| `disk_usage` | Host disk utilization percentage | -| `error_rate` | Pipeline error events per second | -| `discarded_rate` | Pipeline discarded events per second | -| `pipeline_crashed` | Pipeline process has crashed | -
- -
-AlertCondition values - -| Value | Description | -|-------|-------------| -| `gt` | Greater than threshold | -| `lt` | Less than threshold | -| `eq` | Equal to threshold | -
- -### Alert webhooks - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `alert.listWebhooks` | query | VIEWER | `{ environmentId: string }` | List webhook destinations | -| `alert.createWebhook` | mutation | EDITOR | `{ environmentId: string, url: string, headers?: Record, hmacSecret?: string }` | Create a webhook | -| `alert.updateWebhook` | mutation | EDITOR | `{ id: string, url?: string, headers?: Record \| null, hmacSecret?: string \| null, enabled?: boolean }` | Update a webhook | -| `alert.deleteWebhook` | mutation | EDITOR | `{ id: string }` | Delete a webhook | -| `alert.testWebhook` | mutation | EDITOR | `{ id: string }` | Send a test alert payload to a webhook | - -### Alert events - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `alert.listEvents` | query | VIEWER | `{ environmentId: string, limit?: number, cursor?: string }` | Paginated list of alert events | - ---- - -## Template router - -Manage reusable pipeline templates. - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `template.list` | query | VIEWER | `{ teamId: string }` | List all templates for a team | -| `template.get` | query | VIEWER | `{ id: string }` | Get a template with its nodes and edges | -| `template.create` | mutation | EDITOR | `{ name: string, description: string, category: string, teamId: string, nodes: Node[], edges: Edge[] }` | Create a template | -| `template.delete` | mutation | EDITOR | `{ id: string }` | Delete a template | - ---- - -## Secret router - -Manage encrypted secrets for pipeline configurations. - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `secret.list` | query | VIEWER | `{ environmentId: string }` | List secrets (names only, no values) | -| `secret.create` | mutation | EDITOR | `{ environmentId: string, name: string, value: string }` | Create a secret | -| `secret.update` | mutation | EDITOR | `{ id: string, environmentId: string, value: string }` | Update a secret value | -| `secret.delete` | mutation | EDITOR | `{ id: string, environmentId: string }` | Delete a secret | - -{% hint style="info" %} -Secret values are never returned by the API. The `list` endpoint returns only names and timestamps. Values are encrypted at rest and only decrypted during pipeline deployment. -{% endhint %} - ---- - -## Certificate router - -Manage TLS certificates for pipeline components. - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `certificate.list` | query | VIEWER | `{ environmentId: string }` | List certificates (metadata only) | -| `certificate.upload` | mutation | EDITOR | `{ environmentId: string, name: string, filename: string, fileType: "ca" \| "cert" \| "key", dataBase64: string }` | Upload a PEM-encoded certificate | -| `certificate.delete` | mutation | EDITOR | `{ id: string, environmentId: string }` | Delete a certificate | - ---- - -## Dashboard router - -Fetch dashboard statistics and chart data. - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `dashboard.stats` | query | VIEWER | `{ environmentId: string }` | Pipeline count, node count, fleet health, data reduction | -| `dashboard.recentPipelines` | query | VIEWER | *(none)* | Last 5 recently updated pipelines | -| `dashboard.recentAudit` | query | VIEWER | *(none)* | Last 10 audit log entries | -| `dashboard.nodeCards` | query | VIEWER | *(none)* | Node overview cards with metrics and sparklines | -| `dashboard.pipelineCards` | query | VIEWER | `{ environmentId: string }` | Pipeline cards with metrics, rates, and deployment status | -| `dashboard.operationalOverview` | query | VIEWER | *(none)* | Unhealthy nodes, deployed pipelines, recent aggregate metrics | -| `dashboard.chartMetrics` | query | VIEWER | `{ environmentId: string, nodeIds?: string[], pipelineIds?: string[], range?: "1h" \| "6h" \| "1d" \| "7d", groupBy?: "pipeline" \| "node" \| "aggregate" }` | Time-series chart data for dashboards | - ---- - -## Team router - -Manage teams and team membership. - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `team.list` | query | VIEWER | *(none)* | List teams the current user belongs to | -| `team.get` | query | VIEWER | `{ id: string }` | Get team details with members | -| `team.myRole` | query | VIEWER | *(none)* | Get the current user's highest role | -| `team.teamRole` | query | VIEWER | `{ teamId: string }` | Get the current user's role in a specific team | -| `team.create` | mutation | Super Admin | `{ name: string }` | Create a new team | -| `team.delete` | mutation | Super Admin | `{ teamId: string }` | Delete a team (must have no environments) | -| `team.rename` | mutation | ADMIN | `{ teamId: string, name: string }` | Rename a team | -| `team.addMember` | mutation | ADMIN | `{ teamId: string, email: string, role: "VIEWER" \| "EDITOR" \| "ADMIN" }` | Add a user to a team | -| `team.removeMember` | mutation | ADMIN | `{ teamId: string, userId: string }` | Remove a member from a team | -| `team.updateMemberRole` | mutation | ADMIN | `{ teamId: string, userId: string, role: "VIEWER" \| "EDITOR" \| "ADMIN" }` | Change a member's role | -| `team.lockMember` | mutation | ADMIN | `{ teamId: string, userId: string }` | Lock a user account | -| `team.unlockMember` | mutation | ADMIN | `{ teamId: string, userId: string }` | Unlock a user account | -| `team.resetMemberPassword` | mutation | ADMIN | `{ teamId: string, userId: string }` | Reset a member's password (returns temporary password) | -| `team.updateRequireTwoFactor` | mutation | ADMIN | `{ teamId: string, requireTwoFactor: boolean }` | Require 2FA for all team members | - ---- - -## User router - -Manage the current user's profile and two-factor authentication. - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `user.me` | query | VIEWER | *(none)* | Get current user info (name, email, auth method, 2FA status) | -| `user.changePassword` | mutation | VIEWER | `{ currentPassword: string, newPassword: string }` | Change password (min 8 characters) | -| `user.updateProfile` | mutation | VIEWER | `{ name: string }` | Update display name | -| `user.setupTotp` | mutation | VIEWER | *(none)* | Begin TOTP 2FA setup (returns QR URI and backup codes) | -| `user.verifyAndEnableTotp` | mutation | VIEWER | `{ code: string }` | Verify a TOTP code and enable 2FA | -| `user.disableTotp` | mutation | VIEWER | `{ code: string }` | Disable 2FA (requires valid TOTP or backup code) | - ---- - -## Audit router - -Query the audit log. - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `audit.list` | query | VIEWER | `{ action?: string, userId?: string, entityType?: string, search?: string, teamId?: string, environmentId?: string, startDate?: string, endDate?: string, cursor?: string }` | Paginated, filterable audit log | -| `audit.actions` | query | VIEWER | *(none)* | List distinct action values | -| `audit.entityTypes` | query | VIEWER | *(none)* | List distinct entity type values | -| `audit.users` | query | VIEWER | *(none)* | List users who appear in the audit log | - ---- - -## VRL router - -Test VRL (Vector Remap Language) expressions. - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `vrl.test` | mutation | VIEWER | `{ source: string, input: string }` | Execute a VRL program against a test event and return the result | - ---- - -## VRL Snippet router - -Manage the VRL snippet library. - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `vrlSnippet.list` | query | VIEWER | `{ teamId: string }` | List built-in and custom VRL snippets | -| `vrlSnippet.create` | mutation | EDITOR | `{ teamId: string, name: string, description?: string, category: string, code: string }` | Create a custom snippet | -| `vrlSnippet.update` | mutation | EDITOR | `{ id: string, name?: string, description?: string, category?: string, code?: string }` | Update a custom snippet | -| `vrlSnippet.delete` | mutation | EDITOR | `{ id: string }` | Delete a custom snippet | - ---- - -## Admin router - -Manage platform users, super admin privileges, and team assignments. All procedures require **Super Admin** access. - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `admin.listUsers` | query | Super Admin | *(none)* | List all users with team memberships and auth status | -| `admin.createUser` | mutation | Super Admin | `{ email: string, name: string, teamId?: string, role?: Role }` | Create a local user account (returns generated password) | -| `admin.deleteUser` | mutation | Super Admin | `{ userId: string }` | Delete a user and all their data | -| `admin.toggleSuperAdmin` | mutation | Super Admin | `{ userId: string, isSuperAdmin: boolean }` | Grant or revoke super admin privileges | -| `admin.assignToTeam` | mutation | Super Admin | `{ userId: string, teamId: string, role: "VIEWER" \| "EDITOR" \| "ADMIN" }` | Assign a user to a team with a role | -| `admin.removeFromTeam` | mutation | Super Admin | `{ userId: string, teamId: string }` | Remove a user from a team | -| `admin.lockUser` | mutation | Super Admin | `{ userId: string }` | Lock a user account (prevents sign-in) | -| `admin.unlockUser` | mutation | Super Admin | `{ userId: string }` | Unlock a locked user account | -| `admin.resetPassword` | mutation | Super Admin | `{ userId: string }` | Generate a temporary password for a user | -| `admin.listTeams` | query | Super Admin | *(none)* | List all teams | - ---- - -## Settings router - -Configure system-wide settings. All procedures require **Super Admin** access except `checkVersion`. - -### General - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `settings.get` | query | Super Admin | *(none)* | Get all system settings (secrets are masked) | -| `settings.checkVersion` | query | VIEWER | `{ force?: boolean }` | Check server, agent, and dev agent versions | - -### OIDC - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `settings.testOidc` | mutation | Super Admin | `{ issuer: string }` | Test OIDC provider discovery | -| `settings.updateOidc` | mutation | Super Admin | `{ issuer: string, clientId: string, clientSecret: string, displayName?: string, tokenEndpointAuthMethod?: string }` | Update OIDC provider configuration | -| `settings.updateOidcRoleMapping` | mutation | Super Admin | `{ defaultRole: Role, groupsClaim?: string, adminGroups?: string, editorGroups?: string }` | Map OIDC groups to VectorFlow roles | -| `settings.updateOidcTeamMappings` | mutation | Super Admin | `{ mappings: TeamMapping[], defaultTeamId?: string, defaultRole: Role, groupSyncEnabled: boolean, groupsScope?: string, groupsClaim?: string }` | Map OIDC groups to teams with roles | - -
-TeamMapping schema - -Each entry in the `mappings` array: - -| Field | Type | Description | -|-------|------|-------------| -| `group` | `string` | OIDC group name to match | -| `teamId` | `string` | VectorFlow team to map to | -| `role` | `"VIEWER" \| "EDITOR" \| "ADMIN"` | Role to assign in that team | -
- -### Fleet - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `settings.updateFleet` | mutation | Super Admin | `{ pollIntervalMs: number, unhealthyThreshold: number, metricsRetentionDays?: number, logsRetentionDays?: number }` | Update fleet polling interval and data retention | - -### Anomaly detection - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `settings.updateAnomalyConfig` | mutation | Super Admin | `{ baselineWindowDays: number, sigmaThreshold: number, minStddevFloorPercent: number, dedupWindowHours: number, enabledMetrics: string }` | Configure anomaly detection parameters (enabledMetrics is comma-delimited) | - -### Backup & restore - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `settings.createBackup` | mutation | Super Admin | *(none)* | Create a database backup | -| `settings.listBackups` | query | Super Admin | *(none)* | List available backups | -| `settings.previewBackup` | query | Super Admin | `{ filename: string }` | Preview backup contents | -| `settings.deleteBackup` | mutation | Super Admin | `{ filename: string }` | Delete a backup file | -| `settings.restoreBackup` | mutation | Super Admin | `{ filename: string }` | Restore the database from a backup | -| `settings.updateBackupSchedule` | mutation | Super Admin | `{ enabled: boolean, cron: string, retentionCount: number }` | Configure automatic backup schedule | - -### Storage backend - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `settings.testS3Connection` | mutation | Super Admin | `{ bucket: string, region: string, prefix?: string, accessKeyId: string, secretAccessKey: string, endpoint?: string }` | Test S3 bucket connectivity | -| `settings.updateStorageBackend` | mutation | Super Admin | `{ backend: "local" \| "s3", bucket?: string, region?: string, prefix?: string, accessKeyId?: string, secretAccessKey?: string, endpoint?: string }` | Switch backup storage between local filesystem and S3 | - -### SCIM - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `settings.updateScim` | mutation | Super Admin | `{ enabled: boolean }` | Enable or disable SCIM provisioning | -| `settings.generateScimToken` | mutation | Super Admin | *(none)* | Generate a new SCIM bearer token | - ---- - -## Metrics router - -Query pipeline and component metrics from both the database (historical) and in-memory store (live). - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `metrics.getPipelineMetrics` | query | VIEWER | `{ pipelineId: string, minutes?: number }` | Aggregated pipeline metrics from database (default 60 min, max 10080) | -| `metrics.getComponentMetrics` | query | VIEWER | `{ pipelineId: string, minutes?: number }` | Live per-component metrics from in-memory store (default 5 min, max 60) | -| `metrics.getComponentLatencyHistory` | query | VIEWER | `{ pipelineId: string, minutes?: number }` | Per-component latency time series from database (default 60 min, max 1440) | -| `metrics.getNodePipelineRates` | query | VIEWER | `{ nodeId: string }` | Per-pipeline event and byte rates for a specific node | -| `metrics.getLiveRates` | query | VIEWER | `{ environmentId: string }` | Per-pipeline live event and byte rates for the pipelines table | - ---- - -## Analytics router - -Cost analytics and data volume tracking for pipelines, teams, and environments. - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `analytics.costSummary` | query | VIEWER | `{ environmentId: string, range: Range }` | Aggregated cost summary for KPI cards | -| `analytics.costByPipeline` | query | VIEWER | `{ environmentId: string, range: Range }` | Per-pipeline cost breakdown table | -| `analytics.topPipelines` | query | VIEWER | `{ environmentId: string, range: Range }` | Top 5 pipelines by bytes processed | -| `analytics.costByTeam` | query | VIEWER | `{ environmentId: string, range: Range }` | Team-level cost rollup for chargeback (super admins see all teams) | -| `analytics.costByEnvironment` | query | VIEWER | `{ environmentId: string, range: Range }` | Environment comparison view | -| `analytics.costTimeSeries` | query | VIEWER | `{ environmentId: string, range: Range, groupBy?: "pipeline" \| "team" }` | Volume trend time series, grouped by pipeline or team | -| `analytics.costCsv` | query | VIEWER | `{ environmentId: string, range: Range }` | Export cost data as CSV | - -
-Range values - -All analytics queries accept the same range parameter: - -| Value | Description | -|-------|-------------| -| `1h` | Last hour | -| `6h` | Last 6 hours | -| `1d` | Last 24 hours | -| `7d` | Last 7 days | -| `30d` | Last 30 days | -
- ---- - -## Anomaly router - -View and manage anomaly detection events. Anomalies are automatically detected based on the [anomaly configuration](#anomaly-detection) in system settings. - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `anomaly.list` | query | VIEWER | `{ environmentId: string, pipelineId?: string, status?: "open" \| "acknowledged" \| "dismissed", limit?: number, cursor?: string }` | Paginated list of anomalies with optional filters | -| `anomaly.countByPipeline` | query | VIEWER | `{ environmentId: string }` | Count of open anomalies per pipeline | -| `anomaly.maxSeverityByPipeline` | query | VIEWER | `{ environmentId: string }` | Maximum severity level per pipeline | -| `anomaly.acknowledge` | mutation | EDITOR | `{ environmentId: string, anomalyId: string }` | Acknowledge an anomaly | -| `anomaly.dismiss` | mutation | EDITOR | `{ environmentId: string, anomalyId: string }` | Dismiss an anomaly | - ---- - -## Cost Recommendation router - -View and act on cost optimization recommendations generated by automated analysis. - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `costRecommendation.list` | query | VIEWER | `{ environmentId: string, status?: "PENDING" \| "DISMISSED" \| "APPLIED", limit?: number }` | List cost recommendations with optional status filter | -| `costRecommendation.getById` | query | VIEWER | `{ environmentId: string, id: string }` | Get a recommendation with affected pipeline nodes | -| `costRecommendation.summary` | query | VIEWER | `{ environmentId: string }` | Summary stats — pending count and estimated savings | -| `costRecommendation.dismiss` | mutation | EDITOR | `{ environmentId: string, id: string }` | Dismiss a recommendation | -| `costRecommendation.markApplied` | mutation | EDITOR | `{ environmentId: string, id: string }` | Mark a recommendation as applied | -| `costRecommendation.triggerAnalysis` | mutation | ADMIN | `{ environmentId: string }` | Manually trigger cost analysis | - ---- - -## Validator router - -Validate pipeline configuration YAML. - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `validator.validate` | mutation | VIEWER | `{ yaml: string }` | Validate a YAML configuration string and return any errors | - ---- - -## Pipeline Group router - -Organize pipelines into nested folder groups (max 3 levels deep). - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `pipelineGroup.list` | query | VIEWER | `{ environmentId: string }` | List all pipeline groups in an environment | -| `pipelineGroup.create` | mutation | EDITOR | `{ environmentId: string, name: string, color?: string, parentId?: string }` | Create a pipeline group (unique name per parent, max 3-level nesting) | -| `pipelineGroup.update` | mutation | EDITOR | `{ id: string, name?: string, color?: string, parentId?: string }` | Update group name, color, or parent | -| `pipelineGroup.delete` | mutation | ADMIN | `{ id: string }` | Delete a group (child groups are orphaned to root) | - ---- - -## Pipeline Dependency router - -Define and query dependencies between pipelines. Used to warn before deploying or undeploying pipelines with active dependencies. - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `pipelineDependency.list` | query | VIEWER | `{ pipelineId: string }` | List upstream dependencies for a pipeline | -| `pipelineDependency.listCandidates` | query | VIEWER | `{ pipelineId: string, environmentId: string }` | List pipelines available to link as dependencies | -| `pipelineDependency.add` | mutation | EDITOR | `{ upstreamId: string, downstreamId: string, description?: string }` | Add a dependency between two pipelines | -| `pipelineDependency.remove` | mutation | EDITOR | `{ id: string }` | Remove a dependency | -| `pipelineDependency.deployWarnings` | query | VIEWER | `{ pipelineId: string }` | List undeployed upstream dependencies (deploy warnings) | -| `pipelineDependency.undeployWarnings` | query | VIEWER | `{ pipelineId: string }` | List deployed downstream dependents (undeploy warnings) | -| `pipelineDependency.graph` | query | VIEWER | `{ environmentId: string }` | Full dependency graph for an environment | - ---- - -## Promotion router - -Promote pipelines across environments with an optional approval workflow. - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `promotion.preflight` | query | VIEWER | `{ pipelineId: string, targetEnvironmentId: string, name?: string }` | Check promotion readiness — missing secrets, name collisions | -| `promotion.diffPreview` | query | VIEWER | `{ pipelineId: string }` | Generate a YAML diff between source and target | -| `promotion.initiate` | mutation | EDITOR | `{ pipelineId: string, targetEnvironmentId: string, name?: string }` | Initiate a promotion request | -| `promotion.approve` | mutation | EDITOR | `{ requestId: string }` | Approve a pending promotion (self-approval blocked) | -| `promotion.reject` | mutation | EDITOR | `{ requestId: string, note?: string }` | Reject a pending promotion | -| `promotion.cancel` | mutation | EDITOR | `{ requestId: string }` | Cancel a promotion (only the original promoter) | -| `promotion.history` | query | VIEWER | `{ pipelineId: string }` | List promotion history (last 20 requests) | - -
-Promotion statuses - -| Status | Description | -|--------|-------------| -| `PENDING` | Awaiting approval from another team member | -| `DEPLOYED` | Auto-approved and deployed to the target environment | -| `AWAITING_PR_MERGE` | GitOps mode — waiting for the generated PR to be merged | -| `REJECTED` | Rejected by a reviewer | -| `CANCELLED` | Cancelled by the original promoter | -
- ---- - -## Staged Rollout router - -Perform canary deployments by rolling out pipeline changes to a subset of nodes before broadening to the full fleet. - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `stagedRollout.create` | mutation | EDITOR | `{ pipelineId: string, canarySelector: object, healthCheckWindowMinutes: number, changelog: string }` | Create a staged rollout (deploys to canary nodes first) | -| `stagedRollout.broaden` | mutation | EDITOR | `{ rolloutId: string }` | Broaden the rollout to additional nodes | -| `stagedRollout.rollback` | mutation | EDITOR | `{ rolloutId: string }` | Roll back the staged rollout | -| `stagedRollout.getActive` | query | VIEWER | `{ pipelineId: string }` | Get the active rollout for a pipeline (if any) | -| `stagedRollout.list` | query | VIEWER | `{ pipelineId: string }` | List recent rollouts (last 10) | - -
-Rollout statuses - -| Status | Description | -|--------|-------------| -| `CANARY_DEPLOYED` | Deployed to canary nodes, awaiting health check or broadening | -| `HEALTH_CHECK` | Health check window in progress | -| `BROADENED` | Rolled out to all nodes | -| `ROLLED_BACK` | Rolled back to previous version | -
- ---- - -## Node Group router - -Organize fleet nodes into groups based on label criteria. Groups provide aggregated health statistics and compliance tracking. - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `nodeGroup.list` | query | VIEWER | `{ environmentId: string }` | List node groups in an environment | -| `nodeGroup.create` | mutation | ADMIN | `{ environmentId: string, name: string, criteria: Record, labelTemplate?: Record, requiredLabels?: string[] }` | Create a node group with label-based membership criteria | -| `nodeGroup.update` | mutation | ADMIN | `{ id: string, name?: string, criteria?: Record, labelTemplate?: Record, requiredLabels?: string[] }` | Update group properties | -| `nodeGroup.delete` | mutation | ADMIN | `{ id: string }` | Delete a node group | -| `nodeGroup.groupHealthStats` | query | VIEWER | `{ environmentId: string }` | Aggregated health stats per group (includes `__ungrouped__` synthetic entry) | -| `nodeGroup.nodesInGroup` | query | VIEWER | `{ groupId: string, environmentId: string }` | List nodes in a group with status, labels, and compliance info | - ---- - -## Service Account router - -Manage service accounts for programmatic API access via the [REST API](#rest-api-v1). - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `serviceAccount.list` | query | ADMIN | `{ environmentId: string }` | List service accounts for an environment | -| `serviceAccount.create` | mutation | ADMIN | `{ environmentId: string, name: string, description?: string, permissions: Permission[], expiresInDays?: number }` | Create a service account (returns the raw API key once) | -| `serviceAccount.revoke` | mutation | ADMIN | `{ id: string }` | Disable a service account | -| `serviceAccount.delete` | mutation | ADMIN | `{ id: string }` | Permanently delete a service account | - -
-Permission values - -| Permission | Description | -|------------|-------------| -| `pipelines.read` | Read pipeline definitions, versions, and metrics | -| `pipelines.deploy` | Deploy, undeploy, and rollback pipelines | -| `nodes.read` | Read node status, logs, and metrics | -| `nodes.manage` | Manage nodes (maintenance mode, updates) | -| `secrets.read` | List secret names | -| `secrets.manage` | Create, update, and delete secrets | -| `alerts.read` | Read alert rules and events | -| `alerts.manage` | Create, update, and delete alert rules | -| `audit.read` | Read audit log | -
- -{% hint style="info" %} -The raw API key is only returned once during creation. Store it securely — it cannot be retrieved later. Keys are prefixed with `vf_` and hashed (SHA-256) before storage. -{% endhint %} - ---- - -## Webhook Endpoint router - -Manage outbound webhook endpoints for alert notifications. See also [Outbound Webhooks](../operations/outbound-webhooks.md). - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `webhookEndpoint.list` | query | VIEWER | `{ teamId: string }` | List webhook endpoints (secrets excluded) | -| `webhookEndpoint.create` | mutation | ADMIN | `{ teamId: string, name: string, url: string, eventTypes: AlertMetric[], secret?: string }` | Create a webhook endpoint (secret is encrypted at rest) | -| `webhookEndpoint.update` | mutation | ADMIN | `{ id: string, teamId: string, name?: string, url?: string, eventTypes?: AlertMetric[], secret?: string }` | Update a webhook endpoint | -| `webhookEndpoint.delete` | mutation | ADMIN | `{ id: string, teamId: string }` | Delete a webhook endpoint and its delivery history | -| `webhookEndpoint.toggleEnabled` | mutation | ADMIN | `{ id: string, teamId: string }` | Toggle the endpoint enabled/disabled | -| `webhookEndpoint.testDelivery` | mutation | ADMIN | `{ id: string, teamId: string }` | Send a test delivery to the endpoint | -| `webhookEndpoint.listDeliveries` | query | VIEWER | `{ webhookEndpointId: string, teamId: string, take?: number, skip?: number }` | Paginated delivery history (default 20, max 100) | - ---- - -## Git Sync router - -Monitor and manage GitOps synchronization. See also [GitOps](../operations/gitops.md). - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `gitSync.status` | query | VIEWER | `{ environmentId: string }` | Get sync status summary (repo URL, branch, mode, pending/failed counts, last sync) | -| `gitSync.jobs` | query | VIEWER | `{ environmentId: string, status?: "pending" \| "completed" \| "failed", limit?: number }` | List recent sync jobs (default 25, max 100) | -| `gitSync.retryJob` | mutation | EDITOR | `{ jobId: string }` | Retry a single failed sync job | -| `gitSync.retryAllFailed` | mutation | EDITOR | `{ environmentId: string }` | Retry all failed sync jobs | -| `gitSync.importErrors` | query | VIEWER | `{ environmentId: string, limit?: number }` | Get recent import errors from the audit log (default 10, max 50) | - ---- - -## Migration router - -Migrate pipeline configurations from other platforms to VectorFlow. Currently supports Fluentd. - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `migration.list` | query | VIEWER | `{ teamId: string }` | List migration projects for a team | -| `migration.get` | query | VIEWER | `{ id: string, teamId: string }` | Get a migration project with its blocks and status | -| `migration.create` | mutation | EDITOR | `{ teamId: string, name: string, platform: "FLUENTD", originalConfig: string }` | Create a migration project (config max 500 KB) | -| `migration.delete` | mutation | EDITOR | `{ id: string, teamId: string }` | Delete a migration project | -| `migration.parse` | mutation | EDITOR | `{ id: string, teamId: string }` | Parse the original config into translatable blocks | -| `migration.translate` | mutation | EDITOR | `{ id: string, teamId: string }` | Translate all blocks to Vector config via AI | -| `migration.retranslateBlock` | mutation | EDITOR | `{ id: string, teamId: string, blockId: string }` | Re-translate a single block | -| `migration.updateBlockConfig` | mutation | EDITOR | `{ id: string, teamId: string, blockId: string, config: Record }` | Manually edit a translated block's config | -| `migration.validate` | mutation | EDITOR | `{ id: string, teamId: string }` | Validate the translated configuration | -| `migration.generate` | mutation | EDITOR | `{ id: string, teamId: string, environmentId: string, pipelineName: string }` | Generate a VectorFlow pipeline from the translated config | - -{% hint style="info" %} -The `translate` procedure requires an AI provider to be configured for the team. The migration workflow is: **create** → **parse** → **translate** → (optional: edit blocks) → **validate** → **generate**. -{% endhint %} - ---- - -## AI router - -Manage AI assistant conversations for pipeline building, debugging, and VRL authoring. - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `ai.getConversation` | query | VIEWER | `{ pipelineId: string }` | Get the AI conversation for a pipeline | -| `ai.startNewConversation` | mutation | EDITOR | `{ pipelineId: string }` | Start a new AI conversation (replaces existing) | -| `ai.markSuggestionsApplied` | mutation | EDITOR | `{ pipelineId: string, conversationId: string, messageId: string, suggestionIds: string[] }` | Mark AI suggestions as applied to the pipeline | -| `ai.getDebugConversation` | query | VIEWER | `{ pipelineId: string }` | Get the debug-mode AI conversation | -| `ai.getVrlConversation` | query | VIEWER | `{ pipelineId: string, componentKey: string }` | Get the VRL assistant conversation for a component | -| `ai.markVrlSuggestionsApplied` | mutation | EDITOR | `{ pipelineId: string, conversationId: string, messageId: string, suggestionIds: string[] }` | Mark VRL suggestions as applied | - ---- - -## User Preference router - -Store and retrieve per-user UI preferences (e.g., theme, default views, collapsed sidebar sections). - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `userPreference.get` | query | VIEWER | *(none)* | Get all preferences for the current user | -| `userPreference.set` | mutation | VIEWER | `{ key: string, value: string }` | Set a preference (key max 100 chars, value max 500 chars) | -| `userPreference.delete` | mutation | VIEWER | `{ key: string }` | Delete a preference | - ---- - -## Shared Component router - -Manage reusable pipeline components that can be linked across multiple pipelines. When a shared component is updated, linked pipelines show a staleness indicator until the update is accepted. - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `sharedComponent.list` | query | VIEWER | `{ environmentId: string }` | List shared components with linked pipeline count | -| `sharedComponent.getById` | query | VIEWER | `{ id: string, environmentId: string }` | Get a component with linked pipeline details and staleness info | -| `sharedComponent.create` | mutation | EDITOR | `{ environmentId: string, name: string, description?: string, componentType: string, kind: "SOURCE" \| "TRANSFORM" \| "SINK", config: object }` | Create a shared component | -| `sharedComponent.createFromNode` | mutation | EDITOR | `{ nodeId: string, pipelineId: string, name: string, description?: string, environmentId: string }` | Create a shared component from an existing pipeline node | -| `sharedComponent.update` | mutation | EDITOR | `{ id: string, environmentId: string, name?: string, description?: string, config?: object }` | Update a shared component (config changes bump the version) | -| `sharedComponent.delete` | mutation | EDITOR | `{ id: string, environmentId: string }` | Delete a shared component | -| `sharedComponent.acceptUpdate` | mutation | EDITOR | `{ nodeId: string, pipelineId: string }` | Accept the latest shared component config for a single node | -| `sharedComponent.acceptUpdateBulk` | mutation | EDITOR | `{ pipelineId: string }` | Accept updates for all stale nodes in a pipeline | -| `sharedComponent.unlink` | mutation | EDITOR | `{ nodeId: string, pipelineId: string }` | Unlink a node from its shared component (keeps current config) | -| `sharedComponent.linkExisting` | mutation | EDITOR | `{ nodeId: string, pipelineId: string, sharedComponentId: string }` | Link a node to an existing shared component | - ---- - -## Filter Preset router - -Save and manage filter presets for the pipeline list and fleet matrix views. - -| Procedure | Type | Min Role | Input | Description | -|-----------|------|----------|-------|-------------| -| `filterPreset.list` | query | VIEWER | `{ environmentId: string, scope: "pipeline_list" \| "fleet_matrix" }` | List filter presets for a scope | -| `filterPreset.create` | mutation | EDITOR | `{ environmentId: string, name: string, scope: "pipeline_list" \| "fleet_matrix", filters: Record, isDefault?: boolean }` | Create a filter preset (max 20 per scope) | -| `filterPreset.update` | mutation | EDITOR | `{ environmentId: string, id: string, name?: string, filters?: Record }` | Update a preset | -| `filterPreset.delete` | mutation | EDITOR | `{ environmentId: string, id: string }` | Delete a preset | -| `filterPreset.setDefault` | mutation | EDITOR | `{ environmentId: string, id: string, scope: "pipeline_list" \| "fleet_matrix" }` | Set a preset as the default for its scope | -| `filterPreset.clearDefault` | mutation | EDITOR | `{ environmentId: string, scope: "pipeline_list" \| "fleet_matrix" }` | Clear the default preset for a scope | - ---- - -## Error handling - -tRPC errors are returned with a standard error shape: - -```json -{ - "error": { - "json": { - "message": "Pipeline not found", - "code": -32004, - "data": { - "code": "NOT_FOUND", - "httpStatus": 404 - } - } - } -} -``` - -Common error codes: - -| tRPC Code | HTTP Status | Meaning | -|-----------|-------------|---------| -| `UNAUTHORIZED` | 401 | Not signed in | -| `FORBIDDEN` | 403 | Insufficient role or not a team member | -| `NOT_FOUND` | 404 | Resource does not exist | -| `BAD_REQUEST` | 400 | Invalid input | -| `CONFLICT` | 409 | Resource already exists (duplicate name) | -| `PRECONDITION_FAILED` | 412 | Operation requires a precondition (e.g., pipeline must be deployed) | - ---- - -## OpenAPI Specification - -VectorFlow provides a machine-readable [OpenAPI 3.1](https://spec.openapis.org/oas/v3.1.0) specification covering all REST v1 endpoints and key tRPC procedures. - -### Fetching the spec - -```bash -curl -s https://vectorflow.example.com/api/v1/openapi.json | jq .info -``` - -The spec is served at `/api/v1/openapi.json` with CORS enabled — you can fetch it from any origin without credentials. - -### Importing into tools - -**Postman:** File > Import > paste URL `https://vectorflow.example.com/api/v1/openapi.json` - -**Swagger UI / Stoplight:** Point to the spec URL or paste the JSON content. - -### Client generation - -Generate a typed API client in any language using [openapi-generator](https://openapi-generator.tech/): - -```bash -npx @openapitools/openapi-generator-cli generate \ - -i https://vectorflow.example.com/api/v1/openapi.json \ - -g python \ - -o ./vectorflow-client -``` - -### What's included - -The spec documents two API surfaces: - -| Surface | Auth | Endpoints | -|---------|------|-----------| -| REST v1 (`/api/v1/*`) | Service account Bearer token | Pipeline CRUD, deploy, rollback, nodes, secrets, alerts, audit | -| tRPC (`/api/trpc/*`) | Session cookie | Pipeline management, fleet, environments, secrets, deploy, alerts, service accounts | - -{% hint style="info" %} -**tRPC encoding note:** tRPC endpoints use [SuperJSON](https://github.com/blitz-js/superjson) encoding. For queries, input is URL-encoded JSON in `?input=` (wrap as `{"json": }`). For mutations, the body is `{"json": }`. Using a tRPC client is recommended for full type safety; the OpenAPI spec is provided for discoverability and non-TypeScript integrations. -{% endhint %} - ---- - -## REST API (v1) - -The REST API provides a standard HTTP interface for automation and CI/CD. All endpoints require a [Service Account](../operations/service-accounts.md) API key. - -### Authentication - -Include your API key in the `Authorization` header: - -```bash -curl -H "Authorization: Bearer vf_live_abc123..." \ - https://vectorflow.example.com/api/v1/pipelines -``` - -Responses use standard HTTP status codes and return JSON: - -```json -{ "error": "Unauthorized" } // 401 -{ "error": "Forbidden" } // 403 -{ "error": "Pipeline not found" } // 404 -``` - -### Service Account Management - -Service accounts are managed via the tRPC API (Settings UI) or programmatically: - -| Procedure | Type | Min Role | Description | -|-----------|------|----------|-------------| -| `serviceAccount.list` | query | ADMIN | List service accounts for an environment | -| `serviceAccount.create` | mutation | ADMIN | Create a service account (returns raw key once) | -| `serviceAccount.revoke` | mutation | ADMIN | Disable a service account | -| `serviceAccount.delete` | mutation | ADMIN | Permanently delete a service account | - ---- - -### Pipelines - -#### List pipelines - -```bash -GET /api/v1/pipelines -``` - -Permission: `pipelines.read` - -Returns all pipelines in the service account's environment. - -```bash -curl -s https://vectorflow.example.com/api/v1/pipelines \ - -H "Authorization: Bearer vf_live_..." -``` - -Response: - -```json -{ - "pipelines": [ - { - "id": "clxyz123", - "name": "syslog-to-s3", - "description": "Ship syslog to S3", - "isDraft": false, - "deployedAt": "2026-03-01T12:00:00.000Z", - "createdAt": "2026-02-15T10:00:00.000Z", - "updatedAt": "2026-03-01T12:00:00.000Z" - } - ] -} -``` - -#### Get pipeline details - -```bash -GET /api/v1/pipelines/:id -``` - -Permission: `pipelines.read` - -```bash -curl -s https://vectorflow.example.com/api/v1/pipelines/clxyz123 \ - -H "Authorization: Bearer vf_live_..." -``` - -#### Deploy pipeline - -```bash -POST /api/v1/pipelines/:id/deploy -``` - -Permission: `pipelines.deploy` - -Validates the pipeline config, creates a new version, and marks it as deployed. Agents pick up the change on their next poll. - -```bash -curl -s -X POST https://vectorflow.example.com/api/v1/pipelines/clxyz123/deploy \ - -H "Authorization: Bearer vf_live_..." \ - -H "Content-Type: application/json" \ - -d '{"changelog": "Deployed from CI"}' -``` - -Response: - -```json -{ - "success": true, - "versionId": "clversion456", - "versionNumber": 5 -} -``` - -#### Undeploy pipeline - -```bash -POST /api/v1/pipelines/:id/undeploy -``` - -Permission: `pipelines.deploy` - -Marks the pipeline as a draft. Agents stop running it on their next poll. - -```bash -curl -s -X POST https://vectorflow.example.com/api/v1/pipelines/clxyz123/undeploy \ - -H "Authorization: Bearer vf_live_..." -``` - -#### List pipeline versions - -```bash -GET /api/v1/pipelines/:id/versions -``` - -Permission: `pipelines.read` - -```bash -curl -s https://vectorflow.example.com/api/v1/pipelines/clxyz123/versions \ - -H "Authorization: Bearer vf_live_..." -``` - -Response: - -```json -{ - "versions": [ - { - "id": "clversion456", - "version": 5, - "changelog": "Added error handling transform", - "createdById": "user123", - "createdAt": "2026-03-01T12:00:00.000Z" - } - ] -} -``` - -#### Rollback pipeline - -```bash -POST /api/v1/pipelines/:id/rollback -``` - -Permission: `pipelines.deploy` - -Rolls back to a previous version by creating a new version with the target version's config. - -```bash -curl -s -X POST https://vectorflow.example.com/api/v1/pipelines/clxyz123/rollback \ - -H "Authorization: Bearer vf_live_..." \ - -H "Content-Type: application/json" \ - -d '{"targetVersionId": "clversion123"}' -``` - ---- - -### Nodes - -#### List nodes - -```bash -GET /api/v1/nodes -GET /api/v1/nodes?label=role:production -``` - -Permission: `nodes.read` - -Supports optional label filtering via `?label=key:value`. - -```bash -curl -s https://vectorflow.example.com/api/v1/nodes \ - -H "Authorization: Bearer vf_live_..." -``` - -#### Get node details - -```bash -GET /api/v1/nodes/:id -``` - -Permission: `nodes.read` - -```bash -curl -s https://vectorflow.example.com/api/v1/nodes/clnode789 \ - -H "Authorization: Bearer vf_live_..." -``` - -#### Toggle maintenance mode - -```bash -POST /api/v1/nodes/:id/maintenance -``` - -Permission: `nodes.manage` - -```bash -curl -s -X POST https://vectorflow.example.com/api/v1/nodes/clnode789/maintenance \ - -H "Authorization: Bearer vf_live_..." \ - -H "Content-Type: application/json" \ - -d '{"enabled": true}' -``` - ---- - -### Secrets - -#### List secrets - -```bash -GET /api/v1/secrets -``` - -Permission: `secrets.read` - -Returns secret names and timestamps (never values). - -```bash -curl -s https://vectorflow.example.com/api/v1/secrets \ - -H "Authorization: Bearer vf_live_..." -``` - -#### Create secret - -```bash -POST /api/v1/secrets -``` - -Permission: `secrets.manage` - -```bash -curl -s -X POST https://vectorflow.example.com/api/v1/secrets \ - -H "Authorization: Bearer vf_live_..." \ - -H "Content-Type: application/json" \ - -d '{"name": "AWS_ACCESS_KEY", "value": "AKIA..."}' -``` - -#### Update secret - -```bash -PUT /api/v1/secrets -``` - -Permission: `secrets.manage` - -Identify the secret by `id` or `name`: - -```bash -curl -s -X PUT https://vectorflow.example.com/api/v1/secrets \ - -H "Authorization: Bearer vf_live_..." \ - -H "Content-Type: application/json" \ - -d '{"name": "AWS_ACCESS_KEY", "value": "AKIA_NEW..."}' -``` - -#### Delete secret - -```bash -DELETE /api/v1/secrets?name=AWS_ACCESS_KEY -DELETE /api/v1/secrets?id=clsecret123 -``` - -Permission: `secrets.manage` - -```bash -curl -s -X DELETE "https://vectorflow.example.com/api/v1/secrets?name=AWS_ACCESS_KEY" \ - -H "Authorization: Bearer vf_live_..." -``` - ---- - -### Alert Rules - -#### List alert rules - -```bash -GET /api/v1/alerts/rules -``` - -Permission: `alerts.read` - -```bash -curl -s https://vectorflow.example.com/api/v1/alerts/rules \ - -H "Authorization: Bearer vf_live_..." -``` - -#### Create alert rule - -```bash -POST /api/v1/alerts/rules -``` - -Permission: `alerts.manage` - -```bash -curl -s -X POST https://vectorflow.example.com/api/v1/alerts/rules \ - -H "Authorization: Bearer vf_live_..." \ - -H "Content-Type: application/json" \ - -d '{ - "name": "High CPU Alert", - "metric": "cpu_usage", - "condition": "gt", - "threshold": 80, - "durationSeconds": 120, - "teamId": "clteam123" - }' -``` - ---- - -### Audit Log - -#### Poll audit events - -```bash -GET /api/v1/audit -GET /api/v1/audit?after=cursor123&limit=100&action=deploy.agent -``` - -Permission: `audit.read` - -Supports cursor-based pagination for polling: - -| Parameter | Type | Default | Description | -|-----------|------|---------|-------------| -| `after` | string | -- | Cursor from previous response (for pagination) | -| `limit` | number | 50 | Max events to return (1-200) | -| `action` | string | -- | Filter by action type (e.g., `deploy.agent`) | - -```bash -curl -s "https://vectorflow.example.com/api/v1/audit?limit=100" \ - -H "Authorization: Bearer vf_live_..." -``` - -Response: - -```json -{ - "events": [ ... ], - "cursor": "claudit789", - "hasMore": true -} -``` - -To poll for new events, pass the `cursor` from the previous response: - -```bash -curl -s "https://vectorflow.example.com/api/v1/audit?after=claudit789&limit=100" \ - -H "Authorization: Bearer vf_live_..." -``` diff --git a/docs/public/reference/database.md b/docs/public/reference/database.md deleted file mode 100644 index 07b68d36..00000000 --- a/docs/public/reference/database.md +++ /dev/null @@ -1,297 +0,0 @@ -# Database Schema - -{% hint style="info" %} -This reference is for advanced self-hosters who need to understand the data model for backup planning, integrations, or troubleshooting. The schema is managed by Prisma migrations -- you do not need to create tables manually. Running `npx prisma migrate deploy` (or starting the Docker container) applies all pending migrations automatically. -{% endhint %} - -VectorFlow uses **PostgreSQL** as its sole data store. All state -- pipeline definitions, fleet status, metrics, audit logs, secrets, and user accounts -- lives in the database. - ---- - -## Entity relationship diagram - -```mermaid -erDiagram - Team ||--o{ TeamMember : has - Team ||--o{ Environment : owns - Team ||--o{ Template : owns - Team ||--o{ AlertRule : owns - Team ||--o{ VrlSnippet : owns - - User ||--o{ TeamMember : belongs_to - User ||--o{ AuditLog : creates - User ||--o{ VrlSnippet : creates - - Environment ||--o{ VectorNode : contains - Environment ||--o{ Pipeline : contains - Environment ||--o{ Secret : stores - Environment ||--o{ Certificate : stores - Environment ||--o{ AlertRule : has - Environment ||--o{ AlertWebhook : has - - Pipeline ||--o{ PipelineNode : has - Pipeline ||--o{ PipelineEdge : has - Pipeline ||--o{ PipelineVersion : tracks - Pipeline ||--o{ NodePipelineStatus : reports - Pipeline ||--o{ PipelineMetric : records - Pipeline ||--o{ PipelineLog : records - Pipeline ||--o{ AlertRule : monitors - Pipeline ||--o{ EventSampleRequest : requests - Pipeline ||--o{ EventSample : stores - - VectorNode ||--o{ NodePipelineStatus : reports - VectorNode ||--o{ NodeMetric : records - VectorNode ||--o{ PipelineLog : emits - VectorNode ||--o{ AlertEvent : triggers - - AlertRule ||--o{ AlertEvent : fires -``` - ---- - -## Core entities - -| Table | Description | -|-------|-------------| -| `User` | User accounts with authentication credentials, TOTP secrets, and super admin flag | -| `Team` | Organizational unit that groups environments, templates, and members | -| `TeamMember` | Join table linking users to teams with a role (VIEWER, EDITOR, ADMIN) | -| `Environment` | Logical grouping of nodes and pipelines (e.g., Production, Staging) | -| `VectorNode` | An agent node registered in an environment | -| `Pipeline` | A pipeline definition with its visual graph, deployment state, and global config | -| `PipelineNode` | A single component (source, transform, or sink) within a pipeline graph | -| `PipelineEdge` | A connection between two pipeline nodes | -| `PipelineVersion` | An immutable snapshot of a pipeline's generated YAML config at deploy time | -| `NodePipelineStatus` | Per-node runtime status for a deployed pipeline | -| `PipelineMetric` | Time-series pipeline throughput data (events, bytes, errors) | -| `NodeMetric` | Time-series host system metrics (CPU, memory, disk, network) | -| `PipelineLog` | Log lines from pipeline processes, forwarded by agents | -| `Secret` | Encrypted secret values scoped to an environment | -| `Certificate` | Encrypted TLS certificate files scoped to an environment | -| `Template` | Reusable pipeline template stored as JSON nodes/edges | -| `AuditLog` | Immutable record of every significant action | -| `SystemSettings` | Singleton row for global server configuration | -| `AlertRule` | Alert condition definition (metric, threshold, duration) | -| `AlertWebhook` | Webhook destination for alert notifications | -| `AlertEvent` | Record of a fired or resolved alert | -| `VrlSnippet` | Custom VRL code snippet in the team library | -| `EventSampleRequest` | Request to sample live events from a running pipeline | -| `EventSample` | Sampled event data and inferred schema for a pipeline component | -| `Account` | OAuth/OIDC provider accounts linked to users | - ---- - -## Key table details - -### Pipeline - -The central entity. Stores the pipeline definition and tracks deployment state. - -| Field | Type | Description | -|-------|------|-------------| -| `id` | `String` (CUID) | Primary key | -| `name` | `String` | Display name | -| `description` | `String?` | Optional description | -| `environmentId` | `String` | FK to Environment | -| `globalConfig` | `Json?` | Global Vector config (API settings, enrichment tables, log level) | -| `isDraft` | `Boolean` | `true` = not deployed, `false` = actively deployed | -| `isSystem` | `Boolean` | `true` = system pipeline (audit log shipping) | -| `deployedAt` | `DateTime?` | Timestamp of last deployment (null if never deployed) | -| `createdById` | `String?` | FK to User who created the pipeline | -| `updatedById` | `String?` | FK to User who last modified the pipeline | -| `createdAt` | `DateTime` | Creation timestamp | -| `updatedAt` | `DateTime` | Last modification timestamp | - -Relationships: `nodes`, `edges`, `versions`, `nodeStatuses`, `metrics`, `pipelineLogs`, `alertRules`, `sampleRequests`, `eventSamples`. - -### VectorNode - -Represents an enrolled agent node. - -| Field | Type | Description | -|-------|------|-------------| -| `id` | `String` (CUID) | Primary key | -| `name` | `String` | Display name (defaults to hostname at enrollment) | -| `host` | `String` | Hostname or IP address | -| `apiPort` | `Int` | Vector API port (default: 8686) | -| `environmentId` | `String` | FK to Environment | -| `status` | `NodeStatus` | Current health: `HEALTHY`, `DEGRADED`, `UNREACHABLE`, `UNKNOWN` | -| `lastSeen` | `DateTime?` | Last time the server processed a heartbeat from this node | -| `metadata` | `Json?` | Additional node metadata | -| `nodeTokenHash` | `String?` | Hashed node authentication token (null = revoked) | -| `enrolledAt` | `DateTime?` | When the node first enrolled | -| `lastHeartbeat` | `DateTime?` | Timestamp of the last heartbeat | -| `agentVersion` | `String?` | Reported agent binary version | -| `vectorVersion` | `String?` | Reported Vector binary version | -| `os` | `String?` | Operating system and architecture (e.g., `linux/amd64`) | -| `deploymentMode` | `DeploymentMode` | `STANDALONE`, `DOCKER`, or `UNKNOWN` | -| `maintenanceMode` | `Boolean` | Whether the node is in maintenance mode (pipelines are stopped) | -| `maintenanceModeAt` | `DateTime?` | When the node entered maintenance mode (null if not in maintenance) | -| `pendingAction` | `Json?` | Server-initiated action (e.g., self-update command) | -| `createdAt` | `DateTime` | Registration timestamp | - -### Environment - -Logical grouping that contains nodes, pipelines, secrets, and certificates. - -| Field | Type | Description | -|-------|------|-------------| -| `id` | `String` (CUID) | Primary key | -| `name` | `String` | Display name (e.g., "Production", "Staging") | -| `isSystem` | `Boolean` | `true` = internal system environment (hidden from UI) | -| `teamId` | `String?` | FK to Team (null for system environment) | -| `enrollmentTokenHash` | `String?` | Hashed enrollment token for agent registration | -| `enrollmentTokenHint` | `String?` | First few characters of the token for display | -| `secretBackend` | `SecretBackend` | Secret storage: `BUILTIN`, `VAULT`, `AWS_SM`, `EXEC` | -| `secretBackendConfig` | `Json?` | Configuration for external secret backends | -| `gitRepoUrl` | `String?` | HTTPS URL of the Git repository for pipeline audit trail | -| `gitBranch` | `String?` | Git branch to commit pipeline YAML to (default: `main`) | -| `gitToken` | `String?` | Encrypted access token for Git repository authentication | -| `createdAt` | `DateTime` | Creation timestamp | - -### PipelineVersion - -Immutable deployment snapshot. Created each time a pipeline is deployed. - -| Field | Type | Description | -|-------|------|-------------| -| `id` | `String` (CUID) | Primary key | -| `pipelineId` | `String` | FK to Pipeline | -| `version` | `Int` | Auto-incrementing version number | -| `configYaml` | `String` | The generated Vector YAML config | -| `configToml` | `String?` | Optional TOML representation | -| `logLevel` | `String?` | Vector log level at deploy time | -| `globalConfig` | `Json?` | Global config snapshot | -| `createdById` | `String` | FK to User who deployed | -| `changelog` | `String?` | User-provided deploy message | -| `createdAt` | `DateTime` | Deploy timestamp | - -### Secret - -Encrypted secrets scoped to an environment. Referenced in pipeline configs using `SECRET[name]` syntax. - -| Field | Type | Description | -|-------|------|-------------| -| `id` | `String` (CUID) | Primary key | -| `name` | `String` | Secret identifier (unique per environment) | -| `encryptedValue` | `String` | AES-256-GCM encrypted value | -| `environmentId` | `String` | FK to Environment | -| `createdAt` | `DateTime` | Creation timestamp | -| `updatedAt` | `DateTime` | Last update timestamp | - -### Certificate - -Encrypted TLS certificate files. Referenced in pipeline configs using `CERT[name]` syntax. - -| Field | Type | Description | -|-------|------|-------------| -| `id` | `String` (CUID) | Primary key | -| `name` | `String` | Certificate identifier (unique per environment) | -| `filename` | `String` | Original filename (e.g., `ca.pem`) | -| `fileType` | `String` | Type: `ca`, `cert`, or `key` | -| `encryptedData` | `String` | AES-256-GCM encrypted PEM content | -| `environmentId` | `String` | FK to Environment | -| `createdAt` | `DateTime` | Upload timestamp | - -### AuditLog - -Immutable audit trail of all significant actions. - -| Field | Type | Description | -|-------|------|-------------| -| `id` | `String` (CUID) | Primary key | -| `userId` | `String?` | FK to User (null for system actions) | -| `action` | `String` | Action identifier (e.g., `pipeline.created`, `deploy.agent`) | -| `entityType` | `String` | Target entity type (e.g., `Pipeline`, `Environment`) | -| `entityId` | `String` | ID of the affected entity | -| `diff` | `Json?` | Before/after field changes | -| `metadata` | `Json?` | Additional context | -| `ipAddress` | `String?` | Client IP address | -| `userEmail` | `String?` | Denormalized email for display | -| `userName` | `String?` | Denormalized name for display | -| `teamId` | `String?` | Owning team | -| `environmentId` | `String?` | Owning environment | -| `createdAt` | `DateTime` | Timestamp | - ---- - -## Enums - -| Enum | Values | Description | -|------|--------|-------------| -| `Role` | `VIEWER`, `EDITOR`, `ADMIN` | Team membership role | -| `AuthMethod` | `LOCAL`, `OIDC` | User authentication method | -| `NodeStatus` | `HEALTHY`, `DEGRADED`, `UNREACHABLE`, `UNKNOWN` | Agent node health | -| `DeploymentMode` | `STANDALONE`, `DOCKER`, `UNKNOWN` | How the agent is deployed | -| `ComponentKind` | `SOURCE`, `TRANSFORM`, `SINK` | Pipeline node category | -| `ProcessStatus` | `RUNNING`, `STARTING`, `STOPPED`, `CRASHED`, `PENDING` | Pipeline process state | -| `LogLevel` | `TRACE`, `DEBUG`, `INFO`, `WARN`, `ERROR` | Log severity | -| `SecretBackend` | `BUILTIN`, `VAULT`, `AWS_SM`, `EXEC` | Secret storage provider | -| `AlertMetric` | `node_unreachable`, `cpu_usage`, `memory_usage`, `disk_usage`, `error_rate`, `discarded_rate`, `pipeline_crashed` | Metric to evaluate | -| `AlertCondition` | `gt`, `lt`, `eq` | Comparison operator | -| `AlertStatus` | `firing`, `resolved` | Alert event state | - ---- - -## Encryption at rest - -Sensitive fields are encrypted using AES-256-GCM before being stored in the database: - -- **Secret values** (`Secret.encryptedValue`) -- pipeline credentials, API keys, passwords -- **Certificate data** (`Certificate.encryptedData`) -- TLS certificates and private keys -- **TOTP secrets** (`User.totpSecret`) -- two-factor authentication secrets -- **TOTP backup codes** (`User.totpBackupCodes`) -- recovery codes -- **Password hashes** (`User.passwordHash`) -- bcrypt-hashed, not AES-encrypted - -The encryption key is derived from the `NEXTAUTH_SECRET` environment variable. Losing this value means encrypted data cannot be recovered. - ---- - -## Indexes - -Key database indexes for query performance: - -| Table | Index | Purpose | -|-------|-------|---------| -| `PipelineMetric` | `(pipelineId, timestamp)` | Time-range queries for pipeline charts | -| `PipelineMetric` | `(timestamp)` | Retention cleanup | -| `NodeMetric` | `(nodeId, timestamp)` | Time-range queries for node charts | -| `PipelineLog` | `(pipelineId, timestamp)` | Pipeline log pagination | -| `PipelineLog` | `(nodeId, timestamp)` | Node log pagination | -| `AuditLog` | `(entityType, entityId)` | Entity-specific audit queries | -| `AuditLog` | `(userId)` | User activity queries | -| `AuditLog` | `(createdAt)` | Time-range audit queries | -| `AlertRule` | `(environmentId)` | Environment-scoped alert listing | -| `AlertEvent` | `(alertRuleId)` | Alert event history | -| `AlertEvent` | `(firedAt)` | Time-range alert queries | - ---- - -## Data retention - -VectorFlow automatically prunes time-series data based on system settings: - -| Data Type | Default Retention | Setting | -|-----------|------------------|---------| -| Pipeline metrics | 7 days | `metricsRetentionDays` | -| Pipeline logs | 3 days | `logsRetentionDays` | -| Node metrics | 7 days | `metricsRetentionDays` | -| Audit logs | Indefinite | Not automatically pruned | -| Alert events | Indefinite | Not automatically pruned | - -These values are configured in the `SystemSettings` table via the admin UI. - ---- - -## Backup considerations - -{% hint style="warning" %} -The database is the single source of truth for all VectorFlow state. Losing the database without a backup means losing all pipeline definitions, deployment history, secrets, and audit logs. -{% endhint %} - -For backup and restore procedures, see [Backup & Restore](../operations/backup-restore.md). - -Key points: -- Use `pg_dump` for logical backups or continuous archiving for point-in-time recovery -- The `NEXTAUTH_SECRET` environment variable must match between backup and restore -- it is the encryption key for secrets and certificates -- VectorFlow has built-in scheduled backup support (configured via `SystemSettings`) diff --git a/docs/public/reference/pipeline-yaml.md b/docs/public/reference/pipeline-yaml.md deleted file mode 100644 index 671611cf..00000000 --- a/docs/public/reference/pipeline-yaml.md +++ /dev/null @@ -1,282 +0,0 @@ -# Pipeline YAML - -{% hint style="info" %} -Users typically do not write pipeline YAML directly -- the visual editor generates it. This reference is for understanding the generated output, debugging deployment issues, and advanced use cases like importing existing Vector configs. -{% endhint %} - -VectorFlow pipelines are ultimately Vector configuration files. The visual pipeline editor translates your node graph into standard [Vector YAML configuration](https://vector.dev/docs/reference/configuration/) that Vector can execute directly. - ---- - -## How YAML is generated - -The pipeline YAML generation follows this flow: - -``` - Visual Editor Server Agent - ┌───────────┐ ┌──────────────┐ ┌──────────────┐ - │ Drag nodes│ │ Save graph │ │ Receive YAML │ - │ Connect │───▶│ to database │ │ via config │ - │ edges │ │ │ │ endpoint │ - └───────────┘ │ On deploy: │ │ │ - │ 1. Generate │ poll │ Write to disk│ - │ YAML │────────▶ │ Start Vector │ - │ 2. Validate │ │ process │ - │ 3. Version │ └──────────────┘ - └──────────────┘ -``` - -1. **Graph saved**: The visual editor saves pipeline nodes (components) and edges (connections) to the database -2. **YAML generated**: At deploy time, the server converts the graph into Vector YAML -3. **Validation**: The generated YAML is validated using `vector validate --no-environment` -4. **Versioning**: A new `PipelineVersion` record stores the YAML and a version number -5. **Distribution**: Agents poll the server and receive the YAML config for all deployed pipelines -6. **Execution**: The agent writes the YAML to disk and starts a Vector process with it - ---- - -## YAML structure - -The generated YAML follows Vector's standard configuration format with three top-level sections: - -```yaml -sources: - : - type: - # ... source-specific fields - -transforms: - : - type: - inputs: - - - # ... transform-specific fields - -sinks: - : - type: - inputs: - - - # ... sink-specific fields -``` - -### Component keys - -Each node in the visual editor has a **component key** -- a unique, auto-generated identifier within the pipeline (e.g., `http_server_k7xMp2nQ`). Component keys are generated when a node is added and never change, even if you rename the component in the editor. - -Component keys must: -- Start with a letter or underscore -- Contain only letters, numbers, and underscores -- Be between 1 and 128 characters - -These keys become the YAML block names under `sources`, `transforms`, or `sinks`. The human-readable **Name** field in the editor is separate from the component key and does not affect the generated YAML. - -### Connections via `inputs` - -Vector uses an `inputs` field to define data flow. When you draw an edge from node A to node B in the visual editor, the generated YAML adds A's component key to B's `inputs` array. - -Sources never have `inputs` -- they are the entry points. Transforms and sinks always have at least one input. - ---- - -## Complete example - -A pipeline that receives syslog, parses and enriches the events with VRL, then sends to S3: - -**Visual editor graph:** -``` -[syslog_in] ──▶ [parse_logs] ──▶ [s3_output] - (source) (transform) (sink) -``` - -**Generated YAML:** -```yaml -sources: - syslog_in: - type: syslog - address: 0.0.0.0:514 - mode: udp - -transforms: - parse_logs: - type: remap - inputs: - - syslog_in - source: | - .environment = "production" - .processed_at = now() - del(.source_type) - -sinks: - s3_output: - type: aws_s3 - inputs: - - parse_logs - bucket: my-log-bucket - region: us-east-1 - key_prefix: "logs/{{ .environment }}/%Y/%m/%d/" - encoding: - codec: json - auth: - access_key_id: "${VF_SECRET_AWS_ACCESS_KEY}" - secret_access_key: "${VF_SECRET_AWS_SECRET_KEY}" -``` - ---- - -## Global configuration - -Pipelines can include global Vector configuration sections beyond sources, transforms, and sinks. These are set via the pipeline's `globalConfig` field and appear at the top level of the generated YAML. - -Common global config sections: - -- **`api`**: Enable the Vector API and GraphQL playground -- **`enrichment_tables`**: Define lookup tables for enrichment - -The `log_level` key in `globalConfig` is handled specially -- it is **not** included in the generated YAML. Instead, it is passed to the Vector process as the `VECTOR_LOG` environment variable. - ---- - -## Disabled nodes - -Nodes marked as **disabled** in the visual editor are excluded from the generated YAML entirely. Their edges are also removed. This lets you temporarily disable a component without deleting it from the graph. - ---- - -## Secret references - -Secrets stored in VectorFlow can be referenced in pipeline component configurations. When you use a secret in a node's config, the reference is resolved at deploy time. - -### How it works - -1. Create a secret in the environment's secret store (e.g., name: `AWS_ACCESS_KEY`) -2. Reference it in a component config field using the `SECRET[name]` syntax in the visual editor -3. At deploy time, the server resolves all secret references -4. The agent receives the resolved values in the config response and injects them as environment variables -5. In the generated YAML, secrets appear as `${VF_SECRET_AWS_ACCESS_KEY}` -- standard Vector environment variable interpolation (all secrets use the `VF_SECRET_` prefix) - -### Example - -A sink configured with secret references: - -```yaml -sinks: - elasticsearch: - type: elasticsearch - inputs: - - transform_logs - endpoints: - - "https://es.example.com:9200" - auth: - strategy: basic - user: "${VF_SECRET_ES_USER}" - password: "${VF_SECRET_ES_PASSWORD}" -``` - -The agent injects environment variables `VF_SECRET_ES_USER` and `VF_SECRET_ES_PASSWORD` with the decrypted values when starting the Vector process. - ---- - -## Certificate references - -TLS certificates uploaded to VectorFlow are referenced using `CERT[name]` syntax. At deploy time: - -1. Certificate data is sent to the agent in the config response (base64-encoded) -2. The agent writes cert files to `/certs/` -3. The config YAML references the local file path - -### Example - -```yaml -sinks: - kafka_out: - type: kafka - inputs: - - parse_logs - bootstrap_servers: "kafka.example.com:9093" - topic: logs - tls: - ca_file: "/var/lib/vf-agent/certs/ca.pem" - crt_file: "/var/lib/vf-agent/certs/client.crt" - key_file: "/var/lib/vf-agent/certs/client.key" -``` - ---- - -## Validation - -Before any deployment, VectorFlow validates the generated YAML using the Vector binary: - -```bash -vector validate --no-environment -``` - -The `--no-environment` flag skips environment variable validation (since secrets are resolved at runtime by the agent, not at validation time). - -### Validation results - -The validation returns: -- **Valid**: The config is syntactically correct and all component types are recognized -- **Errors**: Specific error messages, often with the affected component key identified -- **Warnings**: Deprecation notices or non-fatal issues - -If validation fails, the deployment is blocked and errors are displayed in the UI. - ---- - -## Metrics sidecar - -When the agent starts a pipeline, it automatically appends a **metrics sidecar config** as a second `--config` argument. This sidecar adds instrumentation without modifying the user's pipeline YAML: - -```yaml -# Auto-generated by the VectorFlow agent -api: - enabled: true - address: "127.0.0.1:" - -sources: - vf_internal_metrics: - type: internal_metrics - vf_host_metrics: - type: host_metrics - -sinks: - vf_metrics_exporter: - type: prometheus_exporter - inputs: - - vf_internal_metrics - - vf_host_metrics - address: "127.0.0.1:" -``` - -Vector merges both config files, so the pipeline's sources/transforms/sinks coexist with the metrics instrumentation. The `vf_` prefix prevents key collisions. - ---- - -## Version history - -Every deployment creates an immutable `PipelineVersion` record containing: - -| Field | Description | -|-------|-------------| -| `version` | Auto-incrementing integer (1, 2, 3, ...) | -| `configYaml` | The exact YAML that was deployed | -| `logLevel` | The Vector log level at deploy time | -| `changelog` | User-provided deploy message | -| `createdById` | Who triggered the deployment | -| `createdAt` | Deployment timestamp | - -You can view previous versions in the pipeline detail page and roll back to any prior version. Rolling back creates a **new** version with the old config, preserving the full history. - ---- - -## Importing existing configs - -VectorFlow supports importing existing Vector YAML or TOML configurations into the visual editor. The importer: - -1. Parses the config file -2. Creates nodes for each source, transform, and sink -3. Creates edges based on `inputs` fields -4. 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