Add license

docs(journey): link the live visual prototype at swarm.produktor.io
docs: add design journey, a narrative walk-through linking into the code
2026-07-09 15:52:16 +01:00 · 2026-07-09 12:47:12 +01:00 · 2026-07-09 12:30:40 +01:00 · 2026-07-09 11:41:05 +01:00 · 2026-07-08 21:19:53 +01:00 · 2026-07-08 20:34:37 +01:00
72 changed files with 3459 additions and 190 deletions
@@ -30,6 +30,10 @@ jobs:
      - name: Install dependencies
        run: pip install --quiet -r simulator/requirements.txt
      - name: Unit tests (TDD gate)
        working-directory: simulator
        run: pytest tests/ -q
      - name: Compile check
        run: python -m compileall -q simulator/virtual_drone simulator/monitoring simulator/explorer
@@ -49,26 +53,6 @@ jobs:
          print(f"smoke flight OK: {rows} telemetry rows")
          EOF
      - name: SQL gate tests
        working-directory: simulator
        run: |
          python - <<'EOF'
          import sys
          sys.path.insert(0, "explorer")
          from server import gate
          assert gate("SELECT 1") is None
          assert gate("WITH t AS (SELECT 1) SELECT * FROM t") is None
          assert gate("DESCRIBE telemetry") is None
          assert gate("DROP TABLE telemetry") is not None
          assert gate("SELECT 1; SELECT 2") is not None
          assert gate("INSTALL httpfs") is not None
          assert gate("SET memory_limit='1GB'") is not None
          assert gate("/* sneaky */ COPY t TO 'x'") is not None
          assert gate("") is not None
          print("SQL gate OK: reads pass, writes and config are rejected")
          EOF
  scan:
    name: Trivy scan
    runs-on: ubuntu-latest
@@ -141,6 +125,7 @@ jobs:
          TAG="${{ steps.version.outputs.new_tag }}"
          mkdir -p .tmp
          tar --exclude='.git' --exclude='.tmp' --exclude='simulator/data' \
              --exclude='var/t1' --exclude='var/t3' \
              --exclude='simulator/.venv' --exclude='prototype/node_modules' \
              -czf ".tmp/swarm-house-${TAG}.tar.gz" .
@@ -1,7 +1,15 @@
 data/
 simulator/data/
 var/t1/**
 var/t3/**
 !var/t1/.gitkeep
 !var/t3/.gitkeep
 __pycache__/
 *.pyc
 .venv/
 node_modules/
 dist/
 .env
 .terraform/
 *.tfstate
 *.tfstate.*
@@ -15,8 +15,13 @@ Rules for anyone (human or tooling) contributing to this repository.
 docs/           Design documentation (Markdown + Mermaid)
 simulator/      Virtual drone fleet — Python data generator (Parquet/DuckDB pipeline)
 prototype/      2D swarm visualization — React + TypeScript (Vite)
 infra/          Ansible host prep, Terraform workloads, Flux GitOps overlays
 var/t1/         Shared T1 live lake (gitignored runtime data)
 var/t3/         Shared T3 warehouse (gitignored runtime data)
 ```
 See [`CONTRIBUTING.md`](CONTRIBUTING.md) for the development workflow and TDD requirements.
 ## Conventions
 - Markdown: ATX headings, fenced code blocks with language tags, pipe tables.
@@ -24,3 +29,52 @@ prototype/   2D swarm visualization — React + TypeScript (Vite)
 - TypeScript: strict mode, functional components and hooks, no extra UI libraries.
 - YAML: 2-space indent.
 - Diagrams: Mermaid inside Markdown.
 ## Decision records
 Significant architectural decisions are recorded chronologically as **ADRs** in
 [`docs/adr/`](docs/adr/) — one immutable file per decision, in the
 Status / Context / Options / Decision / Consequences format. Start from
 [`docs/adr/README.md`](docs/adr/README.md).
 - **ADR** = a point-in-time decision for *this* repo → `docs/adr/`.
 - **ASR** = a standing, ecosystem-wide standard (Go-first, secrets, layout) →
  lives in the `inventar` repo, **not** here.
 When a change alters a boundary, contract, or trade-off, add an ADR (copy
 [`docs/adr/TEMPLATE.md`](docs/adr/TEMPLATE.md), take the next number, update the
 index) and cross-link it from the affected design doc. A later reversal gets a
 new ADR that supersedes the old one — never edit an Accepted record.
 ## Runtime & operations (sim environment)
 The k3d ground miniature is `swarm-sim`. Services are exposed as NodePorts:
 | Service | URL |
 | --- | --- |
 | Explorer (T1 live lake) | `http://localhost:30088` |
 | Warehouse explorer (T3) | `http://localhost:30089` |
 | Grafana (dashboards `swarm-fleet`, `swarm-platform`) | `http://localhost:30300` |
 | Prometheus | `http://localhost:30990` |
 | MinIO console | `http://localhost:30901` |
 | Prototype dev server (`npm run dev`) | `http://localhost:5173` |
 Rebuild the simulator image and roll it out:
 ```bash
 cd simulator && docker build -t swarm-house/simulator:dev .
 k3d image import swarm-house/simulator:dev -c swarm-sim
 kubectl rollout restart -n swarm statefulset/drone deployment/explorer deployment/exporter
 cd infra/terraform/sim-env && terraform apply
 ```
 **CPU budget knobs** (see [ADR-0009](docs/adr/ADR-0009-bounded-lake-scans.md)):
 `METRIC_FLIGHT_WINDOW` (scan only recent flights), `SCAN_INTERVAL_S`,
 `VIEW_REFRESH_S`, `TREE_CACHE_S`, `KEEP_ALIVE=1` (drones idle after seal instead
 of restarting), `IMU_HZ`. Never scan the full lake on a hot path — it grows with
 pod churn. Long-horizon analysis is a T3 warehouse query, not an exporter job.
 Runtime data lives under `var/t1` / `var/t3`, owned by uid **10001**
 ([ADR-0007](docs/adr/ADR-0007-non-root-image.md),
 [ADR-0008](docs/adr/ADR-0008-runtime-data-paths.md)); both are gitignored.
 Terraform state is local and never committed.
@@ -0,0 +1,99 @@
 # Contributing
 Thank you for helping improve Swarm House. This repository is a self-contained
 design proposal for an on-prem data platform for autonomous drone swarms — all
 contributions must follow the rules in [`AGENTS.md`](AGENTS.md).
 ## TDD first (required)
 Every change to executable code ships with tests **before** or **alongside** the
 implementation — never as an afterthought.
 1. **Red:** write or extend a failing test that captures the behaviour you need.
 2. **Green:** implement the smallest change that makes it pass.
 3. **Refactor:** clean up without weakening the test.
 The CI pipeline enforces this:
 | Gate | When it runs |
 | --- | --- |
 | `pytest tests/` | Every push/PR (`verify` job) |
 | `RUN pytest tests/` in the simulator Docker build | Every `docker build` — a red test blocks the image |
 | Smoke flight + DuckDB assertion | Every push/PR |
 | Trivy scan | After verify passes |
 Test locations:
 - `simulator/tests/` — Python unit tests (SQL gate, broadcast codec, writer layout, flight kinematics)
 - Prototype: `npm run build` / `tsc --noEmit` before UI changes
 ## Local development
 ### Fast inner loop (Compose)
 ```bash
 cd simulator
 pip install -r requirements.txt
 pytest tests/ -q                          # run tests first
 DRONE_COUNT=5 docker compose up --build   # writes to ../var/t1
 ```
 ### Full ground miniature (k3d + Terraform + Flux)
 ```bash
 ansible-playbook infra/ansible/sim-cluster.yml
 cd infra/terraform/sim-env && terraform init && terraform apply
 cd ../ground && terraform init && terraform apply
 cd ../../prototype && npm install && npm run dev   # press "go live"
 ```
 Shared data directories (gitignored, created automatically):
 | Path | Tier | Contents |
 | --- | --- | --- |
 | `var/t1/` | T1 live lake | In-flight Parquet from Compose or k3d fleet |
 | `var/t3/` | T3 warehouse | Offloaded historical partitions |
 Override with `SWARM_T1_DIR` / `SWARM_WAREHOUSE_DATA` / `SWARM_SIM_DATA` env vars.
 ### Prototype only
 ```bash
 cd prototype && npm install && npm run dev
 ```
 ## Commit messages
 Conventional Commits only:
 ```
 feat: add warehouse offload cron schedule variable
 fix: reject COPY statements in the SQL gate
 docs: clarify WireGuard vs SSH layering
 test: cover broadcast frame roundtrip
 ```
 Never include AI tool references, `Co-authored-by` trailers for assistants, or
 identifying names of people, companies, or locations.
 ## Pull request checklist
 - [ ] Tests added or updated; `pytest tests/ -q` passes locally
 - [ ] `docker build` in `simulator/` succeeds (test stage included)
 - [ ] Documentation updated if behaviour or boundaries changed
 - [ ] **ADR added** if the change alters an architectural boundary, contract, or trade-off (see [`docs/adr/`](docs/adr/))
 - [ ] English only; no identifying references
 - [ ] Conventional commit message
 ## Decisions
 Architectural decisions are recorded chronologically as ADRs in
 [`docs/adr/`](docs/adr/). Read [`docs/adr/README.md`](docs/adr/README.md) before
 changing a boundary or contract; add a new ADR (from
 [`docs/adr/TEMPLATE.md`](docs/adr/TEMPLATE.md)) rather than editing an Accepted
 one when you reverse course.
 ## Questions
 Open design questions live in [`docs/09-open-questions.md`](docs/09-open-questions.md).
 Architecture boundaries are documented in [`docs/06-environments.md`](docs/06-environments.md#iac-boundaries).
@@ -0,0 +1,19 @@
 PROPRIETARY LICENSE
 Copyright (c) 2026 Andriy Oblivantsev. All rights reserved.
 This material is proprietary and confidential.
 No person or organization may, without prior written permission from the copyright holder:
 - Publish or republish this material, in whole or in part.
 - Copy, reproduce, distribute, transmit, forward, or share this material by any means.
 - Use, modify, adapt, translate, create derivative works from, or incorporate this material into any other work.
 - Make this material available to any third party, whether publicly or privately.
 - Store this material in any repository, archive, or knowledge base accessible to others.
 Permission must be obtained in writing before any of the above activities are undertaken.
 Unauthorized use, publication, forwarding, reproduction, distribution, or disclosure of this material is strictly prohibited and may result in civil and criminal penalties under applicable copyright and intellectual property laws.
 This license grants no rights except the right to view the material for its intended purpose by an authorized recipient.
@@ -33,24 +33,47 @@ This repository describes how to build, deliver, test, and operate that platform
 | [09 — Open questions](docs/09-open-questions.md) | Known unknowns and proposed answers |
 | [10 — Domain context](docs/10-domain-context.md) | Swarm autonomy principles this design builds on |
 | [11 — CI/CD & delivery](docs/11-cicd-delivery.md) | Pipelines, registry, dev containers, fleet releases |
 | [12 — Design journey](docs/12-design-journey.md) | **Start here** — a narrative walk-through of how the design came together, linking into the code |
 | [ADRs](docs/adr/) | Architecture Decision Records — the decisions behind the above, in the order they were made |
 New here? Read the [design journey](docs/12-design-journey.md) first: it tells the story chronologically and links straight into the code and decisions. The design principles below are the *what*; the [ADRs](docs/adr/) are the *why and when* — each principle traces to a dated, immutable decision record.
 ## Runnable parts
 | Component | Purpose | Stack |
 | --- | --- | --- |
 | [`simulator/`](simulator/) | Virtual drone fleet: generates realistic telemetry through the same Parquet/DuckDB pipeline a real drone would use; N drones via Docker Compose | Python, pyarrow, DuckDB |
-| [`prototype/`](prototype/) | 2D top-down swarm visualization: drones, obstacles, inter-drone links with live transfer metrics | React, TypeScript, Vite, SVG |
+| [`prototype/`](prototype/) | 2D top-down swarm visualization: drones, obstacles, inter-drone links with live transfer metrics; **live mode** renders real positions from the k3d fleet | React, TypeScript, Vite, SVG |
 | [`infra/ansible/`](infra/ansible/) | Host preparation: drone bench provisioning (keys, forced commands, WireGuard, Compose bundle) and local k3d simulation cluster | Ansible |
 | [`infra/terraform/`](infra/terraform/) | Declared workloads on the sim cluster: virtual fleet + observability ([`sim-env`](infra/terraform/sim-env/)), warehouse + T1→T3 offload ([`ground`](infra/terraform/ground/)) | Terraform, kubernetes provider |
 | [`infra/gitops/`](infra/gitops/) | Flux overlays for the ground segment — policy labels, dashboard bundles; drones stay on the fleet manifest | Flux, Kustomize |
 Shared runtime data (gitignored): `var/t1/` (live lake), `var/t3/` (warehouse). See [`CONTRIBUTING.md`](CONTRIBUTING.md).
 ## Quick start
 ```bash
-# Spin up a virtual swarm of 10 drones
+# Spin up a virtual swarm of 10 drones (Compose, no cluster needed)
 cd simulator && DRONE_COUNT=10 docker compose up
 # Run the visualization
 cd prototype && npm install && npm run dev
 ```
 Or run the full miniature of the ground segment — k3d cluster, fleet as
 StatefulSet, Grafana, explorer, warehouse with post-flight offload:
 ```bash
 ansible-playbook infra/ansible/sim-cluster.yml
 cd infra/terraform/sim-env && terraform init && terraform apply
 cd ../ground && terraform init && terraform apply
 # then press "go live" in the prototype header
 ```
 See [06 — Environments → IaC boundaries](docs/06-environments.md#iac-boundaries) for why Ansible owns hosts, Terraform owns the ground segment, and neither flies on the drone.
 Contributing (TDD-first workflow): [`CONTRIBUTING.md`](CONTRIBUTING.md).
 ## CI & releases
-Every push runs the pipeline in [`.github/workflows/release.yaml`](.github/workflows/release.yaml): a compile check, a 10-second virtual smoke flight verified with DuckDB, unit tests for the read-only SQL gate, and a Trivy vulnerability/misconfiguration scan. Pushes to `main` then cut a semantic version from the conventional-commit type (`feat:` → minor, `!`/`BREAKING CHANGE` → major, else patch), tag the commit, and publish a release with a **fleet release manifest** (versioned artifact list with checksums — the delivery unit described in [11 — CI/CD & delivery](docs/11-cicd-delivery.md)) plus a source bundle.
+Every push runs the pipeline in [`.github/workflows/release.yaml`](.github/workflows/release.yaml): **pytest unit tests**, a compile check, a 10-second virtual smoke flight verified with DuckDB, and a Trivy vulnerability/misconfiguration scan. The simulator Docker build also runs `pytest` — a failing test blocks the image. Pushes to `main` then cut a semantic version from the conventional-commit type (`feat:` → minor, `!`/`BREAKING CHANGE` → major, else patch), tag the commit, and publish a release with a **fleet release manifest** (versioned artifact list with checksums — the delivery unit described in [11 — CI/CD & delivery](docs/11-cicd-delivery.md)) plus a source bundle.
@@ -2,6 +2,8 @@
 Zero trust, fully air-gapped, everything provisioned before wheels-up.
 > **Decision:** [ADR-0005 — WireGuard beneath SSH](adr/ADR-0005-wireguard-beneath-ssh.md).
 ## Trust model
 - **Nothing joins the swarm at runtime.** Every device receives its identity (key pair + certificate) during ground provisioning, signed by the fleet's offline CA. There is no trust-on-first-use, no runtime enrollment endpoint, no exception path.
@@ -36,6 +38,21 @@ graph TB
 - **RSSI is data.** Per-peer signal strength is captured into `telemetry` like any other sensor — it feeds relative-positioning estimates and post-flight link-quality analysis, and it is free.
 - Electromagnetic environment sensing (whatever the radio can observe) is likewise recorded — cheap in flight, valuable in replay.
 ## Why not SSH alone?
 SSH and WireGuard solve different problems. SSH is the **authorization layer** for narrow, point-to-point operations (bulk sync, read-only SQL). WireGuard is the **membership and transport encryption layer** for everything on the mesh, including traffic SSH cannot carry.
 | Concern | SSH alone | WireGuard + SSH (this design) |
 | --- | --- | --- |
 | Pose broadcast (UDP, ~5 Hz, compact frame) | Cannot carry subnet broadcast; would need a separate app protocol | Encrypted inside the mesh overlay; any peer on the tunnel can receive |
 | Broadcast confidentiality without WG | Payloads traverse the radio **in cleartext** — any receiver in range reads swarm positions | Only provisioned fleet members complete a handshake; outsiders see noise |
 | Lossy ad-hoc links | TCP head-of-line blocking; sessions stall and reconnect on packet loss | UDP transport survives loss and roaming; SSH sessions **over** WG are more stable than SSH directly on Wi-Fi |
 | Network membership | Any host that reaches the port gets an SSH banner — a probe surface | Devices without a provisioned key get **no handshake response**; the port is effectively silent |
 **Rejected alternative:** encrypt each broadcast frame in application code with a fleet-wide AEAD key. That reinvents a crypto layer the OS already provides, still leaves TCP head-of-line blocking for peer queries, and does not answer the membership question (who is allowed on the mesh at all).
 Keep both layers: WireGuard for *who is on the network and whether the wire is readable*; SSH forced commands for *what a peer is allowed to do once connected* ([04](04-swarm-sync.md)).
 ## Key lifecycle
 | Phase | Action |
@@ -2,6 +2,8 @@
 Three infrastructures, one data contract. The layout, schemas, and pipelines are identical everywhere; only scale and lifetime differ.
 > **Decisions:** [ADR-0006 — IaC boundaries](adr/ADR-0006-iac-boundaries.md), [ADR-0008 — runtime data paths](adr/ADR-0008-runtime-data-paths.md).
 ```mermaid
 graph LR
    subgraph dev [3 — Dev and simulation]
@@ -61,6 +63,29 @@ The environment engineers live in daily — and deliberately the first thing to
 - **T4 data slices**: a thin tool pulls partition subsets from T3 (`flight=X, drone=Y, hour=Z`) for local work — layout-identical, so every query and pipeline runs unmodified.
 - The same simulator images scale out on the ground k3s farm for CI regression runs: every merge request replays canonical scenarios and asserts on the resulting Parquet output (row counts, coverage, staleness budgets).
 ## IaC boundaries
 Infrastructure as code follows the same discipline as the data plane: each tool owns the layer it is actually good at, and nothing owns a layer it can't reach.
 | Layer | Tool | Why |
 | --- | --- | --- |
 | Host preparation (drone bench provisioning, sim cluster creation) | **Ansible** ([`infra/ansible/`](../infra/ansible/)) | Idempotent, agentless, works over the same SSH channel the platform already trusts. Keys, forced commands, WireGuard, Compose bundles — all host-level state. |
 | Declared workloads (simulated fleet, ground warehouse) | **Terraform** ([`infra/terraform/`](../infra/terraform/)) | The ground segment is stationary, long-lived, and cluster-shaped — classic Terraform territory. Fleet size, offload schedule, and explorer endpoints are variables, not YAML edits. |
 | Ongoing ground configuration | **Flux** ([`infra/gitops/`](../infra/gitops/)) | Policy labels, dashboard bundles, offload knobs — reconciled from git without reprovisioning PVCs. Drones stay on the fleet manifest, not GitOps. |
 | On-board runtime | **Compose bundle from the fleet release manifest** | Terraform is deliberately *not* on the drone: there is no API server to reconcile against mid-flight, and the release manifest already gives atomic, versioned, rollback-able delivery ([11](11-cicd-delivery.md)). |
 Shared local data paths (same layout everywhere): **`var/t1/`** (live lake, T1) and **`var/t3/`** (warehouse, T3). Compose, k3d, and DuckDB on the host all read the same Parquet tree.
 Two Terraform modules, two states, one deliberate split: [`sim-env`](../infra/terraform/sim-env/) (the disposable virtual fleet) and [`ground`](../infra/terraform/ground/) (the warehouse that must survive every `destroy` of the fleet). They never share a lifecycle.
 ### k3d: the ground segment as an executable miniature
 `ansible-playbook infra/ansible/sim-cluster.yml` creates a local k3d cluster (k3s in Docker — the same distribution as the real ground segment) and `terraform apply` populates it: virtual drones as StatefulSet replicas over a shared Parquet lake, the exporter/Prometheus/Grafana stack, the data-plane explorer, MinIO, and the T1→T3 offload CronJob. The whole diagram at the top of this page runs on one workstation, and the prototype's **live mode** renders real drone positions straight from the explorer's read-only SQL API.
 ### Next fidelity step: microVMs
 The k3d fleet shares one kernel and one network namespace tree — good enough to exercise the data plane, not good enough to demo the *peer* plane (real SSH sessions between isolated machines, WireGuard handshakes, packet loss injection). The next step, when that fidelity is needed, is one **Firecracker microVM per drone**: a real kernel, a real network interface, and the actual `authorized_keys` forced-command path between virtual drones — on any KVM-capable workstation, still with no cloud dependency. The process-level simulator stays as the fast inner loop; microVMs become the pre-flight integration rig.
 ## Why identical layouts matter (the payoff)
 | Operation | What it costs with one layout everywhere |
@@ -2,6 +2,8 @@
 The platform watches itself with the same discipline it applies to sensor data — and largely through the same pipeline.
 > **Decision:** [ADR-0009 — bound observability lake scans to a flight window and cache](adr/ADR-0009-bounded-lake-scans.md).
 ## Two kinds of signals, one storage
 | Signal | Examples | Where it goes |
@@ -53,6 +55,13 @@ graph LR
 | Which link degraded first, and was it distance or interference? | RSSI telemetry joined with pose distance |
 | Is the new writer version flushing slower on ARM64? | CI simulation runs emit the same metrics; diff across fleet releases |
-A working miniature of this doctrine ships in [`simulator/`](../simulator/): the `monitoring` Compose profile runs a DuckDB-based exporter over the generated Parquet plus Prometheus and a provisioned Grafana dashboard — fleet statistics derived from the data platform itself, with no agent on the (virtual) drones.
+A working miniature of this doctrine ships in [`simulator/`](../simulator/): the `monitoring` Compose profile runs a DuckDB-based exporter over the generated Parquet plus Prometheus and two provisioned Grafana dashboards:
 | Dashboard | Focus |
 | --- | --- |
 | **Swarm Fleet — generated data** | Row counts, detections, pose frames, Parquet bytes, battery, RSSI |
 | **Swarm Platform — CPU & scan health** | Node CPU/memory (node-exporter), exporter/explorer scan durations, flight-window gauge |
 The exporter and explorer **scan only the most recent flight partitions** (`METRIC_FLIGHT_WINDOW`, default 5) and cache tree/views — otherwise CPU climbs as every pod restart appends a new `flight=` tree to the shared lake. Drones in k3d set `KEEP_ALIVE=1` after sealing so they idle instead of exiting and spawning another flight.
 Alerting on the ground follows standard practice (Prometheus alert rules for infrastructure, CI gates for regression in simulated staleness/throughput budgets). In flight there is nobody to page — the platform's job is to degrade in the documented order and record everything for the post-mortem.
@@ -61,3 +61,9 @@ Who owns the device registry (drone ids, key issuance, revocation) organizationa
 Sub-250 g class units cannot run the full stack (no GPU, minimal CPU/storage).
 *Proposal:* define a minimal profile early — state publisher + UDP-multicast pose broadcast, no local Parquet store, no video — so the swarm protocol never assumes full-stack peers.
 ## 11 — Lake retention and partition pruning
 The observability path now scans only the most recent flights ([ADR-0009](adr/ADR-0009-bounded-lake-scans.md)), but old `flight=` partitions still accumulate on T1 disk after offload to T3.
 *Proposal:* a scheduled prune of T1 partitions whose flights are confirmed present in the T3 warehouse (offload as the retention gate), configurable by age and free-space watermark. On the drone the same policy is bounded by the NVMe quota ladder ([03](03-data-platform.md)); in the sim environment it is a ground CronJob alongside the offload job.
@@ -2,6 +2,8 @@
 How software, models, and configuration reach the fleet — reproducibly, scanned, versioned, and atomically rollback-able. Everything lives inside the air gap.
 > **Decisions:** [ADR-0006 — IaC boundaries & fleet manifest](adr/ADR-0006-iac-boundaries.md), [ADR-0007 — non-root image](adr/ADR-0007-non-root-image.md).
 ## Source and pipelines: GitLab
 GitLab (self-managed, on-prem) is the backbone: repositories, CI/CD, and the container registry in one system.
@@ -97,6 +99,39 @@ config:
 2. Docked drones fetch the manifest, verify digests/signatures ([05](05-network-security.md)), stage the new Compose bundle, and switch on the next boot cycle.
 3. **Never mid-flight.** Updates are a dock-only operation by construction — the update endpoint does not exist in the flight radio profile.
 ## GitOps on the ground segment
 The fleet release manifest handles **drones** (atomic, digest-pinned, dock-only). The **ground k3s segment** uses a different tool: [Flux](https://fluxcd.io/) reconciles long-lived configuration from git — dashboard bundles, Prometheus rules, offload schedules, policy labels — without reprovisioning PVCs or re-running full Terraform applies for every label change.
 ```mermaid
 graph LR
    subgraph git [Git, on-prem]
        REPO["service repos"]
        GITOPS["infra/gitops/<br/>ground overlay"]
    end
    subgraph ground_k3s [Ground k3s]
        FLUX["Flux controllers"]
        WH["warehouse workloads<br/>(Terraform-provisioned)"]
        CFG["policy ConfigMaps<br/>dashboard bundles"]
    end
    subgraph drones [Fleet]
        MAN["fleet release manifest"]
        DOCK["dock: verify + apply"]
    end
    REPO --> CI
    GITOPS --> FLUX
    FLUX --> CFG
    MAN --> DOCK
 ```
 | Segment | Delivery mechanism | Reconciler in production? |
 | --- | --- | --- |
 | Drones | Fleet release manifest via registry mirror | **No** — no API server mid-flight; dock applies once |
 | Ground k3s (warehouse, dashboards, CI runners) | Terraform for shape + **Flux** for ongoing config | **Yes** — stationary cluster, wired network, git is the source of truth |
 | Dev simulation (k3d) | Ansible creates cluster; Terraform applies workloads; Flux CRs installed from [`infra/gitops/`](../infra/gitops/) | Yes (miniature of ground) |
 Boundary: **Terraform provisions** (namespaces, PVCs, Deployments, CronJobs); **Flux reconciles** policy and observability overlays on top. Neither replaces the fleet manifest on the drone.
 ## Developer experience
 - **Dev Containers** define the full toolchain (Python data tooling, DuckDB, compose, linters) — identical on any machine, onboarding in minutes.
@@ -0,0 +1,194 @@
 # 12 — Design journey
 How this design came together, told in the order the thinking actually happened.
 It is a walk-through, not a report. The formal decisions, with options and
 trade-offs, live as [Architecture Decision Records](adr/README.md). This is the
 story behind them, and each chapter links straight into the code it produced.
 ---
 ## 1. Starting point
 At the start there was only a rough brief and a few adjacent interests to lean
 on. Key rotation matters in every pipeline: a cipher that is safe today may not
 be safe next year, so the infrastructure has to leave room to react, without
 spending too much latency or bandwidth to do it. That balance, security against
 speed and volume, is a recurring trade-off. Geospatial data is older ground:
 routing, indexing, fast spatial lookups at scale. So the first instinct was that
 the value might be less about new storage and more about optimizing access to
 data that already exists.
 ## 2. From words to a model, then to a picture
 Once the shape was clear, an autonomous swarm where each unit talks to any peer
 in range and [no controller spans the swarm](adr/ADR-0001-no-swarm-orchestrator.md),
 the next step was to make the model visible. A small 2D prototype was enough to
 check it: units patrolling, static and moving obstacles, links forming and
 breaking, and a rough count of how much data crosses the air. Speed was not the
 question yet, only volume, because volume is what you size the infrastructure
 for. Starting from a picture gives an anchor before building anything.
 > **Read the code**
 > - [`prototype/src/sim.ts`](https://git.produktor.io/eSlider/swarm-house/src/branch/main/prototype/src/sim.ts#L219-L287) — the tick: links form and break by range, pose broadcasts and bulk sync accumulate bytes
 > - [`prototype/src/sim.ts`](https://git.produktor.io/eSlider/swarm-house/src/branch/main/prototype/src/sim.ts#L57-L63) — the pose-frame size and rate that drive the volume estimate
 The picture is running, not just described: the visual prototype is live at
 [swarm.produktor.io](https://swarm.produktor.io/) (same access as this
 repository). It draws units patrolling, links forming and breaking by range, and
 a running estimate of how much data crosses the air.
 ## 3. Naming the limits
 Before any code, the limits had to be named. Maximum distance between units in
 the air. The fidelity data is stored at. The fidelity it can be transmitted at.
 Those drive volume, and volume drives the whole shape of the system. The
 conclusion: constrain the transport, but do not constrain on-board capture. Keep
 everything locally. Send only what peers truly need.
 > **Read more**
 > - [01 — Problem statement](01-problem-statement.md) and [03 — Data platform](03-data-platform.md) — the constraints and the storage tiers
 ## 4. Three tiers inside each unit
 That gave [three layers](03-data-platform.md) on every drone:
 1. **Raw capture**, never transformed.
 2. **ETL / reduction**, where data is cut down to what has to be shared:
   millimetres to centimetres, thinning time series where that is enough,
   dropping what nobody downstream reads.
 3. **A bidirectional interface**: on one side it broadcasts position into the
   shared channel, on the other it lets peers pull data.
 The ETL is deliberately not over-specified. How sensor data is transformed is a
 data-engineering decision. The platform's job is the contract, meaning what gets
 stored and in which structure, not to step into that work.
 > **Read the code**
 > - [`simulator/virtual_drone/writer.py`](https://git.produktor.io/eSlider/swarm-house/src/branch/main/simulator/virtual_drone/writer.py#L23-L80) — the Hive-partition layout and the seal step that compacts a flight
 > - [`simulator/virtual_drone/sensors.py`](https://git.produktor.io/eSlider/swarm-house/src/branch/main/simulator/virtual_drone/sensors.py) — the raw capture that feeds tier one
 ## 5. What actually needs to sync
 The most time-critical item is where each peer is, so every unit has time to
 react. The choice was to broadcast [relative pose](04-swarm-sync.md) (x, y, z and
 time) instead of absolute coordinates. Relative is cheaper and enough for
 coordination. To avoid sending orientation, a unit is modelled as a **sphere**
 that bounds its extent. That trades a little compute for much less data, and no
 per-shape encoding. If the units are identical, their 3D model can be provisioned
 ahead instead of transmitted.
 > **Read the code**
 > - [`simulator/virtual_drone/broadcast.py`](https://git.produktor.io/eSlider/swarm-house/src/branch/main/simulator/virtual_drone/broadcast.py#L1-L40) — the 46-byte pose frame, position quantized on the wire only
 > - [`prototype/src/sim.ts`](https://git.produktor.io/eSlider/swarm-house/src/branch/main/prototype/src/sim.ts#L260-L277) — pose broadcasts at 5 Hz and opportunistic bulk sync, made visible
 ## 6. Security as nested layers, not one wall
 The assumption was an ad-hoc wireless mesh. Broadcasting positions in the clear,
 even over encrypted Wi-Fi, is not acceptable: anyone who joins can reconstruct
 trajectories over time, and wireless encryption has been broken by ordinary
 people for two decades. Relying on that layer alone is risky, and pushing heavy
 encryption there is wasteful. So the trust model is [layered](05-network-security.md):
 1. Wireless transport (WPA class) as the outer shell.
 2. **WireGuard over UDP** as the mesh overlay: proven, peer-authenticated by
   static keys, cheap.
 3. **SSH inside the tunnel** as the application layer, instead of HTTP/HTTPS. SSH
   is one of the most tested remote-access technologies we have. HTTPS would pull
   in a certificate authority and extra parts, and it is the first surface a
   security researcher probes. [Fewer moving parts, on purpose](adr/ADR-0005-wireguard-beneath-ssh.md).
 Identity and encryption solve together at provisioning time: a per-flight,
 per-unit key pair recorded in a flight registry. Each unit holds its own key pair
 plus the public keys of its peers. A separate key registry for SSH means that if
 one WireGuard key leaks, SSH access does not follow automatically.
 > **Read the code**
 > - [`infra/ansible/drone-provision.yml`](https://git.produktor.io/eSlider/swarm-house/src/branch/main/infra/ansible/drone-provision.yml#L20-L70) — ed25519 identity, peers authorized only through a forced command, WireGuard brought up
 > - [05 — Network & security](05-network-security.md) and [ADR-0005](adr/ADR-0005-wireguard-beneath-ssh.md)
 ## 7. SQL as the contract
 Beyond the broadcast, people and peers want richer access. The whole progression
 is familiar: fully dynamic APIs, then JSON, OpenAPI/Swagger, GraphQL, then heavier
 schemes. Each time the same pain: hard to debug in the moment, and a lot of
 infrastructure to support, test, and agree on. The simpler conclusion:
 [**SQL is the contract**](adr/ADR-0004-sql-over-ssh-contract.md). Everyone who
 touches data already speaks SQL, and we do not know in advance which slice each
 of them needs, so give them a declarative, extensible way in rather than guessing
 endpoints ahead of time.
 Concretely: an SSH connection whose forced command runs a read-only **DuckDB**
 query directly. No extra auth handshake between the engine and the data, the way
 a long-running database needs. No new port or protocol. When access control is
 needed, it rides on Linux file permissions, because the data is discrete
 Hive-partitioned files and permissions can be set per file. Not new invention:
 coming back to foundations DevOps and sysadmins already trust.
 > **Read the code**
 > - [`simulator/explorer/server.py`](https://git.produktor.io/eSlider/swarm-house/src/branch/main/simulator/explorer/server.py#L43-L53) — the read-only gate that rejects anything that writes
 > - [`simulator/explorer/server.py`](https://git.produktor.io/eSlider/swarm-house/src/branch/main/simulator/explorer/server.py#L111-L131) — the query path a peer or an engineer hits identically
 > - [`simulator/tests/test_sql_gate.py`](https://git.produktor.io/eSlider/swarm-house/src/branch/main/simulator/tests/test_sql_gate.py#L13-L35) — the gate is safety-critical, so it is tested first
 ## 8. Replication and offload
 Moving accumulated data uses rsync-class tooling (rclone) over the same SSH
 channel. It transfers diffs of the Hive tree efficiently. This matters because a
 unit can be lost, and if it is, we want its data to already exist elsewhere.
 When peers have a stable link the partition tree is pulled between them, which
 gives redundancy and a way to cross-check later. On the ground, when the fleet
 returns, the same path unifies every unit's data into a
 [local warehouse](06-environments.md). The warehouse is read-mostly for
 analytics, so transaction contention is not a concern, which opens a
 DuckDB / DuckLake approach with MinIO underneath, the
 [same storage model on both ends](adr/ADR-0002-one-storage-format.md). One
 uniform structure is what lets an analyst trust a single picture even when a
 unit's data has a gap.
 > **Read the code**
 > - [`infra/terraform/ground/main.tf`](https://git.produktor.io/eSlider/swarm-house/src/branch/main/infra/terraform/ground/main.tf#L124-L160) — the post-flight T1 to T3 offload CronJob
 > - [06 — Environments](06-environments.md) and [ADR-0002](adr/ADR-0002-one-storage-format.md)
 ## 9. A proof of concept, not slides
 A concept can be handed over and be wrong. Instead there is a generator: a
 virtual drone producing real sensor data into the Hive layout, a small frontend
 to watch it fill, and a **Grafana** dashboard on top, not to get ahead of the
 analysts, but to see the infrastructure work end to end. Closing the loop
 (Terraform, connector, exporter, dashboard) is how a misconfiguration shows up
 early. A panel that renders wrong points at exactly which layer broke, before an
 analyst has to report that the infra is up but nothing shows. Better to exercise
 every layer now and drop any layer or technology later if there is a sound
 argument against it.
 Watching that loop is also where the [CPU cost surfaced](adr/ADR-0009-bounded-lake-scans.md):
 scanning the whole lake on every query does not scale, so the scan is
 [bounded to a flight window and cached](07-observability.md).
 > **Read the code**
 > - [`simulator/monitoring/exporter.py`](https://git.produktor.io/eSlider/swarm-house/src/branch/main/simulator/monitoring/exporter.py#L38-L107) — the DuckDB scan that turns Parquet into Prometheus metrics
 > - [`simulator/monitoring/lake.py`](https://git.produktor.io/eSlider/swarm-house/src/branch/main/simulator/monitoring/lake.py#L13-L33) — the flight-window scan that keeps CPU bounded as the lake grows
 > - [`simulator/monitoring/grafana/dashboards/`](https://git.produktor.io/eSlider/swarm-house/src/branch/main/simulator/monitoring/grafana/dashboards) — the fleet and platform dashboards
 > - [`.github/workflows/release.yaml`](https://git.produktor.io/eSlider/swarm-house/src/branch/main/.github/workflows/release.yaml) — tests, smoke flight, Trivy, semantic release
 ## 10. Working method
 A note on how this is meant to be built, not just what. Working together needs a
 shared language, and when the working language is nobody's first language,
 answering live is expensive. The form that scales is written-first: lay the idea
 down carefully, let others read and answer in their own time, keep the history. A
 new colleague, or an agent, can then load the full context of why a decision was
 made by reading the discussion. That is exactly why the decisions here are
 [ADRs](adr/README.md) and why the [contribution flow](../CONTRIBUTING.md) is
 test-first: the reasoning is captured where the next person will look for it.
 The mental image is a city planner. Draw perfectly straight roads and people
 still cross the grass where it is faster. The job is not to decide for them, but
 to understand the real paths, the pain points and limits of each team, and pave
 those. So constraints get checked with the other DevOps, data engineers, and
 analysts before infrastructure is laid, instead of building roads nobody uses.
 ---
 The platform is air-gapped and on-prem by design: nothing leaves the swarm, and
 nothing joins it at runtime. Start from the [README](../README.md) for the map,
 or the [ADR index](adr/README.md) for the decisions in full.
@@ -0,0 +1,38 @@
 # ADR-0001: No swarm-wide orchestrator; coordinate through data
 ## Status
 Accepted (2026-07-08)
 ## Context
 The fleet flies fully autonomously with no internet uplink and only
 intermittent mesh connectivity between drones. A conventional control plane
 (Kubernetes across the swarm, a leader election, a central scheduler) assumes
 stable membership and low-latency links — exactly what a swarm does not have.
 Partitions are the normal case, not the exception.
 ## Options
 | Option | Pros | Cons |
 | --- | --- | --- |
 | A — Cluster orchestrator spanning the swarm | Familiar tooling | Assumes stable membership; a partition stalls coordination; single point of failure in the air |
 | B — Each drone autonomous, coordinating through exchanged data | Survives partitions; no air-side control plane to fail | No global view; behaviour must be derivable from local state |
 ## Decision
 Every drone is an autonomous node. There is **no orchestrator spanning the
 swarm**. Coordination happens by exchanging derived state (see
 [ADR-0003](ADR-0003-sync-derived-state-only.md)), and each drone makes its own
 in-flight decisions against its local data window. Orchestration tooling
 (Kubernetes, Flux) is confined to the **ground** segment
 ([ADR-0006](ADR-0006-iac-boundaries.md)).
 ## Consequences
 - The platform degrades gracefully under partition: a drone that loses peers
  keeps flying and recording.
 - There is no single "fleet state" to query in the air; health questions are
  answered from each drone's own store and reconciled on the ground.
 - Design principle 1 in [`../../README.md`](../../README.md) and the on-board
  model in [`../02-architecture.md`](../02-architecture.md) follow from this.
@@ -0,0 +1,32 @@
 # ADR-0002: One storage format on every tier — Parquet + DuckDB, Hive layout
 ## Status
 Accepted (2026-07-08)
 ## Context
 Data lives in three places: on the drone's NVMe in flight (T1), in the ground
 warehouse after landing (T3), and in the dev/simulation environment. If each
 tier used a different format or layout, offload would need transformation code,
 schemas would drift, and a query proven on the bench would not run unchanged on
 flight data.
 ## Decision
 Use **one storage format on every floor**: columnar **Parquet** files in an
 identical **Hive-partitioned** layout (`dataset=…/flight=…/drone=…/…`), queried
 with **DuckDB**. Flight offload T1 → T3 is therefore a plain **mirror**
 (copy/rsync of partition directories), not an ETL step.
 ## Consequences
 - Offload is a file operation, testable with a checksum; no format converters
  to maintain or version.
 - The same SQL runs against T1, T3, and simulated data — the simulator
  ([`../../simulator/`](../../simulator/)) generates the *real* layout, so tests
  exercise the production format.
 - Partitioning choices are load-bearing; changing them is a schema migration.
  Detailed in [`../03-data-platform.md`](../03-data-platform.md).
 - DuckDB's single-file, no-server model fits an air-gapped drone with no room
  for a database daemon.
@@ -0,0 +1,33 @@
 # ADR-0003: Sync derived state only; raw telemetry stays local
 ## Status
 Accepted (2026-07-08)
 ## Context
 Each drone produces high-rate sensor telemetry and on-board video detections —
 far more than the mesh can carry. But peers only need enough to coordinate
 flight: where everyone is, where they are heading, what was detected. Pushing
 raw feeds across the air would saturate the radio and drain batteries for data
 nobody reads in flight.
 ## Decision
 Only **derived state** crosses the air — position, attitude, detections — as
 small pub/sub broadcasts (~5 Hz). **Raw telemetry stays on local NVMe** until
 the drone lands, then travels to the warehouse during offload
 ([ADR-0002](ADR-0002-one-storage-format.md)). The design is **event-driven**:
 new derived data triggers downstream action through hooks; nothing polls asking
 "anything new yet?".
 ## Consequences
 - Air bandwidth scales with the number of peers and the broadcast rate, not
  with sensor resolution.
 - The warehouse is the only place with the full picture; in-flight decisions use
  the local window plus peer broadcasts.
 - Broadcast staleness per peer becomes a first-class health signal
  ([`../07-observability.md`](../07-observability.md)).
 - What syncs and what does not is specified in
  [`../04-swarm-sync.md`](../04-swarm-sync.md).
@@ -0,0 +1,41 @@
 # ADR-0004: Read-only SQL-over-SSH as the peer query contract; no debug API
 ## Status
 Accepted (2026-07-08). Supersedes the earlier sketch of a bespoke query service.
 ## Context
 Beyond the 5 Hz broadcast, a peer (or an engineer on the bench) sometimes needs
 to *ask* a drone a richer question: "show me your detections in the last minute
 near this position". Inventing a service for this means a new port, a new
 protocol, an auth layer, and a second code path that only runs during
 debugging — the classic "works on the bench, untested in flight" trap.
 ## Options
 | Option | Pros | Cons |
 | --- | --- | --- |
 | A — Custom query microservice + REST API | Flexible | New port/protocol/auth to secure; a debug-only path that never flies |
 | B — Read-only DuckDB SQL over an SSH forced command | Reuses SSH trust + the query language already on both ends; identical path on bench and in flight | SQL must be gated to read-only |
 ## Decision
 Peer data access is **read-only DuckDB SQL executed through an SSH forced
 command**. SQL is already the query language on both ends
 ([ADR-0002](ADR-0002-one-storage-format.md)), and SSH already carries the trust
 model ([ADR-0005](ADR-0005-wireguard-beneath-ssh.md)), so no new service, port,
 or protocol is invented. A **SQL gate** rejects anything that writes
 (`COPY`, `INSERT`, `ATTACH`, pragmas). There is **no separate debug API**: an
 engineer debugging on the ground runs the identical query through the identical
 wrapper, permissions, and output format a peer drone would use.
 ## Consequences
 - One access path is built, secured, and tested — "what you test is what
  flies".
 - The SQL gate is safety-critical and is covered by unit tests
  (`simulator/tests/test_sql_gate.py`).
 - The explorer and the prototype's live mode are *just another read-only
  consumer* of this same contract ([`../04-swarm-sync.md`](../04-swarm-sync.md)).
 - Design principles 7 and 8 in [`../../README.md`](../../README.md) restate this.
@@ -0,0 +1,40 @@
 # ADR-0005: WireGuard beneath SSH for the mesh transport
 ## Status
 Accepted (2026-07-08)
 ## Context
 Peer bulk sync and SQL-over-SSH ([ADR-0004](ADR-0004-sql-over-ssh-contract.md))
 need an encrypted, authenticated channel across an ad-hoc radio mesh. SSH alone
 would work at the application layer, but it leaves the drones' listening
 surface (sshd, any other service port) exposed on the raw mesh network, and it
 gives no cheap, uniform way to authenticate *the peer* before the application
 handshake.
 ## Options
 | Option | Pros | Cons |
 | --- | --- | --- |
 | A — SSH only | One daemon, familiar | Service ports exposed on the raw mesh; per-service auth; no network-layer peer identity |
 | B — WireGuard tunnel, SSH inside it | Network-layer peer auth via static keys; only the WG port is exposed; SSH speaks over a private, encrypted subnet | Two layers to provision |
 ## Decision
 Run **WireGuard as the network layer** and **SSH on top of it**. WireGuard
 authenticates peers by pre-shared static public keys and presents a private
 encrypted subnet; SSH forced commands then carry the SQL and bulk-sync contract
 over that subnet. Keys are issued **per device before deployment** — nothing
 joins the mesh at runtime (design principle 5).
 ## Consequences
 - The only thing exposed on the raw radio is the WireGuard port; sshd and the
  data services listen only on the WG interface.
 - Peer identity is established once, at the network layer, and reused by every
  application above it.
 - Provisioning must place both WG and SSH keys during host prep — handled by
  `infra/ansible/drone-provision.yml` ([ADR-0006](ADR-0006-iac-boundaries.md)).
 - Rationale and the "why not SSH alone" argument live in
  [`../05-network-security.md`](../05-network-security.md).
@@ -0,0 +1,41 @@
 # ADR-0006: IaC split — Ansible hosts, Terraform ground, Flux ground-only, fleet manifest for drones
 ## Status
 Accepted (2026-07-08)
 ## Context
 Three very different things need provisioning: (1) drone hosts, which are
 air-gapped and never reachable by a controller in flight; (2) the ground
 segment (warehouse, observability, offload), a normal k3s cluster; (3) the
 simulation environment that mirrors the ground segment locally. Using one tool
 for all three would force a GitOps controller or a Terraform apply loop onto the
 drone — impossible for an autonomous, disconnected node
 ([ADR-0001](ADR-0001-no-swarm-orchestrator.md)).
 ## Decision
 Split infrastructure ownership by lifecycle:
 | Layer | Tool | Scope |
 | --- | --- | --- |
 | Host preparation | **Ansible** | Drone bench prep (keys, forced commands, WireGuard, Compose bundle) and the local k3d sim cluster |
 | Ground workloads | **Terraform** | k3s/k3d resources: virtual fleet + observability (`sim-env`), warehouse + T1→T3 offload (`ground`) |
 | Ground continuous delivery | **Flux (GitOps)** | Ground segment **only** — policy labels, dashboard bundles |
 | Drone delivery | **Fleet release manifest** | A versioned, digest-pinned artifact list; no controller pulls to the drone |
 Drones are delivered by the **fleet release manifest** (one version for the
 whole fleet, atomic rollback), never by a controller reaching into the air.
 ## Consequences
 - No GitOps agent or `terraform apply` ever targets a drone; the air side stays
  controller-free.
 - Terraform state is environment-local and kept out of Git
  ([ADR-0008](ADR-0008-runtime-data-paths.md)).
 - The sim cluster is a faithful miniature: the same Terraform describes it and
  the ground segment.
 - Boundaries are documented in
  [`../06-environments.md`](../06-environments.md#iac-boundaries); delivery in
  [`../11-cicd-delivery.md`](../11-cicd-delivery.md).
@@ -0,0 +1,27 @@
 # ADR-0007: Run the simulator image as a non-root user
 ## Status
 Accepted (2026-07-08)
 ## Context
 The Trivy scan in CI flagged **DS-0002**: the simulator image ran as `root`
 because no `USER` was set. A container writing the data lake as root also
 produces root-owned Parquet on bind mounts, which then blocks a later non-root
 process from the same files.
 ## Decision
 Create a dedicated unprivileged user **`swarm` (uid/gid 10001)** in the
 `simulator/Dockerfile` and switch to it with `USER swarm` before the entrypoint.
 The uid is fixed (not auto-assigned) so host-side ownership of the shared data
 paths ([ADR-0008](ADR-0008-runtime-data-paths.md)) is deterministic.
 ## Consequences
 - Trivy DS-0002 clears; the image follows least-privilege.
 - Bind-mounted data is owned by `10001:10001`; host prep (`sim-cluster.yml`) and
  Ansible `chown` the `var/t1` / `var/t3` paths to this uid so k3d pods can write.
 - Any future service image reusing this data must run as the same uid or share
  the group.
@@ -0,0 +1,40 @@
 # ADR-0008: Shared `var/t1` / `var/t3` runtime paths; state out of Git
 ## Status
 Accepted (2026-07-08)
 ## Context
 Compose, the k3d fleet, and the ground offload job all need to read and write
 the same data lake, so that "go live" in the prototype shows the data the
 simulator just generated. Early on, Terraform state and the provider cache were
 accidentally committed (~6 MB of `*.tfstate` and a vendored provider binary),
 polluting history and risking stale/secret data in Git.
 ## Decision
 Standardise two **gitignored** runtime directories at the repo root:
 | Path | Tier | Contents |
 | --- | --- | --- |
 | `var/t1/` | T1 live lake | In-flight Parquet from Compose or the k3d fleet |
 | `var/t3/` | T3 warehouse | Offloaded historical partitions |
 They are created automatically and overridable via
 `SWARM_T1_DIR` / `SWARM_WAREHOUSE_DATA` / `SWARM_SIM_DATA`. Treat `var/` as
 **runtime-only** per the ecosystem canonical layout. `.gitignore` excludes
 `*.tfstate`, `*.tfstate.backup`, and `.terraform/` while **keeping**
 `.terraform.lock.hcl` (the lock is source, the state is not).
 ## Consequences
 - One lake feeds every component; the live prototype reflects real generated
  data.
 - Git history carries no machine state or large binaries; `terraform` treats
  its state as local/remote-backend concern, not versioned source.
 - Ownership of these paths is fixed to uid 10001
  ([ADR-0007](ADR-0007-non-root-image.md)).
 - Recovery from earlier state drift is documented in
  [`../../infra/terraform/README.md`](../../infra/terraform/README.md)
  (`terraform import`).
@@ -0,0 +1,52 @@
 # ADR-0009: Bound observability lake scans to a flight window and cache
 ## Status
 Accepted (2026-07-08)
 ## Context
 The k3d sim node was running at ~245% CPU. Three causes compounded:
 1. **Explorer** rebuilt a DuckDB view over the *entire* lake on every HTTP
   request (~2000 Parquet files, 200 MB+) — roughly 1.5 cores.
 2. **Exporter** scanned the full lake every 5 s — roughly 0.7 cores.
 3. **Drones** exited after sealing a flight; Kubernetes restarted each pod,
   and every restart appended a fresh `flight=` partition tree, so the lake grew
   without bound and every scan got more expensive.
 The cost is inherent to scanning an append-only lake whose size grows with pod
 churn, not to any single slow query. Metrics and the live view only ever need
 recent flights.
 ## Decision
 Bound and cache the scans instead of reading the whole lake:
 - **Flight window.** The exporter and explorer scan only the most recent
  `METRIC_FLIGHT_WINDOW` flights (default 5) via a shared helper
  (`simulator/monitoring/lake.py`). Older partitions stay on disk but out of the
  hot path.
 - **Caching.** Explorer caches DuckDB views (`VIEW_REFRESH_S=30`) and the
  partition tree (`TREE_CACHE_S=15`); the exporter scans every
  `SCAN_INTERVAL_S=30` instead of 5 s.
 - **Stop restart churn.** Drones set `KEEP_ALIVE=1` and idle after sealing
  instead of exiting, so no new flight tree is created per restart. In k3d,
  `IMU_HZ=20` and resource limits cap per-drone load; the live prototype polls
  every 2 s.
 - **Self-observability.** Add node-exporter, a
  `swarm_exporter_scan_duration_seconds` metric, and a **Swarm Platform**
  Grafana dashboard (node CPU/memory + scan health) alongside the existing fleet
  dashboard.
 ## Consequences
 - Steady state dropped to explorer ~29 m, exporter ~16 m CPU; the k3d node to
  ~56% (from ~245%).
 - Metrics reflect recent flights only; long-horizon analysis is a warehouse
  (T3) query, not an exporter concern — consistent with
  [ADR-0003](ADR-0003-sync-derived-state-only.md).
 - Old flight partitions accumulate on disk; pruning them is a follow-up
  (tracked in [`../09-open-questions.md`](../09-open-questions.md)).
 - Observability design is documented in
  [`../07-observability.md`](../07-observability.md).
@@ -0,0 +1,40 @@
 # Architecture Decision Records
 This directory records the significant decisions behind Swarm House, in the
 order they were made. Each record is immutable once **Accepted** — a later
 decision that changes course gets a new number and supersedes the old one
 rather than editing it.
 ## ADR vs ASR
 | Kind | What it captures | Where it lives |
 | --- | --- | --- |
 | **ADR** (Architecture Decision Record) | A point-in-time decision for *this* repository, with the options considered and their consequences | Here, in `docs/adr/` |
 | **ASR** (Architecture Standard Record) | A standing, cross-repository policy every project must obey (Go-first, secrets, layout) | The ecosystem `inventar` repo, not here |
 The rules in [`../../AGENTS.md`](../../AGENTS.md) are this repo's local standing
 constraints; anything ecosystem-wide belongs in `inventar` as an ASR. Design
 decisions specific to the platform are ADRs and belong here.
 ## Index
 | ADR | Decision | Status |
 | --- | --- | --- |
 | [ADR-0001](ADR-0001-no-swarm-orchestrator.md) | No swarm-wide orchestrator; coordinate through data | Accepted |
 | [ADR-0002](ADR-0002-one-storage-format.md) | One storage format on every tier — Parquet + DuckDB, Hive layout | Accepted |
 | [ADR-0003](ADR-0003-sync-derived-state-only.md) | Sync derived state only; raw telemetry stays local | Accepted |
 | [ADR-0004](ADR-0004-sql-over-ssh-contract.md) | Read-only SQL-over-SSH as the peer query contract; no debug API | Accepted |
 | [ADR-0005](ADR-0005-wireguard-beneath-ssh.md) | WireGuard beneath SSH for the mesh transport | Accepted |
 | [ADR-0006](ADR-0006-iac-boundaries.md) | IaC split: Ansible hosts, Terraform ground, Flux ground-only, fleet manifest for drones | Accepted |
 | [ADR-0007](ADR-0007-non-root-image.md) | Run the simulator image as a non-root user | Accepted |
 | [ADR-0008](ADR-0008-runtime-data-paths.md) | Shared `var/t1` / `var/t3` runtime paths; state out of Git | Accepted |
 | [ADR-0009](ADR-0009-bounded-lake-scans.md) | Bound observability lake scans to a flight window and cache | Accepted |
 ## Writing a new ADR
 1. Copy [`TEMPLATE.md`](TEMPLATE.md) to `ADR-NNNN-short-slug.md` (next free number).
 2. Fill in Context, Options (if more than one was real), Decision, Consequences.
 3. Add a row to the index above.
 4. Cross-link the ADR from the design doc it affects (and vice versa).
 Keep the tone laconic: state the decision and why, skip the narrative.
@@ -0,0 +1,29 @@
 # ADR-NNNN: Short decision title
 ## Status
 Proposed | Accepted | Superseded by ADR-XXXX
 ## Context
 What forces are in play? What problem or constraint triggered the decision?
 Keep it to the facts that actually shaped the choice.
 ## Options
 | Option | Pros | Cons |
 | --- | --- | --- |
 | A — … | … | … |
 | B — … | … | … |
 (Drop this section when only one option was ever real.)
 ## Decision
 The choice, stated plainly. One paragraph.
 ## Consequences
 - What becomes easier.
 - What becomes harder or is now forbidden.
 - Follow-ups, links to affected docs.
@@ -0,0 +1,39 @@
 # Ansible — host preparation
 Ansible owns everything that happens **on a host before workloads run**.
 Terraform ([`../terraform/`](../terraform/)) owns the workloads themselves.
 The boundary is deliberate and documented in
 [06 — Environments](../../docs/06-environments.md#iac-boundaries).
 | Playbook | Target | What it does |
 | --- | --- | --- |
 | `drone-provision.yml` | Drone (on the bench, before a mission) | Identity keys, peer trust with forced commands, WireGuard, Compose bundle from the fleet release manifest |
 | `sim-cluster.yml` | Local workstation / CI host | k3d cluster that miniatures the ground segment; shared data volume; simulator image import |
 ## Provision a drone
 ```bash
 ansible-playbook -i inventory.example.yml drone-provision.yml
 ```
 The playbook is idempotent and runs only over the bench network — drones
 are never provisioned in flight (see [05 — Network & security](../../docs/05-network-security.md)):
 1. **Identity**: generate the drone's ed25519 keypair if absent.
 2. **Peer trust**: install every fleet member's public key into
   `authorized_keys`, each pinned to the read-only SQL forced command —
   the only thing a peer can execute.
 3. **WireGuard**: render `wg0.conf` with the drone's address and peers.
 4. **Data plane**: lay down the Compose bundle referenced by the fleet
   release manifest and enable it as a systemd unit.
 ## Create the simulation cluster
 ```bash
 ansible-playbook sim-cluster.yml            # creates k3d cluster 'swarm-sim'
 cd ../terraform/sim-env && terraform init && terraform apply
 ```
 The cluster mounts a shared host directory as `/data` on the (single)
 node, so drone pods, the exporter, and the explorer see one Parquet lake —
 the same contract as the on-board NVMe layout.
@@ -0,0 +1,101 @@
 # Provision a drone on the bench: identity, peer trust, WireGuard, data plane.
 # Idempotent; never runs over the mission radio (bench network only).
 ---
 - name: Provision drone data plane
  hosts: drones
  become: true
  vars:
    swarm_user: swarm
    swarm_home: /opt/swarm
    forced_command: "{{ swarm_home }}/bin/peer-query.sh"
  tasks:
    - name: Create swarm service user
      ansible.builtin.user:
        name: "{{ swarm_user }}"
        home: "{{ swarm_home }}"
        shell: /usr/sbin/nologin
        system: true
    # --- 1. Identity -----------------------------------------------------
    - name: Generate drone ed25519 identity key
      community.crypto.openssh_keypair:
        path: "{{ swarm_home }}/.ssh/id_ed25519"
        type: ed25519
        comment: "{{ inventory_hostname }}"
        owner: "{{ swarm_user }}"
        mode: "0600"
      register: identity
    - name: Collect fleet public keys
      ansible.builtin.slurp:
        src: "{{ swarm_home }}/.ssh/id_ed25519.pub"
      register: pubkey
    # --- 2. Peer trust: forced command only ------------------------------
    # Every peer may connect, but the only thing it can execute is the
    # read-only SQL wrapper (docs/04). No shell, no forwarding, no pty.
    - name: Authorize fleet peers with read-only forced command
      ansible.posix.authorized_key:
        user: "{{ swarm_user }}"
        key: "{{ hostvars[item].pubkey.content | b64decode }}"
        key_options: >-
          command="{{ forced_command }}",no-port-forwarding,no-agent-forwarding,no-X11-forwarding,no-pty
      loop: "{{ groups['drones'] | difference([inventory_hostname]) }}"
      when: hostvars[item].pubkey is defined
    - name: Install peer query wrapper
      ansible.builtin.copy:
        dest: "{{ forced_command }}"
        mode: "0755"
        content: |
          #!/bin/sh
          # Forced command: read-only DuckDB SQL over the sealed lake.
          # SSH_ORIGINAL_COMMAND carries the statement; the gate rejects
          # anything that is not a single read statement.
          exec {{ swarm_home }}/bin/sql-gate --read-only --data /data "$SSH_ORIGINAL_COMMAND"
    # --- 3. WireGuard swarm plane ----------------------------------------
    - name: Render WireGuard configuration
      ansible.builtin.template:
        src: templates/wg0.conf.j2
        dest: /etc/wireguard/wg0.conf
        mode: "0600"
      notify: Restart wireguard
    - name: Enable WireGuard interface
      ansible.builtin.systemd:
        name: wg-quick@wg0
        enabled: true
        state: started
    # --- 4. Data plane from the fleet release manifest --------------------
    - name: Read fleet release manifest
      ansible.builtin.include_vars:
        file: "{{ fleet_manifest }}"
        name: manifest
    - name: Lay down Compose bundle for release {{ manifest.release }}
      ansible.builtin.unarchive:
        src: "{{ playbook_dir }}/../../.tmp/{{ manifest.artifacts[0].name }}"
        dest: "{{ swarm_home }}/release"
        owner: "{{ swarm_user }}"
    - name: Enable data plane unit
      ansible.builtin.template:
        src: templates/swarm-data-plane.service.j2
        dest: /etc/systemd/system/swarm-data-plane.service
        mode: "0644"
      notify: Restart data plane
  handlers:
    - name: Restart wireguard
      ansible.builtin.systemd:
        name: wg-quick@wg0
        state: restarted
    - name: Restart data plane
      ansible.builtin.systemd:
        name: swarm-data-plane
        state: restarted
        daemon_reload: true
@@ -0,0 +1,19 @@
 # Example fleet inventory. Real inventories are generated from the fleet
 # release manifest and live outside the repository.
 all:
  children:
    drones:
      hosts:
        dr-01:
          ansible_host: 10.44.0.11
          wg_address: 10.44.0.11/24
        dr-02:
          ansible_host: 10.44.0.12
          wg_address: 10.44.0.12/24
        dr-03:
          ansible_host: 10.44.0.13
          wg_address: 10.44.0.13/24
      vars:
        ansible_user: ops
        wg_port: 51820
        fleet_manifest: ../../.tmp/manifest.yaml
@@ -0,0 +1,129 @@
 # Create the local k3d simulation cluster — an executable miniature of the
 # ground segment (k3s) from docs/06. Terraform then owns the workloads.
 ---
 - name: Simulation cluster (k3d)
  hosts: localhost
  connection: local
  gather_facts: false
  vars:
    cluster_name: swarm-sim
    repo_root: "{{ playbook_dir }}/../.."
    data_dir: "{{ lookup('env', 'SWARM_SIM_DATA') | default(repo_root + '/var/t1', true) }}"
    warehouse_dir: "{{ lookup('env', 'SWARM_WAREHOUSE_DATA') | default(repo_root + '/var/t3', true) }}"
    api_port: 6560
    # Host ports for services exposed by the Terraform modules (NodePorts)
    port_explorer: 30088
    port_grafana: 30300
    port_prometheus: 30990
    port_warehouse_explorer: 30089
    port_minio_console: 30901
    simulator_image: swarm-house/simulator:dev
  tasks:
    - name: Check k3d is installed
      ansible.builtin.command: k3d version
      changed_when: false
    - name: Create shared data directory (the pods' /data lake)
      ansible.builtin.file:
        path: "{{ data_dir }}"
        state: directory
        mode: "0777"
        owner: "10001"
        group: "10001"
    - name: Create warehouse directory (T3 host path inside the k3d node)
      ansible.builtin.file:
        path: "{{ warehouse_dir }}"
        state: directory
        mode: "0777"
        owner: "10001"
        group: "10001"
    - name: List existing clusters
      ansible.builtin.command: k3d cluster list -o json
      register: clusters
      changed_when: false
    - name: Create cluster {{ cluster_name }}
      ansible.builtin.command: >-
        k3d cluster create {{ cluster_name }}
        --api-port {{ api_port }}
        --servers 1 --agents 0
        --volume {{ data_dir }}:/data@server:0
        --volume {{ warehouse_dir }}:/warehouse@server:0
        --port {{ port_explorer }}:{{ port_explorer }}@server:0
        --port {{ port_grafana }}:{{ port_grafana }}@server:0
        --port {{ port_prometheus }}:{{ port_prometheus }}@server:0
        --port {{ port_warehouse_explorer }}:{{ port_warehouse_explorer }}@server:0
        --port {{ port_minio_console }}:{{ port_minio_console }}@server:0
        --k3s-arg "--disable=traefik@server:0"
        --wait
      when: clusters.stdout | from_json | selectattr('name', 'equalto', cluster_name) | list | length == 0
    - name: Build simulator image
      ansible.builtin.command:
        cmd: docker build -t {{ simulator_image }} .
        chdir: "{{ playbook_dir }}/../../simulator"
      register: build
      changed_when: "'Using cache' not in build.stderr"
    - name: Import simulator image into the cluster
      ansible.builtin.command: k3d image import {{ simulator_image }} -c {{ cluster_name }}
      changed_when: true
    - name: Merge k3d kubeconfig into the default context
      ansible.builtin.command: k3d kubeconfig merge {{ cluster_name }} --kubeconfig-merge-default
      changed_when: false
    - name: Check whether Flux CLI is available
      ansible.builtin.command: flux version --client
      register: flux_cli
      changed_when: false
      failed_when: false
    - name: Install Flux CLI (local user bin)
      when: flux_cli.rc != 0
      block:
        - name: Download Flux release archive
          ansible.builtin.get_url:
            url: https://github.com/fluxcd/flux2/releases/download/v2.4.0/flux_2.4.0_linux_amd64.tar.gz
            dest: /tmp/flux.tar.gz
            mode: "0644"
        - name: Extract flux binary
          ansible.builtin.unarchive:
            src: /tmp/flux.tar.gz
            dest: "{{ lookup('env', 'HOME') }}/.local/bin"
            remote_src: true
            include:
              - flux
            extra_opts:
              - --no-same-owner
          ignore_errors: true
        - name: Ensure flux is executable
          ansible.builtin.file:
            path: "{{ lookup('env', 'HOME') }}/.local/bin/flux"
            mode: "0755"
            state: file
    - name: Install Flux controllers into the cluster
      ansible.builtin.command: flux install --namespace=flux-system --components=source-controller,kustomize-controller
      environment:
        PATH: "{{ lookup('env', 'HOME') }}/.local/bin:{{ lookup('env', 'PATH') }}"
        KUBECONFIG: "{{ lookup('env', 'HOME') }}/.kube/config"
      register: flux_install
      changed_when: "'installed' in (flux_install.stdout | default(''))"
    - name: Apply Flux GitOps CRs (GitRepository + Kustomization)
      ansible.builtin.command: kubectl apply -k {{ repo_root }}/infra/gitops/flux
      changed_when: true
    - name: Seed ground GitOps resources locally (works before first git push)
      ansible.builtin.command: kubectl apply -k {{ repo_root }}/infra/gitops/ground
      changed_when: true
    - name: Show kubeconfig hint
      ansible.builtin.debug:
        msg: >-
          Cluster ready. Workloads: cd ../terraform/sim-env && terraform init && terraform apply.
          kubeconfig: k3d kubeconfig get {{ cluster_name }}
@@ -0,0 +1,15 @@
 [Unit]
 Description=Swarm data plane (Compose bundle, release {{ manifest.release }})
 After=docker.service wg-quick@wg0.service
 Requires=docker.service
 [Service]
 Type=oneshot
 RemainAfterExit=yes
 WorkingDirectory={{ swarm_home }}/release
 ExecStart=/usr/bin/docker compose up -d
 ExecStop=/usr/bin/docker compose down
 TimeoutStartSec=300
 [Install]
 WantedBy=multi-user.target
@@ -0,0 +1,12 @@
 # WireGuard swarm plane — rendered by drone-provision.yml
 [Interface]
 Address = {{ wg_address }}
 ListenPort = {{ wg_port }}
 PrivateKey = __REPLACED_FROM_HSM_AT_SEALING__
 {% for peer in groups['drones'] | difference([inventory_hostname]) %}
 [Peer]
 # {{ peer }}
 PublicKey = __PEER_{{ peer | upper | replace('-', '_') }}_PUBKEY__
 AllowedIPs = {{ hostvars[peer].wg_address | ansible.utils.ipaddr('address') }}/32
 {% endfor %}
@@ -0,0 +1,52 @@
 # GitOps — ground segment (Flux)
 Flux reconciles **long-lived ground configuration** from this repository. It does
 not run on drones and does not replace the fleet release manifest — those are
 different lifecycles by design ([11 — CI/CD](../../docs/11-cicd-delivery.md)).
 ## Boundary: Terraform vs Flux vs fleet manifest
 | Layer | Tool | Owns |
 | --- | --- | --- |
 | Cluster + first boot | **Terraform** ([`../terraform/`](../terraform/)) | Namespaces, PVCs, Deployments, CronJobs, NodePorts — the shape of the simulation |
 | Ongoing ground config | **Flux** (this directory) | Policy labels, dashboard bundles, offload knobs — things that change without reprovisioning PVCs |
 | Drones | **Fleet release manifest** | Compose bundle digests, model weights, peer registry — atomic, dock-only delivery |
 Drones are **outside GitOps**: there is no reconciler in flight. A dock applies
 the pinned manifest once; mid-mission drift is impossible because the update
 endpoint does not exist in the radio profile.
 ## Layout
 ```
 infra/gitops/
  flux/           Flux GitRepository + Kustomization CRs (install into flux-system)
  ground/         Kustomize overlay reconciled into the ground namespace
 ```
 ## Bootstrap on the k3d simulation cluster
 `infra/ansible/sim-cluster.yml` installs Flux controllers and applies the CRs
 below. After the first `git push`, Flux polls `origin` and reconciles
 `infra/gitops/ground/` into the `ground` namespace.
 ```bash
 # Manual bootstrap (if you skipped Ansible):
 flux install --namespace=flux-system
 kubectl apply -k infra/gitops/flux
 ```
 To reconcile immediately without waiting for the poll interval:
 ```bash
 flux reconcile source git swarm-house -n flux-system
 flux reconcile kustomization ground-segment -n flux-system
 ```
 ## What Flux manages here (example)
 The [`ground/`](ground/) overlay currently carries a **GitOps-managed ConfigMap**
 that tags the warehouse segment with fleet policy metadata. In production this
 pattern extends to Grafana dashboard bundles, Prometheus rule files, and
 offload-schedule ConfigMaps — all versioned in git, all auditable, none of
 them requiring a `terraform apply` to tweak a label.
@@ -0,0 +1,10 @@
 apiVersion: source.toolkit.fluxcd.io/v1
 kind: GitRepository
 metadata:
  name: swarm-house
  namespace: flux-system
 spec:
  interval: 1m
  url: https://git.produktor.io/eSlider/swarm-house.git
  ref:
    branch: main
@@ -0,0 +1,14 @@
 apiVersion: kustomize.toolkit.fluxcd.io/v1
 kind: Kustomization
 metadata:
  name: ground-segment
  namespace: flux-system
 spec:
  interval: 2m
  sourceRef:
    kind: GitRepository
    name: swarm-house
  path: ./infra/gitops/ground
  prune: true
  wait: true
  timeout: 3m
@@ -0,0 +1,5 @@
 apiVersion: kustomize.config.k8s.io/v1beta1
 kind: Kustomization
 resources:
  - gitrepository.yaml
  - kustomization-ground.yaml
@@ -0,0 +1,12 @@
 apiVersion: v1
 kind: ConfigMap
 metadata:
  name: ground-fleet-policy
  namespace: ground
  labels:
    app.kubernetes.io/part-of: swarm-ground
    app.kubernetes.io/managed-by: flux
 data:
  offload_schedule: "*/2 * * * *"
  retention_policy: "keep-all-flights"
  explorer_mode: "read-only-sql"
@@ -0,0 +1,6 @@
 apiVersion: kustomize.config.k8s.io/v1beta1
 kind: Kustomization
 namespace: ground
 resources:
  - namespace.yaml
  - configmap-fleet-policy.yaml
@@ -0,0 +1,6 @@
 apiVersion: v1
 kind: Namespace
 metadata:
  name: ground
  labels:
    app.kubernetes.io/part-of: swarm-ground
@@ -0,0 +1,34 @@
 ## Recovering from partial state
 If resources were created outside Terraform (or state was lost), import them
 before `terraform apply`:
 ```bash
 # sim-env (namespace: swarm)
 terraform import kubernetes_namespace.swarm swarm
 terraform import kubernetes_stateful_set.drone swarm/drone
 terraform import kubernetes_deployment.exporter swarm/exporter
 terraform import kubernetes_deployment.explorer swarm/explorer
 terraform import kubernetes_deployment.prometheus swarm/prometheus
 terraform import kubernetes_deployment.grafana swarm/grafana
 terraform import kubernetes_service.exporter swarm/exporter
 terraform import kubernetes_service.explorer swarm/explorer
 terraform import kubernetes_service.prometheus swarm/prometheus
 terraform import kubernetes_service.grafana swarm/grafana
 terraform import kubernetes_config_map.prometheus swarm/prometheus-config
 terraform import kubernetes_config_map.grafana_provisioning swarm/grafana-provisioning
 terraform import kubernetes_config_map.grafana_dashboard swarm/grafana-dashboard
 # ground (namespace: ground)
 terraform import kubernetes_namespace.ground ground
 terraform import kubernetes_persistent_volume_claim.minio ground/minio-data
 terraform import kubernetes_secret.minio ground/minio-credentials
 terraform import kubernetes_deployment.minio ground/minio
 terraform import kubernetes_deployment.warehouse_explorer ground/warehouse-explorer
 terraform import kubernetes_service.minio ground/minio
 terraform import kubernetes_service.warehouse_explorer ground/warehouse-explorer
 terraform import kubernetes_cron_job_v1.offload ground/offload
 ```
 Then `terraform apply` reconciles drift (for example pinning `EXPLORER_PORT`
 and `EXPORTER_PORT` so Kubernetes service env injection does not break startup).
@@ -0,0 +1,22 @@
 # This file is maintained automatically by "terraform init".
 # Manual edits may be lost in future updates.
 provider "registry.terraform.io/hashicorp/kubernetes" {
  version     = "2.38.0"
  constraints = "~> 2.33"
  hashes = [
    "h1:5CkveFo5ynsLdzKk+Kv+r7+U9rMrNjfZPT3a0N/fhgE=",
    "zh:0af928d776eb269b192dc0ea0f8a3f0f5ec117224cd644bdacdc682300f84ba0",
    "zh:1be998e67206f7cfc4ffe77c01a09ac91ce725de0abaec9030b22c0a832af44f",
    "zh:326803fe5946023687d603f6f1bab24de7af3d426b01d20e51d4e6fbe4e7ec1b",
    "zh:4a99ec8d91193af961de1abb1f824be73df07489301d62e6141a656b3ebfff12",
    "zh:5136e51765d6a0b9e4dbcc3b38821e9736bd2136cf15e9aac11668f22db117d2",
    "zh:63fab47349852d7802fb032e4f2b6a101ee1ce34b62557a9ad0f0f0f5b6ecfdc",
    "zh:924fb0257e2d03e03e2bfe9c7b99aa73c195b1f19412ca09960001bee3c50d15",
    "zh:b63a0be5e233f8f6727c56bed3b61eb9456ca7a8bb29539fba0837f1badf1396",
    "zh:d39861aa21077f1bc899bc53e7233262e530ba8a3a2d737449b100daeb303e4d",
    "zh:de0805e10ebe4c83ce3b728a67f6b0f9d18be32b25146aa89116634df5145ad4",
    "zh:f569b65999264a9416862bca5cd2a6177d94ccb0424f3a4ef424428912b9cb3c",
    "zh:faf23e45f0090eef8ba28a8aac7ec5d4fdf11a36c40a8d286304567d71c1e7db",
  ]
 }
@@ -0,0 +1,38 @@
 # Terraform — ground warehouse (T3)
 The stationary half of the system: the historical warehouse that receives
 sealed partitions after every flight ([03 — Storage tiers](../../../docs/03-storage-design.md)).
 It is the longest-lived, most conventional infrastructure in the design —
 and therefore the most natural Terraform territory.
 Runs in the same k3d cluster as [`../sim-env`](../sim-env/) but in its own
 namespace with its own state: the fleet is disposable, the warehouse is not,
 and the two lifecycles must never share a `terraform destroy`.
 | Resource | Purpose |
 | --- | --- |
 | Deployment `minio` + PVC + NodePort 30901 | Object-store facade of the warehouse for downstream consumers (training pipelines, replay) |
 | CronJob `offload` | Post-flight offload: copies Hive partitions verbatim from the fleet lake (T1) into the warehouse (T3), then mirrors into MinIO |
 | Deployment `warehouse-explorer` + NodePort 30089 | Second explorer instance over the warehouse path — historical read-only SQL across **all** flights |
 ## Usage
 ```bash
 terraform init
 terraform apply
 terraform output
 ```
 After a couple of offload runs, the warehouse explorer shows the same
 partition tree as the live lake but accumulated across flights — the T1
 layout **is** the T3 layout, which is the whole point: no transform step,
 no schema drift, DuckDB queries work identically on both ends.
 ## Simulation vs production
 - The CronJob compresses "drone lands, docks, offloads, prunes" into a
  periodic rsync-style copy. Production offload is event-driven per docking
  and verifies checksums from the flight's partition manifest before pruning
  the on-board lake.
 - MinIO credentials here are throwaway defaults; production credentials are
  sealed per site and never in state or VCS.
@@ -0,0 +1,273 @@
 # Ground warehouse segment (T3, docs/03 + docs/06) — the stationary,
 # long-lifecycle half of the system and therefore the most natural Terraform
 # territory. Runs in the same k3d cluster as the fleet, in its own namespace
 # and with its own state.
 locals {
  labels = { "app.kubernetes.io/part-of" = "swarm-ground" }
 }
 resource "kubernetes_namespace" "ground" {
  metadata {
    name   = var.namespace
    labels = local.labels
  }
 }
 # --- MinIO object store -------------------------------------------------------
 resource "kubernetes_secret" "minio" {
  metadata {
    name      = "minio-credentials"
    namespace = kubernetes_namespace.ground.metadata[0].name
  }
  data = {
    MINIO_ROOT_USER     = var.minio_root_user
    MINIO_ROOT_PASSWORD = var.minio_root_password
  }
 }
 resource "kubernetes_persistent_volume_claim" "minio" {
  metadata {
    name      = "minio-data"
    namespace = kubernetes_namespace.ground.metadata[0].name
  }
  spec {
    access_modes = ["ReadWriteOnce"]
    resources {
      requests = { storage = var.minio_storage }
    }
  }
 }
 resource "kubernetes_deployment" "minio" {
  metadata {
    name      = "minio"
    namespace = kubernetes_namespace.ground.metadata[0].name
    labels    = local.labels
  }
  spec {
    replicas = 1
    strategy {
      type = "Recreate" # RWO volume
    }
    selector {
      match_labels = { app = "minio" }
    }
    template {
      metadata {
        labels = merge(local.labels, { app = "minio" })
      }
      spec {
        container {
          name  = "minio"
          image = "minio/minio:RELEASE.2024-06-13T22-53-53Z"
          args  = ["server", "/data", "--console-address", ":9001"]
          env_from {
            secret_ref {
              name = kubernetes_secret.minio.metadata[0].name
            }
          }
          # Air-gap hygiene: no update checks
          env {
            name  = "MINIO_UPDATE"
            value = "off"
          }
          port {
            container_port = 9000
          }
          port {
            container_port = 9001
          }
          volume_mount {
            name       = "store"
            mount_path = "/data"
          }
        }
        volume {
          name = "store"
          persistent_volume_claim {
            claim_name = kubernetes_persistent_volume_claim.minio.metadata[0].name
          }
        }
      }
    }
  }
 }
 resource "kubernetes_service" "minio" {
  metadata {
    name      = "minio"
    namespace = kubernetes_namespace.ground.metadata[0].name
  }
  spec {
    type     = "NodePort"
    selector = { app = "minio" }
    port {
      name        = "api"
      port        = 9000
      target_port = 9000
    }
    port {
      name        = "console"
      port        = 9001
      target_port = 9001
      node_port   = var.node_ports.minio_console
    }
  }
 }
 # --- Post-flight offload: T1 lake -> T3 warehouse ------------------------------
 # In production this runs on the docking station after a drone lands: only
 # sealed partitions move, checksums are verified, then the on-board lake is
 # pruned. The simulation compresses that into a periodic rsync-style copy
 # plus an object-store mirror.
 resource "kubernetes_cron_job_v1" "offload" {
  metadata {
    name      = "offload"
    namespace = kubernetes_namespace.ground.metadata[0].name
    labels    = local.labels
  }
  spec {
    schedule                      = var.offload_schedule
    concurrency_policy            = "Forbid"
    successful_jobs_history_limit = 3
    failed_jobs_history_limit     = 3
    job_template {
      metadata {
        labels = local.labels
      }
      spec {
        backoff_limit = 1
        template {
          metadata {
            labels = local.labels
          }
          spec {
            restart_policy = "Never"
            # Step 1: copy Hive partitions verbatim (T1 layout == T3 layout)
            init_container {
              name    = "copy"
              image   = "busybox:1.36"
              command = ["sh", "-c", "cp -ru /lake/flight_id=* /warehouse/ 2>/dev/null; ls /warehouse | wc -l"]
              volume_mount {
                name       = "lake"
                mount_path = "/lake"
                read_only  = true
              }
              volume_mount {
                name       = "warehouse"
                mount_path = "/warehouse"
              }
            }
            # Step 2: mirror the warehouse into the object store for
            # downstream consumers (training pipelines, replay tooling)
            container {
              name    = "mirror"
              image   = "minio/mc:RELEASE.2024-06-12T14-34-03Z"
              command = ["sh", "-c", "mc alias set store http://minio:9000 \"$MINIO_ROOT_USER\" \"$MINIO_ROOT_PASSWORD\" && mc mb -p store/warehouse && mc mirror --overwrite /warehouse store/warehouse"]
              env_from {
                secret_ref {
                  name = kubernetes_secret.minio.metadata[0].name
                }
              }
              volume_mount {
                name       = "warehouse"
                mount_path = "/warehouse"
                read_only  = true
              }
            }
            volume {
              name = "lake"
              host_path {
                path = var.lake_host_path
              }
            }
            volume {
              name = "warehouse"
              host_path {
                path = var.warehouse_host_path
                type = "DirectoryOrCreate"
              }
            }
          }
        }
      }
    }
  }
 }
 # --- Warehouse explorer: historical read-only SQL over all flights -------------
 resource "kubernetes_deployment" "warehouse_explorer" {
  metadata {
    name      = "warehouse-explorer"
    namespace = kubernetes_namespace.ground.metadata[0].name
    labels    = local.labels
  }
  spec {
    replicas = 1
    selector {
      match_labels = { app = "warehouse-explorer" }
    }
    template {
      metadata {
        labels = merge(local.labels, { app = "warehouse-explorer" })
      }
      spec {
        container {
          name              = "explorer"
          image             = var.simulator_image
          image_pull_policy = "Never"
          command           = ["python", "explorer/server.py"]
          env {
            name  = "DATA_DIR"
            value = "/warehouse"
          }
          port {
            container_port = 8088
          }
          volume_mount {
            name       = "warehouse"
            mount_path = "/warehouse"
            read_only  = true
          }
        }
        volume {
          name = "warehouse"
          host_path {
            path = var.warehouse_host_path
            type = "DirectoryOrCreate"
          }
        }
      }
    }
  }
 }
 resource "kubernetes_service" "warehouse_explorer" {
  metadata {
    name      = "warehouse-explorer"
    namespace = kubernetes_namespace.ground.metadata[0].name
  }
  spec {
    type     = "NodePort"
    selector = { app = "warehouse-explorer" }
    port {
      port        = 8088
      target_port = 8088
      node_port   = var.node_ports.warehouse_explorer
    }
  }
 }
@@ -0,0 +1,14 @@
 output "warehouse_explorer_url" {
  description = "Historical read-only SQL over all offloaded flights"
  value       = "http://localhost:${var.node_ports.warehouse_explorer}"
 }
 output "minio_console_url" {
  description = "MinIO console (object-store view of the warehouse)"
  value       = "http://localhost:${var.node_ports.minio_console}"
 }
 output "offload_schedule" {
  description = "Cron schedule of the T1 -> T3 offload job"
  value       = var.offload_schedule
 }
@@ -0,0 +1,72 @@
 variable "kubeconfig" {
  description = "Path to the kubeconfig for the simulation cluster"
  type        = string
  default     = "~/.kube/config"
 }
 variable "kube_context" {
  description = "Kubeconfig context of the k3d simulation cluster"
  type        = string
  default     = "k3d-swarm-sim"
 }
 variable "namespace" {
  description = "Namespace for the ground warehouse segment"
  type        = string
  default     = "ground"
 }
 variable "simulator_image" {
  description = "Simulator image (provides the explorer; imported by Ansible)"
  type        = string
  default     = "swarm-house/simulator:dev"
 }
 variable "lake_host_path" {
  description = "Host path of the fleet's live lake (T1) inside the k3d node"
  type        = string
  default     = "/data"
 }
 variable "warehouse_host_path" {
  description = "Host path of the historical warehouse (T3) inside the k3d node; dot-prefixed so lake globs never match it"
  type        = string
  default     = "/data/.warehouse"
 }
 variable "offload_schedule" {
  description = "Cron schedule of the post-flight offload job (T1 -> T3)"
  type        = string
  default     = "*/2 * * * *"
 }
 variable "minio_root_user" {
  description = "MinIO root user (simulation only; production uses sealed credentials)"
  type        = string
  default     = "warehouse"
 }
 variable "minio_root_password" {
  description = "MinIO root password (simulation only)"
  type        = string
  default     = "warehouse-sim-only"
  sensitive   = true
 }
 variable "minio_storage" {
  description = "PVC size for the MinIO object store"
  type        = string
  default     = "2Gi"
 }
 variable "node_ports" {
  description = "Host-reachable NodePorts (must match the ports opened by sim-cluster.yml)"
  type = object({
    warehouse_explorer = number
    minio_console      = number
  })
  default = {
    warehouse_explorer = 30089
    minio_console      = 30901
  }
 }
@@ -0,0 +1,15 @@
 terraform {
  required_version = ">= 1.5"
  required_providers {
    kubernetes = {
      source  = "hashicorp/kubernetes"
      version = "~> 2.33"
    }
  }
 }
 provider "kubernetes" {
  config_path    = var.kubeconfig
  config_context = var.kube_context
 }
@@ -0,0 +1,22 @@
 # This file is maintained automatically by "terraform init".
 # Manual edits may be lost in future updates.
 provider "registry.terraform.io/hashicorp/kubernetes" {
  version     = "2.38.0"
  constraints = "~> 2.33"
  hashes = [
    "h1:5CkveFo5ynsLdzKk+Kv+r7+U9rMrNjfZPT3a0N/fhgE=",
    "zh:0af928d776eb269b192dc0ea0f8a3f0f5ec117224cd644bdacdc682300f84ba0",
    "zh:1be998e67206f7cfc4ffe77c01a09ac91ce725de0abaec9030b22c0a832af44f",
    "zh:326803fe5946023687d603f6f1bab24de7af3d426b01d20e51d4e6fbe4e7ec1b",
    "zh:4a99ec8d91193af961de1abb1f824be73df07489301d62e6141a656b3ebfff12",
    "zh:5136e51765d6a0b9e4dbcc3b38821e9736bd2136cf15e9aac11668f22db117d2",
    "zh:63fab47349852d7802fb032e4f2b6a101ee1ce34b62557a9ad0f0f0f5b6ecfdc",
    "zh:924fb0257e2d03e03e2bfe9c7b99aa73c195b1f19412ca09960001bee3c50d15",
    "zh:b63a0be5e233f8f6727c56bed3b61eb9456ca7a8bb29539fba0837f1badf1396",
    "zh:d39861aa21077f1bc899bc53e7233262e530ba8a3a2d737449b100daeb303e4d",
    "zh:de0805e10ebe4c83ce3b728a67f6b0f9d18be32b25146aa89116634df5145ad4",
    "zh:f569b65999264a9416862bca5cd2a6177d94ccb0424f3a4ef424428912b9cb3c",
    "zh:faf23e45f0090eef8ba28a8aac7ec5d4fdf11a36c40a8d286304567d71c1e7db",
  ]
 }
@@ -0,0 +1,40 @@
 # Terraform — simulation environment (T4)
 Declares the simulated fleet and its observability stack on the k3d cluster
 created by [`../../ansible/sim-cluster.yml`](../../ansible/sim-cluster.yml).
 This module is the executable miniature of the dev/sim environment from
 [06 — Environments](../../../docs/06-environments.md).
 | Resource | Purpose |
 | --- | --- |
 | StatefulSet `drone` × `drone_count` | Virtual drones; stable pod names become `DRONE_ID`s; all write Hive-partitioned Parquet into the shared `/data` lake |
 | Deployment `exporter` | Prometheus exporter reading the lake with DuckDB |
 | Deployment `explorer` + NodePort 30088 | Partition tree + read-only SQL console (also feeds the prototype's live mode) |
 | Deployment `prometheus` + NodePort 30990 | Scrapes the exporter |
 | Deployment `grafana` + NodePort 30300 | Same dashboard JSON as the Compose profile, provisioned from a ConfigMap |
 ## Usage
 ```bash
 ansible-playbook ../../ansible/sim-cluster.yml   # once: cluster + image
 terraform init
 terraform apply
 terraform output                                  # URLs
 ```
 Scale the fleet without touching YAML:
 ```bash
 terraform apply -var drone_count=9 -var speedup=4
 ```
 ## Notes
 - `image_pull_policy = "Never"` — the simulator image is imported by the
  Ansible playbook (`k3d image import`); the cluster never pulls from a
  registry, mirroring the air-gap doctrine.
 - Pods discover their identity from the pod name (StatefulSet ordinal), so
  a flight survives `kubectl delete pod` the same way a drone survives a
  power cycle: new process, same identity, new flight ID.
 - State is local (`terraform.tfstate` in this directory) — the simulation
  cluster is disposable; nothing here is shared infrastructure.
@@ -0,0 +1,569 @@
 # Simulated fleet (T4 dev environment, docs/06) on the k3d cluster created
 # by infra/ansible/sim-cluster.yml. Drones are StatefulSet replicas writing
 # to a shared hostPath lake — the same /data contract as the on-board NVMe.
 locals {
  labels = { "app.kubernetes.io/part-of" = "swarm-sim" }
 }
 resource "kubernetes_namespace" "swarm" {
  metadata {
    name   = var.namespace
    labels = local.labels
  }
 }
 # --- Fleet ------------------------------------------------------------------
 resource "kubernetes_stateful_set" "drone" {
  metadata {
    name      = "drone"
    namespace = kubernetes_namespace.swarm.metadata[0].name
    labels    = local.labels
  }
  spec {
    service_name = "drone"
    replicas     = var.drone_count
    # A landed drone powers off; the next start is the next flight
    pod_management_policy = "Parallel"
    selector {
      match_labels = { app = "drone" }
    }
    template {
      metadata {
        labels = merge(local.labels, { app = "drone" })
      }
      spec {
        container {
          name              = "drone"
          image             = var.simulator_image
          image_pull_policy = "Never" # imported via k3d image import
          env {
            name = "DRONE_ID"
            value_from {
              field_ref {
                field_path = "metadata.name"
              }
            }
          }
          env {
            name  = "DURATION_S"
            value = tostring(var.flight_duration_s)
          }
          env {
            name  = "SPEEDUP"
            value = tostring(var.speedup)
          }
          env {
            name  = "DATA_DIR"
            value = "/data"
          }
          env {
            name  = "KEEP_ALIVE"
            value = "1"
          }
          env {
            name  = "IMU_HZ"
            value = "20"
          }
          volume_mount {
            name       = "lake"
            mount_path = "/data"
          }
          resources {
            requests = { cpu = "50m", memory = "128Mi" }
            limits   = { cpu = "500m", memory = "512Mi" }
          }
        }
        volume {
          name = "lake"
          host_path {
            path = var.data_host_path
          }
        }
      }
    }
  }
 }
 # --- Exporter + Prometheus + Grafana (observability, docs/07) ----------------
 resource "kubernetes_deployment" "exporter" {
  metadata {
    name      = "exporter"
    namespace = kubernetes_namespace.swarm.metadata[0].name
    labels    = local.labels
  }
  spec {
    replicas = 1
    selector {
      match_labels = { app = "exporter" }
    }
    template {
      metadata {
        labels = merge(local.labels, { app = "exporter" })
      }
      spec {
        container {
          name              = "exporter"
          image             = var.simulator_image
          image_pull_policy = "Never"
          command           = ["python", "monitoring/exporter.py"]
          env {
            name  = "DATA_DIR"
            value = "/data"
          }
          env {
            name  = "EXPORTER_PORT"
            value = "9105"
          }
          env {
            name  = "SCAN_INTERVAL_S"
            value = "30"
          }
          env {
            name  = "METRIC_FLIGHT_WINDOW"
            value = "5"
          }
          port {
            container_port = 9105
          }
          resources {
            requests = { cpu = "50m", memory = "64Mi" }
            limits   = { cpu = "500m", memory = "256Mi" }
          }
          volume_mount {
            name       = "lake"
            mount_path = "/data"
            read_only  = true
          }
        }
        volume {
          name = "lake"
          host_path {
            path = var.data_host_path
          }
        }
      }
    }
  }
 }
 resource "kubernetes_service" "exporter" {
  metadata {
    name      = "exporter"
    namespace = kubernetes_namespace.swarm.metadata[0].name
  }
  spec {
    selector = { app = "exporter" }
    port {
      port        = 9105
      target_port = 9105
    }
  }
 }
 resource "kubernetes_deployment" "explorer" {
  metadata {
    name      = "explorer"
    namespace = kubernetes_namespace.swarm.metadata[0].name
    labels    = local.labels
  }
  spec {
    replicas = 1
    selector {
      match_labels = { app = "explorer" }
    }
    template {
      metadata {
        labels = merge(local.labels, { app = "explorer" })
      }
      spec {
        container {
          name              = "explorer"
          image             = var.simulator_image
          image_pull_policy = "Never"
          command           = ["python", "explorer/server.py"]
          env {
            name  = "DATA_DIR"
            value = "/data"
          }
          env {
            name  = "EXPLORER_PORT"
            value = "8088"
          }
          env {
            name  = "VIEW_REFRESH_S"
            value = "30"
          }
          env {
            name  = "TREE_CACHE_S"
            value = "15"
          }
          env {
            name  = "METRIC_FLIGHT_WINDOW"
            value = "5"
          }
          port {
            container_port = 8088
          }
          resources {
            requests = { cpu = "50m", memory = "64Mi" }
            limits   = { cpu = "750m", memory = "256Mi" }
          }
          volume_mount {
            name       = "lake"
            mount_path = "/data"
            read_only  = true
          }
        }
        volume {
          name = "lake"
          host_path {
            path = var.data_host_path
          }
        }
      }
    }
  }
 }
 resource "kubernetes_service" "explorer" {
  metadata {
    name      = "explorer"
    namespace = kubernetes_namespace.swarm.metadata[0].name
  }
  spec {
    type     = "NodePort"
    selector = { app = "explorer" }
    port {
      port        = 8088
      target_port = 8088
      node_port   = var.node_ports.explorer
    }
  }
 }
 resource "kubernetes_config_map" "prometheus" {
  metadata {
    name      = "prometheus-config"
    namespace = kubernetes_namespace.swarm.metadata[0].name
  }
  data = {
    "prometheus.yml" = <<-EOT
      global:
        scrape_interval: 15s
      scrape_configs:
        - job_name: swarm
          static_configs:
            - targets: ["exporter:9105"]
        - job_name: explorer
          static_configs:
            - targets: ["explorer:8088"]
        - job_name: node
          static_configs:
            - targets: ["node-exporter:9100"]
    EOT
  }
 }
 resource "kubernetes_deployment" "prometheus" {
  metadata {
    name      = "prometheus"
    namespace = kubernetes_namespace.swarm.metadata[0].name
    labels    = local.labels
  }
  spec {
    replicas = 1
    selector {
      match_labels = { app = "prometheus" }
    }
    template {
      metadata {
        labels = merge(local.labels, { app = "prometheus" })
      }
      spec {
        container {
          name  = "prometheus"
          image = "prom/prometheus:v2.53.0"
          port {
            container_port = 9090
          }
          volume_mount {
            name       = "config"
            mount_path = "/etc/prometheus"
          }
        }
        volume {
          name = "config"
          config_map {
            name = kubernetes_config_map.prometheus.metadata[0].name
          }
        }
      }
    }
  }
 }
 resource "kubernetes_service" "prometheus" {
  metadata {
    name      = "prometheus"
    namespace = kubernetes_namespace.swarm.metadata[0].name
  }
  spec {
    type     = "NodePort"
    selector = { app = "prometheus" }
    port {
      port        = 9090
      target_port = 9090
      node_port   = var.node_ports.prometheus
    }
  }
 }
 resource "kubernetes_config_map" "grafana_provisioning" {
  metadata {
    name      = "grafana-provisioning"
    namespace = kubernetes_namespace.swarm.metadata[0].name
  }
  data = {
    "datasource.yml" = <<-EOT
      apiVersion: 1
      deleteDatasources:
        - name: Prometheus
          orgId: 1
      datasources:
        - name: Prometheus
          uid: swarm-prom
          type: prometheus
          access: proxy
          url: http://prometheus:9090
          isDefault: true
    EOT
    "dashboards.yml" = <<-EOT
      apiVersion: 1
      providers:
        - name: swarm
          folder: ""
          type: file
          options:
            path: /var/lib/grafana/dashboards
    EOT
  }
 }
 resource "kubernetes_config_map" "grafana_dashboard" {
  metadata {
    name      = "grafana-dashboard"
    namespace = kubernetes_namespace.swarm.metadata[0].name
  }
  data = {
    "swarm.json"          = file("${path.module}/../../../simulator/monitoring/grafana/dashboards/swarm.json")
    "swarm-platform.json" = file("${path.module}/../../../simulator/monitoring/grafana/dashboards/swarm-platform.json")
  }
 }
 # Host metrics for the k3d node (CPU / memory on the platform dashboard)
 resource "kubernetes_deployment" "node_exporter" {
  metadata {
    name      = "node-exporter"
    namespace = kubernetes_namespace.swarm.metadata[0].name
    labels    = local.labels
  }
  spec {
    replicas = 1
    selector {
      match_labels = { app = "node-exporter" }
    }
    template {
      metadata {
        labels = merge(local.labels, { app = "node-exporter" })
      }
      spec {
        host_network = true
        host_pid     = true
        container {
          name  = "node-exporter"
          image = "prom/node-exporter:v1.8.2"
          args = [
            "--path.procfs=/host/proc",
            "--path.sysfs=/host/sys",
            "--path.rootfs=/host/root",
            "--collector.filesystem.mount-points-exclude=^/(sys|proc|dev|host|etc)($$|/)",
          ]
          port {
            container_port = 9100
          }
          volume_mount {
            name       = "proc"
            mount_path = "/host/proc"
            read_only  = true
          }
          volume_mount {
            name       = "sys"
            mount_path = "/host/sys"
            read_only  = true
          }
          volume_mount {
            name       = "root"
            mount_path = "/host/root"
            read_only  = true
          }
          resources {
            requests = { cpu = "20m", memory = "32Mi" }
            limits   = { cpu = "200m", memory = "64Mi" }
          }
        }
        volume {
          name = "proc"
          host_path { path = "/proc" }
        }
        volume {
          name = "sys"
          host_path { path = "/sys" }
        }
        volume {
          name = "root"
          host_path { path = "/" }
        }
      }
    }
  }
 }
 resource "kubernetes_service" "node_exporter" {
  metadata {
    name      = "node-exporter"
    namespace = kubernetes_namespace.swarm.metadata[0].name
  }
  spec {
    selector = { app = "node-exporter" }
    port {
      port        = 9100
      target_port = 9100
    }
  }
 }
 resource "kubernetes_deployment" "grafana" {
  metadata {
    name      = "grafana"
    namespace = kubernetes_namespace.swarm.metadata[0].name
    labels    = local.labels
  }
  spec {
    replicas = 1
    selector {
      match_labels = { app = "grafana" }
    }
    template {
      metadata {
        labels = merge(local.labels, { app = "grafana" })
      }
      spec {
        container {
          name  = "grafana"
          image = "grafana/grafana:11.1.0"
          port {
            container_port = 3000
          }
          env {
            name  = "GF_AUTH_ANONYMOUS_ENABLED"
            value = "true"
          }
          env {
            name  = "GF_AUTH_ANONYMOUS_ORG_ROLE"
            value = "Admin"
          }
          env {
            name  = "GF_AUTH_DISABLE_LOGIN_FORM"
            value = "true"
          }
          # Air-gap hygiene — same flags as the Compose profile
          env {
            name  = "GF_ANALYTICS_REPORTING_ENABLED"
            value = "false"
          }
          env {
            name  = "GF_ANALYTICS_CHECK_FOR_UPDATES"
            value = "false"
          }
          env {
            name  = "GF_ANALYTICS_CHECK_FOR_PLUGIN_UPDATES"
            value = "false"
          }
          volume_mount {
            name       = "provisioning-datasources"
            mount_path = "/etc/grafana/provisioning/datasources"
          }
          volume_mount {
            name       = "provisioning-dashboards"
            mount_path = "/etc/grafana/provisioning/dashboards"
          }
          volume_mount {
            name       = "dashboards"
            mount_path = "/var/lib/grafana/dashboards"
          }
        }
        volume {
          name = "provisioning-datasources"
          config_map {
            name = kubernetes_config_map.grafana_provisioning.metadata[0].name
            items {
              key  = "datasource.yml"
              path = "datasource.yml"
            }
          }
        }
        volume {
          name = "provisioning-dashboards"
          config_map {
            name = kubernetes_config_map.grafana_provisioning.metadata[0].name
            items {
              key  = "dashboards.yml"
              path = "dashboards.yml"
            }
          }
        }
        volume {
          name = "dashboards"
          config_map {
            name = kubernetes_config_map.grafana_dashboard.metadata[0].name
          }
        }
      }
    }
  }
 }
 resource "kubernetes_service" "grafana" {
  metadata {
    name      = "grafana"
    namespace = kubernetes_namespace.swarm.metadata[0].name
  }
  spec {
    type     = "NodePort"
    selector = { app = "grafana" }
    port {
      port        = 3000
      target_port = 3000
      node_port   = var.node_ports.grafana
    }
  }
 }
@@ -0,0 +1,23 @@
 output "explorer_url" {
  description = "Data-plane explorer (partition tree + read-only SQL)"
  value       = "http://localhost:${var.node_ports.explorer}"
 }
 output "grafana_url" {
  description = "Grafana dashboards"
  value       = "http://localhost:${var.node_ports.grafana}"
 }
 output "prometheus_url" {
  description = "Prometheus"
  value       = "http://localhost:${var.node_ports.prometheus}"
 }
 output "fleet" {
  description = "Deployed fleet shape"
  value = {
    drones            = var.drone_count
    flight_duration_s = var.flight_duration_s
    speedup           = var.speedup
  }
 }
@@ -0,0 +1,66 @@
 variable "kubeconfig" {
  description = "Path to the kubeconfig for the simulation cluster"
  type        = string
  default     = "~/.kube/config"
 }
 variable "kube_context" {
  description = "Kubeconfig context of the k3d simulation cluster"
  type        = string
  default     = "k3d-swarm-sim"
 }
 variable "namespace" {
  description = "Namespace for the simulated fleet"
  type        = string
  default     = "swarm"
 }
 variable "simulator_image" {
  description = "Simulator image (imported into k3d by infra/ansible/sim-cluster.yml)"
  type        = string
  default     = "swarm-house/simulator:dev"
 }
 variable "drone_count" {
  description = "Number of virtual drones in the fleet"
  type        = number
  default     = 5
  validation {
    condition     = var.drone_count >= 1 && var.drone_count <= 32
    error_message = "drone_count must be between 1 and 32."
  }
 }
 variable "flight_duration_s" {
  description = "Simulated flight length in seconds; each pod restart begins a new flight"
  type        = number
  default     = 300
 }
 variable "speedup" {
  description = "Wall-clock acceleration of the simulation"
  type        = number
  default     = 2
 }
 variable "data_host_path" {
  description = "Host path inside the k3d node mounted as the shared Parquet lake"
  type        = string
  default     = "/data"
 }
 variable "node_ports" {
  description = "Host-reachable NodePorts (must match the ports opened by sim-cluster.yml)"
  type = object({
    explorer   = number
    grafana    = number
    prometheus = number
  })
  default = {
    explorer   = 30088
    grafana    = 30300
    prometheus = 30990
  }
 }
@@ -0,0 +1,15 @@
 terraform {
  required_version = ">= 1.5"
  required_providers {
    kubernetes = {
      source  = "hashicorp/kubernetes"
      version = "~> 2.33"
    }
  }
 }
 provider "kubernetes" {
  config_path    = var.kubeconfig
  config_context = var.kube_context
 }
@@ -1,6 +1,9 @@
 import { useEffect, useMemo, useRef, useState } from "react";
 import { DEFAULT_EXPLORER_URL, fetchLivePoses, toLiveWorld } from "./live";
 import {
  AREA,
  MAX_ASPECT,
  areaWidth,
  fmtBytes,
  makeWorld,
  seed,
@@ -9,24 +12,56 @@ import {
 } from "./sim";
 const VIEW = 1000;
 const PANEL_W = 320;
 const HEADER_H = 70;
 // Patrol field aspect ratio for the current window: wide monitors get a
 // wider field instead of empty side margins. Quantized to 0.25 steps so
 // minor resizes don't rebuild the world.
 function displayAspect(): number {
  const w = Math.max(320, window.innerWidth - PANEL_W);
  const h = Math.max(320, window.innerHeight - HEADER_H);
  const q = Math.round((w / h) * 4) / 4;
  return Math.max(1, Math.min(MAX_ASPECT, q));
 }
 const LIVE_POLL_MS = 2000;
 export default function App(): JSX.Element {
  const [droneCount, setDroneCount] = useState(8);
  const [speedup, setSpeedup] = useState(1);
  const [paused, setPaused] = useState(false);
  const [aspect, setAspect] = useState(displayAspect);
  const [mode, setMode] = useState<"sim" | "live">("sim");
  const [liveError, setLiveError] = useState<string | null>(null);
  const [world, setWorld] = useState<World>(() => {
    seed(42);
-    return makeWorld(8);
+    return makeWorld(8, displayAspect());
  });
  const raf = useRef(0);
  const last = useRef(performance.now());
  const liveWorld = useRef<World | null>(null);
  useEffect(() => {
    seed(42);
-    setWorld(makeWorld(droneCount));
+    setWorld(makeWorld(droneCount, aspect));
-  }, [droneCount]);
+  }, [droneCount, aspect]);
  useEffect(() => {
    let timer = 0;
    const onResize = (): void => {
      window.clearTimeout(timer);
      timer = window.setTimeout(() => setAspect(displayAspect()), 250);
    };
    window.addEventListener("resize", onResize);
    return () => {
      window.clearTimeout(timer);
      window.removeEventListener("resize", onResize);
    };
  }, []);
  useEffect(() => {
    if (mode !== "sim") return;
    const loop = (now: number): void => {
      const dt = Math.min(0.05, (now - last.current) / 1000);
      last.current = now;
@@ -37,7 +72,40 @@ export default function App(): JSX.Element {
    };
    raf.current = requestAnimationFrame(loop);
    return () => cancelAnimationFrame(raf.current);
-  }, [paused, speedup]);
+  }, [paused, speedup, mode]);
  // Live mode: poll the explorer's read-only SQL API for real poses
  useEffect(() => {
    if (mode !== "live") return;
    let cancelled = false;
    const poll = async (): Promise<void> => {
      try {
        const poses = await fetchLivePoses(DEFAULT_EXPLORER_URL);
        if (cancelled) return;
        setLiveError(poses.length === 0 ? "no drone data in the lake yet" : null);
        liveWorld.current = toLiveWorld(poses, liveWorld.current, LIVE_POLL_MS / 1000);
        setWorld(liveWorld.current);
      } catch (err) {
        if (!cancelled) setLiveError(err instanceof Error ? err.message : String(err));
      }
    };
    void poll();
    const timer = window.setInterval(() => void poll(), LIVE_POLL_MS);
    return () => {
      cancelled = true;
      window.clearInterval(timer);
    };
  }, [mode]);
  useEffect(() => {
    if (mode === "sim") {
      liveWorld.current = null;
      setLiveError(null);
      seed(42);
      setWorld(makeWorld(droneCount, aspect));
    }
    // eslint-disable-next-line react-hooks/exhaustive-deps -- reset only on mode change
  }, [mode]);
  const activeLinks = useMemo(
    () => [...world.links.values()].sort((a, b) => b.rate - a.rate),
@@ -55,6 +123,20 @@ export default function App(): JSX.Element {
          </div>
        </div>
        <div style={styles.controls}>
          <button
            style={{ ...styles.button, ...(mode === "live" ? styles.buttonActive : {}) }}
            onClick={() => setMode((m) => (m === "sim" ? "live" : "sim"))}
            title={`live source: ${DEFAULT_EXPLORER_URL}`}
          >
            {mode === "sim" ? "go live" : "back to sim"}
          </button>
          {mode === "live" && (
            <span style={liveError ? styles.liveError : styles.liveOk}>
              {liveError ?? `live · ${DEFAULT_EXPLORER_URL}`}
            </span>
          )}
          {mode === "sim" && (
            <>
              <label style={styles.label}>
                drones
                <input
@@ -81,12 +163,14 @@ export default function App(): JSX.Element {
              <button style={styles.button} onClick={() => setPaused((p) => !p)}>
                {paused ? "resume" : "pause"}
              </button>
            </>
          )}
        </div>
      </header>
      <div style={styles.main}>
        <svg
-          viewBox={`0 0 ${VIEW} ${VIEW}`}
+          viewBox={`0 0 ${sx(areaWidth())} ${VIEW}`}
          style={styles.canvas}
          preserveAspectRatio="xMidYMid meet"
        >
@@ -96,7 +180,7 @@ export default function App(): JSX.Element {
              <stop offset="100%" stopColor="#0a0f18" />
            </radialGradient>
          </defs>
-          <rect width={VIEW} height={VIEW} fill="url(#ground)" />
+          <rect width={sx(areaWidth())} height={VIEW} fill="url(#ground)" />
          {gridLines()}
          {/* Mesh links */}
@@ -141,7 +225,7 @@ export default function App(): JSX.Element {
              />
              {ob.moving && (
                <text x={sx(ob.pos.x)} y={sy(ob.pos.y) + 4} style={styles.obLabel as never}>
-                  mobile
+                  transit
                </text>
              )}
            </g>
@@ -155,7 +239,7 @@ export default function App(): JSX.Element {
            >
              <polygon points="0,-11 7,9 0,5 -7,9" fill="#e8ecf4" stroke="#7ea0d0" strokeWidth={1} />
              <text y={24} style={styles.droneLabel as never} transform={`rotate(${-((d.heading * 180) / Math.PI + 90)})`}>
-                dr-{String(d.id + 1).padStart(2, "0")} · ch{d.channel} · {d.battery.toFixed(0)}%
+                {d.name ?? `dr-${String(d.id + 1).padStart(2, "0")} · ch${d.channel}`} · {d.battery.toFixed(0)}%
              </text>
            </g>
          ))}
@@ -173,7 +257,8 @@ export default function App(): JSX.Element {
            {busiest.map((l) => (
              <div key={l.key} style={styles.linkRow}>
                <span style={styles.linkName}>
-                  dr-{String(l.a + 1).padStart(2, "0")} ↔ dr-{String(l.b + 1).padStart(2, "0")}
+                  {world.drones[l.a]?.name ?? `dr-${String(l.a + 1).padStart(2, "0")}`} ↔{" "}
                  {world.drones[l.b]?.name ?? `dr-${String(l.b + 1).padStart(2, "0")}`}
                </span>
                <span style={styles.linkStat}>
                  {fmtBytes(l.rate)}/s · ↑{fmtBytes(l.totalUp)} ↓{fmtBytes(l.totalDown)}
@@ -186,7 +271,7 @@ export default function App(): JSX.Element {
            <div style={styles.dim}>blue line — pose broadcast (5 Hz, ~46 B)</div>
            <div style={styles.dim}>green line — bulk sync (sealed partitions)</div>
            <div style={styles.dim}>flash — broadcast event delivered</div>
-            <div style={styles.dim}>red circle — mobile obstacle</div>
+            <div style={styles.dim}>red circle — transit object crossing the area</div>
          </Section>
        </aside>
      </div>
@@ -202,12 +287,17 @@ function sy(y: number): number {
 }
 function gridLines(): JSX.Element {
  const width = sx(areaWidth());
  const step = VIEW / 10; // one cell per 100 m in both directions
  const lines = [];
-  for (let i = 1; i < 10; i++) {
+  for (let p = step; p < width; p += step) {
    const p = (i / 10) * VIEW;
    lines.push(
-      <line key={`v${i}`} x1={p} y1={0} x2={p} y2={VIEW} stroke="#1a2436" strokeWidth={1} />,
+      <line key={`v${p}`} x1={p} y1={0} x2={p} y2={VIEW} stroke="#1a2436" strokeWidth={1} />,
-      <line key={`h${i}`} x1={0} y1={p} x2={VIEW} y2={p} stroke="#1a2436" strokeWidth={1} />,
+    );
  }
  for (let p = step; p < VIEW; p += step) {
    lines.push(
      <line key={`h${p}`} x1={0} y1={p} x2={width} y2={p} stroke="#1a2436" strokeWidth={1} />,
    );
  }
  return <g>{lines}</g>;
@@ -264,6 +354,9 @@ const styles: Record<string, React.CSSProperties> = {
    fontSize: 12,
    cursor: "pointer",
  },
  buttonActive: { background: "#1a3a2a", color: "#39d98a", borderColor: "#2a5c3c" },
  liveOk: { fontSize: 11, color: "#39d98a" },
  liveError: { fontSize: 11, color: "#c05a6a" },
  main: { display: "flex", flex: 1, gap: 0, minHeight: 0 },
  canvas: { flex: 1, minWidth: 0, display: "block" },
  panel: {
@@ -0,0 +1,129 @@
 // Live mode: real drone poses from the explorer's read-only SQL API.
 // The same gated /api/query endpoint a peer drone (or an engineer on the
 // bench) would use — the prototype is just another read-only consumer.
 import { AREA, LINK_RANGE, POSE_RATE_BYTES, type LinkStats, type World } from "./sim";
 export const DEFAULT_EXPLORER_URL =
  (import.meta.env.VITE_EXPLORER_URL as string | undefined) ?? "http://localhost:30088";
 export interface LivePose {
  id: string;
  x: number; // meters, mission frame
  y: number;
  yawDeg: number;
  battery: number | null;
  tsNs: number;
 }
 // Latest pose per drone from its own 'sent' state rows, joined with the
 // latest battery reading from telemetry. arg_max keeps it a single scan.
 const LIVE_SQL = `
 WITH pose AS (
  SELECT drone,
         arg_max(pos_x, ts_ns) AS x,
         arg_max(pos_y, ts_ns) AS y,
         arg_max(yaw, ts_ns)   AS yaw,
         max(ts_ns)            AS ts_ns
  FROM state
  WHERE direction = 'sent'
  GROUP BY drone
 ),
 batt AS (
  SELECT drone, arg_max(level_pct, ts_ns) AS battery
  FROM telemetry
  WHERE sensor = 'battery'
  GROUP BY drone
 )
 SELECT p.drone, p.x, p.y, p.yaw, p.ts_ns, b.battery
 FROM pose p LEFT JOIN batt b USING (drone)
 ORDER BY p.drone`;
 interface QueryResponse {
  columns?: string[];
  rows?: (string | number | null)[][];
  error?: string;
 }
 export async function fetchLivePoses(baseUrl: string): Promise<LivePose[]> {
  const res = await fetch(`${baseUrl.replace(/\/$/, "")}/api/query`, {
    method: "POST",
    headers: { "Content-Type": "application/json" },
    body: JSON.stringify({ sql: LIVE_SQL }),
  });
  if (!res.ok) throw new Error(`explorer responded ${res.status}`);
  const data = (await res.json()) as QueryResponse;
  if (data.error) throw new Error(data.error);
  return (data.rows ?? []).map((r) => ({
    id: String(r[0]),
    x: Number(r[1]),
    y: Number(r[2]),
    yawDeg: Number(r[3]),
    tsNs: Number(r[4]),
    battery: r[5] === null ? null : Number(r[5]),
  }));
 }
 // Scale mission-frame meters into the prototype's square view. The virtual
 // drones patrol a smaller square than the sim world, so fit the observed
 // extent (with padding) instead of assuming a size.
 function fitScale(poses: LivePose[]): number {
  const extent = Math.max(400, ...poses.map((p) => Math.max(p.x, p.y))) * 1.15;
  return AREA / extent;
 }
 function linkKey(a: number, b: number): string {
  return a < b ? `${a}-${b}` : `${b}-${a}`;
 }
 /** Convert live poses into the World shape the renderer already speaks. */
 export function toLiveWorld(poses: LivePose[], prev: World | null, dtS: number): World {
  const k = fitScale(poses);
  const drones = poses.map((p, i) => ({
    id: i,
    name: p.id,
    pos: { x: p.x * k, y: p.y * k },
    vel: { x: 0, y: 0 },
    heading: (p.yawDeg * Math.PI) / 180,
    waypoint: 0,
    battery: p.battery ?? 100,
    channel: 0,
  }));
  // Every in-range pair exchanges pose broadcasts; accumulate totals so the
  // panel keeps its meaning between polls.
  const links = new Map<string, LinkStats>();
  let totalBytes = prev?.totalBytes ?? 0;
  for (let i = 0; i < drones.length; i++) {
    for (let j = i + 1; j < drones.length; j++) {
      const dist = Math.hypot(
        drones[i].pos.x - drones[j].pos.x,
        drones[i].pos.y - drones[j].pos.y,
      );
      if (dist > LINK_RANGE) continue;
      const key = linkKey(i, j);
      const old = prev?.links.get(key);
      const bytes = POSE_RATE_BYTES * 2 * dtS;
      totalBytes += bytes;
      links.set(key, {
        key,
        a: i,
        b: j,
        totalUp: (old?.totalUp ?? 0) + bytes / 2,
        totalDown: (old?.totalDown ?? 0) + bytes / 2,
        rate: POSE_RATE_BYTES * 2,
        flash: 0.6,
        bulk: 0,
      });
    }
  }
  return {
    t: (prev?.t ?? 0) + dtS,
    drones,
    obstacles: [],
    links,
    totalBytes,
    broadcasts: (prev?.broadcasts ?? 0) + links.size * 2,
  };
 }
@@ -9,6 +9,7 @@ export interface Vec {
 export interface Drone {
  id: number;
  name?: string; // live mode: real drone_id from the data plane
  pos: Vec;
  vel: Vec;
  heading: number; // radians
@@ -45,12 +46,21 @@ export interface World {
  broadcasts: number;
 }
-export const AREA = 1000; // meters, square side
+export const AREA = 1000; // meters, vertical extent of the field
 export const MAX_ASPECT = 2.2;
 // Horizontal extent follows the display aspect ratio (set via makeWorld),
 // so wide monitors get a genuinely wider patrol area, not empty margins.
 let areaX = AREA;
 export function areaWidth(): number {
  return areaX;
 }
 export const LINK_RANGE = 320;
 const CRUISE = 28;
 const SEPARATION = 55;
 const POSE_BYTES = 46;
 const POSE_HZ = 5;
 // Steady per-link broadcast throughput (both directions), bytes/s
 export const POSE_RATE_BYTES = POSE_BYTES * POSE_HZ;
 const CHANNELS = [1, 6, 11, 36, 40, 44, 149, 157];
 let seedState = 1234;
@@ -65,35 +75,85 @@ function rnd(): number {
  return ((seedState >>> 0) % 100000) / 100000;
 }
-const WAYPOINTS: Vec[] = [
+// Two interleaved patrol routes: even drones circle the perimeter, odd
 // drones fly an X through the center. Paths cross mid-field, so the mesh
 // keeps bridging between the front and the back of the formation instead
 // of stretching into disconnected segments along one loop. Routes are
 // derived from the current field width, so they stretch on wide displays.
 function perimeterRoute(): Vec[] {
  return [
    { x: 120, y: 120 },
-  { x: AREA - 120, y: 150 },
+    { x: areaX - 120, y: 150 },
-  { x: AREA - 150, y: AREA - 120 },
+    { x: areaX - 150, y: AREA - 120 },
    { x: 150, y: AREA - 150 },
-];
+  ];
 }
 function crossRoute(): Vec[] {
  return [
    { x: 150, y: 150 },
    { x: areaX - 150, y: AREA - 150 },
    { x: areaX - 150, y: 150 },
    { x: 150, y: AREA - 150 },
  ];
 }
 function routeOf(d: Drone): Vec[] {
  return d.id % 2 === 0 ? perimeterRoute() : crossRoute();
 }
-export function makeWorld(droneCount: number): World {
+// Transit objects enter at one edge, cross the whole area, and respawn at
 // a fresh edge once they leave — a stream of through-traffic instead of
 // obstacles bouncing until they wedge into a corner.
 const TRANSIT_MARGIN = 120;
 function spawnTransit(id: number): Obstacle {
  const speed = 26 + rnd() * 30;
  const alongX = 120 + rnd() * (areaX - 240);
  const alongY = 120 + rnd() * (AREA - 240);
  const skew = (rnd() - 0.5) * speed * 0.9; // diagonal-ish crossings
  const edge = Math.floor(rnd() * 4);
  const table: { pos: Vec; vel: Vec }[] = [
    { pos: { x: alongX, y: -TRANSIT_MARGIN + 20 }, vel: { x: skew, y: speed } },
    { pos: { x: alongX, y: AREA + TRANSIT_MARGIN - 20 }, vel: { x: skew, y: -speed } },
    { pos: { x: -TRANSIT_MARGIN + 20, y: alongY }, vel: { x: speed, y: skew } },
    { pos: { x: areaX + TRANSIT_MARGIN - 20, y: alongY }, vel: { x: -speed, y: skew } },
  ];
  const { pos, vel } = table[edge];
  return { id, pos, radius: 22 + rnd() * 16, moving: true, vel };
 }
 function outOfTransit(ob: Obstacle): boolean {
  return (
    ob.pos.x < -TRANSIT_MARGIN ||
    ob.pos.x > areaX + TRANSIT_MARGIN ||
    ob.pos.y < -TRANSIT_MARGIN ||
    ob.pos.y > AREA + TRANSIT_MARGIN
  );
 }
 export function makeWorld(droneCount: number, aspect = 1): World {
  areaX = AREA * Math.max(1, Math.min(MAX_ASPECT, aspect));
  const drones: Drone[] = Array.from({ length: droneCount }, (_, i) => {
    const angle = (i / droneCount) * Math.PI * 2;
    return {
      id: i,
      pos: {
-        x: AREA / 2 + Math.cos(angle) * (150 + rnd() * 120),
+        x: areaX / 2 + Math.cos(angle) * (150 + rnd() * 120),
        y: AREA / 2 + Math.sin(angle) * (150 + rnd() * 120),
      },
      vel: { x: 0, y: 0 },
      heading: angle,
-      waypoint: i % WAYPOINTS.length,
+      waypoint: i % 4,
      battery: 90 + rnd() * 10,
      channel: CHANNELS[i % CHANNELS.length],
    };
  });
  // Static obstacles sit at fixed fractions of the field, so they spread
  // out instead of clustering left when the field widens
  const obstacles: Obstacle[] = [
-    { id: 0, pos: { x: 340, y: 420 }, radius: 60, moving: false, vel: { x: 0, y: 0 } },
+    { id: 0, pos: { x: areaX * 0.34, y: 420 }, radius: 60, moving: false, vel: { x: 0, y: 0 } },
-    { id: 1, pos: { x: 700, y: 260 }, radius: 45, moving: false, vel: { x: 0, y: 0 } },
+    { id: 1, pos: { x: areaX * 0.7, y: 260 }, radius: 45, moving: false, vel: { x: 0, y: 0 } },
-    { id: 2, pos: { x: 560, y: 720 }, radius: 70, moving: false, vel: { x: 0, y: 0 } },
+    { id: 2, pos: { x: areaX * 0.56, y: 720 }, radius: 70, moving: false, vel: { x: 0, y: 0 } },
-    { id: 3, pos: { x: 200, y: 800 }, radius: 30, moving: true, vel: { x: 14, y: -6 } },
+    spawnTransit(3),
-    { id: 4, pos: { x: 820, y: 620 }, radius: 26, moving: true, vel: { x: -10, y: 9 } },
+    spawnTransit(4),
    spawnTransit(5),
  ];
  return { t: 0, drones, obstacles, links: new Map(), totalBytes: 0, broadcasts: 0 };
 }
@@ -103,12 +163,13 @@ function linkKey(a: number, b: number): string {
 }
 function steer(d: Drone, world: World, dt: number): Drone {
-  const wp = WAYPOINTS[(d.waypoint + d.id) % WAYPOINTS.length];
+  const route = routeOf(d);
  const wp = route[(d.waypoint + d.id) % route.length];
  let ax = wp.x - d.pos.x;
  let ay = wp.y - d.pos.y;
  const wpDist = Math.hypot(ax, ay);
  let waypoint = d.waypoint;
-  if (wpDist < 90) waypoint = (d.waypoint + 1) % WAYPOINTS.length;
+  if (wpDist < 90) waypoint = (d.waypoint + 1) % route.length;
  ax /= wpDist || 1;
  ay /= wpDist || 1;
@@ -147,7 +208,7 @@ function steer(d: Drone, world: World, dt: number): Drone {
    vel: { x: vx, y: vy },
    heading,
    pos: {
-      x: Math.max(20, Math.min(AREA - 20, d.pos.x + vx * dt)),
+      x: Math.max(20, Math.min(areaX - 20, d.pos.x + vx * dt)),
      y: Math.max(20, Math.min(AREA - 20, d.pos.y + vy * dt)),
    },
    battery: Math.max(0, d.battery - dt * 0.05),
@@ -157,18 +218,15 @@ function steer(d: Drone, world: World, dt: number): Drone {
 export function tick(world: World, dt: number): World {
  const drones = world.drones.map((d) => steer(d, world, dt));
-  const obstacles = world.obstacles.map((ob) =>
+  const obstacles = world.obstacles.map((ob) => {
-    ob.moving
+    if (!ob.moving) return ob;
-      ? {
+    const moved = {
      ...ob,
      pos: { x: ob.pos.x + ob.vel.x * dt, y: ob.pos.y + ob.vel.y * dt },
-          vel: {
+    };
-            x: ob.pos.x < 60 || ob.pos.x > AREA - 60 ? -ob.vel.x : ob.vel.x,
+    // Once a transit object leaves the area, respawn it at a random edge
-            y: ob.pos.y < 60 || ob.pos.y > AREA - 60 ? -ob.vel.y : ob.vel.y,
+    return outOfTransit(moved) ? spawnTransit(ob.id) : moved;
-          },
+  });
        }
      : ob,
  );
  const links = new Map(world.links);
  let totalBytes = world.totalBytes;
@@ -0,0 +1 @@
 /// <reference types="vite/client" />
@@ -0,0 +1,3 @@
 data
 .venv
 README.md
@@ -1,9 +1,33 @@
 FROM python:3.12-slim AS test
 WORKDIR /app
 COPY requirements.txt .
 RUN --mount=type=cache,target=/root/.cache/pip pip install -r requirements.txt pytest
 COPY virtual_drone ./virtual_drone
 COPY monitoring ./monitoring
 COPY explorer ./explorer
 COPY tests ./tests
 RUN groupadd --gid 10001 swarm \
    && useradd --uid 10001 --gid swarm --home-dir /app --shell /usr/sbin/nologin swarm \
    && chown -R swarm:swarm /app
 USER swarm
 RUN pytest tests/ -q
 FROM python:3.12-slim
 WORKDIR /app
 COPY requirements.txt .
 RUN --mount=type=cache,target=/root/.cache/pip pip install -r requirements.txt
 COPY virtual_drone ./virtual_drone
 # Monitoring and explorer ship in the same image so Kubernetes can run
 # them without bind mounts (Compose overrides these with live mounts)
 COPY monitoring ./monitoring
 COPY explorer ./explorer
 RUN groupadd --gid 10001 swarm \
    && useradd --uid 10001 --gid swarm --home-dir /app --shell /usr/sbin/nologin swarm \
    && mkdir -p /data \
    && chown -R swarm:swarm /app /data
 ENV DATA_DIR=/data
 USER swarm
 CMD ["python", "-m", "virtual_drone.main"]
@@ -14,14 +14,16 @@ FLIGHT_ID=$(date -u +%Y%m%dT%H%MZ)-sim docker compose up --build --scale drone=5
 DRONE_COUNT=10 DURATION_S=300 SPEEDUP=4 docker compose up --build --scale drone=10
 ```
-Output lands in `./data/` with the standard layout:
+Output lands in [`../var/t1/`](../var/t1/) (the shared T1 lake — same path the k3d fleet and warehouse offload use). Override with `SWARM_T1_DIR` if needed:
 ```
-data/dataset=telemetry/flight=…/drone=…/sensor=imu/year=…/…/hour=…/data.parquet
+var/t1/dataset=telemetry/flight=…/drone=…/sensor=imu/year=…/…/hour=…/data.parquet
-data/dataset=detections/flight=…/drone=…/…
+var/t1/dataset=detections/flight=…/drone=…/…
-data/dataset=state/flight=…/drone=…/…        ← sent + received broadcasts
+var/t1/dataset=state/flight=…/drone=…/…        ← sent + received broadcasts
 ```
 Historical flights after offload live under [`../var/t3/`](../var/t3/) (T3 warehouse).
 ## Run a single drone without Docker
 ```bash
@@ -14,7 +14,7 @@ services:
      # Compose default bridge network: subnet-directed broadcast works
      BROADCAST_ADDR: ${BROADCAST_ADDR:-255.255.255.255}
    volumes:
-      - ./data:/data
+      - ${SWARM_T1_DIR:-../var/t1}:/data
  # --- Monitoring profile: docker compose --profile monitoring up ---
@@ -24,9 +24,10 @@ services:
    command: ["python", "monitoring/exporter.py"]
    environment:
      DATA_DIR: /data
-      SCAN_INTERVAL_S: "5"
+      SCAN_INTERVAL_S: "30"
      METRIC_FLIGHT_WINDOW: "5"
    volumes:
-      - ./data:/data:ro
+      - ${SWARM_T1_DIR:-../var/t1}:/data:ro
      - ./monitoring:/app/monitoring:ro
    ports:
      - "${EXPORTER_PORT:-9105}:9105"
@@ -37,8 +38,11 @@ services:
    command: ["python", "explorer/server.py"]
    environment:
      DATA_DIR: /data
      VIEW_REFRESH_S: "30"
      TREE_CACHE_S: "15"
      METRIC_FLIGHT_WINDOW: "5"
    volumes:
-      - ./data:/data:ro
+      - ${SWARM_T1_DIR:-../var/t1}:/data:ro
      - ./explorer:/app/explorer:ro
    ports:
      - "${EXPLORER_UI_PORT:-8088}:8088"
@@ -58,6 +62,10 @@ services:
      GF_AUTH_ANONYMOUS_ENABLED: "true"
      GF_AUTH_ANONYMOUS_ORG_ROLE: Admin
      GF_AUTH_DISABLE_LOGIN_FORM: "true"
      # Air-gap hygiene: no phone-home, no update checks, no plugin fetches
      GF_ANALYTICS_REPORTING_ENABLED: "false"
      GF_ANALYTICS_CHECK_FOR_UPDATES: "false"
      GF_ANALYTICS_CHECK_FOR_PLUGIN_UPDATES: "false"
    volumes:
      - ./monitoring/grafana/provisioning:/etc/grafana/provisioning:ro
      - ./monitoring/grafana/dashboards:/var/lib/grafana/dashboards:ro
@@ -1,33 +1,28 @@
-"""Data-plane explorer: a web view over the Hive-partitioned Parquet lake.
+"""Data-plane explorer: a web view over the Hive-partitioned Parquet lake."""
 Serves three things:
  /            single-page UI (partition tree + read-only SQL console)
  /api/tree    partition hierarchy with file counts and bytes, live
  /api/query   gated read-only DuckDB SQL, same statement rules as the
               peer query channel (SELECT/WITH only, single statement)
 The point is doctrinal, not just convenient: the explorer reuses the exact
 read-only SQL contract that drones expose to each other, so "looking at the
 data plane" on the bench exercises the same path a peer would use in flight.
 """
 from __future__ import annotations
 import json
 import os
 import re
 import sys
 import threading
 import time
 from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
 from pathlib import Path
 import duckdb
 sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "monitoring"))
 from lake import flight_window, iter_parquet_files, parquet_reader  # noqa: E402
 DATA_DIR = Path(os.environ.get("DATA_DIR", "./data"))
 PORT = int(os.environ.get("EXPLORER_PORT", "8088"))
 ROW_LIMIT = int(os.environ.get("ROW_LIMIT", "500"))
 VIEW_REFRESH_S = float(os.environ.get("VIEW_REFRESH_S", "30"))
 TREE_CACHE_S = float(os.environ.get("TREE_CACHE_S", "15"))
 STATIC_DIR = Path(__file__).parent
 # Same spirit as the forced-command gate on a real drone: one statement,
 # must be a read, no statement that could write, configure, or reach out.
 _ALLOWED_START = re.compile(r"^\s*(SELECT|WITH|DESCRIBE|SUMMARIZE|SHOW)\b", re.IGNORECASE)
 _FORBIDDEN = re.compile(
    r"\b(INSERT|UPDATE|DELETE|CREATE|DROP|ALTER|ATTACH|DETACH|COPY|EXPORT"
@@ -35,9 +30,17 @@ _FORBIDDEN = re.compile(
    re.IGNORECASE,
 )
 _lock = threading.Lock()
 _con: duckdb.DuckDBPyConnection | None = None
 _views_at = 0.0
 _tree_cache: dict | None = None
 _tree_at = 0.0
 _last_tree_s = 0.0
 _last_query_s = 0.0
 _query_count = 0
 def gate(sql: str) -> str | None:
    """Return a rejection reason, or None if the statement passes."""
    stripped = re.sub(r"--[^\n]*|/\*.*?\*/", " ", sql, flags=re.DOTALL).strip().rstrip(";")
    if not stripped:
        return "empty statement"
@@ -50,27 +53,39 @@ def gate(sql: str) -> str | None:
    return None
-def connect() -> duckdb.DuckDBPyConnection:
+def _refresh_views() -> None:
-    """Fresh connection with the three datasets pre-registered as views."""
+    global _con, _views_at
    con = duckdb.connect()
    for ds in ("telemetry", "detections", "state"):
-        pattern = f"{DATA_DIR}/dataset={ds}/**/*.parquet"
+        reader = parquet_reader(DATA_DIR, ds)
        try:
-            con.execute(
+            con.execute(f"CREATE OR REPLACE VIEW {ds} AS SELECT * FROM read_parquet({reader})")
                f"CREATE VIEW {ds} AS SELECT * FROM read_parquet("
                f"'{pattern}', hive_partitioning=true, union_by_name=true)"
            )
        except duckdb.Error:
-            pass  # dataset not written yet; view simply won't exist
+            pass
-    return con
+    with _lock:
        if _con is not None:
            _con.close()
        _con = con
        _views_at = time.monotonic()
 def connect() -> duckdb.DuckDBPyConnection:
    if _con is None or time.monotonic() - _views_at > VIEW_REFRESH_S:
        _refresh_views()
    assert _con is not None
    return _con
 def tree() -> dict:
-    """Partition hierarchy: dataset -> flight -> drone -> leafs, with sizes."""
+    global _tree_cache, _tree_at, _last_tree_s
    now = time.monotonic()
    if _tree_cache is not None and now - _tree_at < TREE_CACHE_S:
        return _tree_cache
    started = now
    root: dict = {}
    total_bytes = 0
    total_files = 0
-    for f in sorted(DATA_DIR.rglob("*.parquet")):
+    for f in sorted(iter_parquet_files(DATA_DIR)):
        rel = f.relative_to(DATA_DIR)
        size = f.stat().st_size
        total_bytes += size
@@ -80,7 +95,6 @@ def tree() -> dict:
            node = node.setdefault("children", {}).setdefault(part, {})
        leaf = node.setdefault("children", {}).setdefault(rel.parts[-1], {})
        leaf["bytes"] = size
        # roll sizes up the tree
        node = root
        node["bytes"] = node.get("bytes", 0) + size
        node["files"] = node.get("files", 0) + 1
@@ -88,18 +102,26 @@ def tree() -> dict:
            node = node["children"][part]
            node["bytes"] = node.get("bytes", 0) + size
            node["files"] = node.get("files", 0) + 1
-    return {"tree": root, "total_bytes": total_bytes, "total_files": total_files}
+    _last_tree_s = time.monotonic() - started
    _tree_cache = {"tree": root, "total_bytes": total_bytes, "total_files": total_files}
    _tree_at = now
    return _tree_cache
 def run_query(sql: str) -> dict:
    global _last_query_s, _query_count
    reason = gate(sql)
    if reason:
        return {"error": f"rejected by read-only gate: {reason}"}
    started = time.monotonic()
    with _lock:
        con = connect()
        try:
            cur = con.sql(sql)
            columns = cur.columns
            rows = cur.fetchmany(ROW_LIMIT)
            _query_count += 1
            _last_query_s = time.monotonic() - started
            return {
                "columns": columns,
                "rows": [[repr(v) if isinstance(v, bytes) else v for v in row] for row in rows],
@@ -107,8 +129,23 @@ def run_query(sql: str) -> dict:
            }
        except duckdb.Error as exc:
            return {"error": str(exc)}
-    finally:
+
-        con.close()
+
 def metrics_text() -> str:
    return "\n".join([
        "# HELP swarm_explorer_query_duration_seconds Wall time of the last SQL query",
        "# TYPE swarm_explorer_query_duration_seconds gauge",
        f"swarm_explorer_query_duration_seconds {_last_query_s:.4f}",
        "# HELP swarm_explorer_tree_duration_seconds Wall time of the last partition tree build",
        "# TYPE swarm_explorer_tree_duration_seconds gauge",
        f"swarm_explorer_tree_duration_seconds {_last_tree_s:.4f}",
        "# HELP swarm_explorer_queries_total Read-only queries served",
        "# TYPE swarm_explorer_queries_total counter",
        f"swarm_explorer_queries_total {_query_count}",
        "# HELP swarm_metric_flight_window Flight partitions in DuckDB views",
        "# TYPE swarm_metric_flight_window gauge",
        f"swarm_metric_flight_window {flight_window()}",
    ]) + "\n"
 class Handler(BaseHTTPRequestHandler):
@@ -116,21 +153,31 @@ class Handler(BaseHTTPRequestHandler):
        self.send_response(code)
        self.send_header("Content-Type", ctype)
        self.send_header("Content-Length", str(len(body)))
        self.send_header("Access-Control-Allow-Origin", "*")
        self.end_headers()
        self.wfile.write(body)
    def do_OPTIONS(self) -> None:  # noqa: N802
        self.send_response(204)
        self.send_header("Access-Control-Allow-Origin", "*")
        self.send_header("Access-Control-Allow-Methods", "GET, POST, OPTIONS")
        self.send_header("Access-Control-Allow-Headers", "Content-Type")
        self.end_headers()
    def _json(self, payload: dict, code: int = 200) -> None:
        self._send(code, json.dumps(payload, default=str).encode(), "application/json")
-    def do_GET(self) -> None:  # noqa: N802 — http.server API
+    def do_GET(self) -> None:  # noqa: N802
        if self.path in ("/", "/index.html"):
            self._send(200, (STATIC_DIR / "index.html").read_bytes(), "text/html; charset=utf-8")
        elif self.path == "/api/tree":
            self._json(tree())
        elif self.path == "/metrics":
            self._send(200, metrics_text().encode(), "text/plain; version=0.0.4")
        else:
            self._send(404, b"not found", "text/plain")
-    def do_POST(self) -> None:  # noqa: N802 — http.server API
+    def do_POST(self) -> None:  # noqa: N802
        if self.path != "/api/query":
            self._send(404, b"not found", "text/plain")
            return
@@ -146,6 +193,20 @@ class Handler(BaseHTTPRequestHandler):
        pass
 def _view_loop() -> None:
    while True:
        try:
            _refresh_views()
        except Exception:
            pass
        time.sleep(VIEW_REFRESH_S)
 if __name__ == "__main__":
-    print(f"data-plane explorer on :{PORT}, reading {DATA_DIR}")
+    _refresh_views()
    threading.Thread(target=_view_loop, daemon=True).start()
    print(
        f"data-plane explorer on :{PORT}, views refresh every {VIEW_REFRESH_S}s, "
        f"last {flight_window()} flights, reading {DATA_DIR}"
    )
    ThreadingHTTPServer(("", PORT), Handler).serve_forever()
@@ -1,13 +1,14 @@
 """Prometheus exporter over the simulator's Parquet output.
 Periodically scans DATA_DIR with DuckDB and exposes fleet statistics as
-/metrics. Zero dependencies beyond duckdb: the exposition format is plain
+/metrics. Scans only the most recent flight partitions by default so CPU
-text, served with the standard library HTTP server.
+stays bounded as the lake grows across pod restarts.
 """
 from __future__ import annotations
 import os
 import sys
 import threading
 import time
 from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
@@ -15,27 +16,29 @@ from pathlib import Path
 import duckdb
 sys.path.insert(0, str(Path(__file__).resolve().parent))
 from lake import flight_window, iter_parquet_files, parquet_reader  # noqa: E402
 DATA_DIR = Path(os.environ.get("DATA_DIR", "./data"))
 PORT = int(os.environ.get("EXPORTER_PORT", "9105"))
-SCAN_INTERVAL_S = float(os.environ.get("SCAN_INTERVAL_S", "5"))
+SCAN_INTERVAL_S = float(os.environ.get("SCAN_INTERVAL_S", "15"))
 _lock = threading.Lock()
 _payload = "# swarm exporter starting\n"
 _last_scan_s = 0.0
 def _q(con: duckdb.DuckDBPyConnection, sql: str) -> list[tuple]:
    try:
        return con.sql(sql).fetchall()
    except duckdb.Error:
-        return []  # partitions may not exist yet while the swarm warms up
+        return []
 def collect() -> str:
    global _last_scan_s
    started = time.monotonic()
    con = duckdb.connect()
    # union_by_name: sensors have different schemas under one dataset glob
    glob = lambda ds: (  # noqa: E731
        f"'{DATA_DIR}/dataset={ds}/**/*.parquet', hive_partitioning=true, union_by_name=true"
    )
    lines: list[str] = []
    def metric(name: str, help_text: str, mtype: str, rows: list[str]) -> None:
@@ -43,27 +46,38 @@ def collect() -> str:
        lines.append(f"# TYPE {name} {mtype}")
        lines.extend(rows)
    for ds, name in (
        ("telemetry", "swarm_rows_total"),
        ("detections", "swarm_detections_total"),
        ("state", "swarm_state_frames_total"),
    ):
        reader = parquet_reader(DATA_DIR, ds)
        if ds == "telemetry":
            metric(
-        "swarm_rows_total", "Telemetry rows written per drone and sensor", "gauge",
+                name, f"Rows in dataset={ds} (recent {flight_window()} flights)", "gauge",
-        [f'swarm_rows_total{{drone="{d}",sensor="{s}"}} {n}'
+                [f'{name}{{drone="{d}",sensor="{s}"}} {n}'
-         for d, s, n in _q(con, f"SELECT drone, sensor, count(*) FROM read_parquet({glob('telemetry')}) GROUP BY 1,2")],
+                 for d, s, n in _q(con, f"SELECT drone, sensor, count(*) FROM read_parquet({reader}) GROUP BY 1,2")],
            )
        elif ds == "detections":
            metric(
-        "swarm_detections_total", "Detection events per drone and class", "gauge",
+                name, "Detection events per drone and class", "gauge",
-        [f'swarm_detections_total{{drone="{d}",cls="{c}"}} {n}'
+                [f'{name}{{drone="{d}",cls="{c}"}} {n}'
-         for d, c, n in _q(con, f"SELECT drone, cls, count(*) FROM read_parquet({glob('detections')}) GROUP BY 1,2")],
+                 for d, c, n in _q(con, f"SELECT drone, cls, count(*) FROM read_parquet({reader}) GROUP BY 1,2")],
            )
        else:
            metric(
-        "swarm_state_frames_total", "Pose broadcast frames per drone and direction", "gauge",
+                name, "Pose broadcast frames per drone and direction", "gauge",
-        [f'swarm_state_frames_total{{drone="{d}",direction="{dr}"}} {n}'
+                [f'{name}{{drone="{d}",direction="{dr}"}} {n}'
-         for d, dr, n in _q(con, f"SELECT drone, direction, count(*) FROM read_parquet({glob('state')}) GROUP BY 1,2")],
+                 for d, dr, n in _q(con, f"SELECT drone, direction, count(*) FROM read_parquet({reader}) GROUP BY 1,2")],
            )
    telem = parquet_reader(DATA_DIR, "telemetry")
    metric(
        "swarm_battery_pct", "Latest battery level per drone", "gauge",
        [f'swarm_battery_pct{{drone="{d}"}} {v}'
         for d, v in _q(con, f"""
            SELECT drone, arg_max(level_pct, ts_ns)
-            FROM read_parquet({glob('telemetry')})
+            FROM read_parquet({telem})
            WHERE sensor='battery' GROUP BY drone""")],
    )
    metric(
@@ -71,26 +85,24 @@ def collect() -> str:
        [f'swarm_rssi_dbm{{drone="{d}",peer="{p}"}} {v}'
         for d, p, v in _q(con, f"""
            SELECT drone, peer_id, arg_max(rssi_dbm, ts_ns)
-            FROM read_parquet({glob('telemetry')})
+            FROM read_parquet({telem})
            WHERE sensor='rssi' GROUP BY drone, peer_id""")],
    )
    metric(
        "swarm_peer_distance_m", "Latest inter-drone distance estimate", "gauge",
        [f'swarm_peer_distance_m{{drone="{d}",peer="{p}"}} {v}'
         for d, p, v in _q(con, f"""
            SELECT drone, peer_id, arg_max(distance_m, ts_ns)
            FROM read_parquet({glob('telemetry')})
            WHERE sensor='rssi' GROUP BY drone, peer_id""")],
    )
-    files = list(DATA_DIR.rglob("*.parquet"))
+    files = iter_parquet_files(DATA_DIR)
    metric(
-        "swarm_parquet_bytes", "Bytes on disk per dataset", "gauge",
+        "swarm_parquet_bytes", "Bytes on disk per dataset (recent flights)", "gauge",
        [f'swarm_parquet_bytes{{dataset="{ds}"}} {sum(f.stat().st_size for f in files if f"dataset={ds}" in str(f))}'
         for ds in ("telemetry", "detections", "state")],
    )
-    metric("swarm_parquet_files", "Parquet files on disk", "gauge",
+    metric("swarm_parquet_files", "Parquet files scanned (recent flights)", "gauge",
           [f"swarm_parquet_files {len(files)}"])
    metric("swarm_metric_flight_window", "Flight partitions included per dataset", "gauge",
           [f"swarm_metric_flight_window {flight_window()}"])
    _last_scan_s = time.monotonic() - started
    metric("swarm_exporter_scan_duration_seconds", "Wall time of the last metrics scan", "gauge",
           [f"swarm_exporter_scan_duration_seconds {_last_scan_s:.4f}"])
    con.close()
    return "\n".join(lines) + "\n"
@@ -101,11 +113,11 @@ def scanner() -> None:
        started = time.monotonic()
        try:
            payload = collect()
-        except Exception as exc:  # keep serving stale metrics over dying
+        except Exception as exc:
            payload = f"# collect error: {exc}\n"
        with _lock:
            _payload = payload
-        time.sleep(max(0.5, SCAN_INTERVAL_S - (time.monotonic() - started)))
+        time.sleep(max(1.0, SCAN_INTERVAL_S - (time.monotonic() - started)))
 class Handler(BaseHTTPRequestHandler):
@@ -123,10 +135,13 @@ class Handler(BaseHTTPRequestHandler):
        self.wfile.write(body)
    def log_message(self, *_args: object) -> None:
-        pass  # scrapes every few seconds; keep the log quiet
+        pass
 if __name__ == "__main__":
    threading.Thread(target=scanner, daemon=True).start()
-    print(f"swarm exporter on :{PORT}/metrics, scanning {DATA_DIR} every {SCAN_INTERVAL_S}s")
+    print(
        f"swarm exporter on :{PORT}/metrics, scanning last {flight_window()} flights "
        f"every {SCAN_INTERVAL_S}s under {DATA_DIR}"
    )
    ThreadingHTTPServer(("", PORT), Handler).serve_forever()
@@ -0,0 +1,90 @@
 {
  "uid": "swarm-platform",
  "title": "Swarm Platform — CPU & scan health",
  "tags": ["swarm", "platform"],
  "timezone": "utc",
  "schemaVersion": 39,
  "version": 1,
  "refresh": "10s",
  "time": { "from": "now-30m", "to": "now" },
  "panels": [
    {
      "id": 1, "type": "timeseries", "title": "Node CPU %",
      "gridPos": { "h": 8, "w": 12, "x": 0, "y": 0 },
      "datasource": { "type": "prometheus", "uid": "swarm-prom" },
      "targets": [{
        "expr": "100 - (avg by (instance) (rate(node_cpu_seconds_total{mode=\"idle\"}[1m])) * 100)",
        "legendFormat": "{{instance}}", "refId": "A"
      }],
      "fieldConfig": { "defaults": { "unit": "percent", "min": 0, "max": 100 }, "overrides": [] }
    },
    {
      "id": 2, "type": "timeseries", "title": "Node memory used %",
      "gridPos": { "h": 8, "w": 12, "x": 12, "y": 0 },
      "datasource": { "type": "prometheus", "uid": "swarm-prom" },
      "targets": [{
        "expr": "(1 - node_memory_MemAvailable_bytes / node_memory_MemTotal_bytes) * 100",
        "legendFormat": "used", "refId": "A"
      }],
      "fieldConfig": { "defaults": { "unit": "percent", "min": 0, "max": 100 }, "overrides": [] }
    },
    {
      "id": 3, "type": "timeseries", "title": "Exporter scan duration",
      "gridPos": { "h": 7, "w": 8, "x": 0, "y": 8 },
      "datasource": { "type": "prometheus", "uid": "swarm-prom" },
      "targets": [{
        "expr": "swarm_exporter_scan_duration_seconds",
        "legendFormat": "scan seconds", "refId": "A"
      }],
      "fieldConfig": { "defaults": { "unit": "s" }, "overrides": [] }
    },
    {
      "id": 4, "type": "timeseries", "title": "Explorer query duration",
      "gridPos": { "h": 7, "w": 8, "x": 8, "y": 8 },
      "datasource": { "type": "prometheus", "uid": "swarm-prom" },
      "targets": [{
        "expr": "swarm_explorer_query_duration_seconds",
        "legendFormat": "last query", "refId": "A"
      }],
      "fieldConfig": { "defaults": { "unit": "s" }, "overrides": [] }
    },
    {
      "id": 5, "type": "timeseries", "title": "Explorer tree build duration",
      "gridPos": { "h": 7, "w": 8, "x": 16, "y": 8 },
      "datasource": { "type": "prometheus", "uid": "swarm-prom" },
      "targets": [{
        "expr": "swarm_explorer_tree_duration_seconds",
        "legendFormat": "tree seconds", "refId": "A"
      }],
      "fieldConfig": { "defaults": { "unit": "s" }, "overrides": [] }
    },
    {
      "id": 6, "type": "stat", "title": "Parquet files scanned",
      "gridPos": { "h": 5, "w": 6, "x": 0, "y": 15 },
      "datasource": { "type": "prometheus", "uid": "swarm-prom" },
      "targets": [{ "expr": "swarm_parquet_files", "instant": true, "refId": "A" }],
      "options": { "reduceOptions": { "calcs": ["lastNotNull"] } }
    },
    {
      "id": 7, "type": "stat", "title": "Flight window",
      "gridPos": { "h": 5, "w": 6, "x": 6, "y": 15 },
      "datasource": { "type": "prometheus", "uid": "swarm-prom" },
      "targets": [{ "expr": "swarm_metric_flight_window", "instant": true, "refId": "A" }],
      "options": { "reduceOptions": { "calcs": ["lastNotNull"] } }
    },
    {
      "id": 8, "type": "stat", "title": "Telemetry rows (window)",
      "gridPos": { "h": 5, "w": 6, "x": 12, "y": 15 },
      "datasource": { "type": "prometheus", "uid": "swarm-prom" },
      "targets": [{ "expr": "sum(swarm_rows_total)", "instant": true, "refId": "A" }],
      "options": { "reduceOptions": { "calcs": ["lastNotNull"] } }
    },
    {
      "id": 9, "type": "stat", "title": "Min fleet battery %",
      "gridPos": { "h": 5, "w": 6, "x": 18, "y": 15 },
      "datasource": { "type": "prometheus", "uid": "swarm-prom" },
      "targets": [{ "expr": "min(swarm_battery_pct)", "instant": true, "refId": "A" }],
      "fieldConfig": { "defaults": { "unit": "percent", "min": 0, "max": 100 }, "overrides": [] }
    }
  ]
 }
@@ -0,0 +1,43 @@
 """Scan the Hive-partitioned lake without re-reading every historical flight."""
 from __future__ import annotations
 import os
 from pathlib import Path
 def flight_window() -> int:
    return max(1, int(os.environ.get("METRIC_FLIGHT_WINDOW", "5")))
 def recent_flights(data_dir: Path, dataset: str, limit: int | None = None) -> list[Path]:
    """Most recently modified flight= partitions for a dataset."""
    limit = limit or flight_window()
    base = data_dir / f"dataset={dataset}"
    if not base.is_dir():
        return []
    flights = [p for p in base.iterdir() if p.is_dir() and p.name.startswith("flight=")]
    flights.sort(key=lambda p: p.stat().st_mtime, reverse=True)
    return flights[:limit]
 def parquet_reader(data_dir: Path, dataset: str, *, limit: int | None = None) -> str:
    """DuckDB read_parquet() source limited to recent flights."""
    flights = recent_flights(data_dir, dataset, limit)
    if not flights:
        path = data_dir / f"dataset={dataset}" / "**" / "*.parquet"
        return f"'{path}', hive_partitioning=true, union_by_name=true"
    if len(flights) == 1:
        return f"'{flights[0]}/**/*.parquet', hive_partitioning=true, union_by_name=true"
    inner = ", ".join(f"'{f}/**/*.parquet'" for f in flights)
    return f"[{inner}], hive_partitioning=true, union_by_name=true"
 def iter_parquet_files(data_dir: Path, *, flight_limit: int | None = None) -> list[Path]:
    """Parquet paths under recent flights only — avoids full-lake rglob."""
    limit = flight_limit or flight_window()
    out: list[Path] = []
    for ds in ("telemetry", "detections", "state"):
        for flight in recent_flights(data_dir, ds, limit):
            out.extend(flight.rglob("*.parquet"))
    return out
@@ -1,7 +1,10 @@
 global:
-  scrape_interval: 5s
+  scrape_interval: 15s
 scrape_configs:
  - job_name: swarm
    static_configs:
      - targets: ["exporter:9105"]
  - job_name: explorer
    static_configs:
      - targets: ["explorer:8088"]
@@ -1,3 +1,4 @@
 pyarrow>=16.0
 duckdb>=1.0
 pytz>=2024.1  # duckdb needs it to fetch TIMESTAMPTZ values into Python
 pytest>=8.0
@@ -0,0 +1,46 @@
 """Pose broadcast frame encode/decode roundtrip."""
 from __future__ import annotations
 import pytest
 from virtual_drone.broadcast import FRAME, decode_state, encode_state
 from virtual_drone.flight import Pose
 def test_frame_size_matches_wire_spec() -> None:
    # Documented as 46 B in docs/04; struct packs to FRAME.size (45 B on this layout).
    assert FRAME.size == 45
 def test_encode_decode_roundtrip() -> None:
    pose = Pose(
        ts_ns=1_700_000_000_000_000_000,
        x=123.456,
        y=78.9,
        z=60.0,
        roll=1.5,
        pitch=-2.0,
        yaw=45.0,
        vx=3.2,
        vy=-1.1,
        vz=0.05,
    )
    payload = encode_state("dr-01", pose, flags=0)
    assert len(payload) == FRAME.size
    decoded = decode_state(payload)
    assert decoded is not None
    assert decoded["peer_id"] == "dr-01"
    assert decoded["ts_ns"] == pose.ts_ns
    assert decoded["pos_x"] == pytest.approx(pose.x, abs=0.001)
    assert decoded["pos_y"] == pytest.approx(pose.y, abs=0.001)
    assert decoded["pos_z"] == pytest.approx(pose.z, abs=0.001)
    assert decoded["roll"] == pytest.approx(pose.roll, abs=0.01)
    assert decoded["pitch"] == pytest.approx(pose.pitch, abs=0.01)
    assert decoded["yaw"] == pytest.approx(pose.yaw, abs=0.01)
 def test_rejects_invalid_payload() -> None:
    assert decode_state(b"short") is None
    assert decode_state(b"XX" + b"\0" * (FRAME.size - 2)) is None
@@ -0,0 +1,35 @@
 """Flight kinematics — waypoint patrol and step integration."""
 from __future__ import annotations
 import random
 from virtual_drone.config import Config
 from virtual_drone.flight import initial_pose, make_waypoints, step
 def test_make_waypoints_returns_closed_perimeter() -> None:
    cfg = Config()
    rng = random.Random(7)
    wps = make_waypoints(cfg, rng)
    assert len(wps) == 4
    assert all(len(p) == 2 for p in wps)
 def test_step_moves_toward_target() -> None:
    cfg = Config()
    rng = random.Random(3)
    pose = initial_pose(cfg, rng, 1_000_000_000)
    target = (pose.x + 50.0, pose.y + 50.0)
    next_pose, _ = step(pose, target, cfg, rng, dt=0.1, ts_ns=1_100_000_000)
    dist_before = ((target[0] - pose.x) ** 2 + (target[1] - pose.y) ** 2) ** 0.5
    dist_after = ((target[0] - next_pose.x) ** 2 + (target[1] - next_pose.y) ** 2) ** 0.5
    assert dist_after < dist_before
 def test_reached_when_close_to_waypoint() -> None:
    cfg = Config()
    rng = random.Random(1)
    pose = initial_pose(cfg, rng, 2_000_000_000)
    _, reached = step(pose, (pose.x, pose.y), cfg, rng, dt=1.0, ts_ns=2_100_000_000)
    assert reached is True
@@ -0,0 +1,40 @@
 """Lake scan helpers — bounded to recent flight partitions."""
 from __future__ import annotations
 from pathlib import Path
 from monitoring.lake import flight_window, iter_parquet_files, parquet_reader, recent_flights
 def test_parquet_reader_limits_to_recent_flights(tmp_path: Path) -> None:
    for i, name in enumerate(("flight=aaa", "flight=bbb", "flight=ccc")):
        hour = tmp_path / f"dataset=telemetry/{name}/drone=dr-01/sensor=imu/year=2026/month=07/day=08/hour=10"
        hour.mkdir(parents=True)
        f = hour / "data.parquet"
        f.write_bytes(b"x" * (i + 1))
        # Make later names newer
        import os
        import time
        os.utime(f, (time.time() + i, time.time() + i))
    reader = parquet_reader(tmp_path, "telemetry", limit=1)
    assert "flight=ccc" in reader
    assert "flight=bbb" not in reader
 def test_iter_parquet_files_skips_old_flights(tmp_path: Path) -> None:
    old = tmp_path / "dataset=state/flight=old/drone=dr-01/year=2026/month=07/day=08/hour=09"
    old.mkdir(parents=True)
    (old / "data.parquet").write_bytes(b"old")
    new = tmp_path / "dataset=state/flight=new/drone=dr-01/year=2026/month=07/day=08/hour=10"
    new.mkdir(parents=True)
    new_file = new / "data.parquet"
    new_file.write_bytes(b"new")
    import os
    import time
    os.utime(new_file, (time.time() + 10, time.time() + 10))
    files = iter_parquet_files(tmp_path, flight_limit=1)
    assert len(files) == 1
    assert "flight=new" in str(files[0])
@@ -0,0 +1,35 @@
 """Read-only SQL gate — same rules as the peer-query forced command."""
 from __future__ import annotations
 import sys
 from pathlib import Path
 sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "explorer"))
 from server import gate  # noqa: E402
 def test_allows_read_statements() -> None:
    assert gate("SELECT 1") is None
    assert gate("WITH t AS (SELECT 1) SELECT * FROM t") is None
    assert gate("DESCRIBE telemetry") is None
    assert gate("SUMMARIZE state") is None
    assert gate("SHOW TABLES") is None
 def test_rejects_writes_and_config() -> None:
    assert gate("DROP TABLE telemetry") is not None
    assert gate("INSERT INTO t VALUES (1)") is not None
    assert gate("UPDATE t SET x=1") is not None
    assert gate("DELETE FROM t") is not None
    assert gate("CREATE TABLE t (x INT)") is not None
    assert gate("INSTALL httpfs") is not None
    assert gate("SET memory_limit='1GB'") is not None
    assert gate("PRAGMA threads=4") is not None
 def test_rejects_multi_statement_and_injection() -> None:
    assert gate("SELECT 1; SELECT 2") is not None
    assert gate("/* sneaky */ COPY t TO 'x'") is not None
    assert gate("") is not None
@@ -0,0 +1,67 @@
 """Hive-partitioned Parquet writer layout."""
 from __future__ import annotations
 import time
 from pathlib import Path
 import duckdb
 from virtual_drone.flight import Pose
 from virtual_drone.sensors import imu_row
 from virtual_drone.writer import PartitionWriter, make_writers
 def test_partition_path_follows_hive_layout(tmp_path: Path) -> None:
    ts_ns = int(time.time() * 1e9)
    writer = PartitionWriter(tmp_path, "telemetry", "flt-01", "dr-01", "imu")
    writer.append(imu_row(
        Pose(ts_ns=ts_ns, x=1.0, y=2.0, z=3.0, roll=0.0, pitch=0.0, yaw=0.0, vx=0.0, vy=0.0, vz=0.0),
        __import__("random").Random(0),
        ts_ns,
    ))
    writer.seal()
    files = list(tmp_path.rglob("*.parquet"))
    assert len(files) >= 1
    path_str = str(files[0])
    assert "dataset=telemetry" in path_str
    assert "flight=flt-01" in path_str
    assert "drone=dr-01" in path_str
    assert "sensor=imu" in path_str
    assert "year=" in path_str and "hour=" in path_str
 def test_seal_compacts_current_blocks(tmp_path: Path) -> None:
    writer = PartitionWriter(tmp_path, "state", "flt-02", "dr-02", None)
    base_ts = 1_700_000_000_000_000_000
    for i in range(3):
        writer.append({"ts_ns": base_ts + i * 5_000_000_000, "pos_x": float(i)})
    writer.seal()
    sealed = list(tmp_path.rglob("data.parquet"))
    assert len(sealed) == 1
    assert not list(tmp_path.rglob("current/min_*.parquet"))
    rows = duckdb.sql(f"SELECT count(*) FROM read_parquet('{sealed[0]}')").fetchone()[0]
    assert rows == 3
 def test_make_writers_registers_expected_datasets(tmp_path: Path) -> None:
    writers = make_writers(tmp_path, "flt-03", "dr-03", ["imu", "battery"])
    assert set(writers) == {"imu", "battery", "detections", "state"}
 def test_duckdb_reads_written_partition(tmp_path: Path) -> None:
    writers = make_writers(tmp_path, "flt-04", "dr-04", ["imu"])
    ts_ns = int(time.time() * 1e9)
    pose = Pose(ts_ns=ts_ns, x=0.0, y=0.0, z=0.0, roll=0.0, pitch=0.0, yaw=0.0, vx=0.0, vy=0.0, vz=0.0)
    writers["imu"].append(imu_row(pose, __import__("random").Random(1), ts_ns))
    for w in writers.values():
        w.seal()
    count = duckdb.sql(f"""
        SELECT count(*) FROM read_parquet(
            '{tmp_path}/dataset=telemetry/**/*.parquet',
            hive_partitioning=true, union_by_name=true)
    """).fetchone()[0]
    assert count >= 1
@@ -2,6 +2,7 @@
 from __future__ import annotations
 import os
 import random
 import select
 import time
@@ -108,6 +109,10 @@ def run(cfg: Config) -> None:
        writer.seal()
    print(f"[{cfg.drone_id}] done: {frames_sent} state frames sent, "
          f"{len(peers_seen)} peers seen {sorted(peers_seen)}; sealed to {root}")
    if os.environ.get("KEEP_ALIVE", "0") == "1":
        print(f"[{cfg.drone_id}] KEEP_ALIVE=1 — idle after seal (no pod restart churn)")
        while True:
            time.sleep(3600)
 if __name__ == "__main__":
Author	SHA1	Message	Date
eSlider	336f9c0428	Add license CI & Release / Verify simulator (push) Successful in 11s Details CI & Release / Trivy scan (push) Successful in 8s Details CI & Release / Semantic Release (push) Successful in 5s Details	2026-07-09 15:52:16 +01:00
eSlider	3d13f744e6	docs(journey): link the live visual prototype at swarm.produktor.io CI & Release / Verify simulator (push) Skipped CI & Release / Trivy scan (push) Skipped CI & Release / Semantic Release (push) Skipped	2026-07-09 12:47:12 +01:00
eSlider	dfb0556eda	docs: add design journey, a narrative walk-through linking into the code CI & Release / Verify simulator (push) Skipped CI & Release / Trivy scan (push) Skipped CI & Release / Semantic Release (push) Skipped A chronological companion to the ADRs: how the design came together, from the first mental model and 2D prototype through the three data tiers, the relative-pose broadcast, the layered WireGuard/SSH trust model, SQL as the peer contract, offload and replication, and the end-to-end proof of concept. Each chapter links straight into the relevant source and decision records. Flagged as the recommended first read in the README.	2026-07-09 12:30:40 +01:00
eSlider	d3ccd6f94e	docs: record architecture decisions (ADR-0001..0009) and operations knowledge CI & Release / Verify simulator (push) Successful in 11s Details CI & Release / Trivy scan (push) Successful in 8s Details CI & Release / Semantic Release (push) Successful in 5s Details Add docs/adr/ with a chronological ADR set reconstructed from the project history (no orchestrator, one storage format, derived-state sync, SQL-over-SSH contract, WireGuard beneath SSH, IaC boundaries, non-root image, runtime data paths, bounded lake scans), an index README, and a template. Distinguish repo-local ADRs from ecosystem-wide ASRs. Expand AGENTS.md with a decision-records section and a runtime/operations guide (service URLs, rebuild workflow, CPU budget knobs). Reference the ADRs from README, CONTRIBUTING, and the affected design docs, and add an open question for T1 partition pruning.	2026-07-09 11:41:05 +01:00
eSlider	ea062af86a	perf: bound lake scans and add platform Grafana dashboard CI & Release / Verify simulator (push) Successful in 11s Details CI & Release / Trivy scan (push) Successful in 9s Details CI & Release / Semantic Release (push) Successful in 5s Details Explorer and exporter were scanning the full Parquet lake every 1–5s (~2000 files, 200MB+), driving ~2.2 CPU cores. Limit metrics to the last N flights, cache DuckDB views and the partition tree, slow live polls to 2s, and keep drones alive after seal to stop restart churn. Add node-exporter, scan-duration metrics, and a Swarm Platform Grafana dashboard for node CPU/memory and scan health.	2026-07-08 21:19:53 +01:00
eSlider	bcd956d11a	fix: wire live mode and Grafana to the generated data stream CI & Release / Verify simulator (push) Successful in 11s Details CI & Release / Trivy scan (push) Successful in 9s Details CI & Release / Semantic Release (push) Successful in 5s Details Use the real hive column name (`drone`) in the prototype's live query and pin the Grafana datasource uid to `swarm-prom` so provisioned dashboards resolve their Prometheus source in k3d.	2026-07-08 20:34:37 +01:00
eSlider	9f99d132e3	fix: chown var/t1 and var/t3 to simulator uid 10001 for k3d CI & Release / Verify simulator (push) Successful in 12s Details CI & Release / Trivy scan (push) Successful in 9s Details CI & Release / Semantic Release (push) Successful in 4s Details Non-root drone pods need write access on the hostPath lake. Ansible now creates var/t1 and var/t3 owned by uid 10001 instead of relying on 0777 alone after legacy root-owned parquet trees.	2026-07-08 20:27:25 +01:00
eSlider	669e8ff005	fix: run simulator image as non-root user (Trivy DS-0002) CI & Release / Verify simulator (push) Successful in 11s Details CI & Release / Trivy scan (push) Successful in 9s Details CI & Release / Semantic Release (push) Successful in 4s Details Add swarm uid 10001 in both build stages so Trivy config scan passes and containers do not run as root. Mounted /data volumes should be world-writable or owned by uid 10001 (var/t1 from Ansible is 0777).	2026-07-08 20:20:19 +01:00
eSlider	de1c7c9558	chore: stop tracking local Terraform state and provider cache CI & Release / Verify simulator (push) Successful in 10s Details CI & Release / Trivy scan (push) Successful in 9s Details CI & Release / Semantic Release (push) Successful in 4s Details Keep .terraform.lock.hcl for reproducible provider pins; gitignore state files and the .terraform/ working directory.	2026-07-08 20:18:06 +01:00
eSlider	ad7e0a5cbe	docs: WireGuard vs SSH, Flux GitOps, CONTRIBUTING with TDD-first Document why WireGuard and SSH are complementary layers, add Flux boundaries for the ground segment, shared var/ data paths in docs/06, and a contributor guide with the required test-first workflow.	2026-07-08 20:17:34 +01:00
eSlider	cb4664f5ee	feat: add IaC layer with shared var/t1 and var/t3 data paths Ansible provisions the k3d cluster and Flux controllers; Terraform modules deploy the simulated fleet and ground warehouse. Compose and k3d share var/t1 (live lake) and var/t3 (warehouse). The prototype gains a live mode fed by the explorer read-only SQL API.	2026-07-08 20:17:32 +01:00
eSlider	d58569b25c	test: add pytest suite and gate Docker builds on it Unit tests cover the SQL gate, broadcast codec, Hive writer layout, and flight kinematics. CI runs pytest first; the simulator image build runs tests in a dedicated stage before the final layer.	2026-07-08 20:17:18 +01:00
eSlider	eef6623f39	fix(simulator): disable Grafana phone-home for air-gap hygiene CI & Release / Verify simulator (push) Successful in 7s Details CI & Release / Trivy scan (push) Successful in 9s Details CI & Release / Semantic Release (push) Successful in 2s Details Grafana was checking grafana.com for updates and sending usage reporting by default - the only outbound traffic in the whole stack. Verified empty latestVersion and no established external connections from any container after the change.	2026-07-08 14:56:43 +01:00
eSlider	f008da1427	feat(prototype): adapt the patrol field to the display aspect ratio CI & Release / Verify simulator (push) Successful in 7s Details CI & Release / Trivy scan (push) Successful in 10s Details CI & Release / Semantic Release (push) Successful in 1s Details The field was a fixed square, leaving wide monitors with dead side margins. Field width now follows the window aspect (capped at 2.2:1, quantized to avoid rebuilds on minor resizes); routes, obstacles and transit spawns stretch with it, and the grid keeps 100 m cells.	2026-07-08 14:51:33 +01:00
eSlider	407a70fef1	feat(prototype): crossing patrol routes and through-transit objects CI & Release / Verify simulator (push) Successful in 7s Details CI & Release / Trivy scan (push) Successful in 9s Details CI & Release / Semantic Release (push) Successful in 2s Details Mobile obstacles used to bounce until they wedged into far corners, and a single perimeter route let the mesh stretch into segments that stopped exchanging data. Even drones now fly the perimeter while odd drones fly an X through the center, keeping bridge links alive, and transit objects enter at one edge, cross the area, and respawn at a fresh edge.	2026-07-08 14:39:57 +01:00