feat: add docker-compose.yml + .env.example

docker-compose.yml:
- Builds the Dockerfile, exposes :8000
- Mounts ./data (persistent) and ./hadoop-conf (ro) for cluster configs
- Forwards YARN_RESOURCE_MANAGER_URL via env var (fallback for REST client)
- Healthcheck via /openapi.json (cheap liveness probe; MCP initialize
  handshake would be more accurate but requires session-id plumbing)
- restart: unless-stopped for production
- Resource limit section commented out (server itself is lightweight)

.env.example:
- Documents the env vars compose expects
- .env is gitignored

.env.example

@@ -0,0 +1,15 @@
+# Spark Executor MCP — environment template
+# Copy to .env and edit. .env is gitignored.
+
+# YARN ResourceManager URL the REST client falls back to when a Connection
+# was saved without yarn_rm_url, or a Job lacks a snapshot (e.g. if the
+# pending record was created before yarn_rm_url was a field).
+#
+# Examples:
+#   YARN_RESOURCE_MANAGER_URL=http://yarn-rm.prod.internal:8088
+#   YARN_RESOURCE_MANAGER_URL=https://yarn-rm.staging.example.com:8088
+YARN_RESOURCE_MANAGER_URL=
+
+# Optional: JVM flags forwarded to spark-submit. Useful for proxies, custom
+# truststores, or driver memory caps.
+# SPARK_SUBMIT_OPTS=-Dhttps.proxyHost=proxy.corp -Dhttps.proxyPort=3128

docker-compose.yml

@@ -0,0 +1,68 @@
+# spark-executor-mcp — production runtime
+#
+# Bring up with:
+#   docker compose up -d --build
+#
+# Prerequisites (one-time):
+#   1. Put your YARN/Hadoop client configs in ./hadoop-conf/  (must contain
+#      core-site.xml + yarn-site.xml + hdfs-site.xml matching the target
+#      cluster). The Dockerfile's RUN mkdir -p already creates the dir, but
+#      it will be empty until you populate it.
+#   2. Set YARN_RESOURCE_MANAGER_URL in .env (or export it in your shell).
+#      This is the fallback when a Connection was saved without yarn_rm_url.
+#
+# After it starts, the MCP endpoint is at:
+#   http://localhost:8000/spark-executor-mcp   (initialize -> tools/list -> tools/call)
+
+services:
+  spark-executor-mcp:
+    build:
+      context: .
+      dockerfile: Dockerfile
+    image: spark-executor-mcp:latest
+    container_name: spark-executor-mcp
+
+    restart: unless-stopped
+
+    ports:
+      - "8000:8000"
+
+    environment:
+      # Stream Python output to stdout/stderr line-by-line (live loguru output).
+      PYTHONUNBUFFERED: "1"
+
+      # Fallback YARN RM URL used by the REST client when a Connection's
+      # yarn_rm_url is not set or a Job lacks a snapshot. Leave empty if
+      # you always set yarn_rm_url per Connection via save_connection.
+      YARN_RESOURCE_MANAGER_URL: ${YARN_RESOURCE_MANAGER_URL:-}
+
+      # Optional: pass JVM options to spark-submit (e.g. for proxies, memory).
+      # SPARK_SUBMIT_OPTS: "-Dhttps.proxyHost=..."
+
+    volumes:
+      # Persist Connections, PendingSubmissions, and loguru logs across
+      # container restarts. Gitignored.
+      - ./data:/app/data
+
+      # Real Hadoop/YARN client configs read by spark-submit at submit time.
+      # Read-only so the running container cannot mutate cluster config.
+      - ./hadoop-conf:/etc/hadoop/conf:ro
+
+    healthcheck:
+      # The MCP endpoint requires an initialize handshake for real liveness;
+      # /openapi.json (the main app's spec) is a cheap proxy for "process up".
+      # Replace with a /health route once added to main.py (see note below).
+      test: ["CMD", "python", "-c", "import httpx; httpx.get('http://localhost:8000/openapi.json', timeout=5).raise_for_status()"]
+      interval: 30s
+      timeout: 5s
+      retries: 3
+      start_period: 15s
+
+    # No resource limits — spark-submit talks to YARN, which does the actual
+    # heavy lifting. The MCP server itself is lightweight (FastAPI + httpx).
+    # Uncomment to cap if needed:
+    # deploy:
+    #   resources:
+    #     limits:
+    #       cpus: "1.0"
+    #       memory: 1G