Claude
13e39b39f3
common/logging.py used hardcoded 'data/logs/{debug,info}/' paths,
which forced logs into the same dir as connections.json. Operators
couldn't put logs on a dedicated volume or a different disk for
retention policy reasons.
Now the base log dir comes from settings.log_dir (env var
SPARK_EXECUTOR_LOG_DIR, default <data_dir>/logs). Subdirs debug/ and
info/ are auto-created. Same behavior in dev (./data/logs/), but in
prod you can do:
SPARK_EXECUTOR_LOG_DIR=/var/log/spark-executor # dedicated volume
SPARK_EXECUTOR_LOG_DIR=/mnt/slow-storage/logs # cold storage for old logs
Other changes:
- .env.example: new SPARK_EXECUTOR_LOG_DIR entry, split into its own
'Loguru file sinks' section
- docker-compose.yml: forwards SPARK_EXECUTOR_LOG_DIR with the
container-side default /app/data/logs (the existing ./data:/app/data
volume mount already covers this)
- common/config.py: Settings gets a new log_dir field, defaults
derived from data_dir; reload() also resets it
116/116 still pass. Live smoke verified: with
SPARK_EXECUTOR_LOG_DIR=/tmp/spark-logs, both
/tmp/spark-logs/info/2026-06-25.log
/tmp/spark-logs/debug/2026-06-25.log
are created on the first request.
83 lines
3.1 KiB
YAML
83 lines
3.1 KiB
YAML
# spark-executor-mcp — production runtime
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#
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# Bring up with:
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# docker compose up -d --build
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#
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# Prerequisites (one-time):
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# 1. Put your YARN/Hadoop client configs in ./hadoop-conf/ (must contain
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# core-site.xml + yarn-site.xml + hdfs-site.xml matching the target
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# cluster). The Dockerfile's RUN mkdir -p already creates the dir, but
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# it will be empty until you populate it.
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# 2. Set YARN_RESOURCE_MANAGER_URL in .env (or export it in your shell).
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# This is the fallback when a Connection was saved without yarn_rm_url.
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#
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# After it starts, the MCP endpoint is at:
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# http://localhost:8000/spark-executor-mcp (initialize -> tools/list -> tools/call)
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services:
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mcp-server:
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build:
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context: .
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dockerfile: Dockerfile
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image: mcp-server:latest
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container_name: mcp-tools
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restart: unless-stopped
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ports:
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- "8000:8000"
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environment:
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# Stream Python output to stdout/stderr line-by-line (live loguru output).
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PYTHONUNBUFFERED: "1"
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# --- common/config.py knobs (see .env.example for full docs) ---
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# Base dir for connections.json, pending_jobs.json, loguru logs/, jobs/.
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# Defaults to ./data inside the container (mounted from host via volumes below).
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SPARK_EXECUTOR_DATA_DIR: ${SPARK_EXECUTOR_DATA_DIR:-/app/data}
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# Where generate_job_file writes LLM-generated PySpark code.
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# Defaults to <SPARK_EXECUTOR_DATA_DIR>/jobs.
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SPARK_EXECUTOR_JOBS_DIR: ${SPARK_EXECUTOR_JOBS_DIR:-/app/data/jobs}
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# Base dir for loguru file output (debug/ + info/ subdirs are
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# auto-created). Defaults to <SPARK_EXECUTOR_DATA_DIR>/logs.
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SPARK_EXECUTOR_LOG_DIR: ${SPARK_EXECUTOR_LOG_DIR:-/app/data/logs}
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# Fallback YARN RM URL used by the REST client when a Connection's
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# yarn_rm_url is not set or a Job lacks a snapshot. Leave empty if
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# you always set yarn_rm_url per Connection via save_connection.
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YARN_RESOURCE_MANAGER_URL: ${YARN_RESOURCE_MANAGER_URL:-}
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# Loguru verbosity for stderr + info file. DEBUG | INFO.
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SPARK_EXECUTOR_LOG_LEVEL: ${SPARK_EXECUTOR_LOG_LEVEL:-DEBUG}
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# --- gunicorn.conf.py knobs (NOT read by common/config.py) ---
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# 2 workers is a good default for a small MCP service; raise for
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# high-concurrency deploys.
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GUNICORN_WORKERS: ${GUNICORN_WORKERS:-2}
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GUNICORN_TIMEOUT: ${GUNICORN_TIMEOUT:-120}
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GUNICORN_BIND: ${GUNICORN_BIND:-0.0.0.0:8000}
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# Optional: pass JVM options to spark-submit (e.g. for proxies, memory).
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# SPARK_SUBMIT_OPTS: "-Dhttps.proxyHost=..."
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volumes:
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# Persist Connections, PendingSubmissions, and loguru logs across
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# container restarts. Gitignored.
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- ./data:/app/data
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# Real Hadoop/YARN client configs read by spark-submit at submit time.
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# Read-only so the running container cannot mutate cluster config.
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- ./hadoop-conf:/etc/hadoop/conf:ro
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# No resource limits — spark-submit talks to YARN, which does the actual
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# heavy lifting. The MCP server itself is lightweight (FastAPI + httpx).
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# Uncomment to cap if needed:
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# deploy:
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# resources:
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# limits:
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# cpus: "1.0"
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# memory: 1G
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