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.
65 lines
2.5 KiB
Bash
65 lines
2.5 KiB
Bash
# Spark Executor MCP — environment template
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# Copy to .env and edit. .env is gitignored.
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#
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# All env vars in this file are read by common/config.py (the single source
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# of truth for application config). The only exception is the GUNICORN_*
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# block at the bottom — those are read by gunicorn.conf.py.
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# --- Data persistence ---
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# Base directory for connections.json, pending_jobs.json, loguru logs/,
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# and (by default) jobs/. Mount this from the host in production so
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# state survives container restarts. The default ./data/ is fine in dev.
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#
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# SPARK_EXECUTOR_DATA_DIR=./data
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# SPARK_EXECUTOR_DATA_DIR=/var/lib/spark-executor/data
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# --- Job files (LLM-generated PySpark) ---
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# Where generate_job_file writes PySpark source. Defaults to
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# <SPARK_EXECUTOR_DATA_DIR>/jobs. Override to point at a larger disk
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# (e.g. /var/spark-jobs) when the data volume is small.
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#
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# SPARK_EXECUTOR_JOBS_DIR=./data/jobs
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# SPARK_EXECUTOR_JOBS_DIR=/var/spark-jobs
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# --- YARN REST client ---
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# Fallback URL when a Job's yarn_rm_url (snapshotted from its Connection
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# at prepare_submit_job time) is unset. Set this OR per-Connection via
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# save_connection.
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#
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# Examples:
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# YARN_RESOURCE_MANAGER_URL=http://yarn-rm.prod.internal:8088
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# YARN_RESOURCE_MANAGER_URL=https://yarn-rm.staging.example.com:8088
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YARN_RESOURCE_MANAGER_URL=
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# --- Loguru file sinks ---
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# Base directory for loguru output. Subdirs debug/ and info/ are created
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# automatically; rotated daily, gzipped, kept 30 days. Defaults to
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# <SPARK_EXECUTOR_DATA_DIR>/logs. Override to point at a dedicated log
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# volume (e.g. /var/log/spark-executor) or a network mount.
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#
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# SPARK_EXECUTOR_LOG_DIR=./data/logs
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# SPARK_EXECUTOR_LOG_DIR=/var/log/spark-executor
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# --- Loguru verbosity ---
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# For stderr + the info-level file sink. The debug-level file sink
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# always captures full DEBUG (audit trail regardless of level).
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# DEBUG - default; full verbosity
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# INFO - quieter; recommended for production
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#
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# SPARK_EXECUTOR_LOG_LEVEL=DEBUG
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# SPARK_EXECUTOR_LOG_LEVEL=INFO
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# --- Optional: JVM flags forwarded to spark-submit ---
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# Useful for proxies, custom truststores, or driver memory caps.
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# SPARK_SUBMIT_OPTS=-Dhttps.proxyHost=proxy.corp -Dhttps.proxyPort=3128
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# --- Gunicorn process model (see gunicorn.conf.py; NOT read by common/config.py) ---
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# Defaults shown. These are read by gunicorn directly, not by the app.
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# GUNICORN_WORKERS=2
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# GUNICORN_THREADS=1
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# GUNICORN_TIMEOUT=120 # generous; yarn logs can be slow
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# GUNICORN_GRACEFUL_TIMEOUT=30
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# GUNICORN_KEEPALIVE=5
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# GUNICORN_BIND=0.0.0.0:8000
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# GUNICORN_LOGLEVEL=info
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