Spark-submit can fail AFTER YARN has already accepted the application
(lost RM connection, PySpark script exited non-zero on the cluster
side, auth expired mid-submit, ...). In those cases the stderr still
contains 'Submitted application <id>', but the previous
confirm_submit_job implementation threw away that signal and recorded
the pending as FAILED with no application_id. The user had no way to
fetch the YARN logs of the failed job.
Fix:
- run_spark_submit now attaches the failed CompletedProcess to the
SparkSubmitError as .result, so callers can still inspect stderr.
- confirm_submit_job's failure branch tries to parse
(application_id, tracking_url) from exc.result.stderr. If found,
it writes them onto the pending record before re-raising. The
status is still FAILED and the error message is still set — the
user sees the failure normally, but the pending now also has a
log target they can pass to get_application_logs.
- If the stderr has no application_id (e.g. spark-submit failed
locally before reaching YARN), the parse attempt returns None and
the pending is FAILED with no log target — exactly the previous
behavior in that case.
Tests:
- test_confirm_marks_failed_on_spark_submit_error: existing test
now also asserts application_id is None (no result attached).
- test_confirm_recovers_application_id_from_failed_spark_submit:
failed run with 'Submitted application X' in stderr → pending is
FAILED + application_id is set.
- test_confirm_failed_spark_submit_without_application_id_keeps_it_none:
failed run with no application_id in stderr → pending is FAILED
+ application_id is None.
Full suite 248 passed (+2 from 246).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The function had hardcoded --master / --deploy-mode / --queue /
--executor-memory / --executor-cores / --num-executors plus a
`spark_conf` dict for --conf, with no way to pass other flags like
--jars, --py-files, --files, --driver-memory, --name, etc.
Add `extra_args: dict[str, str] | None = None` that emits `--{key}
{value}` pairs, placed after the --conf loop and before the script
path. Default None preserves the existing cmd shape exactly.
Structured (dict) instead of raw list[str] so LLM agents writing
tool-call payloads can't accidentally pack flag+value into a single
string. Key order in the dict is preserved.
Tests: 4 cases — None default, single pair, multiple pairs in order,
no collision with spark_conf. uv run pytest -> 175 passed.
Hardcoding 'spark-submit' as cmd[0] in build_spark_submit_command
breaks for hosts where:
- both Spark 1.x and 2.x/3.x are installed and 'spark-submit' resolves
to the wrong one (use 'spark2-submit' or 'spark3-submit' explicitly)
- the user wants to launch via the PySpark entrypoint ('pyspark')
- a custom wrapper script sits on PATH (e.g. a credentials-injecting
'spark-submit-wrapper')
New env var SPARK_EXECUTOR_SPARK_SUBMIT_BIN. Default is 'spark-submit'
(preserves the current behavior for everyone). Override in .env /
docker-compose.yml to change.
common/config.py:
- new Settings.spark_submit_bin field
- env-var resolution in from_env() with default 'spark-submit'
- included in reload() so tests work
spark_executor/core/spark_submit.py:
- cmd[0] reads settings.spark_submit_bin (was hardcoded 'spark-submit')
.env.example: new section with the override and example values.
docker-compose.yml: forwards the var with the standard 'spark-submit'
default.
Tests: 2 new (settings.spark_submit_bin='spark2-submit', ='pyspark')
plus existing tests updated to use the settings-restore fixture so
mutations don't leak between tests.
165/163 still pass.
- common/logging.py: improve format to timestamp|LEVEL|module:func:line - message
- core/ layer: DEBUG log every subprocess invocation (cmd, rc, byte counts),
JSON load/dump events, parsed application_id. ERROR log on failures.
- tools/ layer: DEBUG log every public tool entry with key parameters,
INFO log on business outcomes (saved/submitted/killed/...).
- New tests/unit/test_logging.py: capture loguru output via in-memory sink
and assert DEBUG + INFO messages are emitted for representative flows.
