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Diagnose multiprocessing pickling failures, memory leaks, unhandled exceptions, and implement structured JSON logging.
In Python's multiprocessing library, functions and arguments passed to worker pools must be serializable via pickle.
pool.map() raises PicklingError: Can't pickle <function <lambda>>.multiprocess (pathos) which uses dill.In production pipelines (Airflow, K8s, AWS CloudWatch), never use plain print() statements. Always emit structured JSON logs with correlation IDs.
Exit Code 137) vs Python MemoryError: Linux kills processes silently with SIGKILL (137) at kernel cgroup limits before Python can even raise MemoryError or flush traceback logs! Always inspect dmesg / Kubernetes OOMKilled pod status AND monitor RSS growth with tracemalloc or psutil.PicklingError in Multiprocessing / Spark: Occurs when closures capture lambda functions, local unpicklable C extensions, or live sockets; move worker targets to top-level module functions!encoding="utf-8" (newline="" with csv module) to prevent Windows/Linux encoding corruption.Review the production code, company interview Q&As, incident traces, and comprehension quiz inside this lesson.
import logging
import json
import time
class JsonFormatter(logging.Formatter):
def format(self, record):
log_record = {
"timestamp": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
"level": record.levelname,
"message": record.getMessage(),
"pipeline": getattr(record, "pipeline", "etl_core"),
"trace_id": getattr(record, "trace_id", "unknown")
}
return json.dumps(log_record)
logger = logging.getLogger("ETLLogger")
handler = logging.StreamHandler()
handler.setFormatter(JsonFormatter())
logger.addHandler(handler)
logger.setLevel(logging.INFO)
# Emit structured log for Datadog / CloudWatch parsing:
logger.info("Batch partition ingestion completed successfully", extra={"pipeline": "orders_bronze", "trace_id": "run-40912"})
_pickle.PicklingError: Can't pickle <function <lambda> at 0x7f8a>: attribute lookup <lambda> on __main__ failed