banner("1) logger.configure(): handlers + customized stage + further + patcher")
mem = MemorySink()
logger.configure(
handlers=[
{"sink": sys.stderr, "format": console_formatter, "level": "DEBUG",
"colorize": True, "backtrace": True, "diagnose": True},
{"sink": mem, "level": "DEBUG", "format": "{message}"},
{"sink": "structured.jsonl", "serialize": True, "level": "DEBUG",
"enqueue": True},
{"sink": "errors.log", "level": "ERROR", "enqueue": True,
"backtrace": True, "diagnose": False,
"format": "{time:YYYY-MM-DD HH:mm:ss} | {level} | "
"{name}:{function}:{line} | {message}"},
],
ranges=[{"name": "NOTICE", "no": 22, "color": "<blue><bold>", "icon": "📢"}],
further={"app": "loguru-advanced"},
patcher=global_patcher,
)
logger.debug("debug"); logger.information("information"); logger.success("SUCCESS stage ships built-in")
logger.warning("warning"); logger.log("NOTICE", "customized stage between INFO and SUCCESS")
banner("2) bind() / contextualize() / patch()")
logger.bind(user_id=42, request_id="abc-123").information("sure context")
with logger.contextualize(job="batch-job", run=7):
logger.information("inside contextualized block")
logger.patch(lambda r: r["extra"].replace(epoch=spherical(time.time()))).information("per-call patched report")
banner("3) @logger.catch + context-manager kind")
def interior(d): return d["a"] / d["b"]
def outer(d): return interior(d)
@logger.catch(reraise=False)
def compute(d): return outer(d)
compute({"a": 1, "b": 0})
with logger.catch(message="dealt with inside a with-block"):
elevate ValueError("increase in block")
banner("4) decide(lazy=True), inline colours, report entry")
logger.decide(lazy=True).debug("lazy sum = {}", lambda: sum(i*i for i in vary(1_000_000)))
logger.decide(colours=True).information("inline <pink>colours</pink> <inexperienced>work</inexperienced>")
logger.decide(report=True).information("emitted from supply line {report[line]}")
Home Artificial Intelligence Coding implementation in Loguru to design a sturdy, structured, concurrent, and production-ready Python logging pipeline.
Coding implementation in Loguru to design a sturdy, structured, concurrent, and production-ready Python logging pipeline.
by root

