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BitLogger/docs/api/configured-logger-pending-count.md
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2026-06-13 23:58:00 +08:00

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name, group, category, update-time, description, key-word
name group category update-time description key-word
configured-logger-pending-count api runtime 20260613 Read the current queued backlog count from a configured runtime logger through RuntimeSink.
logger
runtime
queue
public

Configured-logger-pending-count

Read the current queued backlog count from a ConfiguredLogger. This is the configured logger wrapper over RuntimeSink::pending_count(...) for config-driven queue metrics.

Interface

pub fn ConfiguredLogger::pending_count(self : ConfiguredLogger) -> Int {}

input

  • self : ConfiguredLogger - Config-driven runtime logger whose queue backlog should be inspected.

output

  • Int - Current number returned by the wrapped RuntimeSink::pending_count() call.

Explanation

Detailed rules explaining key parameters and behaviors

  • This helper delegates directly to self.sink.pending_count().
  • Queue-backed runtime sink variants return their live pending count.
  • Plain console and plain file runtime sink variants return 0 because they do not own a pending queue here.
  • This is a point-in-time metric and may change immediately after it is read.

How to Use

Here are some specific examples provided.

When Need Configured Queue Backlog Visibility

When a config-built logger should expose queue pressure:

let pending = logger.pending_count()

In this example, queue backlog is observed without reaching into the sink internals.

When Verify Manual Drain Progress

When drain loops should observe remaining backlog:

ignore(logger.drain(max_items=8))
ignore(logger.pending_count())

In this example, the queue metric helps verify manual drain progress.

Error Case

e.g.:

  • If the configured logger is not queue-backed, the method simply returns 0.

  • If callers need richer file-plus-queue state for file sinks, file_runtime_state() is the better API.

Notes

  1. Use this helper for simple queue visibility on config-built loggers.

  2. Pair it with dropped_count() when investigating loss behavior.