Uses of Interface
org.javasimon.callback.logging.LogMessageSource

Packages that use LogMessageSource

org.javasimon.callback.calltree	CallTreeCallback gathers information for current thread and displays it subsequent calls like a tree.
org.javasimon.callback.lastsplits	LastSplitsCallback retains last splits in a ring a buffer (fixed size FIFO queue).
org.javasimon.callback.logging	LoggingCallback is able to log stopwatch splits and statistics once in a while (N per millisecond, every N split, when split is longer than N ms, etc.)
org.javasimon.callback.quantiles	QuantilesCallback sorts splits to categories (Buckets) based on time ranges.

Uses of LogMessageSource in org.javasimon.callback.calltree

Classes in org.javasimon.callback.calltree that implement LogMessageSource

class

CallTree Call tree contains the root call tree node and the current call stack.

Uses of LogMessageSource in org.javasimon.callback.lastsplits

Classes in org.javasimon.callback.lastsplits that implement LogMessageSource

class

LastSplits Object stored among Stopwatch's attributes in charge of Managing concurrent access to the inner ring buffer through synchronized blocks Computing som statistics (min, max, mean, trend) based on retained values Log retained values and statistics

Uses of LogMessageSource in org.javasimon.callback.logging

Fields in org.javasimon.callback.logging declared as LogMessageSource

private LogMessageSource<String>	LoggingCallback.managerLogMessageSource String to string (no-op) converter.
private LogMessageSource<Split>	LoggingCallback.stopwatchLogMessageSource Split to string converter.

Methods in org.javasimon.callback.logging with parameters of type LogMessageSource

boolean

LogTemplate.log(C context, LogMessageSource<C> messageSource) If enabled, get message for context and log id.

Uses of LogMessageSource in org.javasimon.callback.quantiles

Classes in org.javasimon.callback.quantiles that implement LogMessageSource

class	Buckets List of buckets and quantiles computer.
class	ExponentialBuckets Exponentially organized Buckets.
class	LinearBuckets Linearly organized Buckets For 100-600 range and 5 bucket count, the following buckets are created: Index MinMax Samples Counter 0 -∞100 53 # (1) 1 100200 128,136 ## (2) 2 200300 245,231,264,287,275 ###### (5) 3 300400 356,341 ## (2) 4 400500 461 # (1) 5 500600 801 # (1) 6 600+∞ (0) For a total of 12 splits in this example, we can deduce that Median (6th sample) is in bucket #2 Third quartile (9th sample) is in bucket #3 90% percentile (10,8th sample) is in bucket #4 or #5 (but assume #4).