A simple logging API for julia
Author c42f
25 Stars
Updated Last
2 Years Ago
Started In
February 2017


Build Status


MicroLogging is the prototype implementation of the logging frontend interface which was ported to Base in julia-0.7, and brought most of same features to julia-0.6. See the Base Documentation for an overview of the logging frontend.

Julia Logging Ecosystem

As of the start of 2020, MicroLogging contains some bits and pieces for logging backends, but is somewhat dormant. However, I'm maintaining the following list of libraries from the ecosystem which I hope will eventually mature into a composable system for log routing, storage and pretty printing.

First off, the standard Logging library provides a default logger ConsoleLogger for some basic filtering and pretty printing of log records in the terminal. It combines convenient but non-composable features into a single logger type.

Frontend extensions

  • ProgressLogging.jl provides some convenient frontend macros including @progress which makes it easy to emit log records tracking the progress of looping constructs.

Routing and transformation

  • LoggingExtras.jl provides generic log transformation, filtering and routing functionality. You can use this to mutate messages as they go through the chain, duplicate a stream of log records into multiple streams, discard messages based on a predicate, etc.



pkg> add MicroLogging

Quickstart Example

using MicroLogging

@info "# Logging macros"
@debug "A message for debugging (filtered out by default)"
@info "Information about normal program operation"
@warn "A potential problem was detected"
@error "Something definitely went wrong"
x = [1 2;3 4]
@info "Support for key value pairs" x a=1 b="asdf"

global_logger(ConsoleLogger(stderr, MicroLogging.Info))
@info "# Progress logging"
for i=1:100
    @info "algorithm1" progress=i/100

@info "# Log record filtering"
@debug begin
    error("Should not be executed unless logging at debug level")
    "A message"
@debug "Logging enabled at debug level and above"
for i=1:10
    @warn "Log suppression iteration $i (maxlog=2)" maxlog=2
module LogTest
    using MicroLogging
    function f()
        @debug "Message from LogTest"
        @info  "Message from LogTest"
        @warn  "Message from LogTest"
        @error "Message from LogTest"
configure_logging(LogTest, min_level=:error)
@info "Set log filtering to error level for LogTest module"

@info "# Task-based log dispatch using dynamic scoping"
function some_operation()
    @info "Dispatches to the current task logger, or the global logger"
logstream = IOBuffer()
with_logger(SimpleLogger(logstream)) do
    @info "Logging redirected"
@info "Logs, captured separately in the with_logger() block" logstring=strip(String(take!(logstream)))

@info "# Formatting logs can't crash the application"
@info "Blah $(error("An intentional error"))"


MicroLogging implementation choices

Early filtering

The filtering of log messages should cheap enough that users feel free to leave them available rather than commenting them out or otherwise disabling them at compile time. The only way to achieve this is to filter early, before the entire log message and other log record metadata is fully determined. Thus, we have the following design challenge:

Allow early filtering of log records before the record is fully constructed.

In most logging libraries, a basic level of filtering is achieved based on an ordered log level which represents a verbosity/severity (debug, info, warning, error, etc). Messages more verbose than the currently minimum level are filtered out. This seems simple, effective and efficient as a first pass filter. Naturally, further filtering may also occur based on the log message or other log record metadata.

In MicroLogging, early filtering can be controlled using the configure_logging function, which configures filtering of the current logger:


For even more efficiency, the disable_logging() function can be used to globally disable logging below a given log for all loggers.

Logging macros

Efficiency seems to dictate that some filtering decision is done before any logging-specific user code is run. This implies either a logging macro to insert an early test and branch, or that the log record creation is passed as a closure. We'd also like to gather information from lexical scope, and to look up/create the logger for the current module at compile time.

These considerations indicate that a macro be used, which also has the nice side effect of being visually simple:

x = 42
@info "my value is x = $x"

To achieve early filtering, this example currently expands to something like

if Info >= MicroLogging._min_enabled_level[]
    logger = $(current_logger())
    if shouldlog(logger, Info)
        logmsg(current_logger(), Info, "my value is x = $x", #=...=#)

Logging context and dispatch

Every log record has various types of context which can be associated with it. Some types of context include:

  • static lexical context - based on the location in the code - local variables, line number, file, function, module.
  • dynamic caller context - the current stack trace, and data visible in it. Consider, for example, the context which can be passed with the femtolisp with-bindings construct.
  • dynamic data context - context created from data structures available at log record creation.

Log context can be used in two ways. First, to dispatch the log record to appropriate handler code. Second, to enrich the log record with data about the state of the program.

Which code processes a log message after it is created?

Here we've got to choose between lexical vs dynamic scope to look up the log handler code. MicroLogging chooses a dynamically scoped log handler bound to the current task. To understand why this might be good choice, consider the two audiences of a logging library:

  • Package authors want to emit logs in a simple way, without caring about how they're dispatched.
  • Application authors care about a complete application as built up from many packages. They need to control how log records are dispatched, but don't get any control over how they're created.

Application programmers tend to be calling functions from many different packages to achieve an overall task. With dynamic scoping for log handlers, it's easy to control log dispatch based on task:

logger = MyLogger(#= ... =#)

with_logger(logger) do

Notably, this approach works no matter how deeply nested the call tree becomes within the various functions called by Package1.foo(), without any thought by the author of any of the packages in use.

Most logging libraries seem to look up the log handler in lexical scope, which implies a global entry point for log dispatch. For example, the python community seems to have settled on using per-module contexts to dispatch log messages (TODO: double check how this works).

Which metadata is automatically included with the log record?

Some useful metadata is automatically generated with each record:

  • Module
  • Location - file, line number
  • id - a unique Symbol for the logger invocation

Efficiency - messages you never see should cost almost nothing

The following should be fast

@debug begin
    A = #=Long, complex calculation=#
    "det(A) = $(det(A))"

... FIXME more to write here