In this vignette I suppose that you are already familiar with at least one of the similar logging R packages and you are looking for suggestions on how to switch to logger. Before moving forward, please make sure that you have read the Introduction to logger, The Anatomy of a Log Request and Customizing the Format and the Destination of a Log Record vignettes for a decent background on logger, and use this vignette as a quick-reference sheet to help you migrate from another package.
The logger package has been very heavily inspired by futile.logger and have been using it for many years, also opened multiple pull requests to extend futile.logger before I decided to revamp my ideas into a new R package – but there are still many common things between futile.logger and logger.
Both packages comes with a default log engine / config, so it’s enough to load the packages and those are ready to be used right away:
library(futile.logger)
#>
#> Attaching package: 'futile.logger'
#> The following objects are masked from 'package:logger':
#>
#> DEBUG, ERROR, FATAL, INFO, TRACE, WARN
library(logger)
The most important change is that function names are by snake_case in logger, while futile.logger uses dot.separated expressions, and futile.logger prefixes function names by flog while logger uses log for that:
flog.info('hi there')
#> INFO [2021-10-18 23:25:55] hi there
flog.warn('watch out')
#> WARN [2021-10-18 23:25:55] watch out
log_info('hi there')
#> INFO [2021-10-18 23:25:55] hi there
log_warn('watch out')
#> WARN [2021-10-18 23:25:55] watch out
As you can see above, the default layout of the messages is exactly the same.
Regarding log levels, futile.logger bundles the default log4j levels (TRACE, DEBUG, INFO, WARN, ERROR and FATAL) that is extended by SUCCESS in logger as sometimes it’s worth logging with a higher than INFO level that something succeeded.
Changing layouts is easy in both package, as you simply pass a layout function:
flog.layout(layout.json)
#> NULL
flog.info('hi again')
#> {"level":"INFO","timestamp":"2021-10-18 23:25:55 +0200","message":"hi again","func":"tools::buildVignettes"}
log_layout(layout_json())
log_info('hi again')
#> {"time":"2021-10-18 23:25:55","level":"INFO","ns":"global","ans":"global","topenv":"R_GlobalEnv","fn":"eval","node":"nevermind","arch":"x86_64","os_name":"Linux","os_release":"5.14.8-arch1-1","os_version":"#1 SMP PREEMPT Sun, 26 Sep 2021 19:36:15 +0000","pid":1622485,"user":"daroczig","msg":"hi again"}
As you can see, logger provided a bit more information about the log request compared to futile.logger, but it’s easy to change the list of fields to be used in the JSON – see ?get_logger_meta_variables for a complete list of variable names to be passed to ?layout_json. logger also ships a lot more layouts, eg ?layout_glue_colors or roll out your own via the ?layout_glue_generator factory function.
By default, futile.logger uses an sprintf formatter, while logger passes the objects to be logged to glue:
flog.info('hi')
#> INFO [2021-10-18 23:25:55] hi
flog.info('hi %s', 84/2)
#> INFO [2021-10-18 23:25:55] hi 42
flog.info(paste('hi', 84/2))
#> INFO [2021-10-18 23:25:55] hi 42
flog.info(glue::glue('hi {84/2}'))
#> INFO [2021-10-18 23:25:55] hi 42
log_info('hi')
#> INFO [2021-10-18 23:25:55] hi
log_info('hi {84/2}')
#> INFO [2021-10-18 23:25:55] hi 42
log_formatter(formatter_sprintf)
log_info('hi %s', 84/2)
#> INFO [2021-10-18 23:25:55] hi 42
log_formatter(formatter_paste)
log_info('hi', 84/2)
#> INFO [2021-10-18 23:25:55] hi 42
It’s easy to change this default formatter in both packages: use flog.layout handles this as well in futile.logger, while the formatter is separated from the layout function in logger, so check ?log_formatter instead. logger ships with a bit more formatter functions, eg the default ?formatter_glue and ?formatter_glue_or_sprintf that tries to combine the best from both words.
Setting the destination of the log records works similarly in both packages, although he logger packages bundles a lot more options:
t <- tempfile()
flog.appender(appender.file(t))
#> NULL
flog.appender(appender.tee(t))
#> NULL
t <- tempfile()
log_appender(appender_file(t))
log_appender(appender_tee(t))
Both packages support using different logging namespaces and stacking loggers within the same namespace. Performance-wise, there’s logger seems to be faster than futile.logger, but for more details, check the Simple Benchmarks on Performance vignette.
logger as a drop-in-replacement of futile.loggerlogger has no hard requirements, so it’s a very lightweight alternative of futile.logger. Although the function names are a bit different, and the message formatter also differs, but with some simple tweaks, logger can become an almost perfect drop-in-replacement of futile.logger:
library(logger)
log_formatter(formatter_sprintf)
flog.trace <- log_trace
flog.debug <- log_debug
flog.info <- log_info
flog.warn <- log_warn
flog.error <- log_error
flog.info('Hello from logger in a futile.logger theme ...')
flog.warn('... where the default log message formatter is %s', 'sprintf')
The logging package behaves very similarly to the Python logging module and so thus being pretty Pythonic, while logger tries to accommodate native R users’ expectations – so there are some minor nuances between the usage of the two packages.
In logging, you have to initialize a logger first via addHandler or simply by calling basicConfig, which is not required in logger as it already comes with a default log config:
library(logging)
basicConfig()
library(logger)
After initializing the logging engine, actual logging works similarly in the two packages – with a bit different function names:
logging uses mostly camelCase function names (eg basicConfig), but the logging functions are all lowercase without any separator, such as loginfo or logwarnlogger uses snake_case for the function names, such as log_info and log_warnloginfo('hi there')
#> 2021-10-18 23:25:55 INFO::hi there
logwarn('watch out')
#> 2021-10-18 23:25:55 WARNING::watch out
log_info('hi there')
log_warn('watch out')
As you can see above, the default layout of the log messages is somewhat different:
logging starts with the timestamp that is followed by the log level, optional namespace and the message separated by colonslogger starts with the log level, followed by the timestamp between brackets and then the messageFor the available log levels in logging, check ?loglevels, and ?log_levels for the same in logger:
str(as.list(loglevels))
#> List of 11
#> $ NOTSET : num 0
#> $ FINEST : num 1
#> $ FINER : num 4
#> $ FINE : num 7
#> $ DEBUG : num 10
#> $ INFO : num 20
#> $ WARNING : num 30
#> $ WARN : num 30
#> $ ERROR : num 40
#> $ CRITICAL: num 50
#> $ FATAL : num 50
levels <- mget(ls(
envir = environment(logger), pattern = '^[A-Z]'),
envir = environment(logger))
str(levels[order(-as.numeric(levels))], give.attr = FALSE)
#> Named list()
Performance-wise, there’s no big difference between the two packages, but for more details, check the Simple Benchmarks on Performance vignette.
Getting and setting the layout of the log record should happen up-front in both packages:
getLogger()[['handlers']]$basic.stdout$formatter
#> function (record)
#> {
#> msg <- trimws(record$msg)
#> text <- paste(record$timestamp, paste(record$levelname, record$logger,
#> msg, sep = ":"))
#> return(text)
#> }
#> <bytecode: 0x56308eaf02d8>
#> <environment: namespace:logging>
log_layout()
#> layout_simple
logger provides multiple configurable layouts to fit the user’s need, eg easily show the calling function of the lof request, the pid of the R process, name of the machine etc. or colorized outputs. See Customizing the Format and the Destination of a Log Record vignette for more details.
If you want to pass dynamic log messages to the log engines, you can do that via the hard-coded sprintf in the logging package, while you can set that on a namespaces basis in logger, which is by default using glue:
loginfo('hi')
#> 2021-10-18 23:25:55 INFO::hi
loginfo('hi %s', 84/2)
#> 2021-10-18 23:25:55 INFO::hi 42
loginfo(paste('hi', 84/2))
#> 2021-10-18 23:25:55 INFO::hi 42
loginfo(glue::glue('hi {84/2}'))
#> 2021-10-18 23:25:55 INFO::hi 42
log_info('hi')
log_info('hi {84/2}')
log_formatter(formatter_sprintf)
log_info('hi %s', 84/2)
log_formatter(formatter_paste)
log_info('hi', 84/2)
For even better compatibility, there’s also ?formatter_logging that not only relies on sprintf when the first argument is a string, but will log the call and the result as well when the log object is an R expression:
log_formatter(formatter_logging)
log_info('42')
log_info(42)
log_info(4+2)
log_info('foo %s', 'bar')
log_info(12, 1+1, 2 * 2)
Setting the destination of the log records works similarly in both packages, although he logger packages bundles a lot more options:
?addHandler
?writeToConsole
?writeToFile
?log_appender
?appender_console
?appender_file
?appender_tee
?appender_slack
?appender_pushbullet
Both packages support using different logging namespaces and stacking loggers within the same namespace.
logger as a drop-in-replacement of logginglogger has no hard requirements, so it’s an adequate alternative of logging. Although the function names are a bit different, and the message formatter also differs, but with some simple tweaks, logger can become an almost perfect drop-in-replacement of logging – although not all log levels (eg and ) are supported:
library(logger)
log_formatter(formatter_logging)
log_layout(layout_logging)
logdebug <- log_debug
loginfo <- log_info
logwarn <- log_warn
logerror <- log_error
loginfo('Hello from logger in a logging theme ...')
logwarn('... where the default log message formatter is %s', 'sprintf', namespace = 'foobar')
The log4r package provides an object-oriented approach for logging in R, so the logger object is to be passed to the log calls – unlike in the logger package.
So thus it’s important to create a logging object in log4r before being able to log messages, while that’s automatically done in `logger:
library(log4r)
#>
#> Attaching package: 'log4r'
#> The following object is masked _by_ '.GlobalEnv':
#>
#> logger
#> The following object is masked from 'package:logging':
#>
#> levellog
#> The following object is masked from 'package:logger':
#>
#> logger
#> The following object is masked from 'package:base':
#>
#> debug
logger <- create.logger(logfile = stdout(), level = "INFO")
library(logger)
Please note that in the background, logger does have a concept of logger objects, but that’s behind the scene and the user does not have to specify / reference it. On the other hand, if you wish, you can do that via the namespace concept of logger – more on that later.
While logger has a log_ prefix for all logging functions, log4r has lowercase functions names referring to the log level, which takes a logging object and the log message:
info(logger, 'hi there')
#> INFO [2021-10-18 23:25:55] hi there
warn(logger, 'watch out')
#> WARN [2021-10-18 23:25:55] watch out
log_info('hi there')
log_warn('watch out')
As you can see the default layout of the messages is a bit different in the two packages.
Both packages are based on log4j, and log4r provides DEBUG, INFO, WARN, ERROR and FATAL, while logger also adds TRACE and SUCCESS on the top of these.
To change the log level threshold, use the level function on the logging object in log4r, while it’s log_level in logger.
The log4r provides a logformat argument in create.logger that can be used to override the default formatting, while logger provides formatter and layout functions for a flexible log record design.
By default, log4r logs to a file that can be set to stoud to write to the console, while logger writes to the console by default, but logging to files via the appender_file functions is also possible – besides a number of other log record destinations as well.
Creating objects is the log4r way of handling multiple log environments, while logger handles that via namespaces.
Sorry, no direct replacement for loggit – capturing message, warning and stop function messages, but it’s on the roadmap to provide helper functions to be used as message hooks feed logger.
#> Warning: 'logger' namespace cannot be unloaded:
#> namespace 'logger' is imported by 'logger.tester' so cannot be unloaded