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https://github.com/s-fleck/lgr

A fully featured logging framework for R
https://github.com/s-fleck/lgr

log4j logging r r6

Last synced: about 2 months ago
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A fully featured logging framework for R

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README 

        
---

output: github_document

---



```{r setup, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)



library(lgr)

basic_config(console_connection = stdout())  # ensure default config

# 

```

# lgr



[![CRAN status](https://www.r-pkg.org/badges/version/lgr)](https://cran.r-project.org/package=lgr)

[![Lifecycle: maturing](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://lifecycle.r-lib.org/articles/stages.html)



lgr is a logging package for R built on the back of 

[R6](https://github.com/r-lib/R6) classes. It is designed to be flexible,

performant and extensible. The package 

[vignette](https://s-fleck.github.io/lgr/articles/lgr.html) contains a 

comprehensive description of the features of lgr (some of them unique among

R logging packages) along with many code examples.



Users that have not worked with R6 classes before, will find configuring

Loggers a bit strange and verbose, but care was taken to keep

the syntax for common logging tasks and interactive usage simple and concise.

User that have experience with 

  [shiny](https://github.com/rstudio/shiny), 

  [plumber](https://github.com/rstudio/plumber),

  [python logging](https://docs.python.org/3/library/logging.html) or 

  [Apache Log4j](https://logging.apache.org/log4j/2.x/) will feel

at home. User that are proficient with R6 classes will also find it easy

to extend and customize lgr, for example with their own appenders Loggers or 

Appenders. 



## Features



* *Hierarchical loggers* like in log4j and python logging. This is useful if you

  want to be able to configure logging on a per-package basis.

* An *arbitrary number of appenders* for each logger. A single logger can write

  to the console, a logfile, a database, etc... .

* Support for structured logging. As opposed to many other logging 

  packages for R a log event is not just a message with a timestamp, but an

  object that can contain arbitrary data fields. This is useful for producing 

  machine readable logs.

* *Vectorized* logging (so `lgr$fatal(capture.output(iris))` works)

* Lightning fast *in-memory logs* for interactive use.

* Appenders that write logs to a wide range of destinations:

    * databases (buffered or directly)

    * email or pushbullet

    * plaintext files (with a powerful formatting syntax)

    * JSON files with arbitrary data fields

    * Rotating files that are reset and backed-up after they reach

      a certain file size or age

    * memory buffers

    * (colored) console output

* Optional support to use [glue](https://glue.tidyverse.org/) instead of 

  `sprintf()` for composing log messages.



## Usage



To log an *event* with with lgr we call `lgr$()`. Unnamed 

arguments to the logging function are interpreted by `sprintf()`. For a way

to create loggers that [glue](https://glue.tidyverse.org/) instead

please refer to the vignette.



```{r}

lgr$fatal("A critical error")

lgr$error("A less severe error")

lgr$warn("A potentially bad situation")

lgr$info("iris has %s rows", nrow(iris))



# the following log levels are hidden by default

lgr$debug("A debug message")

lgr$trace("A finer grained debug message")

```



A Logger can have several Appenders. For example, we can add a JSON appender to

log to a file with little effort. 



```{r}

tf <- tempfile()

lgr$add_appender(AppenderFile$new(tf, layout = LayoutJson$new()))

lgr$info("cars has %s rows", nrow(cars))

cat(readLines(tf))

```



By passing a named argument to `info()`, `warn()`, and co you can log not only 

text but arbitrary R objects. Not all appenders support structured logging

perfectly, but JSON does. This way you can create 

logfiles that are machine as well as (somewhat) human readable.



```{r}

lgr$info("loading cars", "cars", rows = nrow(cars), cols = ncol(cars))

cat(readLines(tf), sep = "\n")

```



```{r, echo=FALSE}

unlink(tf)

```



For more examples please see the 

package 

[vignette](https://s-fleck.github.io/lgr/articles/lgr.html) and 

[documentation](https://s-fleck.github.io/lgr/)



## See lgr in action



lgr is used to govern console output in my shiny based csv editor 

[shed](https://github.com/s-fleck/shed)



```{r eval = FALSE}

# install.packages("remotes")

remotes::install_github("s-fleck/shed")

library(shed)



# log only output from the "shed" logger to a file

logfile <- tempfile()

lgr::get_logger("shed")$add_appender(AppenderFile$new(logfile))

lgr::threshold("all")



# edit away and watch the rstudio console!

lgr$info("starting shed")

shed(iris)  

lgr$info("this will not end up in the log file")



readLines(logfile)



# cleanup

file.remove(logfile)

```



## Development status



lgr in general is stable and safe for use, but **the following features are still experimental**: 



* Database appenders which are available from the separate package

  [lgrExtra](https://github.com/s-fleck/lgrExtra). 

* yaml/json config files for loggers (do not yet support all planned features)

* The documentation in general. I'm still hoping for more R6-specific features

  in [roxygen2](https://github.com/r-lib/roxygen2) before I invest more

  time in object documentation.



## Dependencies



[R6](https://github.com/r-lib/R6): The R6 class system provides the framework

on which lgr is built and the **only Package lgr will ever depend on**. If you 

are a **package developer** and want to add logging to your package, this is the

only transitive dependency you have to worry about, as configuring of the 

loggers should be left to the user of your package.



### Optional dependencies



lgr comes with a long list of optional dependencies that make a wide range of

appenders possible. You only need the dependencies for the Appenders you

actually want to use. Care was taken to choose packages that are slim, stable, 

have minimal dependencies, and are well maintained :



  

  Extra appenders (in the main package):

  

  - [jsonlite](https://github.com/jeroen/jsonlite) for JSON logging via 

    `LayoutJson`. JSON is a popular plaintext based file format that is easy to 

    read for humans and machines alike.

    

  - [rotor](https://github.com/s-fleck/rotor) for log rotation via 

    AppenderFileRotating and co. 

    

  - [data.table](https://github.com/Rdatatable/) for fast in-memory 

    logging with `AppenderDt`, and also by all database / DBI Appenders. 



  - [glue](https://glue.tidyverse.org/) for a more flexible formatting syntax

    via LoggerGlue and LayoutGlue.

    

  Extra appenders via [lgrExtra](https://github.com/s-fleck/lgrExtra):

  

  - [DBI](https://github.com/r-dbi/DBI) for logging to databases. lgr is 

    confirmed to work with the following backends: 

    - [RSQLite](https://github.com/r-dbi/RSQLite), 

    - [RMariaDB](https://github.com/r-dbi/RMariaDB) for MariaDB and MySQL,

    - [RPostgres](https://cran.r-project.org/package=RPostgres),

    - [RJDBC](https://github.com/s-u/RJDBC) for DB2, and

    - [odbc](https://github.com/r-dbi/odbc) also for DB2. 

    

    In theory all DBI compliant database packages should work. If you

    are using lgr with a database backend, please report your (positive and

    negative) experiences, as database support is still somewhat experimental.

  

  - [gmailr](https://cran.r-project.org/package=gmailr) or

  

  - [sendmailR](https://cran.r-project.org/package=sendmailR) for email 

      notifications.

      

  - [RPushbullet](https://github.com/eddelbuettel/rpushbullet) for push

    notifications.

    

  - [Rsyslog](https://cran.r-project.org/package=rsyslog) for logging to

    syslog on POSIX-compatible systems.

    

  - [elastic](https://cran.r-project.org/package=elastic) for logging

    to ElasticSearch

  

  Other extra features:

  

  - [yaml](https://CRAN.R-project.org/package=yaml) for configuring loggers 

    via YAML files  

  - [crayon](https://github.com/r-lib/crayon) for colored console output.  

  - [whoami](https://github.com/r-lib/whoami/blob/master/DESCRIPTION) for 

    guessing the user name from various sources. You can also set the user name 

    manually if you want to use it for logging.

  - [desc](https://CRAN.R-project.org/package=desc) for the package development

    convenience function `use_logger()`

  - [cli](https://CRAN.R-project.org/package=cli) for printing the tree 

    structure of registered loggers with `logger_tree()`

    

  Other `Suggests`

  ([future](https://CRAN.R-project.org/package=future), 

  [future.apply](https://CRAN.R-project.org/package=future.apply)) do not 

  provide extra functionality but had to be included for some of 

  the automated unit tests run by lgr.

    



## Installation



You can install lgr from CRAN



```{r eval = FALSE}

install.packages("lgr")

```



Or you can install the current development version directly from github



```{r eval = FALSE}

#install.packages("remotes")

remotes::install_github("s-fleck/lgr")

```



## Outlook



The long term goal is to support (nearly) all features of the python logging

module. If you have experience with python logging or Log4j and are missing

features/appenders that you'd like to see, please feel free to post a feature 

request on the issue tracker.



## Acknowledgement



* [diagrams.net](https://app.diagrams.net/) for the flow chart in the vignette