# Structured Logging

#### Updated as of 2021-11-03

Traditionally, messages emitted from R packages or scripts are unstructured messages, like this one from the shiny package:

Listening on http://localhost:8080

A richer, more structured representation of this log message might be:

level=INFO ts=2021-10-21T20:21:01Z message="starting Shiny" host=localhost port=8080 shiny_version=1.6.0 appdir=projects/mycoolapp

This second message uses structured logging, attaching relevant metadata to a log message as standalone fields.

• They tend to have more standard, predictable content, which can make reading them easier (especially if you did not write the message yourself); and

• They are much, much easier for log management and aggregation systems to query – fields can be used to aggregate logs into metrics like “how many times has X happened” and “how long does task Y take on average”, and individual fields can be used to answer questions like “what happened to user Z over their last few logins”.

log4r includes support for two of the most popular structured logging formats:

• json_log_layout: Emit log messages as JSON, likely the most widely-used.

• logfmt_layout: Emit log messages using the more human-friendly logfmt.

To use these formats, you can pass additional arguments to the existing logging functions info(), warn() and error().

## JSON Logs

The most popular format for structured logging is probably JSON, which you can configure as follows:

logger <- logger(appenders = console_appender(json_log_layout()))

As an example, suppose you are logging unauthorised requests to a Plumber API. You might have a log message with fields like the following:

# Here "req" and "res" are slightly fake request & response objects.
info(
logger, message = "authentication failed",
method = req$REQUEST_METHOD, path = req$PATH_INFO,
params = sub("^\\?", "", req$QUERY_STRING), user_agent = req$HTTP_USER_AGENT,
remote_addr = req$REMOTE_ADDR, status = res$status
)
#> {"message":"authentication failed","method":"POST","path":"/upload","params":"","user_agent":"curl/7.58.0","remote_addr":"124.133.52.161","status":401,"level":"INFO","time":"2021-11-03T16:36:59Z"}

## logfmt Logs

An alternative to JSON is the popular, more human-friendly logfmt style, which you can configure as follows:

logger <- logger(appenders = console_appender(logfmt_log_layout()))

As an example, you might have the following in a script that processes many CSV files:

info(
logger, message = "processed entries", file = filename,
entries = nrow(entries),
elapsed = unclass(difftime(Sys.time(), start, units = "secs"))
)
#> level=INFO ts=2021-11-03T16:36:59Z message="processed entries" file=catpics_01.csv entries=4124 elapsed=0.03846