Extended logging solution:
- transactional logging
- insert log getting sequence id
- evaluate expression with timing and exception handling
- update log with status and metadata
- log to postgres database
- records errors, warnings, messages, interrupts
- log process metadata: in/out nrow, flexible list of custom metadata
- hierarchical logging: parent log id collected in case of nested logging
- high precision timing with optional microbenchmarkCore
- support parallel processing
- email notification on alerts
install.packages("logR", repos = c("https://jangorecki.gitlab.io/logR", "https://cran.rstudio.com"))
For high precision timing install suggested package microbenchmarkCore.
install.packages("microbenchmarkCore", repos = "https://olafmersmann.github.io/drat")
- Connect to postgres db with
?logR_connect
, it will use environment variables if available. Connection is assigned togetOption("logR.conn")
logical value returned, FALSE on failure otherwise TRUE. - Create db table with
?logR_schema
. - Use
?logR
call as wrapper over your processes. - Use
?logR_dump
to dump logs.
An example from SO answer.
library(logR)
# setup connection, default to env vars: `POSTGRES_DB`, etc.
# if you have docker then: docker run --rm -p 127.0.0.1:5432:5432 -e POSTGRES_PASSWORD=postgres --name pg-logr postgres:9.5
logR_connect()
# [1] TRUE
# create logr table
logR_schema()
# make some logging and calls
logR(1+2) # OK
#[1] 3
logR(log(-1)) # warning
#[1] NaN
f = function() stop("an error")
logR(r <- f()) # stop
#NULL
g = function(n) data.frame(a=sample(letters, n, TRUE))
logR(df <- g(4)) # out rows
# a
#1 u
#2 c
#3 w
#4 p
# try CTRL+C / 'stop' button to interrupt
logR(Sys.sleep(15))
# wrapper to: dbReadTable(conn = getOption("logR.conn"), name = "logr")
logR_dump()
# logr_id logr_start expr status alert logr_end timing in_rows out_rows mail message cond_call cond_message
#1: 1 2016-02-08 16:35:00.148 1 + 2 success FALSE 2016-02-08 16:35:00.157 0.000049163 NA NA FALSE NA NA NA
#2: 2 2016-02-08 16:35:00.164 log(-1) warning TRUE 2016-02-08 16:35:00.171 0.000170801 NA NA FALSE NA log(-1) NaNs produced
#3: 3 2016-02-08 16:35:00.180 r <- f() error TRUE 2016-02-08 16:35:00.187 0.000136896 NA NA FALSE NA f() an error
#4: 4 2016-02-08 16:35:00.197 df <- g(4) success FALSE 2016-02-08 16:35:00.213 0.000696145 NA 4 FALSE NA NA NA
#5: 5 2016-02-08 16:35:00.223 Sys.sleep(15) interrupt TRUE 2016-02-08 16:35:05.434 5.202319000 NA NA FALSE NA NA NA
For more details see R scripts in tests
directory and read manual.
Working examples of logR usage can also be found in big.data.table and pg packages. logR can be easily located as suggested dependency and works conditionally.
GPL-3