Logging is critical for monitoring pipelines in a deployed environment. Logging is a way of tracking error messages, warnings, and other user generated informational messages. Maestro provides several options for logging these conditions from pipelines - logging to a file and/or logging to the console. Maestro leverages logger for managing and formatting logs.
R has three types of conditions (increasing in severity): message, warning, and error. Maestro makes use of this system for reporting the statuses of pipelines and for managing logs. For this reason, we recommend using message() for adding informational logs to pipelines in contrast to print() and cat().
There are also several logging packages for R including logger and logr. As we’ll see later, these can be integrated with maestro as well with some special modifications.
Maestro allows for a log file to be continuously appended to with pipeline logs. Conventionally, we give it a name of maestro.log, but this can be any text file. Here we create a sample set of 3 pipelines, one with a message, one with a warning, and another with an error.
#' pipelines/logs.R
#' @maestroFrequency hourly
hello <- function() {
message('hello')
}
#' @maestroFrequency hourly
uhh <- function() {
warning('this could be a problem')
}
#' @maestroFrequency hourly
oh_no <- function() {
stop('this is bad')
}Now we run the orchestrator. For demo purposes, we’ll set run_all = TRUE to allow all pipelines to run regardless of scheduling. We set log_to_file = TRUE argument to tell it to log to a file. This creates (and later appends to) a generic ‘maestro.log’ file in the project directory. We can see the typical output of a maestro schedule as the pipeline runs.
# orchestrator.R
library(maestro)
schedule <- build_schedule(quiet = TRUE)
status <- run_schedule(
schedule,
run_all = TRUE,
log_to_file = TRUE
)── [2024-12-10 13:40:10] Running pipelines ▶ ✔ hello [21ms] ✔ uhh [91ms] ✖ oh_no [16ms] ── [2024-12-10 13:40:11] Pipeline execution completed ■ | 0.172 sec elapsed ✔ 2 successes | → 0 skipped | ! 1 warning | ✖ 1 error | ◼ 3 total ✖ Use `last_run_errors()` to show pipeline errors. ! Use `last_run_warnings()` to show pipeline warnings. ────────────────────────────────────────────────────────────────────────────────
Now let’s take a look at the log file. We can see the logs formatted with the name of the pipeline from where the message came, the type of log (INFO, WARN, or ERROR), the timestamp, and the message itself.
readLines("maestro.log")[1] "[hello] [INFO] [2024-12-10 13:40:10.86378]: hello" [2] "[uhh] [WARN] [2024-12-10 13:40:10.906507]: this could be a problem" [3] "[oh_no] [ERROR] [2024-12-10 13:40:10.999498]: this is bad"
We can also have the logs printed directly to the console using the log_to_console argument.
# orchestrator.R
schedule <- build_schedule(quiet = TRUE)
status <- run_schedule(
schedule,
run_all = TRUE,
log_to_console = TRUE
)── [2024-12-10 13:40:11] Running pipelines ▶ [hello] [INFO] [2024-12-10 13:40:11.21907]: hello ✔ hello [11ms] [uhh] [WARN] [2024-12-10 13:40:11.240539]: this could be a problem ✔ uhh [12ms] [oh_no] [ERROR] [2024-12-10 13:40:11.259333]: this is bad ✖ oh_no [15ms] ── [2024-12-10 13:40:11] Pipeline execution completed ■ | 0.063 sec elapsed ✔ 2 successes | → 0 skipped | ! 1 warning | ✖ 1 error | ◼ 3 total ✖ Use `last_run_errors()` to show pipeline errors. ! Use `last_run_warnings()` to show pipeline warnings. ────────────────────────────────────────────────────────────────────────────────
Now the logs have been interwoven with the output from maestro. Both logging options operate independently, so it is possible to log to a file and to the console.
Maestro uses the concept of log levels (also known as thresholds) to allow users to suppress logs that do not meet a severity threshold. If, for example, you were only concerned with error messages and wanted to ignore warnings and info messages, you would use the maestroLogLevel tag for the relevant pipelines.
#' pipelines/logs.R
#' @maestroFrequency hourly
#' @maestroLogLevel ERROR
hello <- function() {
message('hello')
}
#' @maestroFrequency hourly
#' @maestroLogLevel ERROR
uhh <- function() {
warning('this could be a problem')
}
#' @maestroFrequency hourly
#' @maestroLogLevel ERROR
oh_no <- function() {
stop('this is bad')
}Now, only the error messages are displayed and logged.
# orchestrator.R
schedule <- build_schedule(quiet = TRUE)
status <- run_schedule(
schedule,
run_all = TRUE,
log_to_console = TRUE
)── [2024-12-10 13:40:11] Running pipelines ▶ ✔ hello [9ms] ✔ uhh [8ms] [oh_no] [ERROR] [2024-12-10 13:40:11.452916]: this is bad ✖ oh_no [10ms] ── [2024-12-10 13:40:11] Pipeline execution completed ■ | 0.051 sec elapsed ✔ 2 successes | → 0 skipped | ! 1 warning | ✖ 1 error | ◼ 3 total ✖ Use `last_run_errors()` to show pipeline errors. ! Use `last_run_warnings()` to show pipeline warnings. ────────────────────────────────────────────────────────────────────────────────
By default, pipelines use a log level of INFO, which means that all messages, warnings, and errors are logged.
If you wish to use other logging libraries, we recommend using logger. You can put these statements anywhere in your pipelines and they’ll propagate into the logs that maestro uses. Best approach is to use the namespace argument and reference the name of the pipeline.
#' pipelines/logs.R
#' @maestroFrequency hourly
hello <- function() {
logger::log_info("hi", namespace = "hello")
}
#' @maestroFrequency hourly
uhh <- function() {
logger::log_warn("this could be a problem", namespace = "uhh")
}
#' @maestroFrequency hourly
oh_no <- function() {
logger::log_error("this is bad", namespace = "oh_no")
}# orchestrator.R
schedule <- build_schedule(quiet = TRUE)
status <- run_schedule(
schedule,
run_all = TRUE,
log_to_console = TRUE
)── [2024-12-10 13:40:11] Running pipelines ▶ [hello] [INFO] [2024-12-10 13:40:11.596243]: hi ✔ hello [10ms] [uhh] [WARN] [2024-12-10 13:40:11.613611]: this could be a problem ✔ uhh [12ms] [oh_no] [ERROR] [2024-12-10 13:40:11.633111]: this is bad ✔ oh_no [14ms] ── [2024-12-10 13:40:11] Pipeline execution completed ■ | 0.059 sec elapsed ✔ 3 successes | → 0 skipped | ! 0 warnings | ✖ 0 errors | ◼ 3 total ────────────────────────────────────────────────────────────────────────────────
Note that logger just creates messages and does not actually trigger conditions. This impacts how the statuses of pipelines appear. It is important to use match the namespace with the name of your pipeline (i.e., the function name) for the logs to appear in a log file.