On Thu, Apr 10, 2014 at 5:46 PM, Michael Armbrust
mich...@databricks.com wrote:
... all of the suffer from the fact that the log message needs to be built
even
though it might not be used.
This is not true of the current implementation (and this is actually why
Spark has a logging trait
Another usage that's nice is:
logDebug {
val timeS = timeMillis/1000.0
sTime: $timeS
}
which can be useful for more complicated expressions.
On Thu, Apr 10, 2014 at 5:55 PM, Michael Armbrust mich...@databricks.comwrote:
BTW...
You can do calculations in string interpolation:
sTime:
Hey there,
While going through the try to get the hang of things, I've noticed
several different styles of logging. They all have some downside
(readability being one of them in certain cases), but all of the
suffer from the fact that the log message needs to be built even
though it might not be
Hi Marcelo,
Thanks for bringing this up here, as this has been a topic of debate
recently. Some thoughts below.
... all of the suffer from the fact that the log message needs to be built
even
though it might not be used.
This is not true of the current implementation (and this is actually
BTW...
You can do calculations in string interpolation:
sTime: ${timeMillis / 1000}s
Or use format strings.
fFloat with two decimal places: $floatValue%.2f
More info:
http://docs.scala-lang.org/overviews/core/string-interpolation.html
On Thu, Apr 10, 2014 at 5:46 PM, Michael Armbrust
