Another usage that's nice is:
logDebug {
val timeS = timeMillis/1000.0
s"Time: $timeS"
}
which can be useful for more complicated expressions.
On Thu, Apr 10, 2014 at 5:55 PM, Michael Armbrust <mich...@databricks.com>wrote:
> BTW...
>
> You can do calculations in string interpolation:
> s"Time: ${timeMillis / 1000}s"
>
> Or use format strings.
> f"Float 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 <mich...@databricks.com
> >wrote:
>
> > 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 why
> > Spark has a logging trait instead of just using a logger directly.)
> >
> > If you look at the original function signatures:
> >
> > protected def logDebug(msg: => String) ...
> >
> >
> > The => implies that we are passing the msg by name instead of by value.
> > Under the covers, scala is creating a closure that can be used to
> calculate
> > the log message, only if its actually required. This does result is a
> > significant performance improvement, but still requires allocating an
> > object for the closure. The bytecode is really something like this:
> >
> > val logMessage = new Function0() { def call() = "Log message" +
> someExpensiveComputation() }
> > log.debug(logMessage)
> >
> >
> > In Catalyst and Spark SQL we are using the scala-logging package, which
> > uses macros to automatically rewrite all of your log statements.
> >
> > You write: logger.debug(s"Log message $someExpensiveComputation")
> >
> > You get:
> >
> > if(logger.debugEnabled) {
> > val logMsg = "Log message" + someExpensiveComputation()
> > logger.debug(logMsg)
> > }
> >
> > IMHO, this is the cleanest option (and is supported by Typesafe). Based
> > on a micro-benchmark, it is also the fastest:
> >
> > std logging: 19885.48ms
> > spark logging 914.408ms
> > scala logging 729.779ms
> >
> > Once the dust settles from the 1.0 release, I'd be in favor of
> > standardizing on scala-logging.
> >
> > Michael
> >
>
