I have the following code:
def disableRules(someId: String) = Action.async { implicit req =>
Metrics.measureTime("disableRules") {
someFutureOpr(someId).map(_ => Ok)
.recover {
case e: Exception => handlerError(s"Failure occurred on disableRules request ${e.getMessage}", "disableRules")
}
}
}
def activeRules(someId: String) = Action.async { implicit req =>
Metrics.measureTime("activeRules") {
someFutureOpr2(someId).map(_ => Ok)
.recover {
case e: Exception => handlerError(s"Failure occurred on activeRules request ${e.getMessage}", "activeRules")
}
}
}
...
As you can see, I have mesureTime and handleError functions that I pass to them the name of the function as String, is there way to make it implicitly, I mean its will take the function Name, if not - there way to extract the function Name and print it, also regarding params.
You can solve this with a macro (and no runtime reflection):
import scala.language.experimental.macros
import scala.reflect.macros.blackbox
class CaptureImpl(val c: blackbox.Context) {
import c.universe._
def describe[T: c.WeakTypeTag](
expr: c.Expr[T]
): c.Expr[String] = c.Expr[String](q"(${expr.tree.toString()})")
}
object CaptureMethod {
def apply[T](expr: T): String = macro CaptureImpl.describe[T]
}
Example:
object Test {
def foo(): String = "hello"
def bar(a: Int): Int = a
def baz(s: String): String = s
def main(args: Array[String]): Unit = {
println(CaptureMethod(foo()))
println(CaptureMethod(bar(1)))
println(CaptureMethod(baz("yes")))
}
}
Yields:
Test.this.foo()
Test.this.bar(1)
Test.this.baz("yes")
Calculate it inside Metrics:
object Metrics {
def currentMethodName() : String = Thread.currentThread.getStackTrace()(3).getMethodName
def measureTime(): Unit = {
println(currentMethodName)
}
}
Then for example:
def a1() = {
Metrics.measureTime()
}
def a2() = {
Metrics.measureTime()
}
will output:
a1
a2
Is this a safe operation?
If we had:
def currentMethodName() : String = Thread.currentThread.getStackTrace.toList.mkString("\n")
we get:
java.lang.Thread.getStackTrace(Thread.java:1559)
HelloWorld1$Metrics$.currentMethodName(HelloWorld1.scala:69)
HelloWorld1$Metrics$.measureTime(HelloWorld1.scala:72)
HelloWorld1$.a1(HelloWorld1.scala:77)
HelloWorld1$.main(HelloWorld1.scala:103)
HelloWorld1.main(HelloWorld1.scala)
So we see that:
In index 0 we get getStackTrace.
In index 1 we have currentMethodName.
In index 2 we have measureTime.
Since measureTime is not the first method of the stack trace, fir sure we have another element in the stack trace. Therefore in your case yes, it is safe.
Related
I have an array of Any (in real life, it's a Spark Row, but it's sufficient to isolate the problem)
object Row {
val buffer : Array[Any] = Array(42, 21, true)
}
And I want to apply some operations on its elements.
So, I've defined a simple ADT to define a compute operation on a type A
trait Op[A] {
def cast(a: Any) : A = a.asInstanceOf[A]
def compute(a: A) : A
}
case object Count extends Op[Int] {
override def compute(a: Int): Int = a + 1
}
case object Exist extends Op[Boolean] {
override def compute(a: Boolean): Boolean = a
}
Given that I have a list of all operations and I know which operation is to apply to each element, let's use these operations.
object GenericsOp {
import Row._
val ops = Seq(Count, Exist)
def compute() = {
buffer(0) = ops(0).compute(ops(0).cast(buffer(0)))
buffer(1) = ops(0).compute(ops(0).cast(buffer(1)))
buffer(2) = ops(1).compute(ops(1).cast(buffer(2)))
}
}
By design, for a given op, types are aligned between cast and combine. But unfortunately the following code does not compile. The error is
Type mismatch, expected: _$1, actual: AnyVal
Is there a way to make it work ?
I've found a workaround by using abstract type member instead of type parameter.
object AbstractOp extends App {
import Row._
trait Op {
type A
def compute(a: A) : A
}
case object Count extends Op {
type A = Int
override def compute(a: Int): Int = a + 1
}
case object Exist extends Op {
type A = Boolean
override def compute(a: Boolean): Boolean = a
}
val ops = Seq(Count, Exist)
def compute() = {
val op0 = ops(0)
val op1 = ops(1)
buffer(0) = ops(0).compute(buffer(0).asInstanceOf[op0.A])
buffer(1) = ops(0).compute(buffer(1).asInstanceOf[op0.A])
buffer(2) = ops(1).compute(buffer(2).asInstanceOf[op1.A])
}
}
Is there a better way ?
It seems that your code can be simplified by making Op[A] extend Any => A:
trait Op[A] extends (Any => A) {
def cast(a: Any) : A = a.asInstanceOf[A]
def compute(a: A) : A
def apply(a: Any): A = compute(cast(a))
}
case object Count extends Op[Int] {
override def compute(a: Int): Int = a + 1
}
case object Exist extends Op[Boolean] {
override def compute(a: Boolean): Boolean = a
}
object AbstractOp {
val buffer: Array[Any] = Array(42, 21, true)
val ops: Array[Op[_]] = Array(Count, Count, Exist)
def main(args: Array[String]): Unit = {
for (i <- 0 until buffer.size) {
buffer(i) = ops(i)(buffer(i))
}
println(buffer.mkString("[", ",", "]"))
}
}
Since it's asInstanceOf everywhere anyway, it does not make the code any less safe than what you had previously.
Update
If you cannot change the Op interface, then invoking cast and compute is a bit more cumbersome, but still possible:
trait Op[A] {
def cast(a: Any) : A = a.asInstanceOf[A]
def compute(a: A) : A
}
case object Count extends Op[Int] {
override def compute(a: Int): Int = a + 1
}
case object Exist extends Op[Boolean] {
override def compute(a: Boolean): Boolean = a
}
object AbstractOp {
val buffer: Array[Any] = Array(42, 21, true)
val ops: Array[Op[_]] = Array(Count, Count, Exist)
def main(args: Array[String]): Unit = {
for (i <- 0 until buffer.size) {
buffer(i) = ops(i) match {
case op: Op[t] => op.compute(op.cast(buffer(i)))
}
}
println(buffer.mkString("[", ",", "]"))
}
}
Note the ops(i) match { case op: Opt[t] => ... } part with a type-parameter in the pattern: this allows us to make sure that cast returns a t that is accepted by compute.
As a more general solution than Andrey Tyukin's, you can define the method outside Op, so it works even if Op can't be modified:
def apply[A](op: Op[A], x: Any) = op.compute(op.cast(x))
buffer(0) = apply(ops(0), buffer(0))
If we define the following function:
def methodWithImplicit(explicit: String)(implicit imp: String) = {
println(explicit + imp)
}
we can call it as follows:
methodWithImplicit("abc")("efg") //abc - explicit, efg - imp
And it works fine. Now consider the following TypeClass:
trait MyTypeClass[T] {
def accept(t: T): T
}
which is going to be used inside extractor object:
object TestExtractor {
def unapply(str: String)(implicit myTypeClass: MyTypeClass[String]): Option[String] =
if (!str.isEmpty)
Some(myTypeClass.accept(str))
else
None
}
So if we use it as follows:
implicit val myTypeClass:MyTypeClass[String] = new MyTypeClass[String] {
override def accept(t: String): Unit = t
}
"123" match {
case TestExtractor(str) => println(str)
}
It works ok. But how to pass the parameter explicitly when using with pattern matching? I tried
"123" match {
case TestExtractor(str)(myTypeClass) => println(str) //compile error
}
and
"123" match {
case TestExtractor(myTypeClass)(str) => println(str) //compile error
}
But it does not compile.
Since the left hand side seems to accept essentially nothing but trees built from stable identifiers, constant literals, and lower-case letters for variable names, I don't see any way to get closer to the desired syntax than this:
val `TestExtractor(myTypeClass)` = TestExtractor(myTypeClass)
"hello" match {
case `TestExtractor(myTypeClass)`(str) => println(str)
}
This of course requires that you define the weirdly named value TestExtractor(myTypeClass) (in backticks) right before the match-case, so you can use it as a single symbol.
Full code:
trait MyTypeClass[T] {
def accept(t: T): T
}
object TestExtractor { outer =>
def unapply(str: String)(implicit myTypeClass: MyTypeClass[String]): Option[String] =
if (!str.isEmpty)
Some(myTypeClass.accept(str))
else
None
class ExplicitTestExtractor(tc: MyTypeClass[String]) {
def unapply(t: String) = outer.unapply(t)(tc)
}
def apply(tc: MyTypeClass[String]): ExplicitTestExtractor =
new ExplicitTestExtractor(tc)
}
implicit val myTypeClass:MyTypeClass[String] = new MyTypeClass[String] {
override def accept(t: String): String = t.toUpperCase
}
val `TestExtractor(myTypeClass)` = TestExtractor(myTypeClass)
"hello" match {
case `TestExtractor(myTypeClass)`(str) => println(str)
}
Here is the code:
def transform1(f: String => String): Unit = {
val s = getString
f.andThen(putString)(s)
}
def transform2(f: String => Option[String]): Unit = {
val s = getString
f(s).foreach(putString(_))
}
How do you express these two ideas in one single function?
Method overloading does not work and seems discouraged by the community.
I didn't understand that why anyone may want this but here is a way to do it:
def transform(f: Either[(String => String), (String => Option[String])]: Unit = f match {
case Left(f) => // do transform1 here
case Right(f) => //do transform2 here
}
As I said at the begining you probably shouldn't want to do this; perhaps you should directly ask what you want.
The pattern to avoid overloading is to convert disparate arguments to a common, specific type. There could be any number of such conversions.
Not sure this is the most compelling example, however.
object X {
trait MapFlat[-A, +B] { def apply(x: A): B }
implicit class mapper[A](val f: A => A) extends MapFlat[A, A] {
override def apply(x: A) = {
val res = f(x)
println(res)
res
}
}
implicit class flatmapper[A](val f: A => Option[A]) extends MapFlat[A, Option[A]] {
override def apply(x: A) = {
val res = f(x)
res foreach println
res
}
}
def f[B](g: MapFlat[String, B]) = {
g("abc")
}
}
object Test extends App {
import X._
f((s: String) => s)
f((s: String) => Some(s))
}
One way to do it will be type classes, here's a sample -
trait Transformer[T] {
def transform(foo: String => T)
}
object Transformer {
implicit object StringTransformer extends Transformer[String] {
override def transform(foo: (String) => String): Unit = ??? // Your logic here
}
implicit object OptStringTransformer extends Transformer[Option[String]] {
override def transform(foo: (String) => Option[String]): Unit = ??? // Your logic here
}
}
class SampleClass {
def theOneTransformYouWant[T: Transformer](f: String => T) = {
implicitly[Transformer[T]].transform(f)
}
def canUseBothWays(): Unit = {
theOneTransformYouWant((s: String) => s)
theOneTransformYouWant((s: String) => Some(s))
}
}
Another way would be the magnet pattern
http://spray.io/blog/2012-12-13-the-magnet-pattern/
sealed trait TransformationMagnet {
def apply(): Unit
}
object TransformationMagnet {
implicit def fromString(f: String => String): TransformationMagnet =
new TransformationMagnet {
def apply(): Unit = ??? // Your code goes here
}
implicit def fromOptString(f: String => Option[String]): TransformationMagnet =
new TransformationMagnet {
def apply(): Unit = ??? // your code goes here
}
}
class SampleClass {
def theOneTransformYouWant(f: TransformationMagnet) = {
???
}
def hereWeUseItInBothWays(): Unit = {
theOneTransformYouWant((s: String) => s)
theOneTransformYouWant((s: String) => Some(s))
}
}
add a new parameter on the description typeOfTransform
add a conditional inside the function
if (typeOfTransform == type1){
//functionality1
}else {
//functionality2
}
Just for completeness, you can actually overload methods like this by adding implicit arguments which will always be available:
def transform(f: String => Option[String]): Unit = ...
def transform(f: String => String)(implicit d: DummyImplicit): Unit = ...
Can't I use a generic on the unapply method of an extractor along with an implicit "converter" to support a pattern match specific to the parameterised type?
I'd like to do this (Note the use of [T] on the unapply line),
trait StringDecoder[A] {
def fromString(string: String): Option[A]
}
object ExampleExtractor {
def unapply[T](a: String)(implicit evidence: StringDecoder[T]): Option[T] = {
evidence.fromString(a)
}
}
object Example extends App {
implicit val stringDecoder = new StringDecoder[String] {
def fromString(string: String): Option[String] = Some(string)
}
implicit val intDecoder = new StringDecoder[Int] {
def fromString(string: String): Option[Int] = Some(string.charAt(0).toInt)
}
val result = "hello" match {
case ExampleExtractor[String](x) => x // <- type hint barfs
}
println(result)
}
But I get the following compilation error
Error: (25, 10) not found: type ExampleExtractor
case ExampleExtractor[String] (x) => x
^
It works fine if I have only one implicit val in scope and drop the type hint (see below), but that defeats the object.
object Example extends App {
implicit val intDecoder = new StringDecoder[Int] {
def fromString(string: String): Option[Int] = Some(string.charAt(0).toInt)
}
val result = "hello" match {
case ExampleExtractor(x) => x
}
println(result)
}
A variant of your typed string decoder looks promising:
trait StringDecoder[A] {
def fromString(s: String): Option[A]
}
class ExampleExtractor[T](ev: StringDecoder[T]) {
def unapply(s: String) = ev.fromString(s)
}
object ExampleExtractor {
def apply[A](implicit ev: StringDecoder[A]) = new ExampleExtractor(ev)
}
then
implicit val intDecoder = new StringDecoder[Int] {
def fromString(s: String) = scala.util.Try {
Integer.parseInt(s)
}.toOption
}
val asInt = ExampleExtractor[Int]
val asInt(Nb) = "1111"
seems to produce what you're asking for. One problem remains: it seems that trying to
val ExampleExtractor[Int](nB) = "1111"
results in a compiler crash (at least inside my 2.10.3 SBT Scala console).
I'm having some problems with a macro I've written to help me log metrics represented as case class instances to to InfluxDB. I presume I'm having a type erasure problem and that the tyep parameter T is getting lost, but I'm not entirely sure what's going on. (This is also my first exposure to Scala macros.)
import scala.language.experimental.macros
import play.api.libs.json.{JsNumber, JsString, JsObject, JsArray}
abstract class Metric[T] {
def series: String
def jsFields: JsArray = macro MetricsMacros.jsFields[T]
def jsValues: JsArray = macro MetricsMacros.jsValues[T]
}
object Metrics {
case class LoggedMetric(timestamp: Long, series: String, fields: JsArray, values: JsArray)
case object Kick
def log[T](metric: Metric[T]): Unit = {
println(LoggedMetric(
System.currentTimeMillis,
metric.series,
metric.jsFields,
metric.jsValues
))
}
}
And here's an example metric case class:
case class SessionCountMetric(a: Int, b: String) extends Metric[SessionCountMetric] {
val series = "sessioncount"
}
Here's what happens when I try to log it:
scala> val m = SessionCountMetric(1, "a")
m: com.confabulous.deva.SessionCountMetric = SessionCountMetric(1,a)
scala> Metrics.log(m)
LoggedMetric(1411450638296,sessioncount,[],[])
Even though the macro itself seems to work fine:
scala> m.jsFields
res1: play.api.libs.json.JsArray = ["a","b"]
scala> m.jsValues
res2: play.api.libs.json.JsArray = [1,"a"]
Here's the actual macro itself:
import scala.language.experimental.macros
import scala.reflect.macros.blackbox.Context
object MetricsMacros {
private def fieldNames[T: c.WeakTypeTag](c: Context)= {
val tpe = c.weakTypeOf[T]
tpe.decls.collect {
case field if field.isMethod && field.asMethod.isCaseAccessor => field.asTerm.name
}
}
def jsFields[T: c.WeakTypeTag](c: Context) = {
import c.universe._
val names = fieldNames[T](c)
Apply(
q"play.api.libs.json.Json.arr",
names.map(name => Literal(Constant(name.toString))).toList
)
}
def jsValues[T: c.WeakTypeTag](c: Context) = {
import c.universe._
val names = fieldNames[T](c)
Apply(
q"play.api.libs.json.Json.arr",
names.map(name => q"${c.prefix.tree}.$name").toList
)
}
}
Update
I tried Eugene's second suggestion like this:
abstract class Metric[T] {
def series: String
}
trait MetricSerializer[T] {
def fields: Seq[String]
def values(metric: T): Seq[Any]
}
object MetricSerializer {
implicit def materializeSerializer[T]: MetricSerializer[T] = macro MetricsMacros.materializeSerializer[T]
}
object Metrics {
def log[T: MetricSerializer](metric: T): Unit = {
val serializer = implicitly[MetricSerializer[T]]
println(serializer.fields)
println(serializer.values(metric))
}
}
with the macro now looking like this:
object MetricsMacros {
def materializeSerializer[T: c.WeakTypeTag](c: Context) = {
import c.universe._
val tpe = c.weakTypeOf[T]
val names = tpe.decls.collect {
case field if field.isMethod && field.asMethod.isCaseAccessor => field.asTerm.name
}
val fields = Apply(
q"Seq",
names.map(name => Literal(Constant(name.toString))).toList
)
val values = Apply(
q"Seq",
names.map(name => q"metric.$name").toList
)
q"""
new MetricSerializer[$tpe] {
def fields = $fields
def values(metric: Metric[$tpe]) = $values
}
"""
}
}
However, when I call Metrics.log -- specifically when it calls implicitly[MetricSerializer[T]] I get the following error:
error: value a is not a member of com.confabulous.deva.Metric[com.confabulous.deva.SessionCountMetric]
Why is it trying to use Metric[com.confabulous.deva.SessionCountMetric] instead of SessionCountMetric?
Conclusion
Fixed it.
def values(metric: Metric[$tpe]) = $values
should have been
def values(metric: $tpe) = $values
You're in a situation that's very close to one described in a recent question: scala macros: defer type inference.
As things stand right now, you'll have to turn log into a macro. An alternative would also to turn Metric.jsFields and Metric.jsValues into JsFieldable and JsValuable type classes materialized by implicit macros at callsites of log (http://docs.scala-lang.org/overviews/macros/implicits.html).