Exists Cash to Be Made Spread gclub Betting on Sports?

In some cases a team comes along like the 1999 St. Louis Rams, who won the Super Dish that year in spite of entering the period as a longshot at extraordinary 200/1 probabilities. When such longshots can be found in it demonstrates one more strong appeal of future bets, that a follower can make a small bank on a longshot, appreciate following their results all season, and also just perhaps pay a massive ticket! A more secure method is to pick a playoff group or one that just lost out from the previous year and made some wise relocate to turn them right into a challenger.

Betting the preseason fave like in 2014’s champs is a little bit high-risk considering that their odds are currently so reduced and also a whole lot even more loan would certainly require to be wagered to make it rewarding. Nonetheless, there is still some value in instances such as this if you really feel highly that they can go deep right into the บาคาร่า playoffs again. The more they go, the smaller their chances will likely get on the way and the even more worth you will have by betting far ahead of time.

Sports Betting – Is It Worthwhile?

That’s the lower line with any futures bet – the true value boils down to exactly how you really feel about a group compared to the oddsmaker’s projections. If your Tips to Win point to a team worth banking on and that team eventually follows up, you’ll have a rooting passion all period long and also will obtain the really many out of your futures wager!

Of course, there are various other important details and sports wagering basics that you need to go via if you intend to make better and better wagering choices. For sure, it would certainly be a great deal of fun if you bet in sports as well as you likewise win a lot of the time.

Carolyn Anderson enjoys sharing excellent sources that will assist you to enjoy sports wagering much more. For a total guide on how to make money in sports wagering, check out this Sports Arbitrage. Likewise check out No Threat Betting System a sporting activities wagering system that will help you exactly how to put a safe wager. But your perception of exactly how well a group will certainly carry out and also how you interpret the value of the Tips to Win will establish whether or not a group is worth banking on.

Automated Car Races To Victory smart car parking system

While the SUV might be actually befalling of support as fuel costs remain to go up, the Chevy Tahoe is actually taking concerns in to its very own palms (or even steering wheels?). Lately, the motor vehicle intercrossed the goal of a 55-mile competition along with a vacant motorist chair. Naturally, that Tahoe – nicknamed “Boss” – had actually been actually robotized due to the Tartan Racing crew coming from Carnegie Mellon University. During the course of the DARPA obstacle, the automated car experienced quit indicators, junctions, and also active web traffic regions that it possessed to think out exactly how to acquire around – while still conforming along with California (where the activity was actually carried) web traffic rules.

The lorry additionally possessed to transform streets, combine along with relocating automobiles and also draw in to a parking great smart car parking system deal. Of the eleven automobiles that completed, 6 crossed the end line. While “Junior,” an auto produced through a crew coming from Stanford, was actually the 1st finisher along with an opportunity of only over 4 hrs, Boss ended up simply mins eventually – yet began twenty mins responsible for Junior. While the preliminary requirements were actually located on which lorry performed the finest project of getting through the training program – certainly not that ended up quickest – given that the motor vehicles executed therefore properly, the ultimate opinion arrived down to that possessed the fastest opportunity.

Financial Alleviation

The crew feels one of its own significant perks in the DARPA Challenge was actually a software program system it established phoned TROCS, which made visuals computer animations of Boss’s sensing unit and information inputs as it steered. Via this system, frustrating actions can be actually quickly pinpointed and readjusted. GM contributed the Chevy Tahoe to Tartan Racing for usage in the venture newpark solutions automated parking systems. The provider wishes at some point the innovation may be actually made use of to assist do away with the very most typical reason for car collisions – vehicle driver mistake.

The majority of car storage space solution prepares area wonderful focus on complete access for handicapped individuals, an effectively ignited layout, and also broad doors. The technical car parking and also auto storage space system possesses the terrific benefit of creating all garage straight available. A car may be all set to enter lower than 2 moments due to the fact that the technical car parking does not require to relocate every other motor vehicles so as to obtain it. Various private devices, along with several garages each, could be constructed alongside, consequently raising the system’s ability.

Bitcoin: What Is It, as well as Is It Right for Your Business?

A tool backed by the complete belief and credit rating only of its anonymous designers, in whom I consequently put no confidence, as well as to whom I provide no credit score with the exception of resourcefulness. I wouldn’t touch a bitcoin with a 10-foot USB cord. Yet a fair variety of people currently have, as well as numerous more soon may. This is partly since entrepreneurs Cameron and Tyler Winklevoss, best known for their duty in the beginnings of Facebook, are now looking to utilize their technological savvy, and also money, to bring Bitcoin right into the mainstream.

Bitcoins in friendliness

The Winklevosses hope to begin an exchange-traded fund for bitcoins. An ETF would certainly make Bitcoin extra extensively readily available to financiers that do not have the technological knowledge to buy the digital money straight. As of April, the Winklevosses are claimed to have held around 1 percent of all existing bitcoins. The adjustments came in feedback to a March instruction from Financial Crimes Enforcement Network clearing up the application of its guidelines to digital currencies. The buy bitcoin Winklevosses’ recommended ETF would bring a brand-new degree of accountability.

Bitcoin in the retail setting

Created in 2009 by an anonymous cryptographer, Bitcoin operates the premise that anything, also intangible littles code, can have worth so long as adequate people determine to treat it as beneficial. Bitcoins exist only as digital depictions and are not pegged to any standard currency. Gold is a financial investment of last resort. It has little innate worth. It does not generate passion. However due to the fact that its supply is limited, it is seen as being more stable than kinds of money that can be published at will.

What are its benefits?

The problem with gold is that it doesn’t do anything. Considering that gold coins have befallen of usage, most of the globe’s gold now beings in the vaults of central banks and also various other banks. Therefore, gold has little connection to the actual economy. That can appear like a good idea when the real economy seems like a scary location to be. Yet as soon as other attractive investment choices appear bitcoin mining, gold sheds its luster. That is what we have seen with the current decreases in gold prices.

Exactly how to Find the Right Football Betting Forecasts Solution

Next off, I take a look at the situation of each team, like that they last played as well as that they play next to such as divisional challengers. This aids you in determining if there is a side for either team as a result of their timetable and also existing scenario. Lastly I look for emotional aspects when football handicaps like a team coming off a big win or loss, possible disappointments, bounce backs and more. Football is an extremely emotional video game, as well as you ought to never overstate or underestimate this crucial angle. Free wagering tips are frequently given by tipsters concerning this market.

Discovering the very best Football Betting System is Tough

Put in the time to find out these abilities as well as you will be rewarded. Quickly you will recognize when to make the appropriate wager, as well as when to give up a video game. I desire you all the good luck at ending up being an excellent handicapper at football wagering. I appreciate the football season! An additional common market in football betting is: Both teams to score. This market is commonly presented as GG for both teams to rating and also NG for both teams not rack up. It is additionally a favorable market for many punters. The make was not offered in สมัครแทงบอล numerous websites until Betfred introduced it. Various other betting firms have since included this market in their sites.

Football Betting Systems – Can They Be Profitable?

A predictor can either select that both teams will score or both teams not rack up depending upon their evaluation or feel about goal racking up abilities of the groups dealing with each various other. Free wagering pointers are typically given by informants on this market. Punters who are uncertain which alternative to go with should look for services of informants that carry comprehensive แทงบอล evaluation before making a decision whether both teams will certainly score or otherwise. Such punters can pick their favorite group to win or draw the game. The wager will be a winning wager if the team either win or attract the game. Although chances of this market are small, a number of gamblers choose it considering that it in some way minimizes the risk of losing the wager.

Configuring Docker in order to run properly behind a company proxy

Quick post to remember how to setup Docker in order to run behind a proxy:

1. Create docker service configuration file:

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sudo mkdir /etc/systemd/system/docker.service.d
sudo touch /etc/systemd/system/docker.service.d/http-proxy.conf
sudo nano /etc/systemd/system/docker.service.d/http-proxy.conf

Add the following:

[Service]
Environment="HTTP_PROXY=http://xxx:yyy" 
Environment="HTTPS_PROXY=https://xxx:yyy"
Environment="NO_PROXY=localhost,127.0.0.0"

where xxx is the host and yyy is the port of course!

2. Check and apply configuration:

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sudo systemctl daemon-reload

Check proper configuration:

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sudo systemctl show --property Environment docker

Restart docker if the command output is correct:

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sudo systemctl restart docker

You might wish to customize your bashrc too (~/.bashrc), by adding:

export http_proxy="http://xxx:yyy"
export https_proxy="https://xxx:yyy"

Understanding mutability and immutability in Scala

It’s been 4 months now since I started working with Scala and although I’m far away from being considered a Scala expert (the language is far more complex to master than Java, C# or any other programming language), I’ve got crystal clear how mutability and immutability works in the language and also generally (because this is actually a general, language agnostic concept). Unfortunately it seems to me that several developers are struggling to get it right, so in this post I’m gonna try my best to make it simple and clear for all.
There are two level of mutability/immutability:

1. Variable level
2. Object level

A variable is mutable if defined using the var keyword, it’s instead immutable if defined with the val keyword.
The immutability of a variable means that once it has been defined it can’t be reassigned to something else, the mutability of a variable instead means that it’s possible to assign another object to the variable after its definition.
For example the following code won’t compile:

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object Foo extends App {
    val foo: String = "ciao"
    foo = "hello"
}

It will raise this exception:

error: reassignment to val

This happens because foo is immutable (it’s like a constant, it can’t change).
But if we change val with var all will be fine:

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object Foo extends App {
    var foo: String = "ciao"
    foo = "hello"
}

This because foo is now mutable, so we can assign it to another string (one or more times).
BUT regarding object mutability we are not mutating the string object, because a string either referred with val or var is an immutable object! So the reassignment of foo does not change the string object itself, this is not possible, what happens is that a new string object is created (“hello”) and it’s assigned to the variable, later the previous string object “ciao” will be marked for deletion and the garbage collector will get rid off it.
It’s possible to demonstrate that these objects are different by checking their hash code:

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object Foo extends App {
    var foo: String = "ciao"
    println(foo.hashCode)
    println(foo.hashCode)
    
    foo = "hello"
    println(foo.hashCode)
    println(foo.hashCode)
    
    foo = "hi"
    println(foo.hashCode)
    println(foo.hashCode)
}

The code above will print 2 codes of the same value for the object “ciao”, 2 codes of the same value for the object “hello” and 2 codes of the same value for the object “hi” (we have created 3 string objects in memory and thus we have 3 different hash codes for each one).
Mutability and immutability are the reason behind different collections implementations.
One of the most common collection is the List object for example and it’s an immutable one.
It’s possible to “sum” several lists into one, but in my opinion is not a good practice, because we are creating several unnecessary temporary objects in memory.
For example:

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object Foo extends App {
    var myList: List[Int] = List(1, 2, 3)
    myList = myList ::: List(4, 5, 6)
    myList = myList ::: List(6, 7, 8)
    myList = myList ::: List(9, 10, 11)
}

can be better by using a collection designed for mutability like ListBuffer:

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import scala.collection.mutable.ListBuffer
object Foo extends App {
    val buffer: ListBuffer[Int] = ListBuffer(1, 2, 3)
    buffer.append(4, 5, 6)
    buffer.append(6, 7, 8)
    buffer.append(9, 10, 11)
}

and as you can see I defined the buffer as an immutable constant variable, because I do want the buffer to expand (and this is provided by design since it’s a mutable collection) but I don’t want it to be replaced by another buffer with a new assignment… do you get it?

From Python Generators to Scala Streams

Preface

As a “Pythonista” one cool feature of Python that I was looking for in Scala were generators.
Python generators are a sort of iterators on an incrementally generated collection and they are magically created by using the yield keyword (which is also available in Scala but it has a complete different behavior)*.
For example the following Python code generates as many string objects as the given count on demand (this means that we’ll get only once object in memory on each iteration instead of a collection with multiple objects):

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def stream(count):
    for i in range(count):
        yield "string_{}".format(i)

By calling the function above a generator instance will be returned:

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string_generator = stream(10)
print(type(string_generator))

The output will be:

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<class 'generator'>

By iterating on the returned object:

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for item in string_generator:
    print(item)

We will get the following output:

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string_0
string_1
string_2
string_3
string_4
string_5
string_6
string_7
string_8
string_9

So… you might wonder “what’s all the fuss about printing strings in a loop?”, the cool thing is that those objects are generated one by one on demand if and only if it’s needed, this means that you can generates bazillion of objects by having only one of them in memory at time.
This can be tested by adding a simple print statement before the yield statement.

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def stream(count):
    for i in range(count):
        print("Generating new object ({})".format(i))
        yield "string_{}".format(i)

The output of:

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for item in stream(10):
    print(item)

Will be:

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Generating new object (0)
string_0
Generating new object (1)
string_1
Generating new object (2)
string_2
Generating new object (3)
string_3
Generating new object (4)
string_4
Generating new object (5)
string_5
Generating new object (6)
string_6
Generating new object (7)
string_7
Generating new object (8)
string_8
Generating new object (9)
string_9

As you can see by calling stream() you won’t get a preallocated collection of objects but instead you will pick one by one during the iteration. In practice when Python interpreter find a yield inside a loop it does not run it, but instead it turn that into a special class (generator).
Python generators act like iterators, so they can be “traversed” only once (after the first iteration the generator has been consumed and it won’t return any further data).

Let’s get to Scala now…

How can implement the same thing in Scala?
The answer is: by using Stream!
Here is a basic example:

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def stream(): Stream[String] = {
  "one" #:: "two" #:: "three" #:: "four" #:: Stream.empty[String]
}

And by calling:

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stream().foreach(println)

The output will be:

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one
two
three
four

Ok, but how does it work? Well, basically the sign #:: in plain English means “next in the queue will be”, then at the end a Stream.empty acts as a sentinel value which indicates the end of the stream.
Obviously in the real world an approach like the code above does not make sense since it’s hard-coded. In order to make a stream dynamic like the previous Python example, in Scala we have to use recursion:

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def stream(count: Int): Stream[String] = {
  if (count > 0) {
    s"string_$count" #:: stream(count - 1) // <- go on (recursion)
  } else {
    Stream.empty[String] // <- end of stream
  }
}

Which by calling stream(10).foreach(println) produces the following output:

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string_10
string_9
string_8
string_7
string_6
string_5
string_4
string_3
string_2
string_1

As you should have noticed the output is different from the Python implementation, in order to have the same one we need an helper inner function**:

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def generate(count: Int): Stream[String] = {
    def stream(current: Int): Stream[String] = {
        if (current < count) {
            s"string_$current" #:: stream(current + 1)
        } else {
            Stream.empty[String]
        }
    }
    stream(0) // <- start the generation from 0
}

In this way the count will be the same (from 0 to 9).

One more thing…

While both Python generators and Scala streams are lazy, in Scala, generated objects are kept in memory and it’s possible to use the same stream multiple times (even though objects are generated only the first time).
In practice Scala streams are actually lazy collections.
Fortunately this can be easily “fixed” by returning the stream iterator instead of the stream itself (here the inner function is still present just to maintain the same order of the Python implementation, but it’s not mandatory in other implementations):

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def generate(count: Int): Iterator[String] = {
    def stream(current: Int): Stream[String] = {
        if (current < count) {
            s"string_$current" #:: stream(current + 1)
        } else {
            Stream.empty[String]
        }
    }
    stream(0).iterator
}

So later on:

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val stream: Iterator[String] = generate(10)
while (stream.hasNext) {
    println(stream.next())
}

And in this way we will get one and only one object generated and in memory at time.

Notes

* In Scala yield is used in order to implement the same as Python list comprehension:

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for (i <- 1 to 10; if i % 2 == 0) yield i

The code above will returns a collection containing only even numbers:

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Vector(2, 4, 6, 8, 10)

** Honestly speaking I’m not 100% sure that it’s the only way to do it, but the more clean and simple way I figured out.

Final thoughts

The examples that I’ve used are of course dumb, but I think that they are anyway more practical and more useful than the abused Fibonacci sequence generation you can find everywhere.
It’s been hard but after 1 year, I’m finally appreciating Scala and starting to master all the features of the language, in this case streams, which in the end are more flexible than Python generators even though, as usual the design choices of Scala make it hard to understand and remember how things work… for instance: wouldn’t be more readable one of the following alternative syntax:

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"one" then "two" then "three" then Stream.empty[String]
// or
"one" follow "two" follow "three" follow Stream.empty[String]

Instead of:

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"one" #:: "two" #:: "three" #:: Stream.empty[String]

?
…Fuck yes!

Understanding Scala Generics Variances (Invariant, Covariant, Contravariant)

Since in Scala generics variances are not “verbose” as it might be in Java, I had initially some difficulties in understanding them, until I wrote a super-simple proof of concept in which I tried the three possible variances and “see what happens”.
You know…

So… there are three types of variances:

  1. Invariant: [T]
  2. Covariant: [+T]
  3. Contravariant: [-T]

each one implies a different behavior that I’m gonna to demonstrate by creating a generic class called Box three times, one for each variance. I will also use 2 simple classes that would be placed inside the Box brackets.

1. Invariant: [T]

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class Box[T]
abstract class Item
class Coin extends Item
val b1: Box[Item] = new Box[Item] // ok
val b2: Box[Coin] = new Box[Coin] // ok
val b3: Box[Item] = new Box[Coin] // won't compile!
val b4: Box[Coin] = new Box[Item] // won't compile!

Only b1 and b2 are valid statements, since the invariant [T] is the most restrictive one and it doesn’t allow to see a Box of Coin as it was a Box of Item (the supertype) nor a Boxof Item as it was a Box off Coin (the subtype).

2. Covariant: [+T]

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class Box[+T]
abstract class Item
class Coin extends Item
val b1: Box[Item] = new Box[Item] // ok
val b2: Box[Coin] = new Box[Coin] // ok
val b3: Box[Item] = new Box[Coin] // ok
val b4: Box[Coin] = new Box[Item] // won't compile!

By using the covariant [+T] only the last statement won’t compile. So, basically covariant allows polymorphis thus specific types can be seen as more generic ones.
This type of variance is the one for instance used in Scala collections, otherwise a statement like:

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val coins: List[Item] = List(new Coin, new Coin)

wouldn’t be valid.

3. Contravariant: [-T]

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class Box[-T]
abstract class Item
class Coin extends Item
val b1: Box[Item] = new Box[Item] // ok
val b2: Box[Coin] = new Box[Coin] // ok
val b3: Box[Item] = new Box[Coin] // won't compile!
val b4: Box[Coin] = new Box[Item] // ok

By using contravariant [-T] this time only the third statement won’t compile. This is the most weird one since it doesn’t allow a subtype to be seen as its supertype but the contrary: a supertype can be seen as its subtype!

Scala implicit for mere mortals (like me)

One of the most difficult feature to master in Scala (at least for mere mortals of course) is the implicit keyword and its implications especially because it may appear in different places and for different purposes:

  • Implicit methods
  • Implicit parameters
  • Implicit variables
  • Implicit classes
  • Implicit constructors

So what is it useful for?
Implicits are essentially a way to leverage the power of Scala compiler by avoiding (otherwise) explicit calls to methods in our code or allowing us to extend native or third party classes “dynamically”.

Implicit methods

Implicits are used by Scala itself, for example: have you ever wonder why:

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val value: Double = 1 // <- this is an Int not a Double!

compiles without any issue and returns the expected value (1.0)? Implicit in action!
If we look at scala.Int companion object we will find the following implicit method declaration:

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implicit def int2double(x: Int): Double = x.toDouble

So, at compile-time, once the compiler finds a type mismatch in the code that will generate an exception (in this case an Int instead of a Double), it will check for an implicit in the current scope that is able to convert the wrong type into the correct one. If found, the compiler will replace the code by invoking the method for us:

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val value: Double = int2double(1) // <- explicit replacement

Let’s try to do a similar magic by ourselves. Let’s make the compiler be able to understand booleans as 'yes or 'no symbols:

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object MyApp {
  implicit def symbolToBoolean(symbol: Symbol): Boolean = {
    symbol match {
      case 'yes => true
      case 'no => false
    }
  }
  def main(args: Array[String]): Unit = {
    val scalaIsCool: Boolean = 'yes
    val scalaIsEasy: Boolean = 'no
    println(s"scalaIsCool: $scalaIsCool")
    println(s"scalaIsEasy: $scalaIsEasy")
  }
}

So, once the compiler encounters the following assignment:

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val scalaIsCool: Boolean = 'yes

Since a boolean can be only true or false, it will check for a way to give this symbol a boolean value, so it proceeds by checking the current scope (main() method), here there are no implicit definitions, so it goes up to the parent scope (MyApp), here it finds an implicit that is able to convert the symbol, so it will replace the code with:

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val scalaIsCool: Boolean = symbolToBoolean('yes)

and the same for the next line.
Of course this is just a dumb example and in the real life implicits are usually defined in separated classes.
We could rewrite the example as:

SymbolImplicits.scala

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object SymbolImplicits {
  implicit def symbolToBoolean(symbol: Symbol): Boolean = {
    symbol match {
      case 'yes => true
      case 'no => false
    }
  }
}

AppImportingImplicitMethods.scala

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import SymbolImplicits._
object MyApp {
  def main(args: Array[String]): Unit = {
    val scalaIsCool: Boolean = 'yes
    val scalaIsEasy: Boolean = 'no
    println(s"scalaIsCool: $scalaIsCool")
    println(s"scalaIsEasy: $scalaIsEasy")
  }
}

Implicit classes

One of the best use case for Scala implicit is to extend native or third party classes. Let’s pretend for example that we need to turn an arbitrary String into a slug, we could create an object like StringUtils with a method slufigy(string: String) but wouldn’t be much more clean to be able to call .toSlug against a String instance as we usually do with .toUpperCase and so on? Of course, and by defining an implicit class we can have such convenience:

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object StringExtensions {
  implicit class StringWrapper(string: String) {
    def toSlug: String = {
      string
        .toLowerCase
        .replaceAll("[^a-z\\d]", " ")
        .trim
        .replaceAll("\\s+", "-")
    }
  }
}

Then later on we can:

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import StringExtensions._
object MyApp {
  def main(args: Array[String]): Unit = {
    println(">> This string will be slugified! <<".toSlug)
  }
}

Bear in mind that an implicit class cannot be defined as a top-level object, therefore it must be “wrapped” by an object!

Implicit parameters

Also method parameters can be defined as implicit thus providing a sort of “native dependency injection”.
Let’s create a sample class which is responsible of creating a folder tree by naming folders according to the current date (/yyyy/MM/dd/):

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object FoldersCreator {
  def createDateFolderTree(root: Path)(implicit now: LocalDateTime): Option[Path] = {
    val format = (p: String) => now.format(DateTimeFormatter.ofPattern(p))
    val path: Path = Paths.get(root.toString, format("yyyy"), format("MM"), format("dd"))
    val result: Option[Path] = Try(Files.createDirectories(path)).toOption
    result
  }
  def createDateFolderTree(root: String)(implicit now: LocalDateTime): Option[Path] = {
    createDateFolderTree(Paths.get(root))
  }
}

As you can see I defined a method with an overload and both have a secondary couple of parenthesis containing a parameter now declared as implicit. This means that we will be able to call the methods without the second argument:

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FoldersCreator.createDateFolderTree("a/root/path")

Implicit variables

…provided that in the scope in which they get invoked an implicit variable of type LocalDateTime is found (the name doesn’t matter!).
Moreover as you can see even in the inner call from the overloaded method (createDateFolderTree(rootPath: String)...) to the original signature (createDateFolderTree(rootPath: Path)...) the second argument is omitted.
Let’s see how we can use the previously created object with implicit arguments:

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object MyApp {
  def main(args: Array[String]): Unit = {
    implicit val now: LocalDateTime = LocalDateTime.now()
    FoldersCreator.createDateFolderTree("/home/dave/Desktop")
    FoldersCreator.createDateFolderTree("/home/dave/Desktop/backup")
  }
}

In the code above the implicit variable has been defined just before invoking methods and it has been named like the argument in the signature (now).
However the implicit date can be declared also as class field and with a different name:

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object MyApp {
  private implicit val d: LocalDateTime = LocalDateTime.now()
  def main(args: Array[String]): Unit = {
    FoldersCreator.createDateFolderTree("/home/dave/Desktop")
    FoldersCreator.createDateFolderTree("/home/dave/Desktop/backup")
  }
}

If we define several implicit val, we may face a resolution exception. For example if we add a new implicit val of the same type (LocalDateTime) to the previous code:

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object MyApp {
  private implicit val date: LocalDateTime = LocalDateTime.now()
  private implicit val now: LocalDateTime = LocalDateTime.of(2020, 1, 1, 20, 30)
  def main(args: Array[String]): Unit = {
    FoldersCreator.createDateFolderTree("/home/dave/Desktop")
    FoldersCreator.createDateFolderTree("/home/dave/Desktop/backup")
  }
}

We will get:

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Error:(11, 40) ambiguous implicit values:
 both value date in object AppWithImplicitsVariables of type => java.time.LocalDateTime
 and value now in object AppWithImplicitsVariables of type => java.time.LocalDateTime
 match expected type java.time.LocalDateTime

This is a further proof that variable names don’t matter, only type does and if two variables with the same type are found in the scope the compiler doesn’t know which one to pick up.
Obviously in this case the problem is immediately spottable, but in a real application it might be harder to discover where is the conflict, especially if there are many classes with many implicits, generics and so on.

Implicit constructors

Like methods even constructor can be composed by implicit arguments.

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// an "injectable" object
class Bar
// a class with the implicit
class Foo(implicit val bar: Bar)
// tests app
object FooBarApp {
  private implicit val bar: Bar = new Bar
  def main(args: Array[String]): Unit = {
    val foo: Foo = new Foo
    println(foo.bar.getClass.getName)
  }
}

Final thoughts

The examples that I provided in this post are intentionally dumb and short, however with Scala implicit is possible to achieve complex tasks and automatically data conversion. For example Spark use this feature quite heavily for data encoding. Anyway this powerful tool should be used carefully and not abused in order to avoid painful situations.

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