The configuration of system that execute the tests are:
- (1) = Intel Core i7 4790K @ 4.4GHz, 16Gb DDR3 2400Mhz CL9
- (2) = MacBook Air Intel Core i5-5250U @ 2,7GHz, 4Gb LPDDR3 1600MHz
###map
Running a simple test, we could analysis the performance. It was great! Multiplying values(range between 0...1000000) * 2, it takes 15.805456 millisecond to execute the test on the system (1). Running on device (2), it takes 414.887893 milliseconds! Almost 28 times slower than the i7.
###filter
The filter takes 0.022 milliseconds to run on device (2) to filter a small array, values { 1, 10, 100, 1000 }
###map
With Java <= 7:
public class Foo {
public Integer[] run() {
Integer[] integers = {1,10,100};
Function func = new Function<Integer, Integer>() {
@Override public Integer call(Integer x) {
return x * 2;
}
};
Seq<Integer> values = new Seq<Integer>(integers).map(func);
return values;
}
}With Java >= 8 or Retrolambda:
public class Foo {
public Integer[] run() {
Integer[] integers = {1,10,100};
Seq<Integer> values = new Seq<Integer>(integers).map(x -> x * 2);
return values;
}
}This code will return { 2, 20, 200 }.
###filter
If you need to filter something on your list, you simple can do this:
With Java <= 7:
public class Foo {
public Integer[] run() {
Integer[] integers = { 1, 10, 100, 1000 };
Function func = new Function<Boolean, Integer>() {
@Override public Boolean call(Integer value) {
return value > 1 && value < 1000;
}
};
Seq<Integer> values = new Seq<Integer>(integers).filter(func);
return values;
}
}With Java >= 8 or Retrolambda:
public class Foo {
public Integer[] run() {
Integer[] integers = { 1, 10, 100, 1000 };
Seq<Integer> values = new Seq<Integer>(integers).filter(value > 1 && value < 1000);
return values;
}
}This code will return { 10, 100 }.
Into your build.gradle:
repositories {
maven {
url "https://jitpack.io"
}
}
dependencies {
compile 'com.github.jmonad:seq:0.2.1.0'
}The MIT License (MIT)
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