kind | Kubernetes IN Docker - local clusters for testing Kubernetes | Continuous Deployment library

The difference is in their kinds:

The most basic form of the kind language contains only * (or Type in more modern Haskell; I suspect we'll eventually move away from *) and ->.

But there are more things you can build with that language than you can express by just "counting the number of *s". It's not just the number of * or -> that matter, but how they are nested. For example * -> * -> * is the kind of things that take two type arguments to produce a type, but (* -> *) -> * is the kind of things that take a single argumemt to produce a type where that argument itself must be a thing that takes a type argument to produce a type. data ThreeStars a b = Cons a b makes a type constructor with kind * -> * -> *, while data AlsoThreeStars f = AlsoCons (f Integer) makes a type constructor with kind (* -> *) -> *.

There are several language extensions that add more features to the kind language.

PolyKinds adds kind variables that work exactly the same way type variables work. Now we can have kinds like forall k. (* -> k) -> k.

ConstraintKinds makes constraints (the stuff to the left of the => in type signatures, like Eq a) become ordinary type-level entities in a new kind: Constraint. Rather than the stuff left of the => being special purpose syntax fairly disconnected from the rest of the language, now what is acceptable there is anything with kind Constraint. Classes like Eq become type constructors with kind * -> Constraint; you apply it to a type like Eq Bool to produce a Constraint. The advantage is now we can use all of the language features for manipulating type-level entities to manipulate constraints (including PolyKinds!).

DataKinds adds the ability to create new user-defined kinds containing new type-level things, in exactly the same way that in vanilla Haskell we can create new user-defined types containing new term-level things. (Exactly the same way; the way DataKinds actually works is that it lets you use a data declaration as normal and then you can use the resulting type constructor at either the type or the kind level)

There are also kinds used for unboxed/unlifted types, which must not be ever mixed with "normal" Haskell types because they have a different memory layout; they can't contain thunks to implement lazy evaluation, so the runtime has to know never to try to "enter" them as a code pointer, or look for additional header bits, etc. They need to be kept separate at the kind level so that ordinary type variables of kind * can't be instantiated with these unlifted/unboxed types (which would allow you to pass these types that need special handling to generic code that doesn't know to provide the special handling). I'm vaguely aware of this stuff but have never actually had to use it, so I won't add any more so I don't get anything wrong. (Anyone who knows what they're talking about enough to write a brief summary paragraph here, please feel free to edit the answer)

There are probably some others I'm forgetting. But certainly the kind language is richer than the OP is imagining just with the basic Haskell features, and there is much more to it once you turn on a few (quite widely used) extensions.

Is there a way to automate this for example with some decorator kind of thing?

I'm not aware of a way of doing that currently.

I once developed a method trait to remove the implicit *%_ from the signature of a method. In the hopes I could simplify dispatching on multi methods that take many different (combinations) of named arguments.

It did not end well. I don't recall exactly why anymore, but I decided to postpone trying to do that until after the RakuAST branch has landed.

References can't bind to objects with different type directly. Given const int& s = u;, u is implicitly converted to int firstly, which is a temporary, a brand-new object and then s binds to the temporary int. (Lvalue-references to const (and rvalue-references) could bind to temporaries.) The lifetime of the temporary is prolonged to the lifetime of s, i.e. it'll be destroyed when get out of main.

Since you're already using Docker, I'd suggest using a multi-stage build. Using a standard docker image like golang one can build an executable asset which is guaranteed to work with other docker linux images:

This is a good question... and I would have hoped there would be a practical guide somewhere. One could question if SO would be a good place to ask this question, but regardless, here's my attempt to summarize the various scale_color_*() and scale_fill_*() functions built into ggplot2. Here, we'll describe the range of functions using scale_color_*(); however, the same general rules will apply for scale_fill_*() functions.

There are 22 functions in all, but happily we can group them intelligently based on practical usage scenarios. There are three key criteria that can be used to define practically how to use each of the scale_color_*() functions:

1. Nature of the mapping data. Is the data mapped to the color aesthetic discrete or continuous? CONTINUOUS data is something that can be explained via real numbers: time, temperature, lengths - these are all continuous because even if your observations are 1 and 2, there can exist something that would have a theoretical value of 1.5. DISCRETE data is just the opposite: you cannot express this data via real numbers. Take, for example, if your observations were: "Model A" and "Model B". There is no obvious way to express something in-between those two. As such, you can only represent these as single colors or numbers.

2. The Colorspace. The color palette used to draw onto the plot. By default, ggplot2 uses (I believe) a color palette based on evenly-spaced hue values. There are other functions built into the library that use either Brewer palettes or Viridis colorspaces.

3. The level of Specification. Generally, once you have defined if the scale function is continuous and in what colorspace, you have variation on the level of control or specification the user will need or can specify. A good example of this is the functions: *_continuous(), *_gradient(), *_gradient2(), and *_gradientn().

We can start off with continuous scales. These functions are all used when applied to observations that are continuous variables (see above). The functions here can further be defined if they are either binned or not binned. "Binning" is just a way of grouping ranges of a continuous variable to all be assigned to a particular color. You'll notice the effect of "binning" is to change the legend keys from a "colorbar" to a "steps" legend.

The continuous example (colorbar legend):

To test a MRJAR the classes must be packaged as a jar, so don't use surefire with target/classes, but instead use failsafe during the verify phase. And you must run it at least twice, once per targeted Java version. I would write a unittest, that works for all Java versions, but might skip certain tests.

A deployment is a higher abstraction that manages one or more replicasets to provide controlled rollout of a new version.

As long as you don't have a rollout in progress a deployment will result in a single replicaset with the replication factor managed by the deployment.

It means that particular token is optional. For instance both these declarations work: