When would it make sense to design a data structure that is nesting a Box and a Vec (or vice versa)?
It seems like in most situations where you want to store multiple fixed-size things on the heap, the Box is redundant, since it's only (?) role is to heap-allocate a ~single value, and a normal Vec is already heap allocating its storage.
Context: I am still wrapping my head around the roles of the various Rust types for building up data structures.
There are really only a few times you need to use Box:
Recursive data structures: not relevant for the outermost element, so no need for Vec<Box<T>>.
Owned trait object, which must be Box<Trait> because the size of the object is dynamic;
Things that are sensitive to particular memory addresses, in order that the contained object will keep the same memory location (practically never the case and definitely not the case in any stable public API; some of the handle stuff to do with std::sync::mpsc::Select is the only case that I am aware of; this unsafety and care required is a part of why select! exists. This sort of a thing (Handle.add) is unsafe stuff.
If none of these situations apply, you should not use Box. And Box<Vec<T>> is one such case; the boxing is completely superfluous, adding an additional level of indirection to no benefit whatsoever.
So the simple version is:
Box<Vec<T>>: never.
Vec<Box<T>>: only if T is a trait, i.e. you’re working with trait objects.
Box<Vec<T>> can be useful on rare occasions with Option<Box<Vec<T>>>, if you want to shrink the size of the struct (one word instead of three) and the vector is usually empty. It was used in rustc for that, for example. However, lately it was replaced with the thin-vec crate, that does this better (still one word by storing the length and capacity inline, so does not require too allocations). If you don't want to introduce a dependency this can still be useful, though.
Related
I have learned about dynamic array (non-fixed size array) as dynamic array as vector in C++ and Arraylist in Java
And how can we implement it.
Basically when the array is full we create another array of doubled size and copy the old items to the new array
So can we implement an array of non-fixed size with random access as a vector and Arraylist without spending time copying the old elements?
In other word, Is there data structure like that (dynamic size and random access and no need for copy elements)??
Depending on what you mean by "like", this is trivially impossible to already exists.
First the trivially impossible. When we create an array, we mark a section of memory as being only for that array. If you have 3 such arrays that can grow without bound, one of them will eventually run into another. Given that we can actually create arrays that are bigger than available memory (it just pages to disk), we have to manage this risk, not avoid it.
But how big an issue is it? Copying data is O(1) per element, no matter how big it gets. And the overhead is low. The cost of this dynamicism is that you need to always check where the array starts. But that's a pretty fast check.
Alternately we can move to paged memory. Now an array access looks like, "Check what page it is on, then look at where it is in the page." Now your array can grow, but you never change where anything is. But if you want it to grow without bound, you have to add levels. We can implement it, and it does avoid copying, but this form of indirection has generally NOT proven worth it for general purpose programming. However paging is used in databases. And it is also used by operating systems to manage turning what the program thinks is the address of the data, to the actual address in memory. If you want to dive down that rabbit hole, TLB is worth looking at.
But there are other options that exist as well. Instead of fixed sized pages, we can have variable sized ones. This approach gets very complicated, very quickly. But the result is very useful. Look up ropes for more.
The browser that I wrote this on stores the text of what I wrote using a rope. This is how it can easily offer features like multi-level undo and editing in the middle of the document. However the raw performance of such schemes is significant. It is clearly worthwhile if you need the features, but otherwise we don't do it.
In short, every set of choices we make has tradeoffs. The one you'd like to optimize has what has proven to be the best tradeoff for offering dynamic size and raw performance. That's why it appears everywhere from Python lists to C++ vectors.
I am looking to implement my own collection class. The characteristics I want are:
Iterable - order is not important
Insertion - either at end or at iterator location, it does not matter
Random Deletion - this is the tricky one. I want to be able to have a reference to a piece of data which is guaranteed to be within the list, and remove it from the list in O(1) time.
I plan on the container only holding custom classes, so I was thinking a doubly linked list that required the components to implement a simple interface (or abstract class).
Here is where I am getting stuck. I am wondering whether it would be better practice to simply have the items in the list hold a reference to their node, or to build the node right into them. I feel like both would be fairly simple, but I am worried about coupling these nodes into a bunch of classes.
I am wondering if anyone has an idea as to how to minimize the coupling, or possibly know of another data structure that has the characteristics I want.
It'd be hard to beat a hash map.
Take a look at tries.
Apparently they can beat hashtables:
Unlike most other algorithms, tries have the peculiar feature that the time to insert, or to delete or to find is almost identical because the code paths followed for each are almost identical. As a result, for situations where code is inserting, deleting and finding in equal measure tries can handily beat binary search trees or even hash tables, as well as being better for the CPU's instruction and branch caches.
It may or may not fit your usage, but if it does, it's likely one of the best options possible.
In C++, this sounds like the perfect fit for std::unordered_set (that's std::tr1::unordered_set or boost::unordered_set to you if you have an older compiler). It's implemented as a hash set, which has the characteristics you describe.
Here's the interface documentation. Note that the hash containers actually offer two sets of iterators, the usual ones and local ones which only go through one bucket.
Many other languages have "hash sets" as well, certainly Java and C#.
I'm looking for a container that provides fastest unordered iterations through the encapsulated elements. In other words, "add once, iterate many times".
Is there one among OCaml's standard modules that is fast enough (such that further optimization of it would be useless)? Or some kind of third-party GPL-ready ones?
AFAIK there's just one OCaml compiler, so the concept of being fast is more or less clear...
...But after I saw a couple of answers, it appears, it's not. Of course, there's a plenty of data structures that allow O(n) iteration through container of size n. But the task I'm solving is one of those, where difference between O(n) and O(2n) matters ;-).
I also see that Arrays and Lists provide unnecessary information about the order of elements added, which I don't need. Maybe in "functional world" there exists data structures such that can trade this information for a bit of iteration speed.
In C I would outright pick a plain array. The question is, what should I pick in OCaml?
You are unlikely to do better than built-in arrays and lists, since they are hand-coded in C, unless you bind to your own native implementation of an iterator. An array will behave almost exactly like an array in C (a contiguously allocated block of memory containing a sequence of element values), possibly with some extra pointer indirections due to boxing. List are implemented exactly how you would expect: as cells with a value and a "next" pointer. Arrays will give you the best locality for unboxed types (especially floats, which have a super-special unboxed implementation).
For information about the implementation of arrays and lists, see Section 18.3 of the OCaml manual and the files byterun/mlvalues.h, byterun/array.c, and byterun/alloc.c in the OCaml source code.
From the questioner: indeed, Array appeared to be the fastest solution. However it only outperformed List by 7%. Maybe it was because the type of an array element was not plain enough: it was an algebraic type. Hashtbl performed 4 times worse, as expected.
So, I will pick Array and I'm accepting this one. good.
To know for sure, you're going to have to measure. Based on the machine instructions the compiler is likely to generate, I would try an array, then a list.
Access to an array element requires a bounds check, address arithmetic, and a load
Access to the head of a list requires a load, a test for empty list, and a load at a known compile-time offset.
The details of which is faster probably depend on your application and what else is happening on your machine. They also depend on the type of elements; for example, if they are floating-point numbers, ocamlopt may be clever enough to make an unboxed array, which will save you a level of indirection.
Other common data structures like hash tables or balanced trees generally require that you allocate some context somewhere to keep track of where you are. With an array, keeping track requires only an integer index; with a list, keeping track requires a single pointer. I think this is going to be hard to beat in another data structure.
Finally please note that there may be only one OCaml compiler, but it has two back ends: bytecode and native code. Naturally if you care about this level of performance, you are using the native-code ocamlopt version. Right?
Please take measurements and edit the results into your question.
Don't forget about Bigarrays, they are most close to C arrays (just a flat piece of memory), but cannot contain arbitrary OCaml values. Also consider switching bounds checking off (unsafe_set/get). And of course you should profile first.
The array - a linear piece of memory with the items visited in sequential order - best utilises the CPU's L1 data cache.
All common data structures are iterable in O(n) time, so the differences between data structures will only be constant (and very probably not significant).
At least lists and arrays allow iteration without significant overhead. I can't think of a situation where that would not be fast enough.
I've been coding for quite sometime now. And my work pertains to solving real-world business scenarios. However, I have not really come across any practical usage of some of the data structures like the Linked List, Queues and Stacks etc.
Not even at the business framework level. Of course, there is the ubiquitous HashTable, ArrayList and of late the List...but is there any practical usage of some of the other basic data structures?
It would be great if someone gave a real-world solution where a Doubly Linked List "performs" better than the obvious easily usable counterpart.
Of course it’s possible to get by with only a Map (aka HashTable) and a List. A Queue is only a glorified List but if you use a Queue everywhere you really need a queue then your code gets a lot more readable because nobody has to guess what you are using that List for.
And then there are algorithms that work a lot better when the underlying data structure is not a plain List but a DoublyLinkedList due to the way they have to navigate the list. The same is valid for all other data structures: there’s always a use for them. :)
Stacks can be used for pairing (parseing) such as matching open brackets to closing brackets.
Queues can be used for messaging, or activity processing.
Linked list, or double linked lists can be used for circular navigation.
Most of these algorithms are usually at a lower level than your usual "business" application. For example indices on the database is a variation of a multiply linked list. Implementation of function calling mechanism(or a parse tree) is a stack. Queues and FIFOs are used for servicing network request etc.
These are just examples of collection structures that are optimized for speed in various scenarios.
LIFO-Stack and FIFO-Queue are reasonably abstract (behavioral spec-level) data structures, so of course there are plenty of practical uses for them. For example, LIFO-Stack is a great way to help remove recursion (stack up the current state and loop, instead of making a recursive call); FIFO-Queue helps "buffer up" and "peel away" work nuggets in a coroutine arrangement; etc, etc.
Doubly-linked-List is more of an implementation issue than a behavioral spec-level one, mostly... can be a good way to implement a FIFO-Queue, for example. If you need a sequence with fast splicing and removal give a pointer to one sequence iten, you'll find plenty of other real-world uses, too.
I use queues, linked lists etc. in business solutions all the time.
Except they are implemented by Oracle, IBM, JMS etc.
These constructs are generally at a much lower level of abstaction than you would want while implementing a business solution. Where a business problem would benifit from
such low level constructs (e.g. delivery route planning, production line scheduling etc.) there is usually a package available to do it or you.
I don't use them very often, but they do come up. For example, I'm using a queue in a current project to process asynchronous character equipment changes that must happen in the order the user makes them.
A linked list is useful if you have a subset of "selected" items out of a larger set of items, where you must perform one type of operation on a "selected" item and a default operation or no operation at all on a normal item and the set of "selected" items can change at will (possibly due to user input). Because linked list removal can be done nearly instantaneously (vs. the traversal time it would take for an array search), if the subsets are large enough then it's faster to maintain a linked list than to either maintain an array or regenerate the whole subset by scanning through the whole larger set every time you need the subset.
With a hash table or binary tree, you could search for a single "selected" item, but you couldn't search for all "selected" items without checking every item (or having a separate dictionary for every permutation of selected items, which is obviously impractical).
A queue can be useful if you are in a scenario where you have a lot of requests coming in and you want to make sure to handle them fairly, in order.
I use stacks whenever I have a recursive algorithm, which usually means it's operating on some hierarchical data structure, and I want to print an error message if I run out of memory instead of simply letting the software crash if the program stack runs out of space. Instead of calling the function recursively, I store its local variables in an object, run a loop, and maintain a stack of those objects.
I'm trying to think of a naming convention that accurately conveys what's going on within a class I'm designing. On a secondary note, I'm trying to decide between two almost-equivalent user APIs.
Here's the situation:
I'm building a scientific application, where one of the central data structures has three phases: 1) accumulation, 2) analysis, and 3) query execution.
In my case, it's a spatial modeling structure, internally using a KDTree to partition a collection of points in 3-dimensional space. Each point describes one or more attributes of the surrounding environment, with a certain level of confidence about the measurement itself.
After adding (a potentially large number of) measurements to the collection, the owner of the object will query it to obtain an interpolated measurement at a new data point somewhere within the applicable field.
The API will look something like this (the code is in Java, but that's not really important; the code is divided into three sections, for clarity):
// SECTION 1:
// Create the aggregation object, and get the zillion objects to insert...
ContinuousScalarField field = new ContinuousScalarField();
Collection<Measurement> measurements = getMeasurementsFromSomewhere();
// SECTION 2:
// Add all of the zillion objects to the aggregation object...
// Each measurement contains its xyz location, the quantity being measured,
// and a numeric value for the measurement. For example, something like
// "68 degrees F, plus or minus 0.5, at point 1.23, 2.34, 3.45"
foreach (Measurement m : measurements) {
field.add(m);
}
// SECTION 3:
// Now the user wants to ask the model questions about the interpolated
// state of the model. For example, "what's the interpolated temperature
// at point (3, 4, 5)
Point3d p = new Point3d(3, 4, 5);
Measurement result = field.interpolateAt(p);
For my particular problem domain, it will be possible to perform a small amount of incremental work (partitioning the points into a balanced KDTree) during SECTION 2.
And there will be a small amount of work (performing some linear interpolations) that can occur during SECTION 3.
But there's a huge amount of work (constructing a kernel density estimator and performing a Fast Gauss Transform, using Taylor series and Hermite functions, but that's totally beside the point) that must be performed between sections 2 and 3.
Sometimes in the past, I've just used lazy-evaluation to construct the data structures (in this case, it'd be on the first invocation of the "interpolateAt" method), but then if the user calls the "field.add()" method again, I have to completely discard those data structures and start over from scratch.
In other projects, I've required the user to explicitly call an "object.flip()" method, to switch from "append mode" into "query mode". The nice this about a design like this is that the user has better control over the exact moment when the hard-core computation starts. But it can be a nuisance for the API consumer to keep track of the object's current mode. And besides, in the standard use case, the caller never adds another value to the collection after starting to issue queries; data-aggregation almost always fully precedes query preparation.
How have you guys handled designing a data structure like this?
Do you prefer to let an object lazily perform its heavy-duty analysis, throwing away the intermediate data structures when new data comes into the collection? Or do you require the programmer to explicitly flip the data structure from from append-mode into query-mode?
And do you know of any naming convention for objects like this? Is there a pattern I'm not thinking of?
ON EDIT:
There seems to be some confusion and curiosity about the class I used in my example, named "ContinuousScalarField".
You can get a pretty good idea for what I'm talking about by reading these wikipedia pages:
http://en.wikipedia.org/wiki/Scalar_field
http://en.wikipedia.org/wiki/Vector_field
Let's say you wanted to create a topographical map (this is not my exact problem, but it's conceptually very similar). So you take a thousand altitude measurements over an area of one square mile, but your survey equipment has a margin of error of plus-or-minus 10 meters in elevation.
Once you've gathered all the data points, you feed them into a model which not only interpolates the values, but also takes into account the error of each measurement.
To draw your topo map, you query the model for the elevation of each point where you want to draw a pixel.
As for the question of whether a single class should be responsible for both appending and handling queries, I'm not 100% sure, but I think so.
Here's a similar example: HashMap and TreeMap classes allow objects to be both added and queried. There aren't separate interfaces for adding and querying.
Both classes are also similar to my example, because the internal data structures have to be maintained on an ongoing basis in order to support the query mechanism. The HashMap class has to periodically allocate new memory, re-hash all objects, and move objects from the old memory to the new memory. A TreeMap has to continually maintain tree balance, using the red-black-tree data structure.
The only difference is that my class will perform optimally if it can perform all of its calculations once it knows the data set is closed.
If an object has two modes like this, I would suggest exposing two interfaces to the client. If the object is in append mode, then you make sure that the client can only ever use the IAppendable implementation. To flip to query mode, you add a method to IAppendable such as AsQueryable. To flip back, call IQueryable.AsAppendable.
You can implement IAppendable and IQueryable on the same object, and keep track of the state in the same way internally, but having two interfaces makes it clear to the client what state the object is in, and forces the client to deliberately make the (expensive) switch.
I generally prefer to have an explicit change, rather than lazily recomputing the result. This approach makes the performance of the utility more predictable, and it reduces the amount of work I have to do to provide a good user experience. For example, if this occurs in a UI, where do I have to worry about popping up an hourglass, etc.? Which operations are going to block for a variable amount of time, and need to be performed in a background thread?
That said, rather than explicitly changing the state of one instance, I would recommend the Builder Pattern to produce a new object. For example, you might have an aggregator object that does a small amount of work as you add each sample. Then instead of your proposed void flip() method, I'd have a Interpolator interpolator() method that gets a copy of the current aggregation and performs all your heavy-duty math. Your interpolateAt method would be on this new Interpolator object.
If your usage patterns warrant, you could do simple caching by keeping a reference to the interpolator you create, and return it to multiple callers, only clearing it when the aggregator is modified.
This separation of responsibilities can help yield more maintainable and reusable object-oriented programs. An object that can return a Measurement at a requested Point is very abstract, and perhaps a lot of clients could use your Interpolator as one strategy implementing a more general interface.
I think that the analogy you added is misleading. Consider an alternative analogy:
Key[] data = new Key[...];
data[idx++] = new Key(...); /* Fast! */
...
Arrays.sort(data); /* Slow! */
...
boolean contains = Arrays.binarySearch(data, datum) >= 0; /* Fast! */
This can work like a set, and actually, it gives better performance than Set implementations (which are implemented with hash tables or balanced trees).
A balanced tree can be seen as an efficient implementation of insertion sort. After every insertion, the tree is in a sorted state. The predictable time requirements of a balanced tree are due to the fact the cost of sorting is spread over each insertion, rather than happening on some queries and not others.
The rehashing of hash tables does result in less consistent performance, and because of that, aren't appropriate for certain applications (perhaps a real-time microcontroller). But even the rehashing operation depends only on the load factor of the table, not the pattern of insertion and query operations.
For your analogy to hold strictly, you would have to "sort" (do the hairy math) your aggregator with each point you add. But it sounds like that would be cost prohibitive, and that leads to the builder or factory method patterns. This makes it clear to your clients when they need to be prepared for the lengthy "sort" operation.
Your objects should have one role and responsibility. In your case should the ContinuousScalarField be responsible for interpolating?
Perhaps you might be better off doing something like:
IInterpolator interpolator = field.GetInterpolator();
Measurement measurement = Interpolator.InterpolateAt(...);
I hope this makes sense, but without fully understanding your problem domain it's hard to give you a more coherent answer.
"I've just used lazy-evaluation to construct the data structures" -- Good
"if the user calls the "field.add()" method again, I have to completely discard those data structures and start over from scratch." -- Interesting
"in the standard use case, the caller never adds another value to the collection after starting to issue queries" -- Whoops, false alarm, actually not interesting.
Since lazy eval fits your use case, stick with it. That's a very heavily used model because it is so delightfully reliable and fits most use cases very well.
The only reason for rethinking this is (a) the use case change (mixed adding and interpolation), or (b) performance optimization.
Since use case changes are unlikely, you might consider the performance implications of breaking up interpolation. For example, during idle time, can you precompute some values? Or with each add is there a summary you can update?
Also, a highly stateful (and not very meaningful) flip method isn't so useful to clients of your class. However, breaking interpolation into two parts might still be helpful to them -- and help you with optimization and state management.
You could, for example, break interpolation into two methods.
public void interpolateAt( Point3d p );
public Measurement interpolatedMasurement();
This borrows the relational database Open and Fetch paradigm. Opening a cursor can do a lot of preliminary work, and may start executing the query, you don't know. Fetching the first row may do all the work, or execute the prepared query, or simply fetch the first buffered row. You don't really know. You only know that it's a two part operation. The RDBMS developers are free to optimize as they see fit.
Do you prefer to let an object lazily perform its heavy-duty analysis,
throwing away the intermediate data structures when new data comes
into the collection? Or do you require the programmer to explicitly
flip the data structure from from append-mode into query-mode?
I prefer using data structures that allow me to incrementally add to it with "a little more work" per addition, and to incrementally pull the data I need with "a little more work" per extraction.
Perhaps if you do some "interpolate_at()" call in the upper-right corner of your region, you only need to do calculations involving the points in that upper-right corner,
and it doesn't hurt anything to leave the other 3 quadrants "open" to new additions.
(And so on down the recursive KDTree).
Alas, that's not always possible -- sometimes the only way to add more data is to throw away all the previous intermediate and final results, and re-calculate everything again from scratch.
The people who use the interfaces I design -- in particular, me -- are human and fallible.
So I don't like using objects where those people must remember to do things in a certain way, or else things go wrong -- because I'm always forgetting those things.
If an object must be in the "post-calculation state" before getting data out of it,
i.e. some "do_calculations()" function must be run before the interpolateAt() function gets valid data,
I much prefer letting the interpolateAt() function check if it's already in that state,
running "do_calculations()" and updating the state of the object if necessary,
and then returning the results I expected.
Sometimes I hear people describe such a data structure as "freeze" the data or "crystallize" the data or "compile" or "put the data into an immutable data structure".
One example is converting a (mutable) StringBuilder or StringBuffer into an (immutable) String.
I can imagine that for some kinds of analysis, you expect to have all the data ahead of time,
and pulling out some interpolated value before all the data has put in would give wrong results.
In that case,
I'd prefer to set things up such that the "add_data()" function fails or throws an exception
if it (incorrectly) gets called after any interpolateAt() call.
I would consider defining a lazily-evaluated "interpolated_point" object that doesn't really evaluate the data right away, but only tells that program that sometime in the future that data at that point will be required.
The collection isn't actually frozen, so it's OK to continue adding more data to it,
up until the point something actually extract the first real value from some "interpolated_point" object,
which internally triggers the "do_calculations()" function and freezes the object.
It might speed things up if you know not only all the data, but also all the points that need to be interpolated, all ahead of time.
Then you can throw away data that is "far away" from the interpolated points,
and only do the heavy-duty calculations in regions "near" the interpolated points.
For other kinds of analysis, you do the best you can with the data you have, but when more data comes in later, you want to use that new data in your later analysis.
If the only way to do that is to throw away all the intermediate results and recalculate everything from scratch, then that's what you have to do.
(And it's best if the object automatically handled this, rather than requiring people to remember to call some "clear_cache()" and "do_calculations()" function every time).
You could have a state variable. Have a method for starting the high level processing, which will only work if the STATE is in SECTION-1. It will set the state to SECTION-2, and then to SECTION-3 when it is done computing. If there's a request to the program to interpolate a given point, it will check if the state is SECTION-3. If not, it will request the computations to begin, and then interpolate the given data.
This way, you accomplish both - the program will perform its computations at the first request to interpolate a point, but can also be requested to do so earlier. This would be convenient if you wanted to run the computations overnight, for example, without needing to request an interpolation.