All:
In D3, it often uses .data().enter().append() to reuse existing elements rather than remove everything and add them, but on the other hand, when the DOM structure is very deep, it will involve a lot of this detect(one for every level), I wonder if there is a good way to detect until which level, I need to start use .enter() rather than from the top level?
Thanks
The way I understand your question, you could be asking about one of two possible things. Either:
you're asking about how to use d3's .data() binding method to compute the three sets (enter, update, exit) at multiple levels of a dom hierarchy; or
you already know how to do #1, and are asking about how to NOT do it (i.e. skip calling .data()) in certain cases in order to really optimize performance.
If the question is #1, then check out this tutorial on working with nested selection by passing a function into the first argument of .data().
If the question is #2, then you're taking a risk. By that I mean that you're risking spending a whole lot of time and effort to optimize an aspect of your code that's probably far from being the slowest part of the program. Usually, it's the browser's rendering that's the slowest, while the data binding is quite fast. In fact, following the nested selections pattern from #1 is likely the most effective way to optimize, because it eliminates unnecessary appending to - and re-rendering of - the DOM.
If you really want to do #2 anyway, then I think the way to start is by implementing it using nested selections from #1, and then adding some sort of if statement at every level of the hierarchy that decides whether it's ok to skip calling the .data() method. For that, you have to examine the incoming data vs the outgoing data and deciding whether they're still equal or not. However, since deciding whether things are still equal is roughly what d3's .data() method does, then your optimization of it would have to do even less. Perhaps one way to achieve that level of optimization would involve using immutable data structures, because that's a way to quickly test equality of two nested data structures (that's basically how things work in React.js). It sounds complicated though. That's why I say it's a risk....
There may be another approach, in which you analyze the incoming vs outgoing data and determine which branches of the data hierarchy have changed and then pinpoint the equivalent location in the DOM and use d3 .data() locally within those changed DOM nodes. That sounds even more complex and ambiguous. So to get more help with that on, you'd have to create something like a jsFiddle that recreates your specific scenario.
I'm new to behavior-driven development and I can't find any examples or guidelines that parallel my current problem.
My current project involves a massive 3D grid with an arbitrary number of slots in each of the discrete cells. The entities stored in these slots have their own slots and, thus, an arbitrary nesting of entities can exist. The final implementation of the object(s) used will need be backed by some kind of persistent data store, which complicates the API a bit (i.e. using words like load/store instead of get/set and making sure modifying returned items doesn't modify the corresponding items in the data store itself). Don't worry, my first implementation will simply exist in-memory, but the API is what I'm supposed to be defining behavior against, so the actual implementation doesn't matter right now.
The thing I'm stuck on is the fact that BDD literature focuses on the interactions between objects and how mock objects can help with that. That doesn't seem to apply at all here. My abstract data store's only real "behavior" involves loading and storing data from entities outside those represented by the programming language itself; I can't define or test those behaviors since they're implementation-dependent.
So what can I define/test? The natural alternative is state. Store something. Make sure it loads. Modify the thing I loaded and make sure after I reload it's unmodified. Etc. But I'm under the impression that this is a common pitfall for new BDD developers, so I'm wondering if there's a better way that avoids it.
If I do take the state-testing route, a couple other questions arise. Obviously I can test an empty grid first, then an empty entity at one location, but what next? Two entities in different locations? Two entities in the same location? Nested entities? How deep should I test the nesting? Do I test the Cartesian product of these non-exclusive cases, i.e. two entities in the same location AND with nested entities each? The list goes on forever and I wouldn't know where to stop.
The difference between TDD and BDD is about language. Specifically, BDD focuses on function/object/system behavior to improve design and test readability.
Often when we think about behavior we think in terms of object interaction and collaboration and therefore need mocks to unit test. However, there is nothing wrong with an object whose behavior is to modify the state of a grid, if that is appropriate. State or mock based testing can be used in TDD/BDD alike.
However, for testing complex data structures, you should use a Matchers (e.g. Hamcrest in Java) to test only the part of the state you are interested in. You should also consider whether you can decompose the complex data into objects that collaborate (but only if that makes sense from an algorithmic/design standpoint).
I've been developing an in-house DSP application (Java w/ hooks for Groovy/Jython/JRuby, plugins via OSGi, plenty of JNI to go around) in data flow/diagram style, similar to pure data and simulink. My current design is a push model. The user interacts with some source component causing it to push data onto the next component and so on until a end block (typically a display or file writer). There are some unique challenges with this design specifically when a component starves for input. There is no easy way to request more input. I have mitiated some of this with feedback control flow, ex an FFT block can broadcast that it needs more data to source block of it's chain. I've contemplated adding support for components to be either push/pull/both.
I'm looking for responses regarding the merits of push vs pull vs both/hybrid. Have you done this before? What are some of the "gotchas"? How did you handle them? Is there a better solution for this problem?
Some experience with a "mostly-pull" approach in a large-scale product:
Model: Nodes build a 1:N tree, i.e. each component (except the root) has 1 parent and 1..N children. Data flows almost exclusively from parent to children. Change notifications can originate from any node in the tree.
Implementation: All leafs are notified with the sending node's id and a "generation" counter. Leafs know which node path they depend on, so they know if they need to update. (Any other child node update algorithm would do, too, and might have been better in hindsight).
Leafs query their parent for current data, query bubbles up recursively. The generation counter is included, so the bubble-up stops at the originating node.
Advantages:
parent nodes don't need much/any information about their children. Data can be consumed by anyone - this allowed a generic approach to implementing some (initially not expected) non-UI functionality on top of the data intended for display
Child nodes can aggregate and delay updates (avoiding repaints sure beats fast painting)
inactive leafs do cause no data traffic at all
Disadvantages:
Incremental updates are expensive, as full data is published.
The implementation actually allows for different data packets to be requested (and
the generation counter could prevent unecessary data traffic), but the data packets initially designed are very large. Slicing them was an afterthought, but works ok.
You need a real good generation mechanism. The one initially implemented collided with initial updates (that need special handling - see "incremental updates") and aggregation of updates
the need for data travelling up the tree was greatly underestimated.
publish is cheap only when the node offers read-only access to current data. This might require additional update synchronization, though
sometimes you want intermediate nodes to update, even when all leafs are inactive
some leafs ended up implementing polling, some base nodes ended up relying on that. ugly.
Generally:
Data-Pull "feels" more native to me when data and processing layer should know nothing about the UI. However, it requires a complex change notificatin mechanism to avoid "Updating the universe".
Data-Push simplifies incremental updates, but only if the sender intimately knows the receiver.
I have no experience of similar scale using other models, so I can't really make a recommendation. Looking back, I see that I've mostly used pull, which was less of a hassle. It would be interesting to see other peoples experiences.
I work on a pure-pull image processing library. It's more geared to batch-style operations where we don't have to deal with dynamic inputs and for that it seems to work very well. Pull works especially well for large data sets and for threading: we scale linearly to at least 32 CPUs (depending on the graph being evaluated, of course, heh).
We have a GUI that allows leaves to be dynamic data sources (for example, a video camera delivering frames) and they are handled by throwing away and rebuilding the relevant parts of the graph on a change. This is cheap in our case, so the overhead isn't that high.
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.