next up previous contents
Next: What's wrong? Up: Data-Oriented Design Previous: Maintenance and reuse   Contents

New version now available here at

This page is from the beta release of the Data-Oriented Design book. There are errors, spelling and factual, and this page is only kept for purposes of maintaining old links.

Design Patterns

What caused design patterns

Design patterns came about as a way of writing down and transferring the knowledge gained from experience, originally in architectural design with the term coined as Patterns by Christopher Alexander. Software design patterns came about from experience developing object-oriented software. The new prefixed name was used in the title of the book Design Patterns: elements of Reusable Object-Oriented Software released in 1994, and has stuck ever since.

Using design patterns, it was found that the strategies and solutions to common activities and problems could be expressed as a pattern language of requirement and designs for structuring and the strategies for actions taken on them. This reusable design would be too complex to write as a templated function or a library of reusable code as it describes a solution that would normally require interfacing with and thinking about how it applies to your particular situation and code base. The reason why the solution is too complex to write is not because the problem being solved is complex in itself, but that the solution is normally rooted in the problem domain rather than any specific programming language. However, the solution is only complex because it roots itself in the representation provided by the object-oriented language that was used to bring the design, in this case the problem domain itself, into the code in order for it to be solved. If the problem was not represented by objects but instead left as a technical design, then many design patterns would not be required. This is why when people talk about software design patterns they have to reference them as elements of reusable object-oriented design rather than elements of reusable software engineering.

Data-Oriented Design Patterns

Even though the original design patterns were elements of reusable Object-Oriented Design, there is scope for some pattern language for development given the Data-Oriented approach. Many of the sections so far have had an air of design pattern about them as they tend to solve generic problems with one or more generic solutions. This is where we gather and explain all the core patterns of development that are seen when working with a data-oriented code base, but always remember, a design pattern is a solution looking for a problem, and that alone is enough reason to be wary. In data-oriented design, we prefer to use design patterns as they were initially intended, as a way of communicating between engineers without needing to describe all the reasoning behind it. These architectural design patterns were a kind of written down common sense, which means in some sense, the data-oriented design patterns cannot be like these as common sense is often trumped by profiling or invention. Regardless, the ability to speak a common tongue is enough to merit trying to define some of the patterns present.

A to B transform

A stateless function that takes elements from one container and produces outputs into another container. All variables are constant over the lifetime of the transform.

The normal way of working and the best way to guarantee that your output and input tables don't clash, is to create a new table and transform into it. The A to B transform is also the basis of double buffered state and provides a powerful mechanism for enabling concurrent processing as you can ensure that A is read only, and B is write only for the whole time slice. The A to B transform is the transform performed by shaders and other compute kernels, and the same conditions apply: constants do not change, local variables only, globals are considered to be constants, data can be processed in any order and without any communication to any other element in the transform.

A to B transforms aren't expensive unless the row counts change, and even then you can mitigate those more expensive transforms with conc-trees and other in place transforms that reduce in the case of filtering transforms. There are two things to consider when transforming from one table to another:

Is this a transform to create new, or an updated state? Is this transform going to significantly change the values before they hit the output table?

The simplest transform is an update. This allows for concurrent state updates across multiple associated systems without coupling. The condition table transform is generally used to significantly change the values, putting them into a new table with a different schema, ready for decision transforms. Sometimes a transform will do more than one of these, such as when an update also emits rows into an event table ready for subscribing entities to react to a state change on the data.

An important requirement of the transform is that it is stateless. Transforms operate on data, use constants to adjust how they transform that data, and output in a standardised manner. Some amount of restriction needs to be set up for the transforms to be worth doing, however, and one of those restraints is that the transform must be stateless. The data has state, but the transforms must not. If you look at how the design pattern works for existing streaming transform engines such as GPU shader models, you will see that the shader constants are uniform across all the elements in one render call, this allows for easy distribution of work across many different workers, even on different machines. You can have local working variables, but you cannot store out to some common location as that would introduce some coupling, potentially making one part of a kernel serial in an otherwise completely parallel process. Even accumulation over successive elements would mean that the input to one element depended on the output of another, which would immediately cause the whole process to become serial in nature. If you do this, you are going to break the fundamental parallelisable nature of the transform process, and with it, doom your code to running only as fast as one of your workers can handle the data.

In place transform

A stateless function that changes the data in a container based on constants alone.

There are times when that's not necessary to create a new table with a transform. In update calls for many tables, the update can in place modify rather than run as a double buffered state. The best way to in place modify can change depending on the layout of the data and what needs to be done with the data. Always consider how the data gets into the transform, and how it gets out.

For inputs, make sure you find the optimal way to organise the pre-fetching so that the transform is never starved of data, for outputs, make sure that you write as much as you can in tightly packed consecutive memory to offer opportunity for write combining. The majority of cases have the same restrictions and benefits as A to B transform.

If you are doing in place transforms, always remember to write back once at most for each transform, otherwise aliasing rules can bite, for example, writing to a local accumulator when the type of the accumulator is the same as a type being read from, then aliasing may require your accumulator to be re-read every time it is updated. One way around this is to mark all your elements as restricted, thus ensuring that the compiler will assume it's safe to use the last value without reloading, but this might be vendor specific so test by looking at the assembly produced before declaring that the restrict will fix the problems inherent in accumulators or other load-modify-store values that may be used in in-place transforms.

Another thing to mention is that for both the in-place transform and the A to B transform, using a structure's static member is just as invalid as a global variable.

Generative transform

A function that generates data into an output container based on an algorithm rather than input data.

Sometimes all you need is some constants and only one small piece of data to generate a whole new table. If you've got to build a world from scratch, you can generate your map with procedural generators, but you have to seed a procedural generator and the small inputs or constants. These are what drives these types of transform. You don't need a large input set to drive a file loader, and that can be considered a generative transform. If you put filenames into a load-me-please table, and register a reaction transform into the file-has-loaded event table, then you have a generative function as your streaming engine. You can write your bootstrap as a generative transform that introduces elements into multiple tables. If this transform is a script, then you have the basis for a completely data-driven development. If instead of a filename you provide a seed and expect to get back a chunk of terrain, whether by pure procedural generation, or loading, or some combination, then you have the basis for a distributed processing landscape system. In the chapter on hierarchical level of detail, the just-in-time memento that provides some style for a previously non-existent entity can be considered a generative transform. Taking an ID and generating a coherent collection of attributes that conforms to other constraints that might be present such as district, time of day, difficulty setting, or level of player.

Sorted Table

A container that keeps itself sorted, tunable for different circumstances including only sorting a small subset.

A table that is only ever read after sorting can attempt to keep track of changes rather than sort before use. If the table needs to stay sorted no matter what, it can generate indexes with which it reorders the table on transform. There is hardly ever a good reason to explicitly sort a table, and usually there are better ways to organise data than sorted. Binary searches don't work as well in practice as b-trees due to the cache-friendly operations.

Multi sorted table

A container that keeps multiple sorted representations of itself available, tunable for circumstances including sorting different subsets based on different criteria.

Much like the sorted table, but when a table has multiple queries all requiring different sorting algorithms, then it's better to give up and only use indirection via indexes according to what order each query requires. This is similar to how databases add indexes for each column that requests it.


A method by which large tasks can be split off into multiple parallel tasks and can then be reduced back into one container for further processing.

When you run concurrent processes, but you require the output to be presented in one table, there is a final stage of any transform that turns all the different outputs into one final output. You can reduce the amount of work and memory taken up by using the concatenate function and generate a table that is a conc-tree, or you can use a gather transform. A gather transform has as many inputs are you have parallel tasks leading into the final output table, and each input is a queue of data waiting to enter the final transform. As long as the data is not meant to be sorted, then the gatherer can run while the concurrent transforms are running, peeling the transformed rows out of the processes independent output queues, and dropping them into the final output table. This allows for completely lockless parallelism of many tasks that don't require the data to be in any particular order at the final stage.

If the gatherer is meant to produce the final table in the same order as the input table then you will need to pre-parse the data or have the gatherer use a more complex collating technique. When you pre-parse the data you can produce a count that can be used to initialise the output positions making the output table secure for the processing transforms to write directly into it, but if there is no way to pre-parse the data, then gatherer might need to co-operate with the task scheduler so that the processes are biased to return ealier work faster, making final collation a just in time process for the transfer to the next transform.


When a container is very large, break off pieces and run transforms as concurrent tasks.

When you have a single large table, you can transform it with one transform, or many. Using a tasker to fake multiple independent tables allows for concurrent transforming as each transform can handle their specific part of the input table without having to consider any of the other parts in any way.


Rather than switch which instructions to run based on data in the stream, prefer producing new tables that can each be transformed more precisely.

When operating on data, sometimes the transform is dependent on the data. Normally any enumerable types or booleans that imply a state that can change what transform is meant to run are hidden behind existence based processing tables, but sometimes it's good to store the bool or enum explicitly for size sake. When that is the case, it can be beneficial to pre-process a table and generate a job table, a table with a row for each transform that is meant to be run on the data.

A dispatcher takes on input table and emits into multiple tables based on the data. This allows for rapid execution of the tasks without the overhead of instruction cache misses due to changing what instructions are going to be run based on the data as all the instructions are collected into each of the job tables.

Existing design patterns

Most of the Object-oriented design patterns aren't necessary in data-oriented development, but explaining why can be useful for migrating from the object-oriented way of working. Even though design patterns are founded in the world of object-oriented development, some design patterns have mirrors in the data-oriented world. Sometimes this is because the functionality is incredibly simple (such as with the factory), or because the design pattern is emulated by basic functionality of the data-oriented technique (such as flyweight).

Abstract Factories

The requirement for abstract factories comes from needing to generate new objects based on an overall scheme. This means creation can now be dependent on two data values: the first is the scheme, the second is the type requested for creation. This inherently object-oriented design problem stems from the desire to reuse code to drive object creation, but as C++ stops you from using objects by their features unless already declared in some way, it is hard to retroactively apply old creation techniques to new types. Normally we set up our classes by inheriting from an abstract base class so we can later provide new classes under new creation objects. The abstract factory is the eventual extension of allowing the creation function to be chosen at runtime based on the type of object required (which is statically checked) and the scheme or style of the object (which is dynamically calculated by the abstract factory), so choosing the right concrete factory object to create your new object.

When writing component entity systems, this is the equivalent of having components being inserted into an entity based on two dimensional data. The fact that a component of a certain type needs creating (the create call) is emulated by the row in the component create table, and the abstract factory calling into a concrete factory is emulated by the value causing the create call to be dispatched to an appropriate final factory table. Only once it has been dispatched to the right table does it get inserted into the final concrete component table.

This pattern is useful for multi-platform object-oriented development where you might have to otherwise refactor the internals of your object creators, but with a component driven approach, you can switch your output tables, transforms, or complete tree of transforms without losing consistency where it is necessary.


The builder pattern is all about wrapping up collections of reusable low-level parts of a complex transform in order to provide a tool kit for macro scale programming within a problem domain. The builder pattern is used when you want to be able to switch which tool-kits are used, thus providing a way of giving a high level program the ability to direct the creation of objects through the interface of a collection of tools.

This can be seen to parallel the tool-kit of transforms you tend to build when developing with tables. With a data-oriented functional programming approach, we don't immediately call builders, but instead generate the necessary build instructions that can then dispatched to the correct parallel processes where possible before being reduced into the final data. Usually the builders are stateless transforms that convert the request to build into data outputs, or take a small amount of input data and build it into larger or translated output.

Once you work out that the builder pattern is mostly a rewording of procedural reuse with the addition of switching which procedures are called based on a state in a larger scope, it is easy to see why the pattern is applicable in all programming paradigms.

Factory Method

The factory method allows for data controlled choice of what is created. This is mirrored well in the component driven programming model, as all components are created via predictably called functions, but whether they are called is down to the data. Rather than a method, we use creation tables. Instances of entities and components are created when the system gets around to updating those tables. The creation request table contains the same arguments as the factory method, so the type and its arguments are used in that table update to cause an insert into the component or entity table. There is never any in-place creation, as there is never any need of it. Anything too complex to create as part of the output from a single transform deserves the attention of an explicit creation, and also will find it hard to mitigate the cost of creation out of the order of normal object creation, so will be less tempted to trash the instruction cache or derail the flow of the game for a special case like we find frequently in object-oriented development.


The prototype pattern makes a lot of sense in an object-oriented code base, as the ability to generate more class instances from another class, only to modify it slightly before releasing it as finished almost gives you the flexibility to generate new classes at run-time, but any flexibility still has to be built into the classes before they can be used as the classes can only inherit from types existing at compile time. Some Object-oriented languages do let you generate new features at run-time but C++ doesn't allow for it without considerable coercion.

The existence-based-processing way of working stops you from creating a prototype as its existence would imply that it is in the game. There is no place to hide a prototype. But, there is also little call for it as creating a prototype is about creating from a plan of sorts, and because all the component creation can be controlled via read-only or just-in-time mementos, its very easy to make new classes with their components defined and specialised without resorting to hacks like keeping in memory a runtime template for a class you might need later. Unlike the object-oriented prototype, the component system memento does not have all the feature of the final component set, it only has the features of a creation data-type and thus fit's its purpose more purely. It cannot accidentally be used and damaged in the way prototypes can, for example, prototypes can be damaged by global update functions that accidentally modified the state. Because the only difference between a prototype and the runtime instance is whether the program knows that they are prototypes, it's hard to secure them against unwanted messages.


There is no reason to think that a single instance of a piece of data has any place in a data-oriented development. If the data is definitely unique, then it deserves to be exposed as a system wide global. It is only when globals are used for temporary store that they become dangerous. Singletons are an inherently object-oriented solution to a problem that only persists if you truly want to write platform ignorant code. Initialising and deinitialising managers in specific order for component managers, or for hardware or dependency sake can just as easily be managed explicitly, and is less prone to surprising someone. When you change the initialisation order accidentally by referencing a singleton, that reference won't be seen as changing the order of initialisation and it has been seen to cause quite difficult to debug when the effect of the change in order is subtle enough to not crash immediately.


An adapter provides an API wrapper around an otherwise incompatible class. Classes have their APIs locked down after they are created. If two classes that need to work with each other come from third party software libraries, the likelihood of them being naturally compatible is low to non-existent. An adapter wraps the callee class in a caller friendly API. The adapter turns the caller class friendly API calls into callee class friendly API calls.

When writing programs data-oriented, most data is structs of arrays or tables. In data-oriented development there is no reason for an adapter, as in the first case, a transform can easily be made to take arguments of the specific arrays used, and in the second case, the transform would normally take two or more schema iterators that traversed the table providing arguments to and capturing outputs from the transform. In a sense, every table transform iterator is an adapter because it converts the explicit form of the table into the explicit form accepted by the transform.

Finally, data itself has no API, and thus the idea of an adapter is not appropriate as there is nothing to adapt from, so this relegates the idea to the concept of transforming the data so it can be provided to third party libraries. Preparing data for external processes is part of working with other code, and is one of the prices you pay for not having full control of the source code. The adapter pattern doesn't help this in any way, only provides a way of describing the inevitable.


The bridge pattern is an object-oriented technique for providing encapsulation from implementation when the class's implementation is the functionality that derived classes use to get by in their world. For example, the platform specific code for an object can be privately owned by the parent class, and the concrete class can derive from that class. Then, it can use the implementation of it's parent class, but the implementation of the parent class member functions can be indirectly implemented differently depending on the platform. This way, the child class can call its parent's functions to do what it needs to do, while the parent class can call the implementation's functions which can be defined at runtime or compile time depending on what level of polymorphism is required.

Because all platform specific functionality is defined inside transforms and the way in which they are called, there is no reason to consider this pattern for platform independent reasons. All that is needed to benefit from the side effect of using this pattern is to recognise the boundary between platform specific transforms and platform independent transforms. For situations where the parent class implementation differs from instance to instance, you only need to notice that components can be different, yet still register with the same events. This means, any differing implementations required can be added as additional components which register into the same event tables as the existing components and replace those on creation as part of the different creation sequence.

Also, transforms that take data and do with it something platform specific would be written statically for each platform. This kind of object-oriented approach to everything can be what gives object-oriented development such a bad name when it comes to performance. There's not really any benefit to making your choice of implementation a runtime polymorphic call when the call can only ever be to one concrete method per platform as, at least in C++, there is not such thing as platform independent builds. For C#, it can be different, but the benefit of doing it is just the same, you only benefit very slightly when it comes to readability in the code, but you lose on every call at runtime.


Unlike it's programming namesake, the composite pattern it not about an object being made out of multiple other classes by owning them in the implementation. The composite pattern is the idea that an object that is part of a system can be identified as a single object, or as a group of objects. Usually, a game entity references one renderable unit with a single AI instance, which is why this pattern can be useful. If an enemy unit class can represent a single unit, or a squad of units, then it becomes easier to manage commands in a real time strategy game, or organise AI path finding.

When you don't use objects, the level of detail of individual parts of entities are not so hard tied to each other as the entities are not objects. When you design your software around objects you get caught up thinking about each object as being indivisible, and thus the composite pattern lets you at least handle objects in groups. If you don't limit your data by tying it to objects, then the pattern is less useful as you can already manipulate the data by group in any way you require.


The decorator acts much like adding more components to an entity. Their effect comes online when their existence marks their necessity. Decorators add features, which is exactly like the entity components with their ability to add attributes and functionality to a referenced entity.


Even though there are no tangled messes of interacting objects in the data-oriented approach, sometimes it might be better to channel data through a simpler set of streams. This could be considered to offer the same smaller surface area that the fa├žade offers.


The flyweight pattern offers a way to use very fine grain elements in the same way it uses larger constructs. In data-oriented development there is no need to differentiate between the two, as there is no inherent overhead in interfacing data in a data-oriented manner. The reason for a flyweight is similar to the reason for the bridge pattern, once you start thinking object-oriented and want to keep your whole codebase working like that, you can end up painting yourself into a corner about it and start adding objects to everything, even things that are too small or fragile for such a heavy handed, human centric way of seeing and manipulating the data.


In a sense, the proxy pattern is much like the level of detail system in most games. You don't load final assets until you are near enough to need them. This topic was fully covered in chapter [*].

Chain Of Responsibility

The only problem with the chain of responsibility is that in most implementations it is inherently serial for all the possible recipients interested in responding to it. If we remove the possibility of the event being marked as handled, then the pattern is better handled through existence based processing than through object-oriented design, as existence-based-processing offers a way to subscribe at multiple points in the same entity with different components wanting to respond to different parts of a new event. If you want to enable such things as marking as handled, then it might be better to have a responsibility table for each message or event type with a dispatcher heading off the message before it hits the general message queue.


This is realised fully when using tables to initiate your actions. All actions have to exist as a row in a table at some point, usually as a condition table row in the first place.


This design pattern doesn't seem very well suited to Object-oriented programming at all, though it is rooted in data driven flow control. The problem with the interpreter is it seems to be the design pattern for parsers, but doesn't add anything in itself.


Although the table iterators that convert table schema into transform arguments are iterators, they don't fit into the design pattern because they are a concrete solution to iterating over tables. There is no scope for using them for arguments to functions or using them to erase elements and continue, they are stateful, but restricted to their one job. However, because there are no containers in table oriented solutions other than the tables, we can safely ignore true iterators as a design. In a stream based development, iterators are so common and pure, they don't really exist in the design pattern sense.


The mediator can be thought of as a set up involving attaching event transforms to table updates. When a table entry updates, the callback can then inform the other related tables that they need to consider some new information. However, this data hiding and decoupling is not as necessary in data-oriented development as most solutions are considering all the data and thus don't need to be decoupled at this level.


Stashing a compressed state in a table is very useful when changing level of detail. Much was made of this in count lodding. Mementos also provide a mechanism for undoing changes, which can be essential to low memory usage network state prediction systems, so they can rollback update, and fast forward to the new predicted state.

Null Object

A null object is a pattern to stop unnecessary branching and checking when a fetch operation returns a class instance. If the instance was null, then returning the null object allows the calling code to continue on as if it had received a valid object. This is unnecessary in table driven processing scenarios as the transforms run over data. There is no chance for a transform to run into a null pointer as pointers are banned in general.


The publish and subscribe model presented in the table based event system is an observer pattern and should be used whenever data relies on some other data. The observer patter is high latency, but provides for callbacks, event handling, and job starting. In most games there will be an observer registry for the frame sync, the physics tick, the AI tick and for any IO completion. User defined callback tables will be everywhere, including how many finite state machines are implemented.


Data controlling the flow of the program is considered an anti-pattern in data-oriented development, so this design pattern is to be understood, and never used in the way it is presented. Instead, have your state be implicitly defined by the table in which your entity resides. Using table existince to drive the transform that is called is the basis of finite state machine update, if not the alphabet response.

This design pattern is highly dangerous for any game that wants to run at high speed with a high entity count.


Template Method

The template method can still be used but overriding the calls by removing the template callbacks and adding the specific new callbacks would be how you organise the equivalent of overriding the virtual calls.


The visitor pattern would be fine if it were not for the implicit allowance for it to be stateful. If the visitor was not allowed to accumulate during it's structure traversal, then it would be an in-place transform. By not defining it as a stateless entity, but a stateful object that walks the container, it becomes an inherently serial, cache unfriendly container analyser. This pattern in particular is quoted by people who don't understand the fundamental requirements of a transform function. If you talk to an Object-Oriented programmer about taking a list of data and doing a transform on it, they will tend to mention it as the visitor patter because there is a tendency to fit solutions to problems with design patterns. The best way to define a visitor pattern is an object that changes state based on the incoming data. The final state is dependent on the order in which the data is given to the visitor, thus it is a serial operation. The best analogy to a transform is not compatible with a visitor because a transform pattern denies the possibility of accumulation.

Locking out anti-patterns

In addition to the design patterns, the elements of reusable object-oriented software, there are also the anti-patterns, the elements of miserable software. Sometimes a particularly bad way of doing something turns up way more often than it should, and when it does, it begins to get a name for itself. data-oriented development helps reduce these by both not being as complex to begin with because the problem domain is not dragged into a new language of objects, nor does it have objects themelves, which can cause multiple problems due to their sometimes coarse and sometimes too fine nature.

Abstraction Inversion

Abstraction inversion is when some internal functions of a class are kept hidden, but they are useful, so an external developer has to reinvent those internal functions as external functions by using only the public interface, which itself will probably be using the internal functions these new external functions are trying to replicate. Do this enough times with even the simplest of classes and you can explode the amount of work required to do a simple job.

As there is no inherent data or function hiding during data-oriented development, there can be no barrier to using the functions or the transforms that you want to use.

Acme Pattern / God Class

One giant class the runs the show. One very large data clump that is almost certainly a mess when it comes to organising the data by when it is accessed. Because there are no objects and we normalise our data, this anti-patterns becomes relatively difficult to experience. There is still the chance that someone might try to implement a nosql version of data-oriented development, but even if they do, most of the problems go away under struct of arrays. When it comes to the problem of all the methods in one class, then we know we're fine as there are no such boundaries.

CObject / Cosmic Hierarchy

Almost the pure opposite of the God Class, the CObject does nothing, and is inherited whether directly or indirectly by everything. Many game engines have a concept of an entity or an object, but they generally don't keep it pure out of good practice. The threat of starting a cosmic hierarchy has reduced the chance of people making another CObject. The idea that everything comes from a base object class tends to encourage hard to read code. Code that divines the type of object before use suffers from overly long preamble in functions, and suffers from runtime type checking overhead. It's common knowledge that the preamble code - such things as "if type == foo" or checks for NULL - or the runtime type checking aren't overly expensive. This common knowledge has long outlived it's lifespan though, and

At the root of a lot of object-oriented entity systems lies a core class that everything can be cast to. This usually leads to lots of problematic code that costs cycles and can cause bugs by not correctly identifying itself.

In data-oriented development there is no Cosmic base class, even entities in an entity system only refer to entities. Their components aren't always dependent on the entity alone, such that attachme

Hidden Requirements

Sometimes a basic design change is required by a long standing requirement accidentally omitted from the original and all subsequent design documents. In object-oriented development, making a refactor at a late stage is very costly as object contracts can be hard to adjust without causing a very large amount of related changes to all the code that touches the changed class.

Luckily, in data-oriented development, changing everything does not get much harder the longer the project continues because there are documented tools and techniques for making large scale schema changes in database technologies, and table based development can reap benefits there by literally implementing the same migrational tool-chain that is used in those circumstances.


The more that bugs are fixed and corner cases are added, the harder it can be to refactor an existing Object-oriented codebase. This is not as true in data-oriented as towards the end of a project the difficultly in refactoring the design should be mitigated by the understanding and transparency of the whole program.

Game Development Patterns

The Game/Session/Level/Entity hierarchy still applies, and can be gathered accurately as part of the data flow analysis done during the early stages of design iteration. If you use tables for most of your data, then you will find that each layer has a set of tables.

Tick or update scheduling is normally necessary in order to maintain data scheduling, but as the data flow is paramount in data-oriented development, there is no need for a dedicated scheduler. In fact, the sequences of transforms replace the scheduler normally found in games by having a cascade of dependencies play out in the instance of a new frame being rendered. A frame buffer swap is an event that begins a new sequence of processes. In some games this could be the gathering of controller updates followed by physics and logic updates, finally culminating in an update of the renderable scene.

Double and Triple Buffering, which is normally the only way of getting near a good framerate, by processing multiple stages over multiple frames, may not need to happen so much as it does with object-oriented code as data-oriented code is much more parallelisable, so doesn't have the long critical path between input and rendering output. Removing multiple serial points can drastically reduce your overall gameplay feedback latency.

next up previous contents
Next: What's wrong? Up: Data-Oriented Design Previous: Maintenance and reuse   Contents Beta release of Data-Oriented Design :
Expect errors, spelling and factual. Expect out of date data, or missing stuff. Expect to be bored stiff in some sections, and rushed in others, but most of all, please send any feedback on any of these and any other things that you spot, to

Richard Fabian 2013-06-25