In the GoF design patterns book, when it comes to the implementation part of the Observer pattern, it is stated:
Mapping subjects to their observers The simplest way for a subject to keep track of the observers it should
notify is to store references to them explicitly in the subject. However, such storage may be too expensive
when there are many subjects and few observers. One solution is to trade space for time by using an
associative look-up (e.g., a hash table) to maintain the subject-to-observer mapping. Thus a subject with no
observers does not incur storage overhead. On the other hand, this approach increases the cost of accessing
the observers.
I fail to see how using hash table would improve storage capacity. In Java, for every subject we could have a list of observers List<Observer>. If there are no observers attached to this subject, the list reference would be null. If we use hash table, Map<Subject, List<Observer>, we still have the list, but we also have a reference to the subject, so this way is a bit more memory
inefficient. I don't know whether it is relevant, but the languages used for implementation in the Gof book are Smalltalk and C++.
The point of the quote seems to be that if subjects are responsible for storing their own observers, in a scenario where most subjects are unobserved at a given time, every subject bears the cost of storing an empty list (imagine millions of subjects).
On the other hand, if subject-to-observer mappings are centralized into a single Map, only the (few) observed subjects have any memory footprint. It is correct to point out that the memory cost per observed subject is higher with a centralized mapping, because of the need to store references to subjects, which is why such a design only makes sense, "when there are many subjects and few observers".
Note a more modern example of optimizing code to avoid empty collections: Why overload the varargs method of() in Java Stream interface?
Related
First, I understand the difference between value and reference types -this isn't that question. I am rewriting some of my code in Swift, and decided to also refactor some of the classes. Therefore, I thought I would see if some of the classes make sense as structs.
Memory: I have some model classes that hold very large arrays, that are constantly growing in size (unknown final size), and could exist for hours. First, are there any guidelines about a suggested or absolute size for a struct, since it lives on the stack?
Refactoring Use: Since I'm refactoring what right now is a mess with too much dependency, I wonder how I could improve on that. The views and view controllers are mostly easily, it's my model, and what it does, that's always left me wishing for better examples to follow.
WorkerManager: Singleton that holds one or two Workers at a time. One will always be recording new data from a sensor, and the other would be reviewing stored data. The view controllers get the Worker reference from the WorkerManager, and ask the Worker for the data to be displayed.
Worker: Does everything on a queue, to prevent memory access issues (C array pointers are constantly changing as they grow). Listening: The listening Worker listens for new data, sends it to a Processor object (that it created) that cleans up the data and stores it in C arrays held by the Worker. Then, if there is valid data, the Worker tells the Analyzer (also owned by the worker) to analyze the data and stores it in other C arrays to be fed to views. Both the Processor and Analyzer need state to know what has happened in the past and what to process and analyze next. The pure raw data is stored in a separate Record NSManaged object. Reviewer Takes a Record and uses the pure raw data to recreate all of the analyzed data so that it can be reviewed. (analyzed data is massive, and I don't want to store it to disk)
Now, my second question is, could/should Processor and Analyzer be replaced with structs? Or maybe protocols for the Worker? They aren't really "objects" in the normal sense, just convenient groups of related methods and the necessary state. And since the code is nearly a thousand lines for each, and I don't want to put it all in one class, or even the same file.
I just don't have a good sense of how to remove all of my state, use pure functions for all of the complex mathematical operations that are performed on the arrays, and where to put them.
While the struct itself lives on the stack, the array data lives on the heap so that array can grow in size dynamically. So even if you have an array with million items in it and pass it somewhere, none of the items are copied until you change the new array due to the copy-on-write implementation. This is described in details in 2015 WWDC Session 414.
As for the second question, I think that 2015 WWDC Session 414 again has the answer. The basic check that Apple engineers recommend for value types are:
Use a value type when:
Comparing instance data with == makes sense
You want copies to have independent state
The data will be used in code across multiple threads
Use a reference type (e.g. use a class) when:
Comparing instance identity with === makes sense
You want to create shared, mutable state
So from what you've described, I think that reference types fit Processor and Analyzer much better. It doesn't seem that copies of Processor and Analyzer are valid objects if you've not created new Producers and Analyzers explicitly. Would you not want the changes to these objects to be shared?
I refer to Scott Amblers's Choosing a Primary Key: Natural or Surrogate? page.
Excerpt:
High-low strategy. The basic idea is that your key value, often
called a persistent object identifier (POID) or simply an object
identified (OID), is in two logical parts: A unique HIGH value that
you obtain from a defined source and an N-digit LOW value that your
application assigns itself. Each time that a HIGH value is obtained
the LOW value will be set to zero.
I am interested in DORM (The Delphi ORM by Daniele Teti) and would like to know if somebody has already implemented the high/low strategy for it.
Any input are welcome.
Edit 1:
To narrow the scope of the question:
I want to use Firebird as the RDMS backend
I likely have to implement IdormKeysGenerator similarly to dorm.adapter.Firebird.TFirebirdTableSequence.
Edit 2:
HIGH value is persisted on the Server
LOW value allocation is the client responsability.
I think an usual allocator will do for the LOW value (Implemented as a class).
Currently DORM support only surrogate keys (integer or string). In the internal roadmap is scheduled the natural (multi field keys) key support. Some internal structures are ready to support the multiple fields keys, but still is not implemented. The high-low strategy is not planned, but should not be so difficult to do.
P.S. As is every Open Source project, feel free to contribute :-)
I'm pretty sure this is a silly newbie question but I didn't know it so I had to ask...
Why do we use data structures, like Linked List, Binary Search Tree, etc? (when no dynamic allocation is needed)
I mean: wouldn't it be faster if we kept a single variable for a single object? Wouldn't that speed up access time? Eg: BST possibly has to run through some pointers first before it gets to the actual data.
Except for when dynamic allocation is needed, is there a reason to use them?
Eg: using linked list/ BST / std::vector in a situation where a simple (non-dynamic) array could be used.
Each thing you are storing is being kept in it's own variable (or storage location). Data structures apply organization to your data. Imagine if you had 10,000 things you were trying to track. You could store them in 10,000 separate variables. If you did that, then you'd always be limited to 10,000 different things. If you wanted more, you'd have to modify your program and recompile it each time you wanted to increase the number. You might also have to modify the code to change the way in which the calculations are done if the order of the items changes because the new one is introduced in the middle.
Using data structures, from simple arrays to more complex trees, hash tables, or custom data structures, allows your code to both be more organized and extensible. Using an array, which can either be created to hold the required number of elements or extended to hold more after it's first created keeps you from having to rewrite your code each time the number of data items changes. Using an appropriate data structure allows you to design algorithms based on the relationships between the data elements rather than some fixed ordering, giving you more flexibility.
A simple analogy might help to understand. You could, for example, organize all of your important papers by putting each of them into separate filing cabinet. If you did that you'd have to memorize (i.e., hard-code) the cabinet in which each item can be found in order to use them effectively. Alternatively, you could store each in the same filing cabinet (like a generic array). This is better in that they're all in one place, but still not optimum, since you have to search through them all each time you want to find one. Better yet would be to organize them by subject, putting like subjects in the same file folder (separate arrays, different structures). That way you can look for the file folder for the correct subject, then find the item you're looking for in it. Depending on your needs you can use different filing methods (data structures/algorithms) to better organize your information for it's intended use.
I'll also note that there are times when it does make sense to use individual variables for each data item you are using. Frequently there is a mixture of individual variables and more complex structures, using the appropriate method depending on the use of the particular item. For example, you might store the sum of a collection of integers in a variable while the integers themselves are stored in an array. A program would need to be pretty simple though before the introduction of data structures wouldn't be appropriate.
Sorry, but you didn't just find a great new way of doing things ;) There are several huge problems with this approach.
How could this be done without requring programmers to massively (and nontrivially) rewrite tons of code as soon as the number of allowed items changes? Even when you have to fix your data structure sizes at compile time (e.g. arrays in C), you can use a constant. Then, changing a single constant and recompiling is sufficent for changes to that size (if the code was written with this in mind). With your approach, we'd have to type hundreds or even thousands of lines every time some size changes. Not to mention that all this code would be incredibly hard to read, write, maintain and verify. The old truism "more lines of code = more space for bugs" is taken up to eleven in such a setting.
Then there's the fact that the number is almost never set in stone. Even when it is a compile time constant, changes are still likely. Writing hundreds of lines of code for a minor (if it exists at all) performance gain is hardly ever worth it. This goes thrice if you'd have to do the same amount of work again every time you want to change something. Not to mention that it isn't possible at all once there is any remotely dynamic component in the size of the data structures. That is to say, it's very rarely possible.
Also consider the concept of implicit and succinct data structures. If you use a set of hard-coded variables instead of abstracting over the size, you still got a data structure. You merely made it implicit, unrolled the algorithms operating on it, and set its size in stone. Philosophically, you changed nothing.
But surely it has a performance benefit? Well, possible, although it will be tiny. But it isn't guaranteed to be there. You'd save some space on data, but code size would explode. And as everyone informed about inlining should know, small code sizes are very useful for performance to allow the code to be in the cache. Also, argument passing would result in excessive copying unless you'd figure out a trick to derive the location of most variables from a few pointers. Needless to say, this would be nonportable, very tricky to get right even on a single platform, and liable to being broken by any change to the code or the compiler invocation.
Finally, note that a weaker form is sometimes done. The Wikipedia page on implicit and succinct data structures has some examples. On a smaller scale, some data structures store much data in one place, such that it can be accessed with less pointer chasing and is more likely to be in the cache (e.g. cache-aware and cache-oblivious data structures). It's just not viable for 99% of all code and taking it to the extreme adds only a tiny, if any, benefit.
The main benefit to datastructures, in my opinion, is that you are relationally grouping them. For instance, instead of having 10 separate variables of class MyClass, you can have a datastructure that groups them all. This grouping allows for certain operations to be performed because they are structured together.
Not to mention, having datastructures can potentially enforce type security, which is powerful and necessary in many cases.
And last but not least, what would you rather do?
string string1 = "string1";
string string2 = "string2";
string string3 = "string3";
string string4 = "string4";
string string5 = "string5";
Console.WriteLine(string1);
Console.WriteLine(string2);
Console.WriteLine(string3);
Console.WriteLine(string4);
Console.WriteLine(string5);
Or...
List<string> myStringList = new List<string>() { "string1", "string2", "string3", "string4", "string5" };
foreach (string s in myStringList)
Console.WriteLine(s);
I have a choice.
I have a number of already ordered strings that I need to store and access. It looks like I can choose between using:
A TStringList
A Dynamic Array of strings, and
A Linked List of strings (singly linked)
and Alan in his comment suggested I also add to the choices:
TList<string>
In what circumstances is each of these better than the others?
Which is best for small lists (under 10 items)?
Which is best for large lists (over 1000 items)?
Which is best for huge lists (over 1,000,000 items)?
Which is best to minimize memory use?
Which is best to minimize loading time to add extra items on the end?
Which is best to minimize access time for accessing the entire list from first to last?
On this basis (or any others), which data structure would be preferable?
For reference, I am using Delphi 2009.
Dimitry in a comment said:
Describe your task and data access pattern, then it will be possible to give you an exact answer
Okay. I've got a genealogy program with lots of data.
For each person I have a number of events and attributes. I am storing them as short text strings but there are many of them for each person, ranging from 0 to a few hundred. And I've got thousands of people. I don't need random access to them. I only need them associated as a number of strings in a known order attached to each person. This is my case of thousands of "small lists". They take time to load and use memory, and take time to access if I need them all (e.g. to export the entire generated report).
Then I have a few larger lists, e.g. all the names of the sections of my "virtual" treeview, which can have hundreds of thousands of names. Again I only need a list that I can access by index. These are stored separately from the treeview for efficiency, and the treeview retrieves them only as needed. This takes a while to load and is very expensive memory-wise for my program. But I don't have to worry about access time, because only a few are accessed at a time.
Hopefully this gives you an idea of what I'm trying to accomplish.
p.s. I've posted a lot of questions about optimizing Delphi here at StackOverflow. My program reads 25 MB files with 100,000 people and creates data structures and a report and treeview for them in 8 seconds but uses 175 MB of RAM to do so. I'm working to reduce that because I'm aiming to load files with several million people in 32-bit Windows.
I've just found some excellent suggestions for optimizing a TList at this StackOverflow question:
Is there a faster TList implementation?
Unless you have special needs, a TStringList is hard to beat because it provides the TStrings interface that many components can use directly. With TStringList.Sorted := True, binary search will be used which means that search will be very quick. You also get object mapping for free, each item can also be associated with a pointer, and you get all the existing methods for marshalling, stream interfaces, comma-text, delimited-text, and so on.
On the other hand, for special needs purposes, if you need to do many inserts and deletions, then something more approaching a linked list would be better. But then search becomes slower, and it is a rare collection of strings indeed that never needs searching. In such situations, some type of hash is often used where a hash is created out of, say, the first 2 bytes of a string (preallocate an array with length 65536, and the first 2 bytes of a string is converted directly into a hash index within that range), and then at that hash location, a linked list is stored with each item key consisting of the remaining bytes in the strings (to save space---the hash index already contains the first two bytes). Then, the initial hash lookup is O(1), and the subsequent insertions and deletions are linked-list-fast. This is a trade-off that can be manipulated, and the levers should be clear.
A TStringList. Pros: has extended functionality, allowing to dynamically grow, sort, save, load, search, etc. Cons: on large amount of access to the items by the index, Strings[Index] is introducing sensible performance lost (few percents), comparing to access to an array, memory overhead for each item cell.
A Dynamic Array of strings. Pros: combines ability to dynamically grow, as a TStrings, with the fastest access by the index, minimal memory usage from others. Cons: limited standard "string list" functionality.
A Linked List of strings (singly linked). Pros: the linear speed of addition of an item to the list end. Cons: slowest access by the index and searching, limited standard "string list" functionality, memory overhead for "next item" pointer, spead overhead for each item memory allocation.
TList< string >. As above.
TStringBuilder. I does not have a good idea, how to use TStringBuilder as a storage for multiple strings.
Actually, there are much more approaches:
linked list of dynamic arrays
hash tables
databases
binary trees
etc
The best approach will depend on the task.
Which is best for small lists (under
10 items)?
Anyone, may be even static array with total items count variable.
Which is best for large lists (over 1000 items)?
Which is best for huge lists (over 1,000,000 items)?
For large lists I will choose:
- dynamic array, if I need a lot of access by the index or search for specific item
- hash table, if I need to search by the key
- linked list of dynamic arrays, if I need many item appends and no access by the index
Which is best to minimize memory use?
dynamic array will eat less memory. But the question is not about overhead, but about on which number of items this overhead become sensible. And then how to properly handle this number of items.
Which is best to minimize loading time to add extra items on the end?
dynamic array may dynamically grow, but on really large number of items, memory manager may not found a continous memory area. While linked list will work until there is a memory for at least a cell, but for cost of memory allocation for each item. The mixed approach - linked list of dynamic arrays should work.
Which is best to minimize access time for accessing the entire list from first to last?
dynamic array.
On this basis (or any others), which data structure would be preferable?
For which task ?
If your stated goal is to improve your program to the point that it can load genealogy files with millions of persons in it, then deciding between the four data structures in your question isn't really going to get you there.
Do the math - you are currently loading a 25 MB file with about 100000 persons in it, which causes your application to consume 175 MB of memory. If you wish to load files with several millions of persons in it you can estimate that without drastic changes to your program you will need to multiply your memory needs by n * 10 as well. There's no way to do that in a 32 bit process while keeping everything in memory the way you currently do.
You basically have two options:
Not keeping everything in memory at once, instead using a database, or a file-based solution which you load data from when you need it. I remember you had other questions about this already, and probably decided against it, so I'll leave it at that.
Keep everything in memory, but in the most space-efficient way possible. As long as there is no 64 bit Delphi this should allow for a few million persons, depending on how much data there will be for each person. Recompiling this for 64 bit will do away with that limit as well.
If you go for the second option then you need to minimize memory consumption much more aggressively:
Use string interning. Every loaded data element in your program that contains the same data but is contained in different strings is basically wasted memory. I understand that your program is a viewer, not an editor, so you can probably get away with only ever adding strings to your pool of interned strings. Doing string interning with millions of string is still difficult, the "Optimizing Memory Consumption with String Pools" blog postings on the SmartInspect blog may give you some good ideas. These guys deal regularly with huge data files and had to make it work with the same constraints you are facing.
This should also connect this answer to your question - if you use string interning you would not need to keep lists of strings in your data structures, but lists of string pool indexes.
It may also be beneficial to use multiple string pools, like one for names, but a different one for locations like cities or countries. This should speed up insertion into the pools.
Use the string encoding that gives the smallest in-memory representation. Storing everything as a native Windows Unicode string will probably consume much more space than storing strings in UTF-8, unless you deal regularly with strings that contain mostly characters which need three or more bytes in the UTF-8 encoding.
Due to the necessary character set conversion your program will need more CPU cycles for displaying strings, but with that amount of data it's a worthy trade-off, as memory access will be the bottleneck, and smaller data size helps with decreasing memory access load.
One question: How do you query: do you match the strings or query on an ID or position in the list?
Best for small # strings:
Whatever makes your program easy to understand. Program readability is very important and you should only sacrifice it in real hotspots in your application for speed.
Best for memory (if that is the largest constrained) and load times:
Keep all strings in a single memory buffer (or memory mapped file) and only keep pointers to the strings (or offsets). Whenever you need a string you can clip-out a string using two pointers and return it as a Delphi string. This way you avoid the overhead of the string structure itself (refcount, length int, codepage int and the memory manager structures for each string allocation.
This only works fine if the strings are static and don't change.
TList, TList<>, array of string and the solution above have a "list" overhead of one pointer per string. A linked list has an overhead of at least 2 pointers (single linked list) or 3 pointers (double linked list). The linked list solution does not have fast random access but allows for O(1) resizes where trhe other options have O(lgN) (using a factor for resize) or O(N) using a fixed resize.
What I would do:
If < 1000 items and performance is not utmost important: use TStringList or a dyn array whatever is easiest for you.
else if static: use the trick above. This will give you O(lgN) query time, least used memory and very fast load times (just gulp it in or use a memory mapped file)
All mentioned structures in your question will fail when using large amounts of data 1M+ strings that needs to be dynamically chaned in code. At that Time I would use a balances binary tree or a hash table depending on the type of queries I need to maken.
From your description, I'm not entirely sure if it could fit in your design but one way you could improve on memory usage without suffering a huge performance penalty is by using a trie.
Advantages relative to binary search tree
The following are the main advantages
of tries over binary search trees
(BSTs):
Looking up keys is faster. Looking up a key of length m takes worst case
O(m) time. A BST performs O(log(n))
comparisons of keys, where n is the
number of elements in the tree,
because lookups depend on the depth of
the tree, which is logarithmic in the
number of keys if the tree is
balanced. Hence in the worst case, a
BST takes O(m log n) time. Moreover,
in the worst case log(n) will approach
m. Also, the simple operations tries
use during lookup, such as array
indexing using a character, are fast
on real machines.
Tries can require less space when they contain a large number of short
strings, because the keys are not
stored explicitly and nodes are shared
between keys with common initial
subsequences.
Tries facilitate longest-prefix matching, helping to find the key
sharing the longest possible prefix of
characters all unique.
Possible alternative:
I've recently discovered SynBigTable (http://blog.synopse.info/post/2010/03/16/Synopse-Big-Table) which has a TSynBigTableString class for storing large amounts of data using a string index.
Very simple, single layer bigtable implementation, and it mainly uses disc storage, to consumes a lot less memory than expected when storing hundreds of thousands of records.
As simple as:
aId := UTF8String(Format('%s.%s', [name, surname]));
bigtable.Add(data, aId)
and
bigtable.Get(aId, data)
One catch, indexes must be unique, and the cost of update is a bit high (first delete, then re-insert)
TStringList stores an array of pointer to (string, TObject) records.
TList stores an array of pointers.
TStringBuilder cannot store a collection of strings. It is similar to .NET's StringBuilder and should only be used to concatenate (many) strings.
Resizing dynamic arrays is slow, so do not even consider it as an option.
I would use Delphi's generic TList<string> in all your scenarios. It stores an array of strings (not string pointers). It should have faster access in all cases due to no (un)boxing.
You may be able to find or implement a slightly better linked-list solution if you only want sequential access. See Delphi Algorithms and Data Structures.
Delphi promotes its TList and TList<>. The internal array implementation is highly optimized and I have never experienced performance/memory issues when using it. See Efficiency of TList and TStringList
some questions about records in Delphi:
As records are almost like classes, why not use only classes instead of records?
In theory, memory is allocated for a record when it is declared by a variable; but, and how is memory released after?
I can understand the utility of pointers to records into a list object, but with Generics Containers (TList<T>), are there need to use pointer yet? if not, how to delete/release each record into a Generic Container? If I wanna delete a specific record into a Generic Container, how to do it?
There are lots of differences between records and classes; and no "Pointer to record" <> "Class". Each has its own pros and cons; one of the important things about software development is to understand these so you can more easily choose the most appropriate for a given situation.
This question is based on a false premise. Records are not almost like classes, in the same way that Integers are not almost like Doubles.
Classes must always be dynamically instantiated, whereas this is a possibility, but not a requirement for records.
Instances of classes (which we call objects) are always passed around by reference, meaning that multiple sections of code will share and act on the same instance. This is something important to remember, because you may unintentionally modify an object as a side-effect; although when done intentionally it's a powerful feature. Records on the other hand are passed by value; you need to explicitly indicate if you're passing them by reference.
Classes do not 'copy as easily as records'. When I say copy, I mean a separate instance duplicating a source. (This should be obvious in light of the value/reference comment above).
Records tend to work very nicely with typed files (because they're so easy to copy).
Records can overlay fields with other fields (case x of/unions)
These were comments on certain situational benefits of records; conversely, there are also situational benefits for classes that I'll not elaborate on.
Perhaps the easiest way to understand this is to be a little pedantic about it. Let's clarify; memory is not really allocated 'when its declared', it's allocated when the variable is in scope, and deallocated when it goes out of scope. So for a local variable, it's allocated just before the start of the routine, and deallocated just after the end. For a class field, it's allocated when the object is created, and deallocated when it's destroyed.
Again, there are pros and cons...
It can be slower and require more memory to copy entire records (as with generics) than to just copy the references.
Passing records around by reference (using pointers) is a powerful technique whereby you can easily have something else modify your copy of the record. Without this, you'd have to pass your record by value (i.e. copy it) receive the changed record as a result, copy it again to your own structures.
Are pointers to records like classes? No, not at all. Just two of the differences:
Classes support polymorphic inheritance.
Classes can implement interfaces.
For 1 and 2: records are value types, while classes are reference types. They're allocated on the stack, or directly in the memory space of any larger variable that contains them, instead of through a pointer, and automatically cleaned up by the compiler when they go out of scope.
As for your third question, a TList<TMyRecord> internally declares an array of TMyRecord for storage space. All the records in it will be cleaned up when the list is destroyed. If you want to delete a specific one, use the Delete method to delete by index, or the Remove method to find and delete. But be aware that since it's a value type, everything you do will be making copies of the record, not copying references to it.
One of the main benefits of records is, when you have a large "array of record". This is created in memory by allocating space for all records in one contiguous RAM space, which is extremely fast. If you had used "array of TClass" instead, each object in the array would have to be allocated by itself, which is slow.
There has been a lot of work to improve the speed of allocating memory, in order to improve the speed of strings and objects, but it will never be as fast as replacing 100,000 memory allocations with 1 memory allocation.
However, if you use array of record, don't copy the record around in local variables. That may easily kill the speed benefit.
1) To allow for inheritance and polymorphism, classes have some overhead. Records do not allow them, and in some situations may be somewhat faster and simpler to use. Unlike classes, that are always allocated in the heap and managed through references, records can be allocated on the stack also, accessed directly, and assigned each other without requiring to call an "Assign" method.
Also records are useful to access memory blocks with a given structure, because their memory layout is exactly how you define it. A class instance memory layout is controlled by the compiler and has additional data to make objects work (i.e. the pointer to the Virtual Method Table).
2) Unless you allocate records dynamically, using New() or GetMem(), record's memory is managed by the compiler as ordinals, floats or static arrays: global variables memory is allocated at startup and released when the program terminates, and local variables are allocated on the stack entering a function/procedure/method and released exiting. Allocating/releasing memory in the stack is faster because it doesn't require calls to the memory manager, it's just very few assembler instructions to change the stack registers. But be aware that allocating large structure on the stack may cause a stack overflow, because the maximum stack size is fixed and not very large (see linker options).
If records are fields of a class, they are allocated when the class is created and released when the class is freed.
3) One of the advantages of generics is to eliminate the need of low-level pointer management - but be aware of the inner workings.
There are a few other differences between a class and a record. Classes can use polymorphism, and expose interfaces. Records can not implement destructors (although since Delphi 2006 they can now implement constructors and methods).
Records are very useful in segmenting memory into a more logical structure since the first data item in the record is at the same address point of the pointer to the record itself. This is not the case for classes.