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Referential integrity (RI) refers to the
concept that if one entity references another then that other entity actually
exists. For example, if I claim to
live in a house at 123 Main Street then that house must actually be there,
otherwise we have an RI error. In
relational database design the referential integrity rule (Halpin
2001) states that each non-null value of a foreign key must match the value
of some primary key. In the 1970s, when
relational databases
first came on the scene, the standard implementation technology was procedural
languages such as PL/1, Fortran, and COBOL.
Because these languages didn’t implement anything similar to data
entities and because the relational database did it made sense that relational
databases be responsible for ensuring referential integrity.
Furthermore relational databases back then were relatively simple, they
stored data and supported the ability to implement basic RI constraints.
The end result was that business logic was implemented in the application
code and RI was implemented in the database.
Modern software development isn’t like
this anymore. We now work with
implementation languages such as C# and Java that implement entities called
classes. As a result referential
integrity also becomes an issue within your application code as well as in your
database. Relational database
technology has also improved dramatically, supporting native programming
languages to write stored procedures and triggers and even standard object
programming languages such as Java. It
is now viable to implement business logic in your database as well as in your
application code. The best way to
look at it is that you now have options as to where referential integrity and
business logic is implemented. This
article explores the implications of this observation.
Table of Contents
- How object technology complicates
referential integrity
- Where should you implement?
Modern deployment architectures are complex.
The components of a new application may be deployed across several types
of machines, including various client machines, web servers, application
servers, and databases. Figure 1
depicts a simplified deployment
architecture diagram to provide an overview of
the situation that developers face on a daily basis.
Note that you may not have all of these platforms, or
they might be connected in slightly different ways. The important point is that business logic could be deployed to a wide number of platforms, to any of
the boxes shown in Figure 1,
as could entities. For example, a
new browser-based application could have JavaScript embedded in the HTML code to
perform simply data validation, the primary business objects could reside on the
application servers, these objects in turn invoke several web services which
wrap access to procedures deployed on the mainframe, and several stored
procedures that encapsulate shared functions are implemented in the three
relational databases accessed by the objects.
Figure 1. Modern deployment
architecture.
It is important to recognize that software development has
become more complex over the years. One
of the main reasons why the object-oriented paradigm was embraced so ardently by
software developers is because it helped them to deal with this growing
complexity. Unfortunately the
solution, in this case the common use of object technology within an n-tier
environment, has added a few complications with respect to ensuring referential
integrity. In particular, there are
several aspects of object technology that you need to come to terms with:
- Multiple entity representation
- Object relationship management
- Lazy reads
- Caches
- Association, aggregation,
and composition
- Architectural layering
- Removal from memory vs. persistent deletions
Figure 1 makes
it clear that an entity can be represented in different ways.
For example, customer data can be displayed on an HTML page, be used to
create a customer object that resides on an application server, and be stored in
the database. Keeping these various
representations in sync is a concurrency
control issue. Concurrency
control is nothing new, it is something that you need to deal with in a
multi-user system regardless of the implementation technology being used.
However, when you are using object technology and relational technology
together you are in a situation where you are implementing structure in two
places: In your object schema as classes that have interrelationships and in
your data schema as tables with interrelationships.
You will implement similar structures in each place.
For example you will have an Order object that has a collection of
OrderItem object in your object schema and an Order table that is
related to the OrderItem table. It
should be obvious that you need to deal with referential integrity issues within
each schema.
What isn’t so obvious is that because the same entities are
represented in multiple schemas you have “cross schema” referential
integrity issues to deal with as well.
Let’s work through an example using orders and order items.
To keep things simple, assume that there is a straight one-to-one mapping
between the object and data schemas. Also
assume that we’re working with a fat-client architecture, built in Java, and a
single database. We would have the
same issues that I’m about to describe with an n-tier architecture that
involves a farm of application servers, but let’s keep things simple.
I read an existing order and its order items into memory on my computer.
There are currently two order items, A and B.
Shortly thereafter you read the exact same order and order items into
memory on your computer. You decide
to add a new order item, C, to the order and save it to the database.
The order-order item structure is perfectly fine on each individual
machine – my order object references two order item objects that exist it’s
memory space, your order object references three order item objects that exist
in its memory space, and the three rows in the OrderItem table all
include a foreign key to the row in the Order table representing the
order. When you look at it from the
point of view of the entities, the order and its order items, there is an RI
problem because my order object doesn’t refer to order item C.
A similar situation would occur if you had deleted order item B
– now my order object would refer to an order item that no longer exists.
This assumes of course that the database is the system of record for
these entities. When something is
changed in the system of record it’s considered an “official” change.
This
concept is nothing new. When the
same entities are stored in several databases you have the exact same
referential integrity issues to deal with.
The fundamental issue is that whenever the same entities are represented
in several schemas, regardless of whether they are data schemas or object
schemas, you have the potential for “cross schema” referential integrity
problems.
A common technique to ensure referential integrity is to
use triggers to implement cascades. A
cascade occurs when an action on one table fires a trigger that in turn creates
a similar action in another table, which could in turn fire another trigger and
so on recursively. Cascades,
assuming the triggers are implemented correctly according to the applicable
businesses, effectively support automatic relationship management.
There are three common types of database cascades:
-
Cascading deletes.
The deletion of a row in the Customer table results in the
deletion of all rows referring to the row in the CustomerHistory
table. Each deletion from this
table causes the deletion of a corresponding row, if any, in the CustomerHistoryNotes
table.
-
Cascading inserts.
The insertion of a new row into the Customer table results in
the insertion of a row into the CustomerHistory table to record the
creation.
-
Cascading updates. The update of a row in the OrderItem
table results in an update to the corresponding row in the Item table
to record a change, if any, in the current inventory level.
This change could in turn trigger an update to the row in the DailyInventoryReorder
table representing today’s reorder statistics that in turn triggers an
update to the MonthlyInventoryReorder table.
Most reasonably sophisticated data
modeling tools, such as Computer Associate’s ERWin and Oracle’s
Designer, will automatically generate the stubs for triggers based on your
physical data models. All you need
to do is write the code that makes the appropriate change(s) to the target rows.
The concept of cascades is applicable to object
relationships, and once again there are three types:
-
Cascading deletes.
The deletion of a Customer object results in the deletion of
its corresponding Address object and its ZipCode object.
In languages such as Java and Smalltalk that support automatic
garbage collections cascading deletes, at least of the object in memory, is
handled automatically. However,
you will also want to delete the corresponding rows in the database that
these objects are mapped to.
-
Cascading reads.
When an Order object is retrieved from the database you also
want to automatically retrieve its OrderItem objects and any
corresponding Item objects that describe the order items.
-
Cascading saves. When an Order object is
saved the corresponding OrderItem objects should also be saved
automatically. This may
translate into either inserts or updates into the database as the case may
be.
You have several implementation options for object
cascades, the choice of which should be driven by your database
encapsulation strategy. First,
you can code the cascades. As with
database triggers, sophisticated object
modeling CASE tools such as TogetherCC and Poseidon will automatically
generate operation stubs that you can later write code for.
This approach works well with a brute force, data access object, or
service approach to database encapsulation.
Second, your persistence framework may be sophisticated enough to support
automatic cascades based on your relationship mapping metadata.
There are several important implications of cascades:
-
You have an implementation choice.
First, for a given relationship you need to decide if there are any
cascades that are application and if so where you intend to implement them:
in the database, within your objects, or both.
You may find that you take different implementation strategies with
different relationships. Perhaps
the cascades between customers and addresses are implemented via objects
whereas the cascades originating from order items are implemented in the
database.
-
Beware of cycles.
A cycle occurs when a cascade cycles back to the starting point.
For example a change to A cascades to B that cascades to C that in
turn cascades back to A.
-
Beware of cascades getting out of control.
Although cascades sound great, and they are, there is a significant
potential for trouble. If you
define too many object read cascades you may find that the retrieval of a
single object could result in the cascaded retrieval of thousands of
objects. For example, if you
were to define a read cascade from Division to Employee you
could bring several thousand employees into memory when you read the object
representing the manufacturing division in memory.
Table 1 summarizes
strategies for when to consider defining object cascades on a relationship. For aggregation and composition the whole typically
determines the persistence lifecycle of the parts and thus drives your choice of
cascades. For associations the
primary determining factor is the multiplicity of the association.
For several situations, such as reading in a composition hierarchy, you
almost always want to always do it. In
other situations, such as deleting a composition hierarchy, there is a good
chance that you want to implement a cascade and therefore I indicate that you
should “consider” it. In the
cases where you should consider adding a cascade you need to think through the
business rules pertaining to the entities and their interrelationship(s) as well
as how the entities are used in practice by your application.
Table 1. Strategies for
defining object cascades.
|
Relationship Type
|
Cascading Delete
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Cascading Read
|
Cascading Save
|
|
Aggregation
|
Consider deleting the parts automatically when the
whole is deleted.
|
Consider reading the parts automatically when the
whole is read.
|
Consider saving the parts automatically when the
whole is saved.
|
|
Association (one to one)
|
Consider deleting the corresponding entity when the
multiplicity is 0..1.
Delete the entity when the multiplicity is exactly
one.
|
Consider reading the corresponding entity.
|
Consider saving the corresponding entity.
|
|
Association (one to many)
|
Consider deleting the many entities.
|
Consider reading the many entities.
|
Consider saving the many entities.
|
|
Association (many to one)
|
Avoid this. Deleting
the one entity is likely not an option as other objects (the many) still
refer to it.
|
Consider reading in the one entity.
|
Consider saving the one entity.
|
|
Association (many to many)
|
Avoid this. Deleting the many objects likely isn’t
an option due to other references, and due to the danger of the cascade
getting out of control.
|
Avoid this because the cascade is likely to get out
of control.
|
Avoid this because the cascade is likely to get out
of control.
|
|
Composition
|
Consider deleting the parts automatically when the
whole is deleted.
|
Read in the parts automatically when the whole is
read.
|
Save the parts automatically when the whole is saved.
|
In addition to cascades, you also have the issue of
ensuring that objects reference each other appropriately.
For example, assume that there is a bi-direction association between Customer
and Order. Also assume that
the object representing Sally Jones is in memory but that you haven’t read in
all of the orders that she has made. Now
you retrieve an order that she made last month.
When you retrieve this Order object it must reference the Sally
Jones Customer object that in turn must reference this Order
object. This is called the
“corresponding properties” principle – the values of the properties used
to implement a relationship must be maintained appropriately.
Lazy
reads are a performance enhancing technique common in
object-oriented applications where the
values of high-overhead attributes are defined at the time they are needed.
An example of a high-overhead attribute is a reference to another object,
or a collection of references to other objects, used to implement an object
relationship. In this situation a
lazy read effectively becomes a just in time (JIT) traversal of an object
relationship to read in the corresponding object(s).
What are the trade-offs between a JIT read and a cascading read?
A JIT read provides greater performance because there is the potential
that you never need to traverse the relationship.
A JIT read is a goodstrategy when a relationship
isn’t traversed very often but a bad strategy for relationships that are due
to the additional round-trip to the database. A cascading read is easier to implement because you don’t need to check
to see if the relationship has been initialized (it happens automatically).
A cache is a location where copies of entities are
temporarily kept. Examples of
caches include:
- Object cache.
With this approach copies of business objects are maintained in
memory. Application servers may
put some or all business objects into a shared cache, enabling all the users
that it supports to work with the same copies of the objects. This reduces its number of interactions with the database(s)
because now it can retrieve the objects once and consolidate the changes of
several users before updating the database.
Another approach is to have a cache for each user where updates to
the database are made during off-peak times, an approach that can be taken
by fat client applications as well. An object cache can be implemented easily via the
Identity Map pattern (Fowler
et. al. 2003) that advises use of a collection which supports lookup of
an object by its identity field (the attribute(s) representing the
primary
key within the database, one type of shadow information).
- Database cache.
A database server will cache data in memory enabling it to reduce the
number of disk accesses.
- Client data cache. Client machines may have
their own smaller copies of databases, perhaps a Microsoft Access version of
your corporate Oracle DB, enabling them to reduce network traffic and to run
in disconnected mode. These
database copies are replicated with the database of record (the corporate
DB) to sync them up.
The principle advantage of caches is performance
improvement. Database accesses
often prove to take the majority of processing time in business application, and
caches can dramatically reduce the number of database accesses that your
applications need to make. How you
use a cache is important. If a cache is read-only then chance are good
that you don’t need to refresh it as often as you would an updateable cache. You may want to only cache data that is unlikely to change
very often, such as a list of countries, but not data that is likely to change,
such as customer data.
Unfortunately there are several disadvantages of caches.
First, they add complexity to your application because of the additional
logic required to manage the objects/data in your cache.
This additional logic includes the need to refresh the cache with the
database of record on a regular basis and to handle collisions between the cache
and database (Implementing
Concurrency Control discusses strategies for doing so).
Second, you run the risk of not committing changes to your database if
the machine on which a memory-based cache resides. Third, caches exacerbate cross schema referential integrity
problems discussed earlier. This
happens because caches increase the time that copies of an entity exist in
multiple locations and thus increase the likeliness of a problem occurring.
There are three types of object relationships – aggregation, composition, and
association – that we are interested in. Aggregation represents the
concept that an object may be made up of other objects. For example, in
Figure 2 you see that a flight segment is part of a flight plan.
Composition is a stronger form of aggregation, typically applied to
objects representing physical items such as an engine being part of an airplane.
Association is used to model other types of object relationships, such as
the fact that a pilot flies an airplane and follows a flight plan.
Figure 2. Relationship types.
 |
From a referential integrity perspective the only difference
between association and aggregation/composition relationships is how tightly the
objects are bound to each other. With
aggregation and composition anything that you do to the whole you almost always
need to do to the parts, whereas with association that is often not the case.
For example if you fly an airplane from New York to San Francisco you
also fly the engine there as well. More
importantly, if you retrieve an airplane object from the database then you
likely also want to retrieve its engines (airplanes without engines make little
sense). Similarly a flight plan
without its flight segments offer little value.
You almost always want to delete the parts when you delete the whole, for
example a flight segment doesn’t make much sense outside the scope of a flight
plan. Association is different.
A pilot object without the airplane objects that it flies makes sense,
and if you delete an airplane then the pilot objects that flew it at one point
shouldn’t be affected. |
Clearly the type of relationship between two classes will provide
guidance as to their applicable referential integrity rules.
Composition relationships typically result in more referential integrity
rules than does aggregation, which in turn typically results in more rules than
does association.
It is important to recognize that although inheritance is a type of object
relationship it isn't a factor when it comes to referential integrity between
objects. This is the result of inheritance being natively implemented by
the object-oriented languages. When inheritance
structures are mapped into a relational database you may end up with several
tables and therefore have the normal database referential integrity issues to
deal with.
Layering is the concept of organizing your software design
into layers/collections of classes or components that fulfill a common purpose.
Figure 3 depicts a five-layer
class-type
architecture for the design of object-oriented software. These layers are:
-
Interface layer.
A UI class implements a major UI element of your system such
as a Java Server Page (JSP), an Active Server Page (ASP), a report,
or a graphical user interface (GUI) screen.
-
Process layer.
Controller classes, on the other hand, implement business logic that
involves collaborating with several domain classes or even other controller
classes. In
Enterprise
JavaBeans (EJB) entity beans are domain classes and session beans are
controller classes.
-
Domain layer.
Domain classes implement the concepts pertinent to your business
domain such as customer or order, focusing on the data aspects of the
business objects plus behaviors specific to individual objects.
-
Persistence/data layer.
Persistence classes encapsulate the ability to permanently store,
retrieve, and delete objects without revealing details of the underlying
storage technology (see the essay Encapsulating
Database Access).
-
System layer. System classes provide
operating-system-specific functionality for your applications, isolating
your software from the operating system (OS) by wrapping OS-specific
features, increasing the portability of your application.
Figure 3. Layering your
system based on class types.
Architectural layering is a common design approach because
it improves the modularity, and thus the maintainability, of your system.
Furthermore, it is an approach that is commonly accepted within the
object community and it is one of the reasons why object developers take offense
to the idea of implementing business logic and referential integrity within your
database.
A straightforward but important issue is the distinction between removing an
object from memory and permanently deleting it from the database.
You will often remove an object from memory, an act referred to as
garbage collection, when you no longer require it yet you won’t delete it from
the database because you’ll need it later.
As Figure 1 demonstrates,
you have a choice as to where you implement business logic, including your
referential integrity strategy. Anyone
who tells you that this logic MUST be implemented in the database or MUST be
implemented in business objects is clearly showing their prejudices – this
isn’t a black and white issue. You
have architectural options for how you implement
referential integrity as well as other types of business
logic. Although it may be
painful to admit, there isn’t a perfect solution.
Implementing everything in business objects sounds nice in theory, but in
Database
Encapsulation Strategies you saw that it is common for some applications to
either not use your business objects or simply be unable to due to platform
incompatibilities. Implementing
everything in your database sounds nice in theory, but in Database
Encapsulation Strategies you also saw that it is common to have several
databases within your organization, the implication being that your database
really isn’t the centralized location that you want it to be.
Instead of following strategies that are nice in theory you need to
determine an approach that will actually work for you in practice.
That’s the topic of the rest of this section.
There are two basic philosophies as to where referential integrity
rules should be implement. The
largest camp, the “traditionalists”, maintain that referential integrity
rules should be implemented within the database.
Their argument is that modern databases include sophisticated mechanisms
to support RI and that the database provides an ideal location to centralize RI
enforcement that all applications can take advantage of.
A smaller camp, the “object purists”, maintain that referential
integrity rules should be implemented within the application logic, either the
business objects themselves or within your database
encapsulation layer. Their
argument is that referential integrity is a business issue and therefore should
be implemented within your business layer, not the database.
They also argue that the referential integrity enforcement features of
relational databases reflect the development realities of the 1970s and 1980s,
not the n-tier environment of the 1990s and 2000s.
My belief is that both camps are right and that both camps are
also wrong. The traditionalists’
approach breaks down in a multi-database environment because the database is no
longer a centralized resource in this situation.
It also ignores the need to ensure referential integrity across tiers –
referential integrity is no longer just a database issue.
The object purist approach breaks down when applications exist that
cannot use the business layer. This
includes non-object applications, perhaps written in COBOL or C, as well as
object applications that simply weren’t built to reuse the “standard”
business objects. The reality of
modern software development, apparent even in the simplified deployment diagram
of Figure 1, is that you need
to find the sweet spot between these two extremes.
An agile software developer realizes that there are several
options available to them when it comes to implementing referential integrity.
Table 2 compares and contrasts them from the point of view of each strategy being used
in isolation. The important
thing to realize is that no option is perfect, that each has its trade-offs. For example, within the database community the “declarative
vs. programmatic RI” debate rages on and likely will never be resolved (and
that’s exactly how it should be). A
second important observation is that you can mix and match these techniques.
Today within your organization you are likely using all of them, and you
may even have individual applications that apply each one.
Once again, it isn’t a black and white world.
Table
2. Referential integrity implementation options.
|
Option
|
Description
|
Advantages
|
Disadvantages
|
When to Use
|
|
Business objects
|
Programmatic approach where RI is enforced by operations
implemented by business objects within your application.
For example, as part of deletion an Order object will
automatically delete its associated OrderItem objects.
|
Supports a “pure object” approach.
Testing is simplified because all business logic is
implemented in one place.
|
Every application must be architected to reuse the same
business objects.
Extra programming required to support functionality that is
natively supported by your database.
|
For complex, object-oriented RI rules.
When all applications are built using the same business
object, or better yet
domain component, framework.
|
|
Database constraints
|
This approach, also called declarative referential integrity
(DRI), uses data definition language (DDL) defined constraints to enforce
RI. For example, adding a NOT NULL constraint to a foreign key column.
|
Ensures referential integrity within the database
Constraints can be generated, and reverse engineered, by
data modeling tools.
|
Every application must be architected to use the same
database, or all constraints must be implemented in each database.
Proves to be a performance inhibitor with large tables.
|
When the database is a shared by several applications.
For simple, data-oriented RI.
Use in conjunction with database triggers and possibly updateable views.
For large databases use during development to help you
identify RI bugs, but remove the constraints once you deploy into
production.
|
|
Database triggers
|
Programmatic approach where a procedure is “triggered”
by an event, such as a deletion of a row, to perform required actions to
ensure that other RI is maintained.
|
Ensures referential integrity within the database.
Triggers can be generated, and reverse engineered, by data
modeling tools.
|
Every application must be architected to use the same
database, or all triggers must be implemented in each database.
Proves to be a performance inhibitor in tables with
large numbers of transactions.
|
When the database is shared by several applications.
For complex, data-oriented RI.
Use in conjunction with database constraints and possibly updateable views.
Use during development to discover RI bugs, then remove
once you deploy into production.
|
|
Persistence framework
|
Referential integrity rules are defined as part of the
relationship mappings. The
multiplicity (cardinality and optionality) of relationships are defined in
the meta data along with rules indicating the need for cascading reads,
updates, or deletions.
|
Referential integrity implemented as part of overall object
persistence strategy.
Referential integrity rules can be centralized into a single
meta data repository.
|
Every application must be architected to use the same
persistence framework, or at least work from the same relationship
mappings.
Can be difficult to test meta data driven rules.
|
For simple, object-oriented RI rules.
When all applications are built using the same persistence
framework.
|
|
Updateable Views
|
Referential integrity rules are reflected in the
definition of the view.
|
Referential integrity is enforced within the database.
|
Updateable views are often problematic within relational
databases due to referential integrity problems.
Updateable views that update several tables may not be an
option within your database.
All
applications must use the views, not the source tables.
|
When the database is shared by several applications.
When your RI needs are simple.
Using
in conjunction with database constraints and database triggers.
|
You also have choices when it comes to implementing non-RI business logic and
once again you can apply a combination of technologies.
Luckily this idea does not seem to be contentious, the only real issue is
deciding when to use each option. Table
3 describes each implementation option and provides guidance as to the
effective application of each.
Table 3. Business logic
implementation options.
|
Option
|
Description
|
Advantages
|
Disadvantages
|
When to Use
|
|
Business objects
|
Business objects, both domain and controller objects,
implement the business logic as a collection of operations.
|
Reflects standard layering practices within the development
community.
Business functionality easily accessible by other object
applications.
Very good development tools exist to build business objects.
|
Significant performance problems for data intensive
functions.
Non-object applications may have significant difficulty
accessing functionality.
|
Complex business functionality that does not require
significant amounts of data.
|
|
Services
|
An individual service, such as a web service or CICS
transaction, implements a cohesive business transaction such as
transferring funds between accounts.
|
Services can be accessed in a standard, platform independent
manner.
Promotes reuse.
|
Web services standards still evolving.
Developers are still learning to think in terms of services.
Need tools to manage, find, and maintain services.
|
Wrapper around new or existing business logic implemented by
legacy systems, stored procedures, and business objects.
New functionality that needs to be reused by multiple
platforms.
|
|
Stored procedures
|
Functionality is implemented in the database.
|
Accessible by wide range of applications.
|
Potential for database to become a processing bottleneck.
Requires application programmers to have significant
database development experience in addition to “normal” application
development experience.
Very difficult to port between database vendors.
|
Data intensive functions that produce small result sets.
|
For years I have advised developers to avoid using
stored procedures because they aren’t portable between databases.
During the 1990s I had been involved with several projects that had run
into serious trouble because they needed to port to a new database in order to
scale their application, and as a result they needed to redevelop all of their
stored procedures. Ports such as this were common back then because the database
market hadn’t stabilized yet. It
wasn’t clear back then what products were going to survive and as a result
organizations hadn’t committed yet to a single vendor.
Times have changed. Most
database vendors have solved the scalability issue make it unlikely that you
need to port. Furthermore most organizations have chosen a primary database vendor –
it is quite common for an organization to be an “Oracle shop”, a “DB2
shop”, or a “MySQL shop” – making it unlikely that you will be allowed
to port anyway. Therefore stored
procedures, assuming that they are well written and implemented according to the
guidelines described below, are now a viable implementation option in my
opinion. Use them wisely.
In the previous sections you have seen that you have
several technical alternatives for implementing referential integrity and other
business logic. You have also seen
that each alternative has its strengths and weaknesses.
This section overviews several strategies that you should
consider when deciding where to implement this logic.
These strategies are:
-
Recognize that it isn’t a black and white decision.
I simply can’t say this enough – your technical environment is
likely too complex to support a “one size fits all” strategy.
-
Implement logic on commonly shared tier(s). The
best place to implement commonly used logic is on commonly used tiers.
If your database is the only common denominator between applications,
this is particularly true when applications are built on different platforms
or with different technologies, then your database may be your only viable
option to implement reusable functionality.
-
Implement unique logic in the most appropriate place.
If business logic is unique to an application implement it in the
most appropriate place. If this
happens to be in the same place that you’re implementing shared logic then
implement it in such a way as to distinguish it and better yet keep it
separate so that it doesn’t “get in the way” of everyone else.
-
Implement logic where it’s easiest.
Another factor you need to consider is ease of implementation.
You may have better development tools, or more experience, on one
tier than another. All things
being equal, if it’s easier for you to develop and deploy logic to your
application server than it is into your database server then do so.
-
Be prepared to implement the same logic in several
places. You should always strive to implement logic once, but
sometimes this isn’t realistic. In a multi-database
environment, you may discover that you are implementing the same logic in
each database to ensure consistency. In
a multi-tier environment you may discover that you need to implement most if
not all of your referential integrity rules in both your business layer (so that RI rules are reflected in your object schema)
and
your database.
-
Be prepared to evolve your strategy over time.
Some database refactorings include moving functionality into or out
of your database. Architectural
direction – it might be painful, but you may want to eventually stop
implementing business logic in some places.
However, having said all this the reality is that databases are often the
best choice for implementing RI. The
growing importance of web services and XML point to a trend where application
logic is becoming less object-oriented, even though object technology is the
primary underlying implementation technology for both, and more data-processing
oriented. Nevertheless your team
still needs to work through this critical architectural issue.
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