Comments in your SPFILE

Consider using the COMMENT= clause to document why a particular change was made the next time you make a change to a parameter using an SPFILE:

SQL> alter system set pga_aggregate_target=512m comment='Changed 04-JUN-2018, AWR recommendation, MR';

System altered.

 

SQL> select value, update_comment from v$parameter where name = 'pga_aggregate_target';SQL> select value, update_comment from v$parameter where name = 'pga_aggregate_target';
VALUE      UPDATE_COMMENT
-------------------- ---------------------------------------------------------------------------------------------------
536870912  Changed 04-JUN-2018, AWR recommendation, MR

 

 

 

There is no such thing as a small change

“We want to limit the length of a review in the product to 140 characters, because we may want to use SMS at some stage. That’s a small change, right?”

Wrong.

There are no small changes when you’re committed to delivering quality software. Let’s look at the above case. A naïve programmer may well get this coded in three minutes—after all it’s just an if-statement.

A background in consulting, where you are paid for your time, teaches you to ask a few questions before proceeding with ‘small changes’. Let’s start with some easy questions.

What happens when the review is above 140 characters? Do we crop the string, or display an error message to the user? If we display an error, where does it appear? What does it say? Who is going to write the error message? How do we explain to the user why we’re limiting them to 140 characters? How will these errors look? Do we have a style defined? If not, who is designing it?

But wait, there’s more…

In the unlikely event that we have answers to hand for all of the above concerns, we’re still not finished. Just doing this server-side is a messy way to handle an error. We should do this client-side. But if we’re going to do client-side validation then I’d have a few more questions…

Who’s writing the JavaScript? Does the JavaScript display the same type of error as the server-side code? If not, what’s the new style? How does it behave without JavaScript? How do we ensure that any new update to the 140 character requirement affect both client-side and server-side validation?

We’re still not done. Look at this from a users point of view. They’re already frustrated by having to limit a review to 140 characters for a bizarre reason they won’t understand, and now we’re asking them to guess how long their message is? There must be a better way. Let’s give them a character counter. Oh, well that raises a few more questions…

Nearly there…

Who is going to write this character counter? If we’re using one we found on the net, then who wants to test it in our target browsers (i.e. not just Chrome 27 and beyond).

Also, where is the count of letters displayed on the screen? What does the count look like? Of course, the style should change as the user approaches zero characters, and should definitely look erroneous when they’ve used more than 140 characters—or should it stop accepting input at that point? If so, what happens when they paste something in? Should we let them edit it down, or alert them?

When we’ve implemented the character counter, styled all the errors, implemented the server-side validations, and checked it in all of our supported browsers then it’s just a case of writing tests for it and then deploying it. Assuming your time to production is solid, this bit will be straightforward.

All of this happily ignores the fact that users will wonder why someone wrote an eighty word review just before them and now they’re only allowed write a 140 character one. Obviously we’ll need to keep support in the loop on this, and update our documentation, API, iPhone, and Android apps. Also, what do we do with all the previous reviews? Should we crop them, or leave them as is?

Don’t get me started on how we’re gonna deal with all the funky characters that people use these days… good luck sending them in a text message. We’ll probably need to sanitize the input string of rogue characters, and this means new error messages, new server-side code… the list goes on.

Once you get through all of this you will have your feature in place, and this is just for a character count. Now try something that’s more complex than an if-statement. There are no tiny features when you’re doing things properly. This is why as a UX designer you need a good understanding of what it takes to implement a feature before you nod your head and write another bullet point.

You can’t be serious…

Yes, this was a rant. Yes, most of the above decisions can be made on the fly by experienced developers, but not all of them. Yes, you can use maxlength, but this only addresses one of the points above, and even then only in an HTML5 context.

Often what seems like a two minute job can often turn into a two hour job when the bigger picture isn’t considered. Features that seemed like ‘good value’ at a two minute estimate are rightfully out of scope at two hours.

Key point: Scope grows in minutes, not months. Look after the minutes, and the months take care of themselves.

Agreeing to features is deceptively easy. Coding them rarely is. Maintaining them can be a nightmare. When you’re striving for quality, there are no small changes.

Red means stop, green means go… snow means…?

One more example to remind you that there is no such thing as a “small” change.

LED lights are an excellent lighting solution due to their longevity and power efficiency. Replace all traffic lights with LED lights, “small” change right? Wrong.  It turns out that they may not be the best choice in all conditions. Normally, the excess heat generated by incandescent bulbs is enough to melt the snow off lights so that they remain visible even in freezing conditions. Traffic lights that employ LED lighting, while far more power efficient and reliable than older ones, aren’t able to melt the snow that accumulates.

Snow blocking traffic signals is a significant problem as it has already led to dozens of accidents and at least one fatality.

Aside

University

Universities used to be centres of learning. Now most of them are corporations with huge marketing divisions, massive administration costs, crazy slogans, a fixation with dodgy rankings, an obsession with what is often low grade and banal research and an increasing reliance on casual low-cost staff. Otherwise it’s all good!

Introduction to Database Normalisation

Background

There are two approaches in relational database design.

* TOP DOWN: From Data Modeling (eg. ER Model) to Relational Logical Model for implementation.

* BOTTOM UP: Normalization of Relations

Normalisation is a formal process for deciding which attributes should be grouped together in a relation so that all anomalies are removed. Hence the aim is to successively reduce relations to produce smaller, well structured relations.

Dependencies

Functional Dependency:
The simplest kind of dependency is called functional dependency (FD). The dependencies are best explained through examples.

For example, LecturerID -> LecturerName
is a valid FD because:

For each LecturerID there is at most one LecturerName, or
LecturerName is determined by LecturerID , or
LecturerName is uniquely determined by LecturerID , or
LecturerName depends on LecturerID .

Each of the above statements is equivalent.

Formally;

The FD  X -> Y is a full dependency if no attribute can be removed from X.

LabDate, SubjectCode ->  Tutor is a full dependency, that is, Tutor is fully dependent on both LabDate AND SubjectCode.

Partial Dependency:

The FD  X -> Y is a partial dependency if an attribute can be removed from X.

LecturerID, SubjectCode -> LecturerName is a partial dependency, that is, LecturerName is partially dependent on LecturerID AND SubjectCode.

(to determine LecturerName, I only need to know LecturerID).

Transitive Dependency:

Dependencies can be transitive.

For example, if one lecturer can teach one subject and each subject only has one tutor, then we might have the dependencies:

LecturerID -> SubjectCode
SubjectCode -> Tutor
and, transitively LecturerID -> Tutor.

Normal Forms:

Functional dependencies can be used to decide whether a schema is well designed.

For example, in the following relation:

LecturerSubject (LecturerID, LecturerName, SubjectCode, SubjectName)

Anomalies?
If there is a new subject which has not been allocated a lecturer, can you record the details of this subject in the above table? (Insert Anomaly)

If an existing subject changes the name, can you do the changes to one instance only? (Update Anomaly)

If a lecturer resigns and the details are to be deleted, would there be a chance that some subjects will be removed permanently and we won’t have any track record of those subjects anymore? (Delete Anomaly)

Design errors in relations, such as the potential for certain kinds of anomalies, can be categorised. These categories of error can be successively eliminated by decomposing relations into normal forms.

The major/main normal forms are first (1NF), second (2NF), third (3NF), and Boyce ­Codd (BCNF). Higher/advanced normal forms including fourth (4NF), and fifth (5NF). Because problems with 4NF and 5NF rarely occur, moreover database designers in industry normally do not need to use the highest possible NF for practical reasons. We will focus on satisfying 3NF level.

First Normal Form (1NF):

A relation is in 1NF if:

  • There are no repeating groups
  • A primary key has been defined, which uniquely identifies each row in the relation.
  • All attributes are functionally dependent on all or part of the key.
  • Attributes should be stored as atomic values -> Each field entry can only contain one piece of data. E.g. A name field containing “Fred Smith” has surname and first name, violating 1NF.

Second Normal Form (2NF):

A relation is in 2NF if:

  • The relation is in 1 NF
  • All non-key attributes are fully functionally dependent on the entire key (partial dependency has been removed).

Third Normal Form (3NF):

A relation is in 3NF if:

  • The relation is in 2NF
  • All transitive dependencies have been removed. (Transitive dependency: non-key attribute dependent on another non-key attribute.)

Example

Normalize the ORDER form below:

From the ORDER FORM (user view) we can derive ORDER relation:

Currently in UNF (Un-normalized Form)

ORDER
(Order #, Customer #, Customer Name, Customer Address, City, State, PostCode, Order Date, (Product #, Description, Quantity, Unit Price))

Note that the order form is not in 1NF because there is a repeating group
(Product#, Description….).

To convert the above relation into 1NF, the repeating group must be removed by creating a new relation based on the repeating group along with the primary key of the main relation.

1NF:

ORDER
(Order#, Customer#, Customer Name,  Address, City, State, PostCode, OrderDate)

ORDER_PRODUCT
(Order#, Product#, Description, Quantity, Unit Price)    —-> Note that Order# is also a foreign key as well as a PK

Anomalies:

Insertion Anomalies: cannot insert a new product until there is an order for that product.

Deletion Anomalies: if an order is deleted the whole detail of the product will also be deleted.

Update Anomalies: if the detail of a particular product needs to be updated, each order that contains that product has to be updated.

2NF (Partial dependencies):

The ORDER_PRODUCT relation is not in 2NF because not all non-key attributes are fully dependent on the entire key (e.g. the PK is the combination of order# and product#. But description and unit price depend on product#, not order#)

To convert the ORDER_PRODUCT relation into 2NF, a new relation must be created which consists of part of the keys (becomes the primary key of the new relation) and all non key attributes that are dependent on the partial key.

ORDER_PRODUCT
(Order#, Product#, Quantity)

PRODUCT
(Product#, Description, Unit_Price)

The ORDER relation is already in 2NF as there are no non key attributes that are dependent on partial key (ORDER only has a single key).

Anomalies:

Insert Anomalies: a new customer cannot be inserted until he/she has an order.

Delete Anomalies: if an order is deleted, the whole information of the customer is also deleted.

Update Anomalies: if a customer detail is to be updated, all orders for that  customer need to be updated.

3NF (Transitive Dependencies):

The ORDER relation is not in 3NF because there is a transitive dependency (non-key attribute dependent on another non-key attribute). e.g. customerName, city, Address, etc. all depend on customer#, which is currently a non-key attribute.

To convert the relation into 3NF, a new relation must be created for the non-key attributes that are dependent to another non-key attribute.

CUSTOMER
(Customer#, Customer Name, Customer Address, City, State, PostCode)

ORDER
(Order#Customer#, Order Date)       —-> Remember to always maintain FK links

Both the order ORDER_PRODUCT and the PRODUCT relations are already in 3NF.

ORDER_PRODUCT(Order#, Product#, Quantity)
PRODUCT (Product#, Description, Unit Price)

Example Solution: Final Relations in 3NF and BCNF

ORDER
(Order#Customer#, OrderDate)

CUSTOMER
(Customer#, CustomerName, CustomerAddress, City, State, PostCode)

ORDER_PRODUCT
(Order#, Product#, Quantity)

PRODUCT
(Product#, Description, UnitPrice)

Now an example for everyone at home to try:


Here is a suggested solution:

 

1NF: Identify the PK.

PATIENTHISTORY(PatientNo, name, address, suburb, date, time, drNo, drName, visitCode, description)

2NF: Remove partial dependencies. Notice that currently the PK is the combination of PatientNo, date, time. name, address, and suburb only depend on PART OF THE KEY (patientNo). patientNo, date, time -> drNo so this is not a partial dependency. Same as visitcode. We will deal with drName and description shortly.

PATIENT (PatientNo, name, address, suburb)

PATIENT_HISTORY(PatientNo, date, time, drNo, drName, visitCode, description) —> Note that PatientNo is now a foreign key as well as part of the key.

3NF: Remove transitive dependency. Notice how drName is dependency on the non key drNo. Same principle for description. description is determined by visitCode.

DOCTOR (drNo, drName)

ILLNESS (visitCode, description)

PATIENT (PatientNo, name, address, suburb)

PATIENT_HISTORY (PatientNo, date, timeDrNo, visitCode)  —> DrNo and visitCode are foreign keys (pointing to the doctor and illness tables, respectively).