DateTime, Date and Time – SQL Smells

Date and Time - Point or Period - An SQL Smell?
Date and Time – Point or Period – An SQL Smell?

Phil Factor identifies several SQL Smells associated with the use of DateTime, Date and Time data-types. Using the wrong types will waste space, harm performance and create “odd” behaviour.

If you are clear about what you are recording, you will avoid these issues. I prefer to say what I want, and let an expert choose the best date-types. In other words, I prefer to separate the analyst and designer roles. That way I avoid suggesting the wrong types.

DateTime, Date or Time – Which do you need?

DateTime columns which contain only Date or Time contribute two of SQL Smells. This wastes space, both in storage and in memory (which will degrade performance).

There is something worse here. Using DateTime in this way suggests we are not clear about what we want. This lack of clarity encourages the designer to “hedge their bet” by using DateTime.

How precise do you need to be?

Business Systems need to record dates and times, but they don’t need great precision. For many business transactions, the nearest second or even minute is adequate. In some cases recording extra precision can be misleading.

Storing Durations

For a “Period” you need to know 3 things: Start, End and Duration. You only need to store two out of the 3. The third value can always be calculated if you have the other two.

Finding if an Event is inside a Period is easier using Start and End
Finding if an Event is inside a Period is easier using Start and End

Storing the “start” and “end” is more flexible. It is straightforward to work out if an event took place within a given period.

Finding if Periods Overlap is easy using Start and End times
Finding if Periods Overlap is easy using Start and End times

Telling if periods overlap is easy too.

If you decide to store a duration you must specify the units you intend and the precision you need. (As Phil puts it “milliseconds? Quarters? Weeks?”). Do not be tempted to store a duration in a “Time” data-type! “Time” is intended for “Time-of-day” not duration.

Dates and Times: Choosing the right data-type – Some simple questions

Choosing appropriate data-types for dates and times is not difficult, if you go about it the right way.

Divide the job into two steps: “The Requirement” and the “The Most suitable technical data-type”. Do the two steps separately, taking into account any local standards and conventions.

The Requirement

  1. Is this an “Event” (a “Point in Time”) a “Period” or a “Duration” (both have beginning and an end)?
  2. Event: What is the best way to represent this data? Should it be a “Date” or a “Time”? Does it really need “Date-time”?
  3. Duration: What are the units and scale of this duration?
  4. How precise do you need the value to be? You may surprise yourself.
  5. How long do you expect this system (and the data in it) to last?

The Most Suitable Technical Data-type

Technical Properties of Date and Time data-types in Microsoft SQL Server
Technical Properties of Date and Time data-types in Microsoft SQL Server

Use the table (based on Microsoft’s Documentation) to choose the best data-type for your needs. This table is for SQL Server. Other database managers will have similar tables.

Microsoft recommends using DATETIME2 in preference to DATETIME for new work (it takes up less space and is more accurate). Providing the maximum date is acceptable, I would consider SMALLDATETIME for business transactions (but you do risk creating a “Year 2000 problem” if the data turns out to have a long life).

If your system will span several time-zones, then you should definitely consider the benefits of using the DATETIMEOFFSET data-type.

Summary

The DateTime, Date and Time data-types can all cause SQL Smells when they are used inappropriately. Problems can be avoided by following some simple guidelines.

Where next?

Phil Factor doesn’t like “SELECT *”. Find out why in the next article.

Same column name in different tables- SQL Smells

Two Columns: Same Name, Different Data-type = Nasty! SQL Smell
Two Columns: Same Name, Different Data-type = Nasty! SQL Smell

Here’s another of Phil Factor’s SQL Smells. “Using the same column name in different tables but with different data-types”.

At first glance this seems harmless enough, but it can cause all sorts of problems. All of them are avoidable. If you are an analyst, make sure you are not encouraging this to happen. If you creating the Physical Model or DDL for a database, “Just don’t do it!”

As Phil Factor says, this is: “an accident waiting to happen.”

Two “rights” can make a “wrong”!

The problem here is not “using the same column name in different tables”. That is perfectly ok. Similarly, using “different data-types for different columns” cannot be wrong. That’s exactly what you should expect to do.

The problem is doing both at the same time. The issues are: practical and technical.

The Practical Problem of the same column name with different data-types

Any human user is likely to think that the same name refers to the same type of thing. They won’t check that the definitions of both “names”. No amount of “procedures” or “standards” will make them do anything different.

Sooner or later this will cause one of the technical problems.

The Technical Problems from the same column name with different data-types

Technical problems will occur when a value moves from one column to the other, or when comparing the two columns. Data may be truncated and those data transformations cost effort.

These problems may not manifest themselves immediately. The consequences will be data-dependent bugs and poor performance.

The Solution to this Issue

This smell and the associated problems can be avoided by following some simple rules:

  1. If two columns refer to the same thing (like a foreign key and a primary key), make sure they are the same data type.
  2. If two columns refer to different things, then give them distinct names. (Do not resort to prefixing every column name with the table name. That’s horrible)
  3. Having columns with have different names and the same data-type is perfectly OK.

Summary

“Using the same column name in different tables with different data-types” in an SQL database is simply “an accident waiting to happen.” It is easily avoided. Don’t do it and don’t do anything to encourage it.

Where next?

The next article is about the smells which come from dates and times.

NULLs Data Model and SQL Smells

Nulls - An SQL Smell?
Nulls – An SQL Smell?

NULLs are a perennial problem. Nobody likes them. They confuse developers and users and many analysts do not really understand them.

The concept of NULL allows us to say that there are things that we do not know.

In his article on SQL Smells, Phil Factor associates several smells with NULLs. In this post I’ll explain how to avoid using NULLs and how to use them properly when they are necessary.

Why do we need NULLs at all? What is the benefit and what is the cost?

NULL has a simple meaning with wide-ranging and surprising consequences. NULL means the value is unknown. And this in turn means that the result of any calculation or concatenation which uses this value must also be unknown!

NULL is a feature of SQL. The benefit of allowing NULL or “three valued logic” (TRUE/FALSE/UNKNOWN) is that it allows a database to record that there are things we do not know. The cost of having them is that any calculation or concatenation which uses this value must also be unknown! This confuses many people.

Reasons for needing NULL

There are many reasons why we might not have data to put into a column. Thinking about why we are considering defining a column as NULLable will encourage us to consider alternatives.

The structural NULL – Permanent sub-types

Customer with Sub-types which may cause NULLs
Customer with Sub-types which may cause NULLs

Sometimes we want to combine two entities into a single “super-type” table. There are attributes of Person will never be used for Business (and vice-versa). These missing values will need NULLs.

The structural NULL – Lifecycle subtypes

Order with Lifecycle sub-types which may cause NULLs
Order with Lifecycle sub-types which may cause NULLs

Something similar can happen if we combine all the steps of a entities lifecycle into a single table. The attributes of the later stages will always be empty (NULL) until that stage is reached. These later steps often contain dates or times.

In both cases involving sub-types it may be possible to splitting the sub-types into separate tables. Consider whether it is worth the effort and make sure you avoid the pitfalls of sub-types in SQL.

Data that will never be there

Attributes and Values for an "Address"
Attributes and Values for an “Address”

Entities like “Address” are frequently modelled with attributes like “AddressLine_”. In many cases there will never be values for the later lines. They will not be mandatory in the user interface, but do they need to be NULL? Consider whether allowing them to default to “spaces” or an empty string, would be better and whether it would have any bad effects.

Things which should never allow NULL

Always decide whether you expect an attribute to have a value. Don’t leave it to chance.

There are some things which should hardly ever allow NULL. This includes all keys and identifiers. Avoid allowing short titles or descriptions to be NULL (For long descriptions allowing NULLs is understandable).

Unavoidable NULLs

Attributes and Values for a "Person" Entity - Sometimes NULL is hard to avoid
Attributes and Values for a “Person” Entity – Sometimes NULL is hard to avoid

There are some attributes where allowing NULL is hard to avoid. Life insurance and pensions companies may need a “date of death” for their customers! Having a column with allows NULL is often the easiest way of handling this.

You should resist the temptation to use “magic dates” or inappropriate data-types in order to avoid allowing NULL. The consequences are far worse than the problem.

Summary

NULLs are a problem, nobody likes them but they are necessary. Many problems with NULLs can be avoided by two rules:

  • Remember that NULL means “unknown value” and this has consequences.
  • Ask “_why_ don’t we have this data?”

In many cases NULLs can be avoided by data modelling – that means the analyst has to do work in the Conceptual or Logical Model.

Where next?

The next article is about another smell: having the same name for different things!

Indexes, Analysts and SQL Smells

SQL Indexes - A Goldilocks problem for analysts or an SQL Smell
SQL Indexes – A Goldilocks problem for analysts or an SQL Smell

Database Indexes are something which a lot of analysts ignore as being “too technical”. This is a pity.

Several SQL Smells in Phil Factor’s article point at possible bad decisions. Thinking in the  Logical Model can improve these decisions.

Choosing the correct indexes is a typical “Goldilocks Problem”: not too few, not too many, just the right number! Bad or inadequate requirements will contribute to designers making bad decisions. Phil Factor describes having the wrong indexes as an SQL Smell!

What is an Index and what does it do for us?

If you are an analyst, you may not know exactly what an index is. In non-technical terms, an index provides a quick way for the database manager to find the rows it needs in a table. There are several sorts of index.

An index is a “thing” in its own right. An index takes up space in the database. Updating an index costs effort. The main benefit of an index is that it makes select or read operations faster.

Types: Unique, Non-Unique, Clustered

There are three main kinds of index: Unique, Non-Unique and Clustered.

Imagine that we have a very simply database consisting of 3 tables:

  • Customer (not shown in the diagram)
  • Order
  • OrderLine
Two tables: Order and OrderLine - Where to put the indexes?
Two tables: Order and OrderLine – Where to put the indexes?

Unique Indexes:

Candidate Unique Indexes on an "Order" SQL table
Candidate Unique Indexes on an “Order” SQL table

In the Order table we can see three columns which might be used to identify the order

If you are not familiar with GUIDs, they are a way of assigning identifiers or “keys”. They are worth finding out about. It would be unusual to expect a human being to type in a GUID. An “OrderNum” (which paradoxically might contain letters!) would be more convenient for the users.

We expect all three: OrderId, GUID and OrderNum, to be unique. Therefore, all three are candidates for Unique Indexes. If an application attempts to create a duplicate value in a column which has a unique index, then the database manager will raise an error and reject the transaction.

Non-Unique Indexes for Foreign Keys

Candidate Indexes on an "OrderLine" SQL table
Candidate Indexes on an “OrderLine” SQL table

In the OrderLine table you can see two columns which identify things in other tables: OrderId and ProductId. These are Foreign Keys. In this case we cannot say they are unique, but they are candidates for non-unique indexes.

We could also have used OrderNum or OrderGUID as Foreign Keys into Order.

It is good practice for the rows in the OrderLine table to have a unique identifier. There are two common ways of doing this.

  • We can assign an OrderLineId (which is unique across the whole table) or
  • we can use the combination of OrderId and OrderLineNum which together would identify a row.

In this example, both OrderLineId and the OrderId and OrderLineNum combination are (seperate) candidate unique indexes.

Clustered Indexes

The order of the rows in an SQL table is specified by the Clustered Index. Each table can have only one clustered index. The clustered index must be unique.

People often make the “primary key” the clustered index, but it is worth considering other options. In the example, OrderLines can be added to an order after it has been created.

Using the OrderId, OrderLineNum index as the clustered index would make the database store all the “lines” for one order together (whenever they were added to the order). That may be more efficient for retrieval. Phil Factor identifies two smells with the choice of clustered indexes.

Non-Unique Indexes for Searching

Candidate Indexes on a "Customer" SQL table - A Non-Unique index on Name would help searches
Candidate Indexes on a “Customer” SQL table – A Non-Unique index on Name would help searches

Columns which will be used for searching should be considered candidates for a non-unique index.

The role of Analysts in choosing Indexes and Index types

Indexes are usually specified in the “Physical Model”. The analyst can help the database designer make the right decisions, by applying a little thought. The analyst should not try to pre-empt the designers decisions. They should aim to assist by identifying relevant “candidates”.

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Summary

Indexes enforce business rules like uniqueness in an SQL database. They influence database performance. Considering candidate indexes in the Logical Model and even the Conceptual Model will help database designers make better decisions.

Where next?

The next article is about the smell of nothing, or “Nulls”. Nulls present problems for developers and database designers.

Check Constraint to limit values – SQL Smell?

Do Check Constraints Cause an SQL Smell?
Do Check Constraints Cause an SQL Smell?

In his article on SQL Smells, Phil Factor is not in favour of using Check Constraint to limit the values in columns.

In my previous post I explained why Phil Factor recommends referential integrity. I am going to explain this apparent contradiction.

What are CHECK CONSTRAINTs and what do they do for us?

Using CHECK CONSTRAINT to Restrict Allowed Values
Using CHECK CONSTRAINT to Restrict Allowed Values

Like REFERENCES, the CHECK CONSTRAINT also says what are values can be stored in a column. The logical expression must be true for the value to be allowed.

The logical expression can be limits or even a list of permitted values (as in the illustration).

What is the problem with Check Constraints?

This sounds like a marvellous idea! The benefit is clear. Constraints will exclude invalid data from the database, even when it is loaded using a utility (eg BULK INSERT). We can define constraints which will protect the database from bad data. So what is the problem?

Why does Phil Factor regard this as an SQL Smell? The answer is that using Check Constraints in this way embeds constant values in the structure of the SQL database!

The database structure is “locked down” in commercial systems. Only authorised people are allowed to make changes to the structure. Phil Factor wants to avoid these changes.

When should we use Check Constraints?

This criticism does not mean that we should never use Check Constraints. The ideal constraint will not change in the lifetime of the database.

Possible candidates for using CHECK CONSTRAINTs to Restrict Values
Possible candidates for using CHECK CONSTRAINTs to Restrict Values
  • For attributes which represent “classifications” and “types” we should note how many different values we are expecting, and how frequently the allowed values change. Short lists which change very rarely may be acceptable.
  • On the other hand, consider re-designing a CHECK CONSTRAINT as a FOREIGN KEY by adding an additional table to contain the valid values. This has the benefit of making adding a new value a simple data change!
  • Do not use Check Constraints to enforce arbitrary limits.

Check Constraint and Requirements

We can identify candidates for Check Constraint when we construct the Conceptual Model. We should note:

  • The number of options
  • The expected frequency of change.
  • That information will enable us to make an informed decision about how to validate the values of that column.

Unfortunately, some of the examples using Check Constraints perform the checks against arbitrary values. These examples will work technically but copying them may cause the problems Phil Factor wants us to avoid.

Summary

Check Constraints provide a way of validating data values. They are appropriate for checking against values which do not change.

For lists, lookup-tables with Foreign Key Constraints may be better.

Do not use Check Constraints against arbitrary values or values which change frequently.

Where next?

The next article covers “Indexes”. I will explain how an Analyst can influence some design decisions.