Typical errors in a database

typical errors in a database

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Typical errors in a database - think, that

Summary: 

This blog help you in exploring the common Access Database mistakes that you need to avoid for superb performance of your database. So, let’s get started with the right ways to avoid maximum of your mistakes occurred Access Database handling.

Microsoft Access Usage And Benefits

Microsoft Access is used by many users and professionals and it has many good points to use it. However building an access database requires effort, time and of course enough knowledge. In fact Access database provide enough tools to guide and make you get up again to run the database in minutes.

But there are some tools which are not enough to help user and many times, developers just ignore which affect the performance of the database such as slowing working of Access database.

But one thing to consider for every database user and that is optimization. This should seriously followed and applied so that the database can be in control order and its performance increases.

Access database help users to build forms, tables, query, reports etc. Due to its benefits, this database is used by many users and it forms are very powerful. But sometimes, the developers make some common Access Database mistakes with the database which can lead to poor performance.

Why You Need To Avoid Silly Access Database Mistakes?

Why You Need To Avoid Silly Access Database Mistakes?

However to ensure that you don’t make common mistakes with your database, here we have come up with 10 common mistakes of Access Database and how to avoid it. You will get information regarding your mistakes and reviewing them will help you to save your rework data and performance will increase.

But before going for the common MS Access mistakes, it is also important to know about the common factors which are responsible for access database corruption. Many times the database gets corrupted and users don’t even know how it happened. So few reasons are mentioned below.

13Common Access Database Handling Mistakes To Avoid

Database Mistake #1: sing the same table for all data

Those people who are new the access database, they face trouble with tables. Actually they don’t know how to store the different data in different table. For an instance, a bill consists of customer information, data or purchase and list of product purchased. Now here new users make a concept to fit everything in one table.

It happens now that product and customer information are constant for every bill. Because of this, the information created becomes wasted. Instead, users should use correct way to create three tables for three invoice such as customers, products and date. By this way, every item has a unique identifier and every item is once entered but billing object ties all together.

Database Mistake #2: Monitor your Hotkeys

You should be sure that accelerator keys also known as Hotkeys are not duplicate. This keys are great but should be very careful while assigning them. If keys are duplicate than it can create trouble. This key allow users to use Alt key and letter to jump to control. This is set by “&” character in captions trailed by letter. So, it&#;s better to monitor and test hotkeys two times while making changes to database to avoid any type of Access database mistakes.

Database Mistake #3: Handle queries properly

If you are using queries then it can really slow the multiple table joins which are in use. If more tables are joined with several sorting or criteria then query will become slower to run and it can just be a waste of time. In this case, learning index key fields can really help to improve the speed of query. So placing the primary and secondary keys in tables properly will help to manage the performance of query.

Database Mistake #4: Spell check after every update

Spell check is one of the essential step but it is ignored many times. Though it does not takes much time to check but still it is skipped frequently. Also don’t forget that some hidden texts in labels and validation text fields are there which should be checked. They are missed because they are hidden. Such mistake only happens when text is copied from one box to another and fails to update it. So users should avoid Access database mistakes.

Database Mistake #5: Set field size

While designing tables by adding fields, it’s general that developers fail to use the correct data type and their field size.  Consider a text field is set between 1 to characters which is almost equal to 1 byte per character. Now here if your field requires 5 characters then set the field size to 5 saving a maximum of bytes per record. After that multiply several thousand records and you can easily handle optimization in Access database.

Database Mistake #6: Verify the order of tab

Users always expect tabs to work effectively. But if the tab does not work as the user wanted then it may create problem to search the data and even the data is entered wrong. So by default, the tab should go from left to right and top to bottom. If it does not works in this manner then users may face trouble. Make sure the tab works exactly the way you want. However if you want to change the order then it can be set under view, tab order menu.

Database Mistake #7: Avoid Missing Code

Always make sure that whatever event procedure designed has a defined event. Here users can make a mistake by only assigning the event without hitting on the write code for it. This might occur when you rename a control and soon after that fails to rename the event procedures related to old names.

Database Mistake #8: Making wrong data type field

Access database has several data types which are useful but many times the correct type is not the correct one. Just for an example, phone number consists of numbers and so it should be a numeric field. But here it is wrong because numeric field is used for those values which is used in calculations.

Database Mistake #9: Error in report

The reports made in access will run in several pages and preview can take some time to display. This is similar to forms. Consider decreasing the recordset with deep query along with key fields indexed. Moreover, if you have sub reports than it can cause performance problems because each report contains own data source. Therefore don’t have more than one sub-report because the database performance will decrease.

Database Mistake # Make sure AutoCenter is yes

It is really very frustrating when you open a form to enter data in it and find that it is half hidden outside the screen. With the help of AutoCenter feature, this situation is stopped. When it is set to yes then the form automatically comes in the center. This allow users to see from the beginning actually what they require.

Furthermore, there are also several other reasons which degrade the performance of Access database with some other techniques to optimize all essential data. But mentioned above 10 points are the best to give a good start to know about common mistakes that user or developers make while using Access database.

Database Mistake # Repeating Fields in a Table

The very important part to make Access database mistakes free is by recognize repeating data and to keep those repeating columns in your table. Well repeating fields in table is quiet common to those who are previously using the spreadsheets but when spreadsheet transformed to database, it should be relational.

So, instead of having single table containing entire stuffs and all information together, make tables for distinct piece of information. Also observe how each table keeps its own unique ID field. Link tables using primary key value as foreign key in other table.

Database Mistake # Embedding a Table in a Table

At the time of designing database, you need to be sure that, entire data contained in your table related to that table. You can consider it as “odd one out” game.

This type of designing also helps you in easy addition of extra information to any specific table without creating a nightmare of clutter. Also it simplifies the tracking of your information present in specific table.

Database Mistake # Not Using a Naming Convention

After the beginning of your access database designing when you reached to the point of queries writing against database to extract information. At that time naming convention works great to keep track of field name. Well two popular naming conventions that you can follow are capitalizing the first letter of every word or separating words by giving underscores.

Suppose in case, if names stored as FirstName, LastName in one table and first_name, last_name in another table. What naming convention you approach for is not important but the process of executing the naming convention in a right is more important.

How To Keep Your Access Database Safe Side From Such Mistakes?

access database mistakes

Let’s know about some useful tips and tricks to avoid Access database mistakes and to improve Access database performance.

Always maintain right and updated backup of your Access Database files is a good and secure way to avoid MS Access Data Loss.  If you regularly backup your files then it’s become too easy to recover your database even if gets inaccessible or corrupted.

  • Deletion Of Unnecessary Data Sheets:

Time to time deletion of unnecessary data files that are no more of use is the best way to prevent off your access database corruption.  As collection of junk or unnecessary file enormously increases the size of your access database and ultimately this leads to the Corruption Of Access Database Files. so you need to prevent yourself from doing this Access database mistake.

If multiple users are accessing the database at same time then it’s a good option to split your Access database. It can be splitted into two parts:

1) Front end: it contains queries, reports, forms and data Access pages.

2) Back end: it includes complete table of your MS Access and the data stored in it.

If your database is already get corrupted then try out the following manual techniques to repair corrupt Access Database files.

  • Properly Exit From Access:

Exiting or closing MS Access application properly is important. Because, if it is not done properly then it may cause your Access database files to get corrupt. Follow the steps for proper closing of Access application: File Tab – And the click Exit.

  • Frequent Compacting Your Database:

Microsoft Access offers the “Compact and repair” inbuilt utility tool to resolve minor error blots occurred in your Access Database.  With this tool, you can compact your files properly and also you can repair and fix corrupt Access files. Besides that it also boosts up  Access Database performance.

Conclusion

All the above mentioned mistakes generally made by users or developers and they should try to avoid such misconception. Never make silly mistakes which can completely ruin your application. Just be little bit attentive and properly read the errors which are stated as following those can really help to avoid any type of database mistakes.

tip Still having issues?Fix them with this Access repair tool:

This software repairs & restores all ACCDB/MDB objects including tables, reports, queries, records, forms, and indexes along with modules, macros, and other stuffs effectively.

  1. Download Stellar Repair for Access rated Great on Cnet (download starts on this page).
  2. Click Browse and Search option to locate corrupt Access database.
  3. Click Repair button to repair & preview the database objects.

 

Further Reading:

How to Repair MDB Files When Compact and Repair Does Not Work

Guide to Compact and Repair Microsoft Access Database File: An Effective One

5 Ways To Backup And Restore Access Database In ///?

How To Fix Ms Access Error ‘’: “The Changes You Requested To The Table Were Not Successful “?

Pearson Willey

Pearson Willey is a website content writer and long-form content planner. Besides this, he is also an avid reader. Thus he knows very well how to write an engaging content for readers. Writing is like a growing edge for him. He loves exploring his knowledge on MS Access & sharing tech blogs.

Categories TutorialTags 10 mistakes affecting Access performance, Access database mistakes, Access mistakes to avoid, boosts up  Access Database performance, common access database mistakes, Common mistakes of Access file

One of the most common reasons for software applications’ failures is running a database with errors. As a software developer, you might be given a project that you need to design from scratch.

In such situations, you will try as much as you can to make sure that you have followed all the required guidelines to design the database to avoid some common database errors.

However, in some situations, you might get an already existing project to work on. The original developers might have rushed the database design, leaving many errors and making the project fail.

In such a situation, you might find it very difficult to work on the project without having to first fix the database errors.

The sole purpose of designing a database is to make sure that an application can easily store and access data. It is, therefore, very crucial for developers to ensure that they have designed good databases.

This is because all the application data &#; might be about a company and its operations &#; is stored in the database.

There are a number of common database errors every developer needs to avoid when designing and working on databases. They include;

Poor Normalization

Different software developers might design different databases following all normalization rules and regulations and still come up with databases whose data layout is different. This depends on their creativity. However, some techniques must be followed, no matter how creative a database designer might be. 

Some designers fail to follow the basic normalization principles, leading to errors when doing data queries. Every data should be normalized to at least the third normal form.

If you are given a project whose database is not normalized to any form, then you should design the tables again. This will help your project in the future.

The N+1 Problem

This is a common problem that occurs when one needs to display the contents of the children in parent-child relationships.

Lazy loading is usually enabled in Object-Relational Mappings by default. This means that queries are issued to the parent and a single query to each of the child records. With this, running N+1 queries means that a database would be flooded with more queries. 

Database designers can solve this error through eager loading. This allows them to defer an object’s initialization until that time when the object is needed. By doing this, they will have improved the efficiency of the operations of their databases and applications at large.

Redundancy

This is one of the most common database errors that developers struggle with, especially when they are forced to keep different versions of the same data updated.

Even though it is required in some designs due to different requirements of an application, it should be clearly documented and used only in the situations where it must be used. 

Redundancy leads to inconsistent data, large database sizes, data corruption, and inefficient databases. To avoid all these problems, developers need to make sure that they have thoroughly followed all the normalization guidelines.

Problems with Service Manager Server

Sometimes, you might get a database error indicating that the service manager server is not running. This is a common problem that new developers might find difficult to handle.

The first thing to do when you run through this error is to, of course, check whether the server is running. If it is, then try connecting again via the service manager.

If your login credentials are wrong, the service manager will require you to enter the correct credentials. To ensure that this does not happen again, modify the program so that your credentials are automatically accepted.

Ignoring Data Requirements

We create databases to store data that we can consume when the need arises. It is, therefore, an expectation to store and retrieve the data easily and efficiently.

Some designers start working on a database without knowing the kind of data that it will store, how and when the data will be retrieved, and how it will be used. This creates more problems later when the project is done.

To avoid errors caused by data requirements, developers should understand the data system and its purpose before they start designing the database.

This will help them when choosing the right database engine, the format, and size of records, the database entities, and the required management policies.

Avoiding database errors and updating I.T. services helps in improving the productivity of any business. In addition, a good database will help any developer when it comes to saving storage space and avoiding issues with their applications.

It also helps in making sure that data is reliable, precise and makes it possible for them to access the data in different ways. Finally, it is easy to maintain, use, and modify when they need to make changes.

Ten Common Database Design Mistakes

No list of mistakes is ever going to be exhaustive. People (myself included) do a lot of really stupid things, at times, in the name of &#;getting it done.&#; This list simply reflects the database design mistakes that are currently on my mind, or in some cases, constantly on my mind. I have done this topic two times before. If you&#;re interested in hearing the podcast version, visit Greg Low&#;s super-excellent SQL Down Under. I also presented a boiled down, ten-minute version at PASS for the Simple-Talk booth. Originally there were ten, then six, and today back to ten. And these aren&#;t exactly the same ten that I started with; these are ten that stand out to me as of today.

Before I start with the list, let me be honest for a minute. I used to have a preacher who made sure to tell us before some sermons that he was preaching to himself as much as he was to the congregation. When I speak, or when I write an article, I have to listen to that tiny little voice in my head that helps filter out my own bad habits, to make sure that I am teaching only the best practices. Hopefully, after reading this article, the little voice in your head will talk to you when you start to stray from what is right in terms of database design practices.

So, the list:

  1. Poor design/planning
  2. Ignoring normalization
  3. Poor naming standards
  4. Lack of documentation
  5. One table to hold all domain values
  6. Using identity/guid columns as your only key
  7. Not using SQL facilities to protect data integrity
  8. Not using stored procedures to access data
  9. Trying to build generic objects
  10. Lack of testing

Poor design/planning

&#;If you don&#;t know where you are going, any road will take you there&#; &#; George Harrison

Prophetic words for all parts of life and a description of the type of issues that plague many projects these days.

Let me ask you: would you hire a contractor to build a house and then demand that they start pouring a foundation the very next day? Even worse, would you demand that it be done without blueprints or house plans? Hopefully, you answered &#;no&#; to both of these. A design is needed make sure that the house you want gets built, and that the land you are building it on will not sink into some underground cavern. If you answered yes, I am not sure if anything I can say will help you.

Like a house, a good database is built with forethought, and with proper care and attention given to the needs of the data that will inhabit it; it cannot be tossed together in some sort of reverse implosion.

Since the database is the cornerstone of pretty much every business project, if you don&#;t take the time to map out the needs of the project and how the database is going to meet them, then the chances are that the whole project will veer off course and lose direction. Furthermore, if you don&#;t take the time at the start to get the database design right, then you&#;ll find that any substantial changes in the database structures that you need to make further down the line could have a huge impact on the whole project, and greatly increase the likelihood of the project timeline slipping.

Far too often, a proper planning phase is ignored in favor of just &#;getting it done&#;. The project heads off in a certain direction and when problems inevitably arise &#; due to the lack of proper designing and planning &#; there is &#;no time&#; to go back and fix them properly, using proper techniques. That&#;s when the &#;hacking&#; starts, with the veiled promise to go back and fix things later, something that happens very rarely indeed.

Admittedly it is impossible to predict every need that your design will have to fulfill and every issue that is likely to arise, but it is important to mitigate against potential problems as much as possible, by careful planning.

Ignoring Normalization

Normalization defines a set of methods to break down tables to their constituent parts until each table represents one and only one &#;thing&#;, and its columns serve to fully describe only the one &#;thing&#; that the table represents.

The concept of normalization has been around for 30 years and is the basis on which SQL and relational databases are implemented. In other words, SQL was created to work with normalized data structures. Normalization is not just some plot by database programmers to annoy application programmers (that is merely a satisfying side effect!)

SQL is very additive in nature in that, if you have bits and pieces of data, it is easy to build up a set of values or results. In the FROM clause, you take a set of data (a table) and add (JOIN) it to another table. You can add as many sets of data together as you like, to produce the final set you need.

This additive nature is extremely important, not only for ease of development, but also for performance. Indexes are most effective when they can work with the entire key value. Whenever you have to use SUBSTRING, CHARINDEX, LIKE, and so on, to parse out a value that is combined with other values in a single column (for example, to split the last name of a person out of a full name column) the SQL paradigm starts to break down and data becomes become less and less searchable.

So normalizing your data is essential to good performance, and ease of development, but the question always comes up: &#;How normalized is normalized enough?&#; If you have read any books about normalization, then you will have heard many times that 3rd Normal Form is essential, but 4th and 5th Normal Forms are really useful and, once you get a handle on them, quite easy to follow and well worth the time required to implement them.

In reality, however, it is quite common that not even the first Normal Form is implemented correctly.

Whenever I see a table with repeating column names appended with numbers, I cringe in horror. And I cringe in horror quite often. Consider the following example Customer table:

imagegif

Are there always 12 payments? Is the order of payments significant? Does a NULL value for a payment mean UNKNOWN (not filled in yet), or a missed payment? And when was the payment made?!?

A payment does not describe a Customer and should not be stored in the Customer table. Details of payments should be stored in a Payment table, in which you could also record extra information about the payment, like when the payment was made, and what the payment was for:

imagegif

In this second design, each column stores a single unit of information about a single &#;thing&#; (a payment), and each row represents a specific instance of a payment.

This second design is going to require a bit more code early in the process but, it is far more likely that you will be able to figure out what is going on in the system without having to hunt down the original programmer and kick their butt&#;sorry&#; figure out what they were thinking

Poor naming standards

&#;That which we call a rose, by any other name would smell as sweet&#;

This quote from Romeo and Juliet by William Shakespeare sounds nice, and it is true from one angle. If everyone agreed that, from now on, a rose was going to be called dung, then we could get over it and it would smell just as sweet. The problem is that if, when building a database for a florist, the designer calls it dung and the client calls it a rose, then you are going to have some meetings that sound far more like an Abbott and Costello routine than a serious conversation about storing information about horticulture products.

Names, while a personal choice, are the first and most important line of documentation for your application. I will not get into all of the details of how best to name things here- it is a large and messy topic. What I want to stress in this article is the need for consistency. The names you choose are not just to enable you to identify the purpose of an object, but to allow all future programmers, users, and so on to quickly and easily understand how a component part of your database was intended to be used, and what data it stores. No future user of your design should need to wade through a page document to determine the meaning of some wacky name.

Consider, for example, a column named, X_DSCR. What the heck does that mean? You might decide, after some head scratching, that it means &#;X description&#;. Possibly it does, but maybe DSCR means discriminator, or discretizator?

Unless you have established DSCR as a corporate standard abbreviation for description, then X_DESCRIPTION is a much better name, and one leaves nothing to the imagination.

That just leaves you to figure out what the X part of the name means. On first inspection, to me, X sounds like more like it should be data in a column rather than a column name. If I subsequently found that, in the organization, there was also an X and X then I would flag that as an issue with the database design. For maximum flexibility, data is stored in columns, not in column names.

Along these same lines, resist the temptation to include &#;metadata&#; in an object&#;s name. A name such as tblCustomer or colVarcharAddress might seem useful from a development perspective, but to the end user it is just confusing. As a developer, you should rely on being able to determine that a table name is a table name by context in the code or tool, and present to the users clear, simple, descriptive names, such as Customer and Address.

A practice I strongly advise against is the use of spaces and quoted identifiers in object names. You should avoid column names such as &#;Part Number&#; or, in Microsoft style, [Part Number], therefore requiring you users to include these spaces and identifiers in their code. It is annoying and simply unnecessary.

Acceptable alternatives would be part_number, partNumber or PartNumber. Again, consistency is key. If you choose PartNumber then that&#;s fine &#; as long as the column containing invoice numbers is called InvoiceNumber, and not one of the other possible variations.

Lack of documentation

I hinted in the intro that, in some cases, I am writing for myself as much as you. This is the topic where that is most true. By carefully naming your objects, columns, and so on, you can make it clear to anyone what it is that your database is modeling. However, this is only step one in the documentation battle. The unfortunate reality is, though, that &#;step one&#; is all too often the only step.

Not only will a well-designed data model adhere to a solid naming standard, it will also contain definitions on its tables, columns, relationships, and even default and check constraints, so that it is clear to everyone how they are intended to be used. In many cases, you may want to include sample values, where the need arose for the object, and anything else that you may want to know in a year or two when &#;future you&#; has to go back and make changes to the code.

NOTE:
Where this documentation is stored is largely a matter of corporate standards and/or convenience to the developer and end users. It could be stored in the database itself, using extended properties. Alternatively, it might be in maintained in the data modeling tools. It could even be in a separate data store, such as Excel or another relational database. My company maintains a metadata repository database, which we developed in order to present this data to end users in a searchable, linkable format. Format and usability is important, but the primary battle is to have the information available and up to date.

Your goal should be to provide enough information that when you turn the database over to a support programmer, they can figure out your minor bugs and fix them (yes, we all make bugs in our code!). I know there is an old joke that poorly documented code is a synonym for &#;job security.&#; While there is a hint of truth to this, it is also a way to be hated by your coworkers and never get a raise. And no good programmer I know of wants to go back and rework their own code years later. It is best if the bugs in the code can be managed by a junior support programmer while you create the next new thing. Job security along with raises is achieved by being the go-to person for new challenges.

One table to hold all domain values

&#;One Ring to rule them all and in the darkness bind them&#;

This is all well and good for fantasy lore, but it&#;s not so good when applied to database design, in the form of a &#;ruling&#; domain table. Relational databases are based on the fundamental idea that every object represents one and only one thing. There should never be any doubt as to what a piece of data refers to. By tracing through the relationships, from column name, to table name, to primary key, it should be easy to examine the relationships and know exactly what a piece of data means.

The big myth perpetrated by architects who don&#;t really understand relational database architecture (me included early in my career) is that the more tables there are, the more complex the design will be. So, conversely, shouldn&#;t condensing multiple tables into a single &#;catch-all&#; table simplify the design? It does sound like a good idea, but at one time giving Pauly Shore the lead in a movie sounded like a good idea too.

For example, consider the following model snippet where I needed domain values for:

  • Customer CreditStatus
  • Customer Type
  • Invoice Status
  • Invoice Line Item BackOrder Status
  • Invoice Line Item Ship Via Carrier

On the face of it that would be five domain tables&#;but why not just use one generic domain table, like this?

imagegif

This may seem a very clean and natural way to design a table for all but the problem is that it is just not very natural to work with in SQL. Say we just want the domain values for the Customer table:

SELECT *
FROM Customer
  JOIN GenericDomain as CustomerType
    ON sprers.euerTypeId = sprers.eucDomainId
      and sprers.eudToTable = &#;Customer&#;
      and  sprers.eudToColumn = &#;CustomerTypeId&#;
  JOIN GenericDomain as CreditStatus
    ON  sprers.euStatusId = sprers.eucDomainId
      and sprers.eudToTable = &#;Customer&#;
      and sprers.eudToColumn = &#; CreditStatusId&#;

As you can see, this is far from being a natural join. It comes down to the problem of mixing apples with oranges. At first glance, domain tables are just an abstract concept of a container that holds text. And from an implementation centric standpoint, this is quite true, but it is not the correct way to build a database. In a database, the process of normalization, as a means of breaking down and isolating data, takes every table to the point where one row represents one thing. And each domain of values is a distinctly different thing from all of the other domains (unless it is not, in which case the one table will suffice.). So what you do, in essence, is normalize the data on each usage, spreading the work out over time, rather than doing the task once and getting it over with.

So instead of the single table for all domains, you might model it as:

imagegif

Looks harder to do, right? Well, it is initially. Frankly it took me longer to flesh out the example tables. But, there are quite a few tremendous gains to be had:

  • Using the data in a query is much easier:

1

SELECT*sprers.euerTypeId=sprers.euStatusId=sprers.euStatusId

  • Data can be validated using foreign key constraints very naturally, something not feasible for the other solution unless you implement ranges of keys for every table &#; a terrible mess to maintain.
  • If it turns out that you need to keep more information about a ShipViaCarrier than just the code, &#;UPS&#;, and description, &#;United Parcel Service&#;, then it is as simple as adding a column or two. You could even expand the table to be a full blown representation of the businesses that are carriers for the item.
  • All of the smaller domain tables will fit on a single page of disk. This ensures a single read (and likely a single page in cache). If the other case, you might have your domain table spread across many pages, unless you cluster on the referring table name, which then could cause it to be more costly to use a non-clustered index if you have many values.
  • You can still have one editor for all rows, as most domain tables will likely have the same base structure/usage. And while you would lose the ability to query all domain values in one query easily, why would you want to? (A union query could easily be created of the tables easily if needed, but this would seem an unlikely need.)

I should probably rebut the thought that might be in your mind. &#;What if I need to add a new column to all domain tables?&#; For example, you forgot that the customer wants to be able to do custom sorting on domain values and didn&#;t put anything in the tables to allow this. This is a fair question, especially if you have of these tables in a very large database. First, this rarely happens, and when it does it is going to be a major change to your database in either way.

Second, even if this became a task that was required, SQL has a complete set of commands that you can use to add columns to tables, and using the system tables it is a pretty straightforward task to build a script to add the same column to hundreds of tables all at once. That will not be as easy of a change, but it will not be so much more difficult to outweigh the large benefits.

The point of this tip is simply that it is better to do the work upfront, making structures solid and maintainable, rather than trying to attempt to do the least amount of work to start out a project. By keeping tables down to representing one &#;thing&#; it means that most changes will only affect one table, after which it follows that there will be less rework for you down the road.

Using identity/guid columns as your only key

First Normal Form dictates that all rows in a table must be uniquely identifiable. Hence, every table should have a primary key. SQL Server allows you to define a numeric column as an IDENTITY column, and then automatically generates a unique value for each row. Alternatively, you can use NEWID() (or NEWSEQUENTIALID()) to generate a random, 16 byte unique value for each row. These types of values, when used as keys, are what are known as surrogate keys. The word surrogate means &#;something that substitutes for&#; and in this case, a surrogate key should be the stand-in for a natural key.

The problem is that too many designers use a surrogate key column as the only key column on a given table. The surrogate key values have no actual meaning in the real world; they are just there to uniquely identify each row.

Now, consider the following Part table, whereby PartID is an IDENTITY column and is the primary key for the table:

 

PartID

PartNumber

Description

1

XXXXXXXX

The X part

2

XXXXXXXX

The X part

3

YYYYYYYY

The Y part

How many rows are there in this table? Well, there seem to be three, but are rows with PartIDs 1 and 2 actually the same row, duplicated? Or are they two different rows that should be unique but were keyed in incorrectly?

The rule of thumb I use is simple. If a human being could not pick which row they want from a table without knowledge of the surrogate key, then you need to reconsider your design. This is why there should be a key of some sort on the table to guarantee uniqueness, in this case likely on PartNumber.

In summary: as a rule, each of your tables should have a natural key that means something to the user, and can uniquely identify each row in your table. In the very rare event that you cannot find a natural key (perhaps, for example, a table that provides a log of events), then use an artificial/surrogate key.

Not using SQL facilities to protect data integrity

All fundamental, non-changing business rules should be implemented by the relational engine. The base rules of nullability, string length, assignment of foreign keys, and so on, should all be defined in the database.

There are many different ways to import data into SQL Server. If your base rules are defined in the database itself can you guarantee that they will never be bypassed and you can write your queries without ever having to worry whether the data you&#;re viewing adheres to the base business rules.

Rules that are optional, on the other hand, are wonderful candidates to go into a business layer of the application. For example, consider a rule such as this: &#;For the first part of the month, no part can be sold at more than a 20% discount, without a manager&#;s approval&#;.

Taken as a whole, this rule smacks of being rather messy, not very well controlled, and subject to frequent change. For example, what happens when next week the maximum discount is 30%? Or when the definition of &#;first part of the month&#; changes from 15 days to 20 days? Most likely you won&#;t want go through the difficulty of implementing these complex temporal business rules in SQL Server code &#; the business layer is a great place to implement rules like this.

However, consider the rule a little more closely. There are elements of it that will probably never change. E.g.

  • The maximum discount it is ever possible to offer
  • The fact that the approver must be a manager

These aspects of the business rule very much ought to get enforced by the database and design. Even if the substance of the rule is implemented in the business layer, you are still going to have a table in the database that records the size of the discount, the date it was offered, the ID of the person who approved it, and so on. On the Discount column, you should have a CHECK constraint that restricts the values allowed in this column to between and (or whatever the maximum is). Not only will this implement your &#;maximum discount&#; rule, but will also guard against a user entering a % or a negative discount by mistake. On the ManagerID column, you should place a foreign key constraint, which reference the Managers table and ensures that the ID entered is that of a real manager (or, alternatively, a trigger that selects only EmployeeIds corresponding to managers).

Now, at the very least we can be sure that the data meets the very basic rules that the data must follow, so we never have to code something like this in order to check that the data is good:

 

We can feel safe that data meets the basic criteria, every time.

Not using stored procedures to access data

Stored procedures are your friend. Use them whenever possible as a method to insulate the database layer from the users of the data. Do they take a bit more effort? Sure, initially, but what good thing doesn&#;t take a bit more time? Stored procedures make database development much cleaner, and encourage collaborative development between your database and functional programmers. A few of the other interesting reasons that stored procedures are important include the following.

Maintainability

Stored procedures provide a known interface to the data, and to me, this is probably the largest draw. When code that accesses the database is compiled into a different layer, performance tweaks cannot be made without a functional programmer&#;s involvement. Stored procedures give the database professional the power to change characteristics of the database code without additional resource involvement, making small changes, or large upgrades (for example changes to SQL syntax) easier to do.

Encapsulation

Stored procedures allow you to &#;encapsulate&#; any structural changes that you need to make to the database so that the knock on effect on user interfaces is minimized. For example, say you originally modeled one phone number, but now want an unlimited number of phone numbers. You could leave the single phone number in the procedure call, but store it in a different table as a stopgap measure, or even permanently if you have a &#;primary&#; number of some sort that you always want to display. Then a stored proc could be built to handle the other phone numbers. In this manner the impact to the user interfaces could be quite small, while the code of stored procedures might change greatly.

Security

Stored procedures can provide specific and granular access to the system. For example, you may have 10 stored procedures that all update table X in some way. If a user needs to be able to update a particular column in a table and you want to make sure they never update any others, then you can simply grant to that user the permission to execute just the one procedure out of the ten that allows them perform the required update.

Performance

There are a couple of reasons that I believe stored procedures enhance performance. First, if a newbie writes ratty code (like using a cursor to go row by row through an entire ten million row table to find one value, instead of using a WHERE clause), the procedure can be rewritten without impact to the system (other than giving back valuable resources.) The second reason is plan reuse. Unless you are using dynamic SQL calls in your procedure, SQL Server can store a plan and not need to compile it every time it is executed. It&#;s true that in every version of SQL Server since this has become less and less significant, as SQL Server gets better at storing plans ad hoc SQL calls (see note below). However, stored procedures still make it easier for plan reuse and performance tweaks. In the case where ad hoc SQL would actually be faster, this can be coded into the stored procedure seamlessly.

In , there is a database setting (PARAMETERIZATION FORCED) that, when enabled, will cause all queries to have their plans saved. This does not cover more complicated situations that procedures would cover, but can be a big help. There is also a feature known as plan guides, which allow you to override the plan for a known query type. Both of these features are there to help out when stored procedures are not used, but stored procedures do the job with no tricks.

And this list could go on and on. There are drawbacks too, because nothing is ever perfect. It can take longer to code stored procedures than it does to just use ad hoc calls. However, the amount of time to design your interface and implement it is well worth it, when all is said and done.

Trying to code generic T-SQL objects

I touched on this subject earlier in the discussion of generic domain tables, but the problem is more prevalent than that. Every new T-SQL programmer, when they first start coding stored procedures, starts to think &#;I wish I could just pass a table name as a parameter to a procedure.&#; It does sound quite attractive: one generic stored procedure that can perform its operations on any table you choose. However, this should be avoided as it can be very detrimental to performance and will actually make life more difficult in the long run.

T-SQL objects do not do &#;generic&#; easily, largely because lots of design considerations in SQL Server have clearly been made to facilitate reuse of plans, not code. SQL Server works best when you minimize the unknowns so it can produce the best plan possible. The more it has to generalize the plan, the less it can optimize that plan.

Note that I am not specifically talking about dynamic SQL procedures. Dynamic SQL is a great tool to use when you have procedures that are not optimizable / manageable otherwise. A good example is a search procedure with many different choices. A precompiled solution with multiple OR conditions might have to take a worst case scenario approach to the plan and yield weak results, especially if parameter usage is sporadic.

However, the main point of this tip is that you should avoid coding very generic objects, such as ones that take a table name and twenty column names/value pairs as a parameter and lets you update the values in the table. For example, you could write a procedure that started out:

CREATE PROCEDURE updateAnyTable
@tableName sysname,
@columnName1 sysname,
@columnName1Value varchar(max)
@columnName2 sysname,
@columnName2Value varchar(max)
&#;

The idea would be to dynamically specify the name of a column and the value to pass to a SQL statement. This solution is no better than simply using ad hoc calls with an UPDATE statement. Instead, when building stored procedures, you should build specific, dedicated stored procedures for each task performed on a table (or multiple tables.) This gives you several benefits:

  • Properly compiled stored procedures can have a single compiled plan attached to it and reused.
  • Properly compiled stored procedures are more secure than ad-hoc SQL or even dynamic SQL procedures, reducing the surface area for an injection attack greatly because the only parameters to queries are search arguments or output values.
  • Testing and maintenance of compiled stored procedures is far easier to do since you generally have only to search arguments, not that tables/columns/etc exist and handling the case where they do not

A nice technique is to build a code generation tool in your favorite programming language (even T-SQL) using SQL metadata to build very specific stored procedures for every table in your system. Generate all of the boring, straightforward objects, including all of the tedious code to perform error handling that is so essential, but painful to write more than once or twice.

In my Apress book, Pro SQL Server Database Design and Optimization, I provide several such &#;templates&#; (manly for triggers, abut also stored procedures) that have all of the error handling built in, I would suggest you consider building your own (possibly based on mine) to use when you need to manually build a trigger/procedure or whatever.

Lack of testing

When the dial in your car says that your engine is overheating, what is the first thing you blame? The engine. Why don&#;t you immediately assume that the dial is broken? Or something else minor? Two reasons:

  • The engine is the most important component of the car and it is common to blame the most important part of the system first.
  • It is all too often true.

As database professionals know, the first thing to get blamed when a business system is running slow is the database. Why? First because it is the central piece of most any business system, and second because it also is all too often true.

We can play our part in dispelling this notion, by gaining deep knowledge of the system we have created and understanding its limits through testing.

But let&#;s face it; testing is the first thing to go in a project plan when time slips a bit. And what suffers the most from the lack of testing? Functionality? Maybe a little, but users will notice and complain if the &#;Save&#; button doesn&#;t actually work and they cannot save changes to a row they spent 10 minutes editing. What really gets the shaft in this whole process is deep system testing to make sure that the design you (presumably) worked so hard on at the beginning of the project is actually implemented correctly.

But, you say, the users accepted the system as working, so isn&#;t that good enough? The problem with this statement is that what user acceptance &#;testing&#; usually amounts to is the users poking around, trying out the functionality that they understand and giving you the thumbs up if their little bit of the system works. Is this reasonable testing? Not in any other industry would this be vaguely acceptable. Do you want your automobile tested like this? &#;Well, we drove it slowly around the block once, one sunny afternoon with no problems; it is good!&#; When that car subsequently &#;failed&#; on the first drive along a freeway, or during the first drive through rain or snow, then the driver would have every right to be very upset.

Too many database systems get tested like that car, with just a bit of poking around to see if individual queries and modules work. The first real test is in production, when users attempt to do real work. This is especially true when it is implemented for a single client (even worse when it is a corporate project, with management pushing for completion more than quality).

Initially, major bugs come in thick and fast, especially performance related ones. If the first time you have tried a full production set of users, background process, workflow processes, system maintenance routines, ETL, etc, is on your system launch day, you are extremely likely to discover that you have not anticipated all of the locking issues that might be caused by users creating data while others are reading it, or hardware issues cause by poorly set up hardware. It can take weeks to live down the cries of &#;SQL Server can&#;t handle it&#; even after you have done the proper tuning.

Once the major bugs are squashed, the fringe cases (which are pretty rare cases, like a user entering a negative amount for hours worked) start to raise their ugly heads. What you end up with at this point is software that irregularly fails in what seem like weird places (since large quantities of fringe bugs will show up in ways that aren&#;t very obvious and are really hard to find.)

Now, it is far harder to diagnose and correct because now you have to deal with the fact that users are working with live data and trying to get work done. Plus you probably have a manager or two sitting on your back saying things like &#;when will it be done?&#; every 30 seconds, even though it can take days and weeks to discover the kinds of bugs that result in minor (yet important) data aberrations. Had proper testing been done, it would never have taken weeks of testing to find these bugs, because a proper test plan takes into consideration all possible types of failures, codes them into an automated test, and tries them over and over. Good testing won&#;t find all of the bugs, but it will get you to the point where most of the issues that correspond to the original design are ironed out.

If everyone insisted on a strict testing plan as an integral and immutable part of the database development process, then maybe someday the database won&#;t be the first thing to be fingered when there is a system slowdown.

Summary

Database design and implementation is the cornerstone of any data centric project (read % of business applications) and should be treated as such when you are developing. This article, while probably a bit preachy, is as much a reminder to me as it is to anyone else who reads it. Some of the tips, like planning properly, using proper normalization, using a strong naming standards and documenting your work- these are things that even the best DBAs and data architects have to fight to make happen. In the heat of battle, when your manager&#;s manager&#;s manager is being berated for things taking too long to get started, it is not easy to push back and remind them that they pay you now, or they pay you later. These tasks pay dividends that are very difficult to quantify, because to quantify success you must fail first. And even when you succeed in one area, all too often other minor failures crop up in other parts of the project so that some of your successes don&#;t even get noticed.

The tips covered here are ones that I have picked up over the years that have turned me from being mediocre to a good data architect/database programmer. None of them take extraordinary amounts of time (except perhaps design and planning) but they all take more time upfront than doing it the &#;easy way&#;. Let&#;s face it, if the easy way were that easy in the long run, I for one would abandon the harder way in a second. It is not until you see the end result that you realize that success comes from starting off right as much as finishing right.

When you run an append query in an Access desktop database, you may receive an error message that says, "Microsoft Access can't append all the records in the append query.”

This error message can appear for one of the following reasons:

Type conversion failures    You may be trying to append data of one type into a field of another type. For example, appending text into a field whose data type is set to Number will cause the error to appear. Check the data types of fields in the destination table, and then make sure you’re appending the correct type of data into each one.

Key violations    You may be trying to append data into one or more fields that are part of the table’s primary key, such as the ID field. Check the design of the destination table to see if the primary key (or any index) has the No Duplicates property set to Yes. Then, check the data you are appending to make sure it doesn’t violate the rules of the destination table.

Lock violations    If the destination table is open in Design view or open by another user on the network, this could result in record locks that would prevent the query from being able to append records. Make sure everyone’s closed out of the database.

Validation rule violations    Check the design of the destination table to see what validation rules exist. For example, if a field is required and your query doesn’t provide data for it, you’ll get the error. Also, check the destination table for any Text fields where the Allow Zero Length property is set to No. If your query doesn’t append any characters into such a field, you’ll get the error. Other validation rules may also be causing the problem—for example, you may have the following validation rule for the Quantity field:

>=10

In this case, you cannot append records with a quantity less than

For more information about creating append queries, see Add records to a table by using an append query.

Why Talk About Errors?

The art of designing a good database is like swimming. It is relatively easy to start and difficult to master. If you want to learn to design databases, you should for sure have some theoretic background, like knowledge about database normal forms and transaction isolation levels. But you should also practice as much as possible, because the sad truth is that we learn most… by making errors.

In this article we will try to make learning database design a little simpler, by showing some common errors people make when designing their databases.

Note that we will not talk about database normalization – we assume the reader knows database normal forms and has a basic knowledge of relational databases. Whenever possible, covered topics will be illustrated by models generated using Vertabelo and practical examples.

This article covers designing databases in general, but with emphasis on web applications, so some examples may be web application-specific.

Model Setup

Let’s assume we want to design a database for an online bookstore. The system should allow customers to perform the following activity:

  • browse and search books by book title, description and author information,
  • comment on books and rate them after reading,
  • order books,
  • view status of order processing.

So the initial database model could look like this:




To test the model, we will generate SQL for the model using Vertabelo and create a new database in PostgreSQL RDBMS.

The database has eight tables and no data in it. We have populated the database with some manually created test data. Now the database contains some exemplary data and we are ready to start the model inspection, including identifying potential problems that are invisible now but can arise in the future, when the system will be used by real customers.

1 – Using Invalid Names

Here you can see that we named a table with a word “order”. However, as you probably remember, “order” is a reserved word in SQL! So if you try to issue a SQL query:

SELECT * FROM ORDER ORDER BY ID

the database management system will complain. Luckily enough, in PostgreSQL it is sufficient to wrap the table name in double quotes, and it will work:

SELECT * FROM "order" ORDER BY ID

Wait, but “order” here is lowercase!

That’s right, and it is worth digging deeper. If you wrap something in double quotes in SQL, it becomes a delimited identifier and most databases will interpret it in a case-sensitive way. As “order” is a reserved word in SQL, Vertabelo generated SQL which wrapped it automatically in double quotes:

CREATE TABLE "order" ( id int NOT NULL, customer_id int NOT NULL, order_status_id int NOT NULL, CONSTRAINT order_pk PRIMARY KEY (id) );

But as an identifier wrapped in double quotes and written in lower case, the table name remained lower case. Now, if I wanted to complicate things even more, I can create another table, this time named (in uppercase), and PostgreSQL will not detect a naming conflict:

CREATE TABLE "ORDER" ( id int NOT NULL, customer_id int NOT NULL, order_status_id int NOT NULL, CONSTRAINT order_pk2 PRIMARY KEY (id) );

The magic behind that is that if an identifier is not wrapped in double quotes, it is called “ordinary identifier” and automatically converted to upper case before use – it is required by SQL 92 standard. But identifiers wrapped in double quotes – so called “delimited identifiers” – are required to stay unchanged.

The bottom line is – don’t use keywords as object names. Ever.

Did you know that maximum name length in Oracle is 30 chars?

The topic of giving good names to tables and other elements of a database – and by good names I mean not only “not conflicting with SQL keywords”, but also possibly self-explanatory and easy to remember – is often heavily underestimated. In a small database, like ours, it is not a very important matter indeed. But when your database reaches , or tables, you will know that consistent and intuitive naming is crucial to keep the model maintainable during the lifetime of the project.

Remember that you name not only tables and their columns, but also indexes, constraints and foreign keys. You should establish naming conventions to name these database objects. Remember that there are limits on the length of their names. The database will complain if you give your index a name which is too long.

Hints:

  • keep names in your database:
    • possibly short,
    • intuitive and as correct and descriptive as possible,
    • consistent;
  • avoid using SQL and database engine-specific keywords as names;
  • establish naming conventions (read more about planning and setting up a naming convention for your database)

Here is the model with table renamed to :




Changes in the model were as follows:

Don't use word 'order' in the names of database tables


2 – Insufficient Column Width

Let’s inspect our model further. As we can see in table, the column’s type is . What does this mean?

If the field will be plain-text in the GUI (customers can enter only unformatted comments) then it simply means that the field can store up to characters of text. And if it is so – there is no error here.

But if the field allows some formatting, like bbcode or HTML, then the exact amount of characters a customer can enter is in fact unknown. If they enter a simple comment, like this:

I like that book!

then it will take only 17 characters. But if they format it using bold font, like this:

I like that book!

then it will take 24 characters to store while the user will see only 17 in the GUI.

So if the bookstore customer can format the comment using some kind of WYSIWYG editor then limiting the size of the “comment” field can be potentially harmful because when the customer exceeds the maximum comment length ( characters in raw HTML) then the number of characters they see in the GUI can still be much below In such a situation just change the type to and don’t bother limiting length in the database.

However, when setting text field limits, you should always remember about text encoding.

Type means  characters in PostgreSQL but  bytes in Oracle!

Instead of explaining it in general, let’s see an example. In Oracle, the column type is limited to bytes. And it is a hard limit – there is no way you can exceed that. So if you define a column with type , then it means that you can store characters in that column, but only if it does not use more than bytes on disk. If it exceeds the limit, Oracle will throw an error when attempting to save the data to the database. And why could a character text exceed bytes on disk? If it is in English it cannot. But in other languages it may be possible. For example, if you try to save the word “mother” in Chinese – 母親 – and your database encoding is UTF-8, then such a string will have 2 characters but 6 bytes on disk.

Note that different databases can have different limitations for varying character and text fields. Some examples:

BMP (Basic Multilingual Plane, Unicode Plane 0) is a set of characters that can be encoded using 2 bytes per character in UTF Luckily, it covers most characters used in all the world.

  • Oracle has the aforementioned limit of bytes for column,
  • Oracle will store CLOBs of size below 4 KB directly in the table, and such data will be accessible as quickly as any column, while bigger CLOBs will take longer to read as they are stored outside of the table,
  • PostgreSQL will allow an unlimited column to store even a gigabyte-long string, silently storing longer strings in background tables to not decrease performance of the whole table.

Hints:

  • limiting length of text columns in the database is good in general, for security and performance reasons,
  • but sometimes it may be unnecessary or inconvenient to do;
  • different databases may treat text limits differently;
  • always remember about encoding if using language other than English.

Here is the model with type changed to :




The change in the model was as follows:

The table with comment type changed to text


3 – Not Indexing Properly

There is a saying that “greatness is achieved, not given”. It is the same for performance – it is achieved by careful design of the database model, tuning of database parameters, and by optimizing queries run by the application on the database. Here we will focus on the model design, of course.

In our example let’s assume that the GUI designer of our bookstore decided that 30 newest comments will be shown in the home screen. So to select these comments, we will use the following query:

select comment, send_ts from book_comment order by send_ts desc limit 30;

How fast does this query run? It takes less than 70 milliseconds on my laptop. But if we want our application to scale (work fast under heavy load) we need to check it on bigger data. So let’s insert significantly more records into the table. To do so, I will use a very long word list, and turn it into SQL using a simple Perl command.

Now I will import this SQL into the PostgreSQL database. And while it is importing, I will check time of execution of the previous query. The results are summarized in the table below:


Rows in “book_comment”Time of query execution [s]
100,
,0,
,0,
,0,
,0,
,0,


As you can see, with increasing number of rows in table it takes proportionally longer to return the newest 30 rows. Why does it take longer? Let’s see the query plan:

db=# explain select comment, send_ts from book_comment order by send_ts desc limit 30; QUERY PLAN Limit (cost= rows=30 width=17) -> Sort (cost= rows= width=17) Sort Key: send_ts -> Seq Scan on book_comment (cost= rows= width=17)

The query plan tells us how the database is going to process the query and what the possible time cost of computing its results will be. And here PostgreSQL tells us it is going to do a “Seq Scan on book_comment” which means that it will check all records of table, one by one, to sort them by value of column. It seems PostgreSQL is not wise enough to select 30 newest records without sorting all , of them.

Luckily, we can help it by telling PostgreSQL to sort this table by , and save the results. To do so, let’s create an index on this column:

create index book_comment_send_ts_idx on book_comment(send_ts);

Now our query to select the newest 30 out of , records takes 67 milliseconds again. The query plan now is quite different:

db=# explain select comment, send_ts from book_comment order by send_ts desc limit 30; QUERY PLAN Limit (cost= rows=30 width=17) -> Index Scan Backward using book_comment_send_ts_idx on book_comment (cost= rows= width=17)

Index Scan” means that instead of browsing the table, row by row, the database will browse the index we’ve just created. And estimated query cost is less than here, 28 thousand times lower than before.

You have a performance problem? The first attempt to solve it should be to find long running queries, ask your database to explain them, and look for sequential scans. If you find them, probably adding some indexes will speed things up a lot.

Yet, database performance design is a huge topic and it exceeds the scope of this article.
Let’s just mark some important aspects of it in the hints below.

Hints:

  • always check long running queries, possibly using the feature; most modern databases have it;
  • when creating indexes:
    • remember they will not always be used; the database may decide not to use an index if it estimates the cost of using it will be bigger that doing a sequential scan or some other operation,
    • remember that using indexes comes at a cost – s and s on indexed tables are slower
    • consider non-default types of indexes if needed; consult your database manual if your index does not seem to be working well
  • sometimes you need to optimize the query, and not the model;
  • not every performance problem can be solved by creating an index; there are other possible ways of solving performance problems:
    • caches in various application layers,
    • tuning your database parameters and buffer sizes,
    • tuning your database connection pool size and/or thread pool size,
    • adjusting your database transaction isolation level,
    • scheduling bulk deletes at night, to avoid unnecessary table locks,
    • and many others.

The model with an index on column:

Index was created on the 'send_ts' column


4 – Not Considering Possible Volume or Traffic

You often have additional information about the possible volume of data. If the system you’re building is another iteration of an existing project, you can estimate the expected size of the data in your system by looking at the data volume in the old system. You can use this information when you design a model for a new system.

If your bookstore is very successful, the volume of data in the table can be very high. The more you sell the more rows there will be in the table. If you know this in advance, you can separate current, processed purchases from completed purchases. Instead of a single table called , you can have two tables: , for current purchases, and , for completed orders. Current purchases are retrieved all the time: their status is updated, the customers often check info on their order. On the other hand, completed purchases are only kept as historical data. They are rarely updated or retrieved, so you can deal with longer access time to this table. With separation, we keep the frequently used table small, but we still keep the data.

You should similarly optimize data which are frequently updated. Imagine a system where parts of user info are frequently updated by an external system (for example, the external system computes bonus points of a kind). Then there are also other information in the table, for example their basic info like login, password and full name. The basic info is retrieved very often. The frequent updates slow down getting basic info of the user. The simplest solution is to split data into two tables: one for basic info (often read), the other for bonus points info (frequently updated). This way update operations don’t slow down read operations.

Separating frequently and infrequently used data into multiple tables is not the only way of dealing with high volume data. For example, if you expect the book description to be very long, you can use application-level caching so that you don’t have to retrieve this heavyweight data often. Book description is likely to remain unchanged, so it is a good candidate to be cached.

Hints:

  • Use business, domain-specific knowledge your customer has to estimate the expected volume of the data you will process in your database.
  • Separate frequently updated data from frequently read data
  • Consider using application-level caching for heavyweight, infrequently updated data.

Here is our bookstore model after the changes:




5 – Ignoring Time Zones

What if the bookstore runs internationally? Customers come from all over the world and use different time zones. Managing time zones in date and datetime fields can be a serious issue in a multinational system.

The system must always present the correct date and time to users, preferably in their own time zone.

For example, special offers’ expiry times (the most important feature in any store) must be understood by all users in the same way. If you just say “the promotion ends on December 24”, they will assume it ends on midnight of December 24 in their own time zone. If you mean Christmas Eve midnight in your own time zone, you must say “December 24, UTC” (or whatever your time zone is). For some users, it will be “December 24, ”, for others it will be “December 25, ”. The users must see the promotion date in their own time zone.

In a multi-time zone system date column type efficiently does not exist. It should always be a type.

A similar approach should be taken when logging events in a multi-time zone system. Time of events should always be logged in a standardized way, in one selected time zone, for example UTC, so that you could be able to order events from oldest to newest with no doubt.

In case of handling time zones the database must cooperate with application code. Databases have different data types to store date and time. Some types store time with time zone information, some store time without time zone. Programmers should develop standardized components in the system to handle time zone issues automatically.

Hints:

  • Check the details of date and time data types in your database. Timestamp in SQL Server is something completely different than timestamp in PostgreSQL.
  • Store date and time in UTC.
  • Handling time zones properly requires cooperation between database and code of the application. Make sure you understand the details of your database driver. There are quite a lot of catches there.

6 – Missing Audit Trail

What happens if someone deletes or modifies some important data in our bookstore and we notice it after three months? In my opinion, we have a serious problem.

Perhaps we have a backup from three months ago, so we can restore the backup to some new database and access the data. Then we will have a good chance to restore it and avert the loss. But to do that, several factors must contribute:

  • we need to have the proper backup – which one is proper?
  • we must succeed in finding the data,
  • we must be able to restore the data without too much work.

And when we eventually restore the data (but is it the correct version for sure?), there comes the second question – who did it? Who damaged the data three months ago? What is their IP/username? How do we check that? To determine this, we need to:

  • keep access logs for our system for three months at least – and this is unlikely, they are probably already rotated,
  • be able to associate the fact of deleting the data with some URL in our access log.

This will for sure take a lot of time and it does not have a big chance of success.

What our model is missing, is some kind of audit trail. There are multiple ways of achieving this goal:

  • tables in the database can have creation, and update timestamps, together with indication of users who created / modified rows,
  • full audit logging can be implemented using triggers or other mechanisms available to the database management system being used; such audit logs can be stored in separate schemas to make altering and deleting impossible,
  • data can be protected against data loss, by:
    • not deleting it, but marking as deleted instead,
    • versioning changes.

As usual, it is best to keep the proverbial golden mean. You should find balance between security of data and simplicity of the model. Keeping versions and logging events makes your database more complex. Ignoring data safety may lead to unexpected data loss or high costs of recovery of lost data.

Hints:

  • consider which data is important to be tracked for changes/versioned,
  • consider the balance between risk and costs; remember the Pareto principle stating that roughly 80% of the effects come from 20% of the causes; don’t protect your data from unlikely accidents, focus on the likely ones.

Here is our bookstore model with very basic audit trail for and tables.




Changes in the model were as follows (on the example of the table):

Store at least basic information about changes made to the database


7 – Ignoring Collation

The last error is a tricky one as it appears only in some systems, mostly in multi-lingual ones. We add it here because we encounter it quite often, and it does not seem to be widely known.

Most often we assume that sorting words in a language is as simple as sorting them letter by letter, according to the order of letters in the alphabet. But there are two traps here:

  • first, which alphabet? If we have content in one language only, it is clear, but if we have content in 15 or 30 languages, which alphabet should determine the order?
  • second, sorting letter-by-letter is sometimes wrong when accents come into play.

We will illustrate this with a simple SQL query on French words:

db=# select title from book where id between 1 and 4 order by title collate "POSIX"; title cote coté côte côté

This is a result of sorting words letter-by-letter, left to right.

But these words are French, so this is correct:

db=# select title from book where id between 1 and 4 order by title collate "en_GB"; title cote côte coté côté

The results differ, because the correct order of words is determined by collation – and for French the collation rules say that the last accent in a given word determines the order. It is a feature of this particular language. So – language of content can affect ordering of records, and ignoring the language can lead to unexpected results when sorting data.

Hints:

  • in single-language applications, always initialize the database with a proper locale,
  • in multi-language applications, initialize the database with some default locale, and for every place when sorting is available, decide which collation should be used in SQL queries:
    • probably you should use collation specific to the language of the current user,
    • sometimes you may want to use language specific to data being browsed.
  • if applicable, apply collation to columns and tables – see article for more details.

Here is the final version of our bookstore model:




Most Common Mistakes In A Database Design

Some of these problems are unavoidable and beyond your control. However, some of them are due to the quality of the database design.

Poor Pre-planning

If you are building a house, you would not hire a contractor and would immediately require them to start laying the foundation in an hour.

Bad design planning can lead to structural problems that would be costly to resolve once the database has been implemented.

Inadequate standardization

Database design is not a rigidly deterministic process. Two developers could follow the same design rules but still end up with completely different data designs.

That&#;s largely due to the inherent place of creativity in any software engineering project.

However, there are certain basic design principles that are vital to ensure that the database works optimally. One of these principles is standardization.

Standardization refers to the techniques used to disaggregate tables into constituent parts.

This is done until each table represents a single thing, while the columns describe the attributes of the element that the table represents.

Standardization is an old computing concept and has been around for more than three decades.

Bad indexing

Sometimes a user or an application may need to query numerous columns of a table.

As the number of records in the table increases, the time it takes for these queries will constantly increase.

To speed up queries and reduce the impact of overall table size, it is useful to index table columns so that the entries in each are available almost immediately when a SELECT query is invoked.

Unfortunately, accelerating the SELECT statement generally results in a slowdown of the INSERT, UPDATE, and DELETE statements.

A single table for all domain values

An all-encompassing domain table is not the best approach to database design.

Remember that relational databases are based on the idea that each object in the database is representative of one thing.

There should be no ambiguity about any dataset.

When navigating through the primary key, table name, column name, and relationships, one must quickly decipher what a data set means.

However, a persistent misconception about database design is that the more tables there are, the more confusing and complex the database will become.

This is often the reason for condensing multiple tables into one table, assuming it will simplify the layout.

This is true from an implementation point of view, but it is not the best way to design a database.

  • Small domain tables will fit on a single page on your hard drive, unlike a large domain table that will likely span multiple sections of the disk. Having the tables on a single page means that data extraction can be accomplished with a single disk read.
  • Having multiple domain tables does not prevent you from using an editor for all rows. Domain tables probably have the same underlying usage/structure.

You’ve probably made some of these mistakes when you were starting your database design career. Maybe you’re still making them, or you’ll make some in the future. We can’t go back in time and help you undo your errors, but we can save you from some future (or present) headaches.

Reading this article might save you many hours spent fixing design and code problems, so let’s dive in. I’ve split the list of errors into two main groups: those that are non-technical in nature and those that are strictly technical. Both these groups are an important part of database design.

Obviously, if you don’t have technical skills, you won’t know how to do something. It’s not surprising to see these errors on the list. But non-technical skills? People may forget about them, but these skills are also a very important part of the design process. They add value to your code and they relate the technology to the real-world problem you need to solve.

So, let’s start with the non-technical issues first, then move to the technical ones.

Non-Technical Database Design Errors

#1 Poor Planning

This is definitely a non-technical problem, but it is a major and common issue. We all get excited when a new project starts and, going into it, everything looks great. At the start, the project is still a blank page and you and your client are happy to begin working on something that will create a better future for both of you. This is all great, and a great future will probably be the final result. But still, we need to stay focused. This is the part of the project where we can make crucial mistakes.

Before you sit down to draw a data model, you need to be sure that:

  • You’re completely aware of what your client does (i.e. their business plans related to this project and also their overall picture) and what they want this project to achieve now and in the future.
  • You understand the business process and, if or when needed, you’re ready to make suggestions to simplify and improve it ( e.g. to increase efficiency and income, reduce costs and working hours, etc).
  • You understand the data flow in the client’s company. Ideally, you’d know every detail: who works with the data, who makes changes, which reports are needed, when and why all of this happens.
  • You can use the language/terminology your client uses. While you might or might not be an expert in their area, your client definitely is. Ask them to explain what you don’t understand. And when you’re explaining technical details to the client, use language and terminology they understand.
  • You know which technologies you’ll use, from the database engine and programming languages to other tools. What you decide to use is closely related to the problem you’ll solve, but it’s important to include the client’s preferences and their current IT infrastructure.

During the planning phase, you should get answers to these questions:

  • Which tables will be the central tables in your model? You’ll probably have a few of them, while the other tables will be some of the usual ones (e.g. user_account, role). Don’t forget about dictionaries and relations between tables.
  • What names will be used for tables in the model? Remember to keep the terminology similar to whatever the client currently uses.
  • What rules will apply when naming tables and other objects? (See Point 4 about naming conventions.)
  • How long will the whole project take? This is important, both for your schedule and for the client’s timeline.

Only when you have all these answers are you ready to share an initial solution to the problem. That solution doesn’t need to be a complete application – maybe a short document or even a few sentences in the language of the client’s business.

Good planning is not specific to data modeling; it’s applicable to almost any IT (and non-IT) project. Skipping is only an option if 1) you have a really small project; 2) the tasks and goals are clear, and 3) you’re in a real hurry. A historical example is the Sputnik 1 launching engineers giving verbal instructions to the technicians who were assembling it. The project was in a rush because of the news that the US was planning to launch their own satellite soon – but I guess you won’t be in such a hurry.

#2 Insufficient Communication with Clients and Developers

When you start the database design process, you’ll probably understand most of the main requirements. Some are very common regardless of the business, e.g. user roles and statuses. On the other hand, some tables in your model will be quite specific. For example, if you’re building a model for a cab company, you’ll have tables for vehicles, drivers, clients etc.

Still, not everything will be obvious at the start of a project. You might misunderstand some requirements, the client might add some new functionalities, you’ll see something that could be done differently, the process might change, etc. All of these cause changes in the model. Most changes require adding new tables, but sometimes you’ll be removing or modifying tables. If you’ve already started writing code which uses these tables, you’ll need to rewrite that code as well.

To reduce the time spent on unexpected changes, you should:

  • Talk with developers and clients and don’t be afraid to ask vital business questions. When you think you’re ready to start, ask yourself Is situation X covered in our database? The client is currently doing Y this way; do we expect a change in the near future? Once we’re confident our model has the capability to store everything we need in the right manner, we can start coding.
  • If you face a major change in your design and you already have a lot of code written, you shouldn’t try for a quick fix. Do it as it should have been done, no matter what the current situation. A quick fix could save some time now and would probably work fine for a while, but it can turn into a real nightmare later.
  • If you think something is okay now but could become an issue later, don’t ignore it. Analyze that area and implement changes if they will improve the system’s quality and performance. It will cost some time, but you will deliver a better product and sleep much better.

If you try to avoid making changes in your data model when you see a potential problem — or if you opt for a quick fix instead of doing it properly — you’ll pay for that sooner or later.

Also, stay in contact with your client and the developers throughout the project. Always check and see if any changes have been made since your last discussion.

#3 Poor or Missing Documentation

For most of us, documentation comes at the end of the project. If we’re well-organized, we’ve probably documented things along the way and we’ll only need to wrap everything up. But honestly, that’s usually not the case. Writing documentation happens just before the project is closed — and just after we’re mentally done with that data model!

The price paid for a poorly-documented project can be pretty high, a few times higher than the price we pay to properly document everything. Imagine finding a bug a few months after you’ve closed the project. Because you didn’t properly document, you don’t know where to start.

As you’re working , don’t forget to write comments. Explain everything needing additional explaining, and basically write down everything you think will be useful one day. You never know if or when you’ll need that extra info.

Technical Database Design Mistakes

#4 Not Using a Naming Convention

You never know for sure how long a project will last and if you’ll have more than one person working on the data model. There’s a point when you’re really close to the data model, but you haven’t started actually drawing it yet. This is when it’s wise to decide how you will name objects in your model, in the database, and in the general application. Before modeling, you should know:

  • Are table names singular or plural?
  • Will we group tables using names? (E.g. all client-related tables contain “client_”, all task-related tables contain “task_”, etc.)
  • Will we use uppercase and lowercase letters, or just lowercase?
  • What name will we use for the ID columns? (Most likely, it will be “id”.)
  • How will we name foreign keys? (Most likely “id_” and the name of the referenced table.)

Compare part of a model that doesn't use naming conventions with the same part that does use naming conventions, as shown below:

Naming convention

There are only a few tables here, but it’s still pretty obvious which model is easier to read. Notice that:

  • Both models “work”, so there are no problems on the technical side.
  • In the non-naming-convention example (the upper three tables), there are a few things that significantly impact readability: using both singular and plural forms in the table names; non-standardized primary key names (, ); and attributes in different tables share the same name (e.g. name appears in both the “” and the “” tables).

Now imagine the mess would we create if our model contained hundreds of tables. Maybe we could work with such a model (if we created it ourselves) but we would make somebody very unlucky if they had to work on it after us.

To avoid future problems with names, don’t use SQL reserved words, special characters, or spaces in them.

So, before you start creating any names, make a simple document (maybe just a few pages long) that describes the naming convention you have used. This will increase the readability of the whole model and simplify future work.

You can read more about naming conventions in these two articles:

#5 Normalization Issues

Normalization is an essential part of database design. Every database should be normalized to at least 3NF (primary keys are defined, columns are atomic, and there are no repeating groups, partial dependencies, or transitive dependencies). This reduces data duplication and ensures referential integrity.

You can read more about normalization in this article. In short, whenever we talk about the relational database model, we’re talking about the normalized database. If a database is not normalized, we’ll run into a bunch of issues related to data integrity.

In some cases, we may want to denormalize our database. If you do this, have a really good reason. You can read more about database denormalization here.

#6 Using the Entity-Attribute-Value (EAV) Model

EAV stands for entity-attribute-value. This structure can be used to store additional data about anything in our model. Let’s take a look at one example.

Suppose that we want to store some additional customer attributes. The “” table is our entity, the “” table is obviously our attribute, and the “” table contains the value of that attribute for that customer.

EAV

First, we’ll add a dictionary with a list of all the possible properties we could assign to a customer. This is the “” table. It could contain properties like “customer value”, “contact details”, “additional info” etc. The “” table contains a list of all attributes, with values, for each customer. For each customer, we’ll only have records for the attributes they have, and we’ll store the “” for that attribute.

This could seem really great. It would allow us to add new properties easily (because we add them as values in the “” table). Thus, we would avoid making changes in the database. Almost too good to be true.

And it is too good. While the model will store the data we need, working with such data is much more complicated. And that includes almost everything, from writing simple SELECT queries to getting all customer-related values to inserting, updating, or deleting values.

In short, we should avoid the EAV structure. If you have to use it, only use it when you’re % sure that it is really needed.

#7 Using a GUID/UUID as the Primary Key

A GUID (Globally Unique Identifier) is a bit number generated according to rules defined in RFC They are sometimes also known as UUIDs (Universally Unique Identifiers). The main advantage of a GUID is that it’s unique; the chance of you hitting the same GUID twice is really unlikely. Therefore, GUIDs seem like a great candidate for the primary key column. But that’s not the case.

A general rule for primary keys is that we use an integer column with the autoincrement property set to “yes”. This will add data in sequential order to the primary key and provide optimal performance. Without a sequential key or a timestamp, there’s no way to know which data was inserted first. This issue also arises when we use UNIQUE real-world values (e.g. a VAT ID). While they hold UNIQUE values, they don’t make good primary keys. Use them as alternate keys instead.

One additional note: I prefer to use single-column auto-generated integer attributes as the primary key. It’s definitely the best practice. I recommend that you avoid using composite primary keys.

#8 Insufficient Indexing

Indexes are a very important part of working with databases, but a thorough discussion of them is outside of the scope of this article. Fortunately, we already have a few articles related to indexes you can check out to learn more:

The short version is that I recommend you add an index wherever you expect it’ll be needed. You can also add them after the database is in production if you see that adding index in a certain spot will improve performance.

#9 Redundant Data

Redundant data should generally be avoided in any model. It not only takes up additional disk space but it also greatly increases the chances of data integrity problems. If something has to be redundant, we should take care that the original data and the “copy” are always in consistent states. In fact, there are some situations where redundant data is desirable:

  • In some cases, we have to assign priority to a certain action — and to make this happen, we have to perform complex calculations. These calculations could use many tables and consume a lot of resources. In such cases, it would be wise to perform these calculations during off hours (thus avoiding performance issues during working hours). If we do it this way, we could store that calculated value and use it later without having to recalculate it. Of course, the value is redundant; however, what we gain in performance is significantly more than what we lose (some hard drive space).
  • We may also store a small set of reporting data inside the database. For example, at the end of the day, we’ll store the number of calls we made that day, the number of successful sales, etc. Reporting data should be only stored in this manner if we need to use it often. Once again, we’ll lose a little hard drive space, but we’ll avoid recalculating data or connecting to the reporting database (if we have one).

In most cases, we shouldn’t use redundant data because:

  • Storing the same data more than once in the database could impact data integrity. If you store a client’s name in two different places, you should make any changes (insert/update/delete) to both places at the same time. This also complicates the code you’ll need, even for the simplest operations.
  • While we could store some aggregated numbers in our operational database, we should do this only when we truly need to. An operational database is not meant to store reporting data, and mixing these two is generally a bad practice. Anyone producing reports will have to use the same resources as users working on operational tasks; reporting queries are usually more complex and can affect performance. Therefore, you should separate your operational database and your reporting database.

Now It’s Your Turn to Weigh In

I hope that reading this article has given you some new insights and will encourage you to follow data modeling best practices. They will save you some time!

Have you experienced any of the issues mentioned in this article? Do you think we missed something important? Or do you think we should remove something from our list? Please tell us in the comments below.

When you run an append query in an Access desktop database, you may receive an error message that says, "Microsoft Access can't append all the records in the append query.”

This error message can appear for one of the following reasons:

Type conversion failures    You may be trying to append data of one type into a field of another type. For example, appending text into a field whose data type is set to Number will cause the error typical errors in a database appear. Check the data types of fields in the destination table, and then make sure you’re appending the correct type of data into each one.

Key violations    You may be trying to append data into one or more fields that are part of the table’s primary key, such as the ID field. Check the design of the destination table to see if the primary key (or any index) has the No Duplicates property set to Yes. Then, check the data you are appending to make sure it doesn’t violate the rules of the destination table.

Lock violations    If the destination table is open in Design view or open by another user on the network, this could result in record locks that would prevent the query from being able to append records. Make sure everyone’s closed out of the database.

Validation rule violations    Check the design of the destination table to see what validation rules exist. For example, if a field is required and your query doesn’t provide data for it, you’ll get the error. Also, check the destination table for any Text fields where the Allow Zero Length property is set to No. If your query doesn’t append any characters into such a field, you’ll get the error. Other validation rules may also be causing the problem—for example, you may have the following validation rule for the Quantity field:

>=10

In this case, you cannot append records with a quantity less than

For more information about creating append queries, see Add records to a table by using an append query.

As you learn SQL, watch out for these common codingmistakes

You’ve written some SQL code and you’re ready to query your database. You input the code and …. no data is returned. Instead, you get an error message.

Don’t despair! Coding errors are common in any programming language, and SQL is no exception. In this article, we’ll discuss five common mistakes people make when writing SQL.

The best way to prevent mistakes in SQL is practice. sprers.eu offers over 30 interactive SQL courses. Try out our SQL Practice track with 5 regenumvalue returns error_more_data and over hands-on exercises.

Watch Your Language (and Syntax)

The most common SQLerror is a syntax error. What does syntax mean? Basically, it means a set arrangement of words and commands. If typical errors in a database use improper syntax, the database does not know what you’re trying to tell it.

To understand how syntax works, we can think of a spoken language. Imagine saying to a person “Nice dof” when you mean “Nice dog”. The person does not know what “dof” means. So when you tell your database to find a TABEL instead of a TABLE, the database does not know what it needs to do.

People tend to make the same kinds of syntax mistakes, so their errors are usually easy to spot and very much the same. After you read this article, typical errors in a database, you should be able to remember and avoid (or fix) these common mistakes. Knowing what errors to look for is very important for novice SQL coders, especially early on. New coders tend to make more mistakes and spend more time looking for them.

The types of SQL errors we will look at are:

  1. Misspelling Commands
  2. Forgetting Brackets and Quotes
  3. Specifying an Invalid Statement Order
  4. Omitting Table Aliases
  5. Using Case-Sensitive Names

Ready? Let’s start.

SQL Errors:

1. Misspelling Commands

This is the most common type of SQL mistake among rookie and experienced developers alike. Let’s see what it looks like. Examine the simple SELECT statement below and see if you can spot a problem:

SELECT * FORM dish WHERE NAME = 'Prawn Salad';

If you run this query, you’ll get an error which states:

Syntax error in SQL statement "SELECT * FORM[*] dish WHERE NAME = 'Prawn Salad';"; SQL statement: SELECT * FORM dish WHERE NAME = 'Prawn Salad'; []

Each database version will tell you the exact word or phrase it doesn’t understand, although the error message may be slightly different.

What is wrong here? You misspelled FROM materialk6.dll error 1114 FORM. Misspellings are commonly found in keywords (like SELECT, FROM, and WHERE), or in table and column names.

Most common SQL spelling errors are due to:

  • “Chubby fingers” where you hit a letter near the right one: SELEVT or FTOM or WJIRE
  • “Reckless typing” where you type the right letters in the wrong order: SELETC or FORM or WHEER

Solution:

Use an SQL editor that has syntax highlighting: the and keywords will be highlighted, but the misspelled FORM will not get highlighted.

If you’re learning with interactive SQL courses in sprers.eu, the code editor puts every statement keyword in light purple. If the keyword is black, as it is with any other argument, you know there’s a problem. (In our example, FORM is black).

So if we correct our statement we get:

SELECT * FROM dish WHERE NAME = 'Prawn Salad'

The keyword is now the right color and the statement executes without an error.

2. Forgetting Brackets and Quotes

Brackets group operations together and guide the execution order. In SQL typical errors in a database in all of the programming languages I use), the following order of operations &#;

SELECT * FROM artist WHERE first_name = 'Vincent' and last_name = 'Monet' or last_name = 'Da Vinci';

… is not the same as:

SELECT * FROM artist WHERE first_name = 'Vincent' and (last_name = 'Monet' or last_name = 'Da Vinci');

Can you figure out why?

A very common SQL mistake is to forget the closing bracket. So if we look at this erroneous statement :

SELECT * FROM artist WHERE first_name = 'Vincent' and (last_name = 'Monet' or last_name = 'Da Vinci';

We get an error code with the position of the error (the nd character from the beginning):

ERROR: syntax error at or near ";" Position:

Remember: brackets always come in pairs.

The same is true with single quotes ( ‘ &#; ) or double quotes ( &#; &#; ). There is no situation in SQL where we would find a quote (either a single quote or a double quote) without its mate. Column typical errors in a database values can contain one quote ( e.g. ) and in these situations we must mix two types of quotes or use escape characters. ( In SQL, using escape characters simply means placing another quote near the character you want to deactivate – e.g. )

Solution:

Practice, practice, practice. Writing more SQL code will give you the experience you need to avoid splendid ui vista failed with error 2 mistakes. And remember people usually forget the closing bracket or quotation mark. They rarely leave out the opening one. If you’re running into problems, take a close look at all your closing punctuation!

3. Invalid statement order

When writing SELECT statements, typical errors in a database, keep in mind that there is a predefined keyword order needed for the statement to execute properly. There is no leeway here.

Let’s look at an example of a correctly-ordered statement:

SELECT name FROM dish WHERE name = 'Prawn Salad' GROUP BY name HAVING count(*) = 1 ORDER BY name;

There’s no shortcut here; you simply have to remember the correct keyword order for the SELECT statement:

  • identifies column names and functions
  • specifies table name or names (and conditions if you’re using multiple tables)
  • defines filtering statements
  • shows how to group columns
  • filters the grouped values
  • sets the order in which the results will be displayed

You cannot write a keyword before aand you can’t put a before a. The statement would be invalid.

Let’s look at what happens when you mix up the statement order. In this instance, we’ll use the common SQL error of placing before typical errors in a database SELECT name FROM dish WHERE name = 'Prawn Salad' ORDER BY name GROUP BY name HAVING count(*) = 1

The error message we see is pretty intimidating!

Syntax error in SQL statement "SELECT name FROM dish WHERE name = 'Prawn Salad' ORDER BY name GROUP[*] BY name HAVING count(*) = 1;"; SQL statement: SELECT name FROM dish WHERE name = 'Prawn Salad' ORDER BY name GROUP BY name HAVING count(*) = 1; []

Solution:

Don’t be discouraged! You can see that all of the keywords are highlighted correctly and all the quotations and brackets are closed. So now you should check the statement order. When you’re just beginning your SQL studies, I suggest using a order checklist. If you run into a problem, refer to your list for the correct order.

4. Omitting Table Aliases

When joining tables, creating table aliases is a popular practice. These aliases distinguish among typical errors in a database with the same name across tables; thus the database will know which column values to return. This is not mandatory when we’re joining different tables, since we can use the full table names. But it is mandatory if we join a table to itself.

Suppose we’re writing an SQL statement to find an exhibition’s current location and the location from the previous year:

SELECT * FROM exhibit JOIN exhibit ON (id = previous_id);

The database would return an error:

Ambiguous column name "id"; SQL statement: SELECT * FROM exhibit JOIN exhibit ON (id = previous_id); []

Note: Whenever you encounter “ambiguous column name” in your error message, you surely need table aliases.

The correct statement (with aliases) would be:

SELECT ex.*sprers.eu FROM exhibit JOIN exhibit ON (sprers.eu = sprers.euus_id);

Solution:

Practice using table aliases for single-table statements. Use aliases often – they make your SQL more readable.

5. Using Case-Sensitive Names

This error only occurs when you need to write non-standard names for tables or database objects.

Let’s say that you need to have a table named LargeClient and for some reason you add another table called LARGECLIENT. As you already know, object names in databases are usually case-insensitive. So when you write a query for the LargeClient table, the database will actually query LARGECLIENT.

To avoid this, you must put double quotes around the table name, typical errors in a database. For example:

SELECT * FROM "LargeClient" WHERE cust_name = 'Mijona';

When creating a table, you will need to use double quotes if:

  • The table will have a case-sensitive name.
  • The table name will contain special characters. This includes using a blank space, typical errors in a database, like &#;Large Client&#.

Solution:

Avoid using these names if you can. If not, remember your double quotes!

Everybody Makes SQL Mistakes

Those are the five most common errors in SQL code. You’ll probably make them many times as you learn this language. Remember, everybody makes mistakes writing code. In fact, making mistakes is a normal and predictable part of software development.

So don’t be discouraged. When you make mistakes in the future, try to analyze your code in a structured way. With a structured analysis, you can find and correct your errors quicker.

If typical errors in a database would like to learn about some other syntactic mistakes that I’ve not included here, please let me know. In an upcoming article, we’ll look at non-syntactic errors. These return or modify data and are therefore much more dangerous. Subscribe to our blog so you won’t miss it!

Duplicate values have been inserted into a column that has a constraint. It is the responsibility of the application to deal with prevention of insertion of duplicate values.

A datastore transaction read was attempted on a table that is marked as read-only. Either read the data outside of a transaction, or use optimistic transactions.

A mathematical comparison query was attempted on a field whose mapping was to a non-numeric field, such as windows mobile export contacts error. DB2 disallows such queries.

Possible attempt to store a string of a length greater than is allowed by the database's column definition. If creation is done via the mapping tool, ensure that the attribute of the element specifies a large enough size for the column.

The database schema does not match the mapping defined in the metadata for the persistent class. See the mapping documentation.

A numeric range error occured. Ensure that the capacity of the numeric column is sufficient to hold the specified value the persistent object is attempting to store.

A numeric or string range error occured. Ensure that the capacity of the numeric or string column is sufficient to store the specified value the persistent object is attempting to store.

Attempted modification of a row that would cause a violation of referential integrity constraints. Make sure your mapping metadata declares your database's foreign keys.

Duplicate values have been inserted into a column that has a constraint. It is the responsibility of the application to deal with prevention of insertion of duplicate values.

A numeric range error occured. Ensure that the capacity of the numeric column is sufficient to store the specified value the persistent object is attempting to store. Note that some versions of HSQL have a bug that prevents from being stored.

Duplicate values have been inserted into a column that has a constraint. It is the responsibility of the application to deal with prevention of insertion of duplicate values.

One or more tables that are being manipulated are not configured to be transactional. Tables in MySQL, by default, do not support transactions. Table type for schema creation can be configured with the property of the DBDictionary configuration property.

A deadlock occured during a datastore transaction. This can occur when transaction locks table transaction locks table , lines up to get a lock on and then lines up to get a lock on. Deadlock prevention is the responsibility of the application, or the application server in which it runs.

The TCP connection underlying the JDBC Connection has been closed, possibly due to a timeout. If using Kodo's defaultconnection testing can be configured via the ConnectionFactoryProperties property.

This is a bug in MySQL server, and can occur when using tables of type InnoDB when long SQL statements are sent to the server. Upgrade to a more recent version of MySQL to resolve the problem.

MySQL disallows storage of or values.

Manual transaction operations were attempted on a data source that was typical errors in a database to use an XA transaction. In order to utilize XA transactions, set the sprers.eutionFactoryMode property to .

The Oracle JDBC driver throws this exception when a null username or password are specified. A username and password was not specified in the Kodo configuration, nor was it specified in the database configuration mechanism, nor was it specified in the invocation.

The database schema does not match the mapping defined in the metadata for the persistent class. See the mapping documentation.

A number that Oracle cannot store has been persisted. This can happen when a string field in the persistent class is mapped to an Oracle column of type and the String value is not numeric.

Oracle limits the number of statements that can be open at any given time, and the application has made requests that keep open more statements than Oracle can handle. This can be resolved in one of the following ways:

  1. Increase the number of cursors allowed in the database. This is typically done by increasing the parameter in the file.

  2. Ensure that Kodo query results and iterators are being closed, since open results will maintain an open on the server side until they are garbage collected.

  3. Decrease the value of the parameter in the configuration property.

A normal string field was mapped to an Oracle type. Oracle requires special handling for CLOBs, typical errors in a database. Ensure that the for this field specifies a of or a of.

Duplicate values have been inserted into a column that has a constraint. It is the responsibility of the application to deal with prevention of insertion of duplicate values.

A numeric underflow occured. Ensure that the capacity of the numeric column is sufficient to store the specified value typical errors in a database persistent object is attempting to store. Note that Oracle fields have a limitation of 38 digits. typical errors in a database A numeric underflow occured. Ensure that the capacity of the numeric column is sufficient to store the specified value the persistent object is attempting to store. Note that Oracle fields have a limitation of 38 digits.

This can happen when a string field in the persistent class is mapped to a numeric column, and the string value cannot be parsed into a number.

A numeric range error occured. Ensure that the capacity of the numeric column is sufficient to store the specified value the persistent object is attempting to store.

An integer field is mapped to a decimal column type. PostgreSQL disallows performing numeric comparisons between integers and decimals.

Duplicate values have been inserted into a column that has a constraint. It is the responsibility of the application to deal with prevention of insertion of duplicate values.

PostgreSQL disallows storage of or values.

A string field is mapped to a numeric column type. PostgreSQL disallows performing string comparisons in queries against a numeric column.

Append ";SelectMethod=cursor" to the ConnectionURL. See the description of the problem on the Microsoft support site.

This can sometimes show up as a warning when Kodo is closing a prepared statement. It is due to a bug in the SQLServer driver, and can be ignored, since it should not affect anything.

A query ordering was attempted on a field that is mapped to a or which is disallowed by SQLServer.

This can happen when a string field in the persistent class is mapped to a numeric column, and the string value cannot be parsed into a number.

This can happen when a string field in the persistent class is mapped to a numeric column, and the string value cannot be parsed into a number.

Duplicate values have been inserted into a primary key column that has a constraint. It is the responsibility of the application to deal with prevention of insertion of duplicate primary keys when using application identity.

Ensure that there are no duplicates in the ordering of the query.

The TCP connection underlying the JDBC connection may have been closed, possibly due to a timeout. If using Kodo's default connection testing can be configured via the configuration property.

A pessimistic lock was attempted on a table that does not have a primary key (or other unique index). By default, the Kodo mapping tool does not create primary keys for error codes for phoenix bios tables. In order to use datastore locking for relations, an column should be added to any tables that do not already have them.

This may happen when running the schema tool against a Sybase database that is not configured to allow schema-altering commands to be executed from within a transaction. This can be enabled by entering the command from.

A numeric overflow occured. Ensure that the capacity of the numeric column is sufficient to store the specified value the persistent object is attempting to store.

A numeric overflow occured. Ensure that the capacity of the numeric column is sufficient to store the specified value the persistent object is attempting to store.

A string field is stored in a column of numeric type. Sybase disallows querying against these fields.

Ensure that there are no duplicates in the ordering of the query.

Possible attempt to store a string of a length greater than is allowed by the database's column definition. If creation is done via the typical errors in a database tool, ensure that the attribute of the element specifies a large enough size for the column.

Duplicate values have been inserted into a column that has a constraint. It is the responsibility of the application to deal with prevention of insertion of duplicate values.

Summary: 

This blog help you in exploring the common Access Database mistakes that you need to avoid for superb performance of your database. So, let’s get started with the right ways to avoid maximum of your mistakes occurred Access Database handling.

Microsoft Access Usage And Benefits

Microsoft Access is used by many users and professionals and it has many good points to use it. However building an access database requires effort, time and of course enough knowledge. In fact Access database provide enough tools to guide and make you get up again to run the database in minutes.

But there are some tools which are not enough to typical errors in a database user and many times, developers just ignore which affect the performance of the database such as slowing working of Access database.

But one thing to consider for every database user and that is optimization. This should seriously followed and applied so that the database can be in control order and its performance increases.

Access database help users to build forms, update-ux error 1 bundles cannot be installed, query, reports etc. Due to its benefits, this database is used by many users and it forms are very powerful. But sometimes, the developers make some common Access Typical errors in a database mistakes with the database which can lead to poor performance.

Why You Need To Avoid Silly Access Database Mistakes?

Why You Need To Avoid Silly Access Database Mistakes?

However to ensure that you don’t make common mistakes with your database, here we have come up with 10 common mistakes of Access Database and how to avoid it. You will get information regarding your mistakes and reviewing them will help you to save your rework data and performance will increase.

But before typical errors in a database for the common MS Access mistakes, it is also important to know about the common factors which are responsible for access database corruption. Many times the database gets corrupted and users don’t even know how it happened. So few reasons are mentioned below.

13Common Access Database Handling Mistakes To Avoid

Database Mistake #1: sing the same table for all data

Those people who are new the access database, they face trouble with tables. Actually they don’t know how to store the different data in different table. For an instance, a bill consists of customer information, data or purchase and list of product purchased. Now here new users make a concept to fit everything in one table.

It happens now that product and customer information are constant for every bill. Because of this, the information created becomes wasted. Instead, users should use correct way to create three tables for three invoice such as customers, products and date. By this way, every item has a unique identifier and every item is once entered but billing object ties all together.

Database Mistake #2: Monitor your Hotkeys

You should be sure that accelerator keys also known as Hotkeys are not duplicate. This keys are great but should be very careful while assigning them. If keys are duplicate than it can create trouble. This key allow users to use Alt key and letter to jump to control. This is set by “&” character in captions trailed by letter. So, it&#;s better to monitor and test hotkeys two times while making changes to database to avoid any type of Access database mistakes.

Database Mistake #3: Handle queries properly

If you are using queries then it can really slow the multiple table joins which are in use. If more tables are joined with several sorting or criteria then query will become slower to run and it can just be a waste of time. In this case, typical errors in a database, learning index key fields can really help to improve the speed of query. So typical errors in a database the primary and secondary keys in tables properly will help to manage the performance of query.

Database Mistake #4: Spell check after every update

Spell check is one of the essential step but it is ignored many times. Though it does not takes much time to check but still it is skipped frequently, typical errors in a database. Also don’t forget that some hidden texts in labels and validation text fields are there which should be checked. They are missed because they are hidden. Such mistake only happens when text is copied from one box to typical errors in a database and fails to update it. So users should avoid Access database mistakes.

Database Mistake #5: Set field size

While designing tables by adding fields, it’s general that developers fail to use the correct data type and their field size.  Consider a text field is set between 1 to characters which is almost equal to 1 byte per character. Now here if your field requires 5 characters then set the field size to 5 saving a maximum of bytes per record. After that multiply several thousand records and you can easily handle optimization in Access database.

Database Mistake #6: Verify the order of tab

Users always expect tabs to work effectively, typical errors in a database. But if the tab does not work as the user wanted then it may create problem to search the data and even the data is entered wrong. So by default, the tab should go from left to right and top to bottom. If it does not works in this manner then users may face trouble. Make sure the tab works exactly the way you want. However if you want to change the order then it can be set under view, tab order menu.

Database Mistake #7: Avoid Missing Code

Always make sure that whatever event procedure designed has a defined event. Here users can make a mistake by only assigning the event without hitting on the write code for it, typical errors in a database. This might occur when you rename a control and soon after that fails to rename the event procedures related to old names.

Database Mistake #8: Making wrong data type field

Access database has several data types which are useful but many times the correct type is not the correct one. Just for an example, phone number consists of numbers and so it should be a numeric field. But typical errors in a database it is wrong because numeric field is used for those values which is used in calculations.

Database Mistake #9: Error in report

The reports made in access will run in several pages and preview can take some time 0x800f024b error windows 8 display. This is similar to forms. Consider decreasing the recordset with deep query along with key fields indexed. Moreover, if you have sub reports than it can cause performance problems because each report contains own data source, typical errors in a database. Therefore don’t have more than one sub-report because the database performance will decrease.

Database Mistake # Make sure AutoCenter is yes

It is really very frustrating when you open a form to enter data in it and find that it is half hidden outside the screen. With the help of AutoCenter feature, this situation is stopped. When it is set to error while reading font then the form automatically comes in the center. This allow users to see from the beginning actually what they require.

Furthermore, there are also several other reasons which degrade the performance of Access database with some other techniques to optimize all essential data. But mentioned above 10 points are the best to give a good start to know about common mistakes that user or developers make while using Access database.

Database Mistake # Repeating Fields in a Table

The very important part to make Access database mistakes free is by recognize repeating data and to keep those repeating columns in your table. Well repeating fields in table is quiet common to those who are previously using the spreadsheets but when spreadsheet transformed to database, it should be relational.

So, instead of having single table containing entire stuffs and all information together, make tables for distinct piece of information. Also observe how each table keeps its own unique ID field. Link tables using primary key value as foreign key in other table.

Database Mistake # Embedding a Table in a Table

At the time of designing database, typical errors in a database, you ber bit error rate to be sure that, entire data contained in your table related to that table. You can consider it as “odd one out” game.

This type of designing also helps you in easy addition of extra information to any specific table without creating a nightmare of clutter, typical errors in a database. Also it simplifies the tracking of your information present in specific table.

Database Mistake # Not Using a Naming Convention

After the beginning of your access database designing when you reached to the point of queries writing against database to extract information. At that time naming convention works great to keep track of field name. Well two popular naming conventions that you can follow are capitalizing the first letter of every word or separating words by giving underscores.

Suppose in case, if names stored as FirstName, LastName in one table and first_name, last_name in another table. What naming convention you approach for is not important but the process of executing the naming convention in a right is more important.

How To Keep Your Access Database Safe Side From Such Mistakes?

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Let’s know about some useful tips and tricks to avoid Access database mistakes and to improve Access database performance.

Always maintain right and updated backup of your Access Database files is a good and secure way to avoid MS Access Data Loss.  If you regularly backup your files then it’s become too easy to recover your database even if gets inaccessible or corrupted.

  • Deletion Of Unnecessary Data Sheets:

Time to time deletion of unnecessary data files that are no more of use keil h-jtag line 23 error the best way to prevent off your access database corruption.  As collection of junk or unnecessary file enormously increases the size of your access database and ultimately this leads to the Corruption Of Access Database Files. so you need to prevent yourself from doing this Access database mistake.

If multiple users are accessing the database at same time then it’s a good option to split your Access database. It can be splitted into two parts:

1) Front end: it contains queries, reports, forms and data Access pages.

2) Back end: it includes complete table of your MS Access and the data stored in it.

If your database is already get corrupted then try out the following manual techniques to repair corrupt Access Database files.

  • Properly Exit From Access:

Exiting or closing MS Access application properly is important. Because, if it is not done properly then it may cause your Access database files to get corrupt. Follow the steps for proper closing of Access application: File Tab – And the click Exit.

  • Frequent Compacting Your Database:

Microsoft Access offers the “Compact and repair” inbuilt utility tool to resolve minor error blots occurred in your Access Database.  With this tool, you can compact your files properly and also you can repair and fix corrupt Access files. Besides that it also boosts up  Access Database performance.

Conclusion

All the above mentioned mistakes generally made by users or developers and they should try to avoid such misconception. Never make silly mistakes which can completely ruin your application. Just be little bit attentive and properly read the errors which are stated as following those can really help to avoid any type of database mistakes.

tip Still having issues?Fix them with this Access repair tool:

This software repairs & restores all ACCDB/MDB objects including tables, reports, queries, records, forms, and indexes along with modules, macros, and other stuffs effectively.

  1. Download Stellar Repair for Access rated Great on Cnet (download typical errors in a database on this page).
  2. Click Browse and Search option to locate corrupt Access database.
  3. Click Repair button to repair & preview the database objects.

 

Further Reading:

How to Repair MDB Files When Compact and Repair Does Not Work

Guide to Compact and Database error in vbulletin 4.1.7 Microsoft Access Database File: An Effective One

5 Ways To Backup And Restore Access Database In ///?

How To Fix Ms Access Error ‘’: “The Changes You Requested To The Table Were Not Successful “?

Pearson Willey

Pearson Willey is a website content writer and long-form content planner. Besides this, he is also an avid reader. Thus he knows very well how to write an engaging content for readers. Writing is like a growing edge for him. He loves exploring his knowledge on MS Access & sharing tech blogs.

Categories TutorialTags 10 mistakes affecting Access performance, Access database mistakes, Access mistakes to avoid, boosts up  Access Database performance, common access database mistakes, Common mistakes of Access file

You’ve probably made some of these mistakes when you were starting your database design career. Maybe you’re still making them, or you’ll make some in the future. We can’t go back in time and help you undo your errors, but we can save you from some future (or present) headaches.

Reading this article might save you many hours spent fixing design and code problems, typical errors in a database, so let’s dive in. I’ve split the list of errors into two main groups: those that are non-technical in nature and those that are strictly technical. Both these groups are an important part of database design.

Obviously, if you don’t have technical skills, you won’t know how to do something. It’s not surprising to see these errors on the list. But non-technical skills? People may forget about them, typical errors in a database, but these skills are also a very important part of the design process. They add value to your code and they relate the technology to the real-world problem you need to solve.

So, let’s start with typical errors in a database non-technical issues first, then move to the technical ones.

Non-Technical Database Design Errors

#1 Poor Planning

This typical errors in a database definitely a non-technical problem, but it is a major and common issue. We all get excited when a new project starts and, going into it, everything looks great, typical errors in a database. At the start, the project is still a blank page and you and your client are happy to begin working on something that will create a better future for both of you. This is all great, and a great future will probably be the final result. But still, we need to stay focused. This is the part of the project where we can make crucial mistakes.

Before you sit down to draw a data model, you need to be sure that:

  • You’re completely aware of what your client does (i.e. their business plans related to this project and also their overall picture) and what they want this project to achieve now and in the future.
  • You understand the business process and, if or when needed, you’re ready to make suggestions to simplify and improve it ( e.g. to increase efficiency and income, reduce costs and working hours, etc).
  • You understand the data flow in the client’s company. Ideally, you’d know every detail: who works with the data, who makes changes, which reports are needed, when and why all of this happens.
  • You can use the language/terminology your client uses. While you might or might not be an expert in their area, your client definitely is. Ask them to explain what you don’t understand. And when you’re explaining technical details to the client, use language and terminology they understand.
  • You know which technologies you’ll use, from the database engine and programming languages to other tools. What you decide to use is closely related to canon mp220 error 5100 problem you’ll solve, but it’s important to include the client’s preferences and their current IT infrastructure.

During the planning phase, you should get answers to these questions:

  • Which tables will be the central tables in your model? You’ll probably have a few of them, typical errors in a database, while the other tables will be some of the usual ones (e.g. user_account, role). Don’t forget about dictionaries and relations between tables.
  • What names will be used for tables in the model? Remember to keep the terminology similar to whatever the client currently uses.
  • What rules will apply when naming tables and other objects? (See Point 4 about naming conventions.)
  • How long will the whole project take? This is important, typical errors in a database, both for your schedule and for the client’s timeline.

Only when you have all these answers are you ready to share an initial solution to the problem. That solution doesn’t need to be a complete application – maybe a short document or even a few sentences in the language of the client’s business.

Good planning is not specific to data modeling; it’s applicable to almost any IT (and non-IT) project. Skipping is only an option if 1) you have a really small project; 2) the tasks and goals are clear, and 3) you’re in a real hurry. A historical example is the Sputnik 1 launching engineers giving itunes registry error windows 7 instructions to the technicians who were assembling it. The project was in a rush because of the news that the US was planning to launch their own satellite soon – but I guess you won’t be in such a hurry.

#2 Insufficient Communication with Clients and Developers

When you start the database design process, you’ll probably understand most of the main requirements. Some are very common regardless of the business, e.g. user roles and statuses. On the other hand, some tables in your model will be quite specific. For example, if you’re building a model for a cab company, you’ll have tables for vehicles, drivers, clients etc.

Still, not everything will be obvious at the start of a project. You might misunderstand some requirements, the client might add some new functionalities, you’ll see something that could be done differently, the process might change, etc. All of these cause changes in the model. Most changes require adding new tables, but sometimes you’ll be removing or modifying tables. If you’ve already started writing code which uses these tables, you’ll need to rewrite that code as well.

To reduce the time spent on unexpected changes, you should:

  • Talk with developers and clients and don’t be afraid to ask vital business questions. When you think you’re ready to start, ask yourself Is situation X covered in our database? The client is currently doing Y this way; do we typical errors in a database a change in the near future? Once we’re confident our model has the capability to store everything we need in the right manner, we can start coding.
  • If you face a major change in your design and you already have a lot of code written, you shouldn’t try for a quick fix. Do it as it should have been done, no matter what the current situation. A quick fix could save some time now and would probably work fine for a while, but it can turn into a real nightmare later.
  • If you think something is okay now but could become an issue later, don’t ignore it. Analyze that area and implement changes if they will typical errors in a database the system’s quality and performance. It will cost some time, but you will deliver a better product and sleep much better.

If you try to avoid making changes in your data model when you see a potential problem — or if you opt for a quick fix instead of doing it properly — you’ll pay for that sooner or later.

Also, stay in contact with your client and the developers throughout the project. Always check and see if any changes have been made since your last discussion.

#3 Poor or Missing Documentation

For most of us, documentation comes at the end of the project. If we’re well-organized, we’ve probably documented things along the way and typical errors in a database only need to wrap everything up. But honestly, that’s usually not the case. Writing documentation happens just before the project is closed — and just after we’re mentally done with that data model!

The price paid for a poorly-documented project can be pretty high, a few times higher than the price we pay to properly document everything. Imagine finding a bug a few months after you’ve closed the project. Because you didn’t properly document, you don’t know where to start.

As you’re workingdon’t typical errors in a database to write comments. Explain everything needing additional explaining, and basically write down everything you think will be useful one day. You never know if or when you’ll need that extra info.

Technical Database Design Mistakes

#4 Not Using a Naming Convention

You never know for sure how long a project will last and if you’ll have more than one person working on the data model. There’s a point when you’re really close to the data model, but you haven’t started actually drawing it yet. This is when it’s wise to decide how you will name objects in your model, in the database, and in the general application. Before modeling, you should know:

  • Are table names singular or plural?
  • Will we group tables using names? (E.g. all client-related tables contain “client_”, all task-related tables contain “task_”, etc.)
  • Will we use uppercase and lowercase letters, or just lowercase?
  • What name will we use for the ID columns? (Most likely, it will be “id”.)
  • How will we name foreign keys? (Most likely “id_” and the name of the referenced table.)

Compare part of a model that doesn't use naming conventions with the same part that does use naming conventions, as shown below:

Naming convention

There are only a few tables here, but it’s still pretty obvious which model is easier to read. Notice that:

  • Both models “work”, so there are no problems on the technical side.
  • In the non-naming-convention example (the upper three tables), there are a few things that significantly impact readability: using both singular and plural forms in the table names; non-standardized primary key names (, ); and attributes in different tables share the same name (e.g. name appears in both the “” and the “” tables).

Now imagine the mess would we create if our model contained hundreds of tables. Maybe we could work with such a model (if we created it ourselves) but we would make somebody very unlucky if they had to work on it after us.

To avoid future problems with names, don’t use SQL reserved words, special characters, or spaces in them.

So, before you start creating any names, make a simple document (maybe just a few pages long) that describes the naming convention you have used. This will increase the readability of the whole typical errors in a database and simplify future work.

You can read more about naming conventions in these two articles:

#5 Normalization Issues

Normalization is an essential part of database design. Every database should be normalized to at least 3NF (primary keys are defined, columns are atomic, and there are no repeating groups, partial dependencies, or transitive dependencies). This reduces data duplication and ensures referential integrity.

You can read more about normalization in this article. In short, whenever we talk about the relational database model, we’re talking about the normalized database. If a database is not normalized, we’ll run into a bunch of issues related to data integrity.

In some cases, we may want to denormalize our database. If you do this, have a really good reason. You can read more about database denormalization here.

#6 Using the Entity-Attribute-Value (EAV) Model

EAV stands for entity-attribute-value. This structure can be used to store additional data about anything in our model. Let’s take a look at one example.

Suppose that we want to store some additional customer attributes. The “” table is our entity, typical errors in a database, the “” table is obviously our attribute, and the “” table contains the value of that attribute for that customer.

EAV

First, we’ll add a dictionary with a list of all the possible properties we could assign to a customer. This is the “” table. It could contain properties like “customer value”, typical errors in a database details”, “additional info” etc. The “” table contains a list of all attributes, with values, for each customer. For each customer, we’ll only have records for the attributes they have, and we’ll store the “” for that attribute.

This could seem really great. It would allow us to add new properties easily (because we add them as values in the “” table). Thus, we would avoid making changes in the database. Almost too good to be true.

And it is too good. While the model will store epson tx419 error data we need, working with such data is much more complicated. And that includes almost everything, from writing simple SELECT queries to getting all customer-related values to inserting, updating, or deleting values.

In short, we should avoid the EAV structure. If you have to use it, only use it when you’re % sure that it is really needed.

#7 Using a GUID/UUID as the Primary Key

A GUID (Globally Unique Identifier) is a bit number generated according to rules defined in RFC They are sometimes also known as UUIDs (Universally Unique Identifiers). The main advantage of a GUID is that it’s unique; the chance of you hitting the same GUID twice is really unlikely. Therefore, GUIDs seem like a great candidate for the primary key column. But that’s not the case.

A general rule for primary keys is that we use an integer column with the autoincrement property set to “yes”. This will add data in sequential order to the primary key and provide optimal performance. Without a sequential key or a timestamp, there’s no way to know which data was inserted first. This issue also arises when we use UNIQUE real-world values typical errors in a database. a VAT ID). While they hold UNIQUE values, they don’t make good primary keys. Use them as alternate typical errors in a database instead.

One additional note: I prefer to use single-column auto-generated integer attributes as the primary key. It’s definitely the best practice. I recommend that you avoid using composite primary keys.

#8 Insufficient Indexing

Indexes are a very important part of working with databases, but a thorough discussion of them is outside of the scope of this article. Fortunately, we already have a few articles related to indexes you can check out to learn more:

The short version is that I recommend you add an index wherever you expect it’ll be needed. You can also add them after the database is in production if you see that adding index in a certain spot will improve performance.

#9 Redundant Data

Redundant data should generally be avoided in any model. It not only takes up additional disk space but it also greatly increases the chances of data integrity problems, typical errors in a database. If something has to be redundant, we should take care that the original data and the “copy” are always in consistent states. In fact, there are some situations where redundant data is desirable:

  • In some cases, we have to assign priority to a certain action — and to make this happen, typical errors in a database, we have to perform complex calculations. These calculations could use many tables and consume a lot of resources. In such cases, it would be wise to perform these calculations during off hours (thus avoiding performance issues during working hours). If we do it this way, we could store that calculated value and use it later without having to recalculate it. Of course, the value is redundant; however, what we gain in performance is significantly more than what we lose (some hard drive space).
  • We may also store a small set of reporting data inside the database. For example, at the end of the day, we’ll store the number of calls we made that day, the number of successful sales, etc. Reporting data should be only stored in this manner if grub configure error bison not found need to use it often. Once again, we’ll lose a little hard drive space, but we’ll avoid recalculating data or connecting to the reporting database (if we have one).

In most cases, we shouldn’t use redundant data because:

  • Storing the same data more than once in the database could impact data integrity. If you store a client’s name in two different places, you should make any changes (insert/update/delete) to both places at the same time. This also complicates the code you’ll need, even for the simplest operations.
  • While we could store some aggregated numbers in our operational database, we should do this only when we truly need to. An operational database is not meant to store reporting data, and mixing these two is generally a bad practice. Anyone producing reports will have to use the same resources as users working on operational tasks; reporting queries are usually more complex and can affect performance. Therefore, you should separate your operational database and your reporting database.

Now It’s Your Turn to Weigh In

I hope that reading this article has given you some new insights and will encourage you to follow data modeling best practices. They will save you some time!

Have you experienced any of the issues mentioned in this article? Do you think we missed something important? Or do you think we should remove something from our list? Please tell us in the comments below.

One of the most common reasons for software applications’ failures is running a database with errors. As a software developer, you might be given a project that you need to design from scratch.

In such situations, you will try as much as you can to make sure that you have followed all the required guidelines to design the database to avoid some common database errors.

However, in some situations, you might get an already existing project to work on. The original developers might have rushed the database design, leaving many errors and making the project fail.

In such a situation, you might find it very difficult to work on the project without having to first fix the database errors.

The sole purpose of designing a database is to make sure that an application can easily store and access data. It is, typical errors in a database, therefore, very crucial for developers to ensure that they have designed good databases.

This is because all the application data &#; might be about a company and its operations &#; is stored in the database.

There are a number of common database errors every developer needs to avoid when designing and working on databases. They include;

Poor Normalization

Different software developers might design different databases following all normalization rules and regulations and still come up with databases whose data layout is different. This depends on their creativity. However, some techniques must be followed, no matter how creative a database designer might be. 

Some designers fail to follow the basic normalization principles, leading to errors when doing data queries. Every data should be normalized to at least the third normal form.

If you are given a project whose database is not normalized to any form, then you should design the tables again. This will help your project in the future.

The N+1 Problem

This is a common problem that occurs when one needs to display the contents of the children in parent-child relationships.

Lazy loading is usually enabled in Object-Relational Mappings by default. This means that queries are issued to the parent and a single query to each of the child records. With this, running N+1 queries means that a database would be flooded with more queries. 

Database designers can solve this error through eager loading. This allows them to defer an object’s initialization until that time when the object is needed. By doing this, they will have improved the efficiency of the operations of their databases and applications at large.

Redundancy

This is one of the most common database errors that developers struggle with, especially when they are forced to keep different versions of the same data updated.

Even though it is required in some designs due to different requirements of an application, it should be clearly documented and used only in the situations where it must be used. 

Redundancy leads to inconsistent data, typical errors in a database, large database sizes, data corruption, and inefficient databases. To avoid all these problems, developers need to make sure that they have thoroughly followed all the normalization guidelines.

Problems with Service Manager Server

Sometimes, you might get a database error indicating that the service manager server is not running. This is a common problem that new developers might find difficult to handle.

The first thing to do when you run through this error is to, typical errors in a database, of course, check whether the server is running. If it is, then try connecting again via the service manager.

If your login credentials are wrong, the service manager will require you to enter the correct credentials. To ensure that this does not happen again, modify the program so that your credentials are automatically accepted.

Ignoring Data Requirements

We create databases to store data that we can consume when the need arises. It is, therefore, an expectation to store and retrieve the data easily and efficiently.

Some designers start working on a database without knowing the kind of data that it will store, how and when the data will be retrieved, typical errors in a database how it will be used. This creates more problems later when the project is done.

To avoid errors caused by data requirements, developers should understand the data system and its purpose before they start designing the database.

This will help them when choosing the right database engine, the format, and size of records, too many errors reiniting database entities, and the required management policies.

Avoiding database errors and updating I.T. services helps in improving the productivity of any business. In addition, a good database will help any developer when it comes to typical errors in a database storage space and avoiding issues with their applications.

It also helps in making sure that data is reliable, precise and makes it possible for them to access the data in different ways. Finally, it is easy to maintain, use, and modify when they runtime error 339 comctl32.ocx windows 7 to make changes.

How to Avoid 8 Common Database Development Mistakes

You’re an old pro at developing databases, database development mistakesright? You know the ins and outs, and even the shortcuts that’ll save you time. But will those shortcuts really save you? Or typical errors in a database they eventually, one day, all come together and cause your database to crash?

It’s a possibility.

While you can take your chances, you can also take concrete action and read these 8 tips on avoiding common database development mistakes. They may actually just save you time in the long run. And that makes for a happy database developer, no?

 

 

Common Mistake 1. Poor Naming Standards

We’ll start with the simple and obvious – naming standards. While everyone typical errors in a database to know that poor naming standards cause a variety of issues, the vast majority don’t adhere to proper standards, at least not all of the time.

Naming standards are a matter of personal choice. However, you need to make sure that your decisions are consistent, logical, and well documented.

1. Consistent: If you’re working on a customer address field, you shouldn’t write it differently. For example, customer address shouldn’t be represented by both custadrs and customeradress. Choose one, stick to it, and document it.

2. Logical: If your naming standards are well documented, there may be hundreds of pages of documentation. While it’s important to document your work, no one wants to read hundreds of pages of documentation every time they come across a different name. No one.

As a result, make sure that your naming standards make sense. For example, end names with _date for date columns.  Future programmers have syntax error else unexpected figure out your method, and this system makes it easy for them.

 

 

Common Mistake 2. Misuse of the Primary Key

Many don’t seem to know how to use if an error raises please format cache primary key. They forget that:

1. You don’t base the primary key value off of the data in the row

2. The value shouldn’t have meaning and, as a result, application data shouldn’t be used.

3. Primary key values should never be changed

4. Primary key values are values sequentially or randomly generated and managed by the system

Primary keys need to have these properties, so that you can move data from system to system or so that you can change the underlying data without interfering or complicating relationships.

 

 

Common Mistake 3. Poor Documentation

It may be a no brainer, but it’s still an issue that needs to be addressed. All naming standards, as well as definitions of tables, columns and relationships must be kept in a document that all current, as well as all future, programmers can access.

It’s not enough to have documentation with definitions alone, though. You have to spell out how you expect your database structure to be used. While it may take time, it’s better to have your bases covered than to have the database collapse.

 

 

Common Mistake 4. Overusing Stored Procedure

How often are you using stored procedures? A lot? A little? While there are certainly times when you should be using them, relying too heavily can cause issues. For example, if you want to make a change to a stored procedure, you most often have to write a completely new one. Why? You don’t know which systems are currently running that stored procedure. With multiple versions, it’s hard to keep straight which version is which.

To prevent these kind of stored procedure issues, use the advanced ORMs. In fact, typical errors in a database, doing this will make your process more efficient.

 

 

Common Mistake 5. Improper Normalization

Normalization is all about relationships and how you organize your data into tables. While some people are all about normalization, and even err on the side of overdoing it, others don’t do nearly enough.

Make sure that you’re in the middle.

General rules? If your data is shared among multiple rows, keep it in the same table if the change in one shouldn’t affect the other rows. However, if the change in one should affect the other rows, the data goes to another table.

 

 

Common Mistake 6. Not Using Appropriate Indexes

As with normalization, make sure that you are using the appropriate amount of indexes. You can run query analysis typical errors in a database help you decide how many indexes are needed. You may also check server performance to see how locking indexes affect it.

Outside of testing this, some general guidelines are that:

1. Foreign keys should have an index

2. WHERE fields should have an index

 

 

Common Mistake 7. Hard Deletes

If you’re anything like me, you delete something only to realize down the line that you need it. Retrieving it is suddenly crucial to saving several hours of your day.

Sound familiar?

That’s exactly why you should perform soft deletes. With soft deletes, you’re marking a row as inactive, and can retrieve it at a later time. With a hard delete, you’ll spend hours searching through transaction logs but soft deletes save you time.

 

Common Mistake 8. Using Exclusive Arcs Incorrectly

Exclusive arcs add greater complexity, which often lead to database development issues. As a result, you should only use exclusive arcs in certain cases, and in those typical errors in a database, arcs can typical errors in a database be used in these circumstances:

1.  If one relationship in the arc provides the primary key, and each of the other possible relationships can as well

2. If the relationships in the arc all have the same optionality

3. If the arc is being used for only one entity

4. If the relationship is only in one arc

 

 

If you push yourself to better documentation and naming standards, typical errors in a database, as well as remember the simple rules surrounding appropriate indexes, exclusive arcs, normalization, fatal error cannot redeclare geoip_country_code_by_name primary keys, you’ll be in great typical errors in a database. You’ll avoid all of those time consuming mistakes that make you wish that you were anything but a database developer.

 

What other mistakes are made during database development? How do you avoid them? Let us know in the comments section, or join the conversation on Facebook, Twitter, and LinkedIn.

 

Thanks to youngthousands for the use of their respective photographs.

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