I'm designing a database model where there are agents that can have customers.
From a best practice standpoint, I'd like to know what is best relationship to use.
The thing is, a customer could be working with multiple agents. If we want to consider that the customer should be treated as if they are being worked from a different angle, is it best practice to design the model as a one to many relationship instead of a many to many?
In otherwords, if Agent A and Agent B are working with John Doe, should we treate John Doe as separate entities for each agent, even though the record of John Doe may be the same (think like contact details).
It sounds like you simply want a junction/association table with columns such as:
CustomerId
AgentId
You may also want dates, descriptions and other information describing the relationship being worked on.
You mean to have agent_customer table? Which has the auto increment ID as PK, agentID and customerID so one agent can have multiple customers and a customer can have multiple agents. Make sure to make agentID and customerID unique to avoid redundancy.
I was studying the article on Database Normalization 2NF in Wikipedia at Wikipedia, when I came across the last example, on tournament winners. There it says that because there is a transitive dependency in the table, it is not in 2NF, and further optimization is needed (like splitting it into further tables) to restore that to 3NF and eliminating chances of data corruption. Can anybody tell me what kinds of 'corruption' can creep in to "show the same person from being shown with different dates of birth on different records"?
Imagine once you insert this row:
Indiana Invitational 1998 Al Fredrickson 21 July 1975
and later you insert this (say, by mistake):
Des Moines Masters 1999 Al Fredrickson 20 June 1985
As you can see, the same person has 2 different birthdays in this table. What that Wikipedia article says is that the birthday depends on the person, which is not a primary key. This means that the person can repeat and if you don't pay attention, its dependent attributes (such as birth date) can change.
What you should do is to make another table with the person name as primary attribute and move its dependent data over to that table (such as the person's birth date). This way, you will avoid the redundant (name, birthday) tuples and prevent possible corruption (as well as save memory).
I have this table phonebook SQL Server 2005:
username(PK) Serial(PK) contact_name contact_adr contact_email contact_phone
bob 1 Steve 12 abc street steve#bb.com 1234
bob 2 John 34 xyz street john#bb.com 5345
bob 3 Mark 98 ggs street mark#bb.com 1234
patrick 4 lily 77 fgs street lily#bb.com 1234
patrick 5 mily 76 fgs street mily#bb.com 1234
von 8 jim 6767 jsd way jim#bb.com 4564
Now you can see the phonebook stores all contacts of same user together.
Storing this way has advantages which I can't avoid.
My question is:
If I have 100 million entries in the table for all users, will my future insertion in the above table be very expensive?
Since SQL Engine needs to find the actual location where to enter the data (I mean under which username)
I tested with 1 million rows, I don't see noticeable issues.
I am asking if anyone has this experience or suggestions for me?
Thanks
The approach that is optimal for an address book is a NOSQL hashed-table. There's no need for an index on the PK. The algorithm returns the "page" where the row identified by the PK can be found. The address book of the user is also stored with the user, as a denormalized relation. Insert overhead is negligible. Hashed-PK is optimized for insert/retrieval when the PK is known. Excellent for OLTP systems. Now if you want to do something like figure out who knows whom, so that a given user's contacts need to be related to the contacts of all other users, then you have a different can of worms. But a straightforward address-book application, where the contacts of a given user remain "private" to that user, then a hashed primary key system is superb.
One of the first principles in db design is data non-redundancy: your db table design doesn't comply to that principle as you have same data repeated many times. A resonable solution would be to create separate table for users, a separate table for contacts and a table for realationship between users and contacts.
It depends on the underlying database. Every implementation has something different under its sleeves.
But! Performance will almost definitely suffer if you use indexes on that table and you have many, many, many, many rows inside of it.
First of all, username doesn't seem to be a primary key for your table by itself. You will probably have to use it in combination with another field if you want it to work. At this point, I would rather use your serial column as primary key, and have an index on username to answer the query get bob's contacts efficiently.
You insert will certainly become slower as your table grow. But I don't think it will be too slow to avoid following this approach.
You can't force the data to be stored together. Are you re-sequencing the Serial upon an insert? How are you ensuring the data is "stored together"?
If you mean putting all this data in one table, then it really depends on your index structure. The more indexes on the table, the more processing that takes place on very insert. Since user tables are usually heavily queried and rarely inserted (relatively), they are usually indexed heavily, in which case inserts can be slow. The answer, as with almost every DB question is: "It depends".
Pawnshop business model:
CLIENTES (customer table), LOTES (lot table), ARTICULOS (item table) and TRANSACCIONES (transaction table).
The reason I defined a lot table is because when customers pawn or sell items, the pawnshop groups all these items into one lot, calculates the total loan or purchase amount, stores these values under one transaction and prints the ticket with a description of all the items and total amount. So I want the ability to say, if customer defaults on interest payments or does not redeem pawn, then customer forfeits all items and pawnshop may choose to sell some items to gold refinery and/or transfer other non-gold items to inventory to sell to the public. In other words, I want the ability to do a break-out explosions of each item.
Would the above ER be adequate for this capability?
From the point of view of a logical model, you probably don't want store_id on the lot (as it comes from the customer) or the transactions or articles (as they get it through the lot and customer). At the physical level you might have those as attributes (called denormalisation), you have the risk of data showing, for example, LOT 1234 being on CUSTOMER C12 and at STORE S1, while the customer table has C12 being at store S2.
Of course it is possible that you allow Mr Smith to pawn an item at one store but make payments on it at another. Or perhaps an item might be pawned at one store but physically relocated to a different one for security or space reasons. If so, then it is appropriate to have distinct store ids on these entities.
However that doesn't sit comfortably with the 'store' being an attribute of the customer, since that implies they have a relationship with only one store.
Also consider what happens if MR P BROKER has three stores, but decides to close one and move the business to one of the others. You need to merge the stores but do you update the store id on all the transactions and articles and lots (including ones that are 'in progress' and those redeemed) or do you leave them with the original store id ?
Another common data modelling issue is identifying customers. Is Mr Smith one customer and Mrs Smith another, or can Mr and Mrs Smith be 'parts' of the same customer ? If Mr Smith pawns something, can Mrs Smith redeem it ? I'm thinking family squabbles, disputed heirlooms.... Perhaps she can't redeem it, but can make payments on it.
If an item (eg a watch) is included in one lot, then redeemed, then included in a different lot, does it get a different item_id ?
When a client buys an article offered to the general public, is that a transaction? Or does your database only track transactions about lots?
Can an item exist in your system without being part of any lot? You can't express that fact in the ER model you've presented.
Your ER model doesn't show any many to many relationships. That makes me suspicious. I've never worked in a pawnshop, so I can't say for sure. But every other enterprise database I've ever seen has at least one many-to-many relationship. Sometimes a relationship is treated as though it were an entity, and appears with a box of its own. But that box would be on the "infinity" end of more than one relationship, something I don't see in your diagram.
Buena suerte.
I understand the concept of database normalization, but always have a hard time explaining it in plain English - especially for a job interview. I have read the wikipedia post, but still find it hard to explain the concept to non-developers. "Design a database in a way not to get duplicated data" is the first thing that comes to mind.
Does anyone has a nice way to explain the concept of database normalization in plain English? And what are some nice examples to show the differences between first, second and third normal forms?
Say you go to a job interview and the person asks: Explain the concept of normalization and how would go about designing a normalized database.
What key points are the interviewers looking for?
Well, if I had to explain it to my wife it would have been something like that:
The main idea is to avoid duplication of large data.
Let's take a look at a list of people and the country they came from. Instead of holding the name of the country which can be as long as "Bosnia & Herzegovina" for every person, we simply hold a number that references a table of countries. So instead of holding 100 "Bosnia & Herzegovina"s, we hold 100 #45. Now in the future, as often happens with Balkan countries, they split to two countries: Bosnia and Herzegovina, I will have to change it only in one place. well, sort of.
Now, to explain 2NF, I would have changed the example, and let's assume that we hold the list of countries every person visited.
Instead of holding a table like:
Person CountryVisited AnotherInformation D.O.B.
Faruz USA Blah Blah 1/1/2000
Faruz Canada Blah Blah 1/1/2000
I would have created three tables, one table with the list of countries, one table with the list of persons and another table to connect them both. That gives me the most freedom I can get changing person's information or country information. This enables me to "remove duplicate rows" as normalization expects.
One-to-many relationships should be represented as two separate tables connected by a foreign key. If you try to shove a logical one-to-many relationship into a single table, then you are violating normalization which leads to dangerous problems.
Say you have a database of your friends and their cats. Since a person may have more than one cat, we have a one-to-many relationship between persons and cats. This calls for two tables:
Friends
Id | Name | Address
-------------------------
1 | John | The Road 1
2 | Bob | The Belltower
Cats
Id | Name | OwnerId
---------------------
1 | Kitty | 1
2 | Edgar | 2
3 | Howard | 2
(Cats.OwnerId is a foreign key to Friends.Id)
The above design is fully normalized and conforms to all known normalization levels.
But say I had tried to represent the above information in a single table like this:
Friends and cats
Id | Name | Address | CatName
-----------------------------------
1 | John | The Road 1 | Kitty
2 | Bob | The Belltower | Edgar
3 | Bob | The Belltower | Howard
(This is the kind of design I might have made if I was used to Excel-sheets but not relational databases.)
A single-table approach forces me to repeat some information if I want the data to be consistent. The problem with this design is that some facts, like the information that Bob's address is "The belltower" is repeated twice, which is redundant, and makes it difficult to query and change data and (the worst) possible to introduce logical inconsistencies.
Eg. if Bob moves I have to make sure I change the address in both rows. If Bob gets another cat, I have to be sure to repeat the name and address exactly as typed in the other two rows. E.g. if I make a typo in Bob's address in one of the rows, suddenly the database has inconsistent information about where Bob lives. The un-normalized database cannot prevent the introduction of inconsistent and self-contradictory data, and hence the database is not reliable. This is clearly not acceptable.
Normalization cannot prevent you from entering wrong data. What normalization prevents is the possibility of inconsistent data.
It is important to note that normalization depends on business decisions. If you have a customer database, and you decide to only record a single address per customer, then the table design (#CustomerID, CustomerName, CustomerAddress) is fine. If however you decide that you allow each customer to register more than one address, then the same table design is not normalized, because you now have a one-to-many relationship between customer and address. Therefore you cannot just look at a database to determine if it is normalized, you have to understand the business model behind the database.
This is what I ask interviewees:
Why don't we use a single table for an application instead of using multiple tables ?
The answer is ofcourse normalization. As already said, its to avoid redundancy and there by update anomalies.
This is not a thorough explanation, but one goal of normalization is to allow for growth without awkwardness.
For example, if you've got a user table, and every user is going to have one and only one phone number, it's fine to have a phonenumber column in that table.
However, if each user is going to have a variable number of phone numbers, it would be awkward to have columns like phonenumber1, phonenumber2, etc. This is for two reasons:
If your columns go up to phonenumber3 and someone needs to add a fourth number, you have to add a column to the table.
For all the users with fewer than 3 phone numbers, there are empty columns on their rows.
Instead, you'd want to have a phonenumber table, where each row contains a phone number and a foreign key reference to which row in the user table it belongs to. No blank columns are needed, and each user can have as few or many phone numbers as necessary.
One side point to note about normalization: A fully normalized database is space efficient, but is not necessarily the most time efficient arrangement of data depending on use patterns.
Skipping around to multiple tables to look up all the pieces of info from their denormalized locations takes time. In high load situations (millions of rows per second flying around, thousands of concurrent clients, like say credit card transaction processing) where time is more valuable than storage space, appropriately denormalized tables can give better response times than fully normalized tables.
For more info on this, look for SQL books written by Ken Henderson.
I would say that normalization is like keeping notes to do things efficiently, so to speak:
If you had a note that said you had to
go shopping for ice cream without
normalization, you would then have
another note, saying you have to go
shopping for ice cream, just one in
each pocket.
Now, In real life, you would never do
this, so why do it in a database?
For the designing and implementing part, thats when you can move back to "the lingo" and keep it away from layman terms, but I suppose you could simplify. You would say what you needed to at first, and then when normalization comes into it, you say you'll make sure of the following:
There must be no repeating groups of information within a table
No table should contain data that is not functionally dependent on that tables primary key
For 3NF I like Bill Kent's take on it: Every non-key attribute must provide a fact about the key, the whole key, and nothing but the key.
I think it may be more impressive if you speak of denormalization as well, and the fact that you cannot always have the best structure AND be in normal forms.
Normalization is a set of rules that used to design tables that connected through relationships.
It helps in avoiding repetitive entries, reducing required storage space, preventing the need to restructure existing tables to accommodate new data, increasing speed of queries.
First Normal Form: Data should be broken up in the smallest units. Tables should not contain repetitive groups of columns. Each row is identified with one or more primary key.
For example, There is a column named 'Name' in a 'Custom' table, it should be broken to 'First Name' and 'Last Name'. Also, 'Custom' should have a column named 'CustiomID' to identify a particular custom.
Second Normal Form: Each non-key column should be directly related to the entire primary key.
For example, if a 'Custom' table has a column named 'City', the city should has a separate table with primary key and city name defined, in the 'Custom' table, replace the 'City' column with 'CityID' and make 'CityID' the foreign key in the tale.
Third normal form: Each non-key column should not depend on other non-key columns.
For example, In an order table, the column 'Total' is dependent on 'Unit price' and 'quantity', so the 'Total' column should be removed.
I teach normalization in my Access courses and break it down a few ways.
After discussing the precursors to storyboarding or planning out the database, I then delve into normalization. I explain the rules like this:
Each field should contain the smallest meaningful value:
I write a name field on the board and then place a first name and last name in it like Bill Lumbergh. We then query the students and ask them what we will have problems with, when the first name and last name are all in one field. I use my name as an example, which is Jim Richards. If the students do not lead me down the road, then I yank their hand and take them with me. :) I tell them that my name is a tough name for some, because I have what some people would consider 2 first names and some people call me Richard. If you were trying to search for my last name then it is going to be harder for a normal person (without wildcards), because my last name is buried at the end of the field. I also tell them that they will have problems with easily sorting the field by last name, because again my last name is buried at the end.
I then let them know that meaningful is based upon the audience who is going to be using the database as well. We, at our job will not need a separate field for apartment or suite number if we are storing people's addresses, but shipping companies like UPS or FEDEX might need it separated out to easily pull up the apartment or suite of where they need to go when they are on the road and running from delivery to delivery. So it is not meaningful to us, but it is definitely meaningful to them.
Avoiding Blanks:
I use an analogy to explain to them why they should avoid blanks. I tell them that Access and most databases do not store blanks like Excel does. Excel does not care if you have nothing typed out in the cell and will not increase the file size, but Access will reserve that space until that point in time that you will actually use the field. So even if it is blank, then it will still be using up space and explain to them that it also slows their searches down as well.
The analogy I use is empty shoe boxes in the closet. If you have shoe boxes in the closet and you are looking for a pair of shoes, you will need to open up and look in each of the boxes for a pair of shoes. If there are empty shoe boxes, then you are just wasting space in the closet and also wasting time when you need to look through them for that certain pair of shoes.
Avoiding redundancy in data:
I show them a table that has lots of repeated values for customer information and then tell them that we want to avoid duplicates, because I have sausage fingers and will mistype in values if I have to type in the same thing over and over again. This “fat-fingering” of data will lead to my queries not finding the correct data. We instead, will break the data out into a separate table and create a relationship using a primary and foreign key field. This way we are saving space because we are not typing the customer's name, address, etc multiple times and instead are just using the customer's ID number in a field for the customer. We then will discuss drop-down lists/combo boxes/lookup lists or whatever else Microsoft wants to name them later on. :) You as a user will not want to look up and type out the customer's number each time in that customer field, so we will setup a drop-down list that will give you a list of customer, where you can select their name and it will fill in the customer’s ID for you. This will be a 1-to-many relationship, whereas 1 customer will have many different orders.
Avoiding repeated groups of fields:
I demonstrate this when talking about many-to-many relationships. First, I draw 2 tables, 1 that will hold employee information and 1 that will hold project information. The tables are laid similar to this.
(Table1)
tblEmployees
* EmployeeID
First
Last
(Other Fields)….
Project1
Project2
Project3
Etc.
**********************************
(Table2)
tblProjects
* ProjectNum
ProjectName
StartDate
EndDate
…..
I explain to them that this would not be a good way of establishing a relationship between an employee and all of the projects that they work on. First, if we have a new employee, then they will not have any projects, so we will be wasting all of those fields, second if an employee has been here a long time then they might have worked on 300 projects, so we would have to include 300 project fields. Those people that are new and only have 1 project will have 299 wasted project fields. This design is also flawed because I will have to search in each of the project fields to find all of the people that have worked on a certain project, because that project number could be in any of the project fields.
I covered a fair amount of the basic concepts. Let me know if you have other questions or need help with clarfication/ breaking it down in plain English. The wiki page did not read as plain English and might be daunting for some.
I've read the wiki links on normalization many times but I have found a better overview of normalization from this article. It is a simple easy to understand explanation of normalization up to fourth normal form. Give it a read!
Preview:
What is Normalization?
Normalization is the process of
efficiently organizing data in a
database. There are two goals of the
normalization process: eliminating
redundant data (for example, storing
the same data in more than one table)
and ensuring data dependencies make
sense (only storing related data in a
table). Both of these are worthy goals
as they reduce the amount of space a
database consumes and ensure that data
is logically stored.
http://databases.about.com/od/specificproducts/a/normalization.htm
Database normalization is a formal process of designing your database to eliminate redundant data. The design consists of:
planning what information the database will store
outlining what information users will request from it
documenting the assumptions for review
Use a data-dictionary or some other metadata representation to verify the design.
The biggest problem with normalization is that you end up with multiple tables representing what is conceptually a single item, such as a user profile. Don't worry about normalizing data in table that will have records inserted but not updated, such as history logs or financial transactions.
References
When not to Normalize your SQL Database
Database Design Basics
+1 for the analogy of talking to your wife. I find talking to anyone without a tech mind needs some ease into this type of conversation.
but...
To add to this conversation, there is the other side of the coin (which can be important when in an interview).
When normalizing, you have to watch how the databases are indexed and how the queries are written.
When in a truly normalized database, I have found that in situations it's been easier to write queries that are slow because of bad join operations, bad indexing on the tables, and plain bad design on the tables themselves.
Bluntly, it's easier to write bad queries in high level normalized tables.
I think for every application there is a middle ground. At some point you want the ease of getting everything out a few tables, without having to join to a ton of tables to get one data set.