Database Cardinality

When dealing with databases, there are several concepts to learn. Modeling and designing a database might seem difficult at first. The same can be said for writing, updating, and deleting the data from a database. But, what if there is more than one database?

DBMS are made of sets of data. RDBMS, to be even more specific, are made of relational sets. These sets are normally called tables. These tables, may correlate with each other. The relationship among these tables, as well as, the number of distinct records in these tables will determine the cardinality of a database.

This post will go deeply on the meaning of cardinality and how to design an efficient database with database cardinality in mind.

Introduction to Database Cardinality

Good data relationships and organization are important in developing goals, baselines, and benchmarks. Even though data is everywhere, organizations sometimes need specific data types for unique identification. Many types of data can be stored in databases, including simple and complex data. But regardless of the type and the source, the relationship between different data types is important.

In schools, for instance, students are usually given ID numbers for easier identification. In the same institutions, many students can belong to one faculty. Meanwhile, one tutor can be linked to many students. From this overview, it’s clear that several relationships can be formulated based on values and their uniqueness. This is what database cardinality is about.

In database design, cardinality refers to the uniqueness of data values stored in a column. It can also refer to the relationship between different tables. IT professionals such as database administrators use different notations to highlight cardinality relationships. This is integral in the design of the database and maintenance.

So, what are the different types of cardinality, and how can you find it? Besides that, what are the characteristics of cardinality values? This article offers a comprehensive overview of cardinality, including some of the frequently asked questions.

Cardinality in Mathematics

The term cardinality originates from relational algebra, a mathematical subfield. Its normally used to find the number of elements in a mathematics set. For instance, a set X= {a, b, c, d, e} has a cardinality of 5, i.e., for all the five elements found in it.

A set is a collection of elements, and these can be variables, symbols, shapes, and others. In math, a set is defined as a collection of numbers. So set A= {1, 2, 3} has a cardinality of three.

Cardinality in Databases

Cardinality and databases go hand in hand since the functions of relational databases are based on relational algebra. There are many relational algebra concepts used to implement the database engine.

To find the connection between math, relational databases, and relational algebra, it can be said that the equivalent of sets in relational algebra is a simple table in relational databases.

Generally, sets are used as containers to hold elements. A table stores elements in a container featuring rows. Tables are better than sets since they can store simple and complex elements. This is because a row can have stored information in multiple columns.

Generally, database cardinality is diverse and different from mathematical cardinality. That’s why the definition might be different in this case since the cardinality of relational databases doesn’t involve counting the number of elements in a set but rather finding the number of unique elements in a specific set.

Database cardinality focuses on columns because this is where you need to select the unique elements. Thus, each column will have its own different cardinality due to the different number of unique elements stored.

It’s also important to note that in math, the cardinality of a set aims to find a solution. In databases, cardinality is critical for tuning performance, therefore, making it easy to run queries.

Importance of Cardinality in Databases

Cardinality can help you identify database areas where the normalization process might have gone wrong. It helps to effectively create links from one entity or table to the other. This is usually done in a structured way. The whole process is critical for query execution- a sequence of steps that a user can take while trying to access and search for data stored in a database.

For instance, a bank can have one database table with a column listing all the regular clients. The institution can also have another database table that lists all their loans over a specific time duration. Thus, a relationship can be created between these two tables. This can be a one to one or one to many relationships, depending on what the banking institution wants to achieve. The initial goal of using cardinality, in this case, is to create a natural relationship between two tables.

Note that the way database queries are written isn’t the same way the commands are executed by the database engine. For starters, the engine needs to analyze the query’s syntax to determine the table and the columns that should be accessed.

After that, the data will be filtered, and if necessary, it will be grouped for easier accessibility. It’s clear that database query operations are normally done in steps, and SQL EXECUTION PLAN is responsible for determining the order of the query operations.

What happens is that the Execution Plan will search and find the optimal path that will quickly use minimal server resources and still return the appropriate data quickly. The efficiency of the Execution Plan is enhanced by data access operators.

Types of Database Cardinality / Mapping Cardinality/ Cardinality Ratios

There are three main types of cardinality that can be used in databases. This includes one to one, one to many, and many to many cardinality relationships. Below is an overview of each one of them and examples.

I.One-to-one relationship

One-to-one relationship (1:1) means that one entity instance only relates to one instance of another entity. If you are working with a database, then you might come across a 1:1 mapping in one row found in a specific table that’s connected to one row in another table.

For Example: A banking institution can use a 1:1 cardinality relationship in their customer database to show the connection between each customer and their national ID number. Generally, there is one ID per person, which means that the institution can use this data modeling concept to find dormant accounts, etc.

II.One-to-Many Relationship

This type of cardinality relationship, also written as (1: many), describes one instance of an entity relating to many instances in another entity. You can find this type of database cardinality if a row in a table is linked to several rows in another different table.

For example: a banking institution can use people’s addresses as an identifier. This means that each person that banks with them should have a house address. However, many people can live in the same house and share the same address. In this case, there is only one address that’s shared by multiple other users.

III.Many-to-Many Relationship

It’s also written as (M: M) relationship. So, there are many instances in one entity that relate to many other instances in another entity. So, several rows in a database table are mapped to several rows in a different table.

For Example: M: M can describe a relationship between customers and products. It’s possible for customers to purchase multiple products. On the other hand, many products can be bought by many customers.

IV. Many-to-One Relationship

It’s also written as (m:1), and it means there is more than one entity instance that forms a relationship with a single instance of another entity.

For Example: There can be many employees working for a single department, i.e. Many people are linked to only one department

The One-to-many and Many-to-one is basically the same thing with a different point of view. From the example above, imagine that a department can have many employees. I mentioning this Many-to-one, because many time developers get lost in the technicality of things when it does not matter.

Values in Cardinality

There are three main types of cardinality in columnar value sets: low cardinality, normal cardinality, and high cardinality.

I.Low Cardinality

These are columns that have few unique values and commonly feature Boolean values, status flags, and major classifications like gender. A low-cardinality data table column could be a STUDENT table with a column labeled NEW_STUDENT, for instance.

So, the NEW_STUDENT column will only consist of two distinct values, i.e., Y and N, which indicate if the student is new or not.

II.High-Cardinality

A high cardinality refers to those database columns with unique values. These values are usually identification numbers, user names, and email addresses. It could be a data table column for MEMBERS whose column name is MEMBER_ID. The unique values in such a column would be 1-n, and so when every new member is added, a new unique number would be created for them in this column for unique identification.

You can also have an EMPLOYEE column labeled EMPLOYEE gender. This column can have two types of unique values, i.e., Male or Female.

III.Normal Cardinality

It refers to database columns with uncommon values. Generally, columns with normal cardinality consist of information such as names or street addresses. For instance, a normal cardinality can have a CLIENT data table column labeled CLIENT_ADDRESS, which contains the clients’ home addresses.

In relational databases, low cardinality refers to a column whose data range is repeated many times. On the other hand, a high cardinality column will have a significantly high percentage of completely unique values.

It should be noted that high cardinality isn’t necessarily a bad thing since this helps database users to understand the complexity of the stored data. However, it usually makes it challenging to identify data patterns due to the many different values.

Higher cardinality provides a better-read performance, but its storage is less efficient.

The lower the cardinality, the more the database will have duplicated elements in the column. This way, it’s easier to identify patterns since a column with the lowest cardinality would most likely have the same value type in each row.

Cardinality’s Impact on a Relational Database

Initially, cardinality in a database looks like a simple concept that simply represents row numbers in a table. However, it’s clear that it’s much more than that and has a significant impact.

Therefore, it’s important for you to determine the right cardinality before designing your database. This is because its cardinality will directly impact your queries’ performance. Choosing the right cardinality will help you generate the right type of data model. Besides that, it will be easier to make queries, and this will consequentially lead to better performance. Additionally, you will use fewer resources and a higher level of efficiency.

Cardinality Constraints

Cardinality constraints highlight the number of an entity instance that associates with another entity’s instance. There are four main types of cardinality constraints, including:

I.Mandatory one – it’s mandatory to have one element

II.Mandatory many – it’s mandatory to have many elements

III.Optional one – there is only one optional element

IV.Optional many – there are many optional elements

In mandatory cardinality, the number of an element’s occurrences is managed using the properties – Max
Occurs and Min Occurs. Using these two properties, you can define an element as mandatory repeating or optional. It should be noted that a mandatory element usually has a minimum occurrence greater or equal to 1, i.e., Min Occurs >=.1. This means that a mandatory element should occur in an input message at least once.

When dealing with optional cardinality constraints, any instance of (many or one) entities might be used in relation to another entity instance, but this isn’t compulsory. An optional cardinality element has a minimal occurrence requirement of zero, i.e., Min Occurs = 0. Such an element can be completely removed from the input message.

But what’s the importance of cardinality constraints?

A constraint is an important restriction that allows relationship limitations based on the number of entity instances.

Example:

In cardinality, one table’s row and another table’s row are the same. If you decide to use mandatory cardinality, then you have only two options, mandatory one and mandatory many.

This can be translated to a CUSTOMER making One ORDER or a CUSTOMER making several ORDERS.

How to Find Cardinality?

In terms of database SQL statements, cardinality is simply the uniqueness of values. However, you can still calculate it in the following way:

Select distinct count (column_name) from’ Tablename;

In simple terms,

You need to first know the total rows found in your table and then:

Select count (x) from {Table}

Some database admins usually use cardinality to count database tables and values.

To determine the uniqueness of values stored in a database column and within an index, users normally use Index Cardinality. So:

Index Cardinality = the total number of distinct values for a specified column or column group.

What’s the Cardinality of a Set?

The standard definition of cardinality is based on the number of values in a set. How many are they? But in the context of databases that deal with a massive amount of data sets, this would mean the number of unique values in a column compared to the number of rows, all in one table.

A database’s cardinality is status is never expressed in the form of a number. At no point will you have to deal with values such as 1,23….10. Everything is different but straightforward and that’s why we talk about high, normal, and low cardinality.

Cardinality and Data Modeling

When designing a database, the initial stages usually involve data modeling phases. Data modeling is important for the creation of a simplified and logical database. This helps to reduce data storage requirements, eliminate redundancy, and enhance efficient data retrieval.

The combination of cardinality and data modeling makes things even better since this will produce high-quality data that can be stored in a structured and consistent format. As a result, businesses can easily attain consistent query results.

Once data has undergone modeling, it needs to pass through the logical modeling phase, where the logical entities are divided into several entities, which is usually based on the rules of principles of normalization.

In data modeling, a join cardinality between two tables helps form the numerical relationship between them. But for ease of classification and use, data elements in data modeling are usually grouped into data tables containing various data field name groups known as database attributes.

Generally, “key fields” are used to link different tables. It should be noted that a field assigned a “primary key” must be assigned to a special-order table.

Data modeling is important in the real world since data tends to change, and its growth can be rapid, leading to the accumulation of voluminous elements. So, keys must be used and linked to tables to facilitate easier data access and retrieval.

A poorly constructed data model will consume many resources and give the end-user an inappropriate response after a significant time delay.

Cardinality and Indexes

For performance purposes, indexes should be created based on cardinality of columns. When creating filtering clauses (join and where), we create them based on the relation of the primary and foreign keys. The primary key will always represent the highest cardinality in a table since they are unique in a table.

However, the foreign key, will not always represent that. When people say that Foreign key should always have an index associated with it. It may not always be true. The index should be based on the cardinality being joined. That will ensure faster results in the join. If we only use FKs for joins, then we are good, but if not, then no.

Also the order that we create an index based on cardinality matters very much. How may times have I see index that are created based on columns with the name type. Type, usually means only few different values are available. Those indexes may help a little, but not a whole lot, specially on skewed data.

Cardinality FAQs

Q: What’s Database Cardinality?

A: It’s a term that refers to data value uniqueness in a particular database table’s column. The higher the
cardinality, the fewer the number of duplicated elements in a specified column.

Q: What are the Types of Cardinality

There are four types of connections that can be made by cardinality:

• One to one: only one row in the first table connects to one row in the second table.
• One to many: one row in the first table connects to many rows in the second table.
• Many to one: many rows in the first table connect to one row in the second table.
• Many to many: many rows in the first table connect to many rows in the second table.

Q: What are the Four Main Types of Cardinality Constraints?

• Mandatory to have one
• Mandatory to have many
• Optional one
• Many options

Why is Database Cardinality Important?

It’s vital since it provides a structured way of linking one entity or table to the other. This impacts your
query execution as you search and try to access stored data in the database. Business administrators use
cardinality since the model helps them effectively and efficiently analyze customer information stored in the inventory.

What’s the Difference Between High and Low Cardinality?

Low-cardinality’s large data set quantity is highly likely to have the same entities or values. There are
many repeatable entities with a significantly low variety.
On the other hand, high cardinality has a data set with an incredibly large number of unique entities or
values. As a result, there is a significantly high level of diversity due to the minimal repetition.

How Can I Achieve Cardinality in a Database?

To achieve cardinality, every value or group of values should have a unique database identifier. So, each
value can have a cardinality relationship with values in other entities. This way, you can conveniently add
new values with distinct attributes and conveniently access them when needed. As a result, you can easily
keep multiple and frequently changing data sets optimally organized and distinct.

Is it Possible to Reduce Cardinality?

Yes. Sometimes this is required due to the “curse of dimensionality”. The reduction process usually
involves the use of an aggregation function. Throughout the whole process, you need to choose the right
threshold and then sort the different unique values found in the column based on their frequency, usually in descending order. For programmers, the whole process requires writing code.

What Should I Do If the Cardinality is High?

Sometimes, you don’t need to do anything since a high cardinality is still good in some instances.
Sometimes knowing that you are dealing with complex data is important since this can help you find certain issues uniquely tied to it.
But if you are experiencing reduced database performance or facing database stability issues, you might
need to lower the cardinality.
But before you lower the cardinality, ask yourself how unique you need the stored data to be.

Why is High Cardinality Expensive?

The number of unique tag combinations in a database determines the cost. So, the more the number, the
more expenses you will incur. This sometimes means that even adding a single vale whose attribute is still
unique will increase resource utilization and overall cost. If you are going to a column with a high
cardinality value, then be prepared for more expenses.

Is it Hard to Find Cardinality?

To most people, database cardinality is a complex topic and that’s why putting an effort to find cardinality
might be off the cards. However, the whole process is straightforward. Furthermore, it’s possible to repeat
the whole process for a finite set of elements.

Can the Order of Values in a Set Affect Cardinality?

No. the order of set values doesn’t have any impact on cardinality. You can opt to arrange your data in
any preferred order, and the cardinality will still be the same. However, you should understand that even
though different sets may have the same cardinality, this doesn’t mean that they are now equal.
This is because two sets can still have the same number of values, but they are different. The fact that their values aren’t identical means that they are different.

Should I Worry About Cardinality?

No, you don’t need to compute cardinality, and this is definitely good news. But if you are interested in
complex math, then you can learn how cardinality is usually computed – the set theory.
The most important aspect of database cardinality is understanding the relationship between different data sets. This is because data will have a direct effect on the stability and performance of the database.

But people generally try to avoid complex data with extremely high cardinality due to the high level of
uniqueness. Note that the more complex a data is, the more resources it needs. This makes it more expensive to write, store, or retrieve.

What’s the Difference Between Database Cardinality and Database Modality?

It’s easier to confuse these two modeling concepts since they are both used to analyze the relationship
between entities in database design. However, there is a notable difference between them.

Database cardinality refers to the maximum number of connections made between table rows. On the other hand, modality refers to the minimal number of connections between rows.

The common types of cardinality include many-to-many, one-to-one, and one-to-many. Meanwhile,
modality can be nullable or not nullable. A nullable column can accept an empty field while the converse is true.

What is the Most Common Cardinality?

One-to-many, i.e. (1: N) relationship. It’s commonly used since it helps to highlight the relationship
between most tables found in a database. This type of relationship means that one row in a table directly
relates to multiple rows in the other table.

Can Cardinality be Limited?

Yes, it’s possible to limit cardinality. Understand that as cardinality goes higher, the data volume also
increases. This leads to the need for more computing power and storage options to ensure that data can still be processed quickly and effectively.

However, resources are generally limited, and some organizations might not have the necessary tools
required to scale up the database to meet the growing demand.

This can result in the following limitations:

• Limits on the number of data dimensions you are allowed to add
• Increased costs
• Limited storage

What is an Entity-Relationship (ER) Diagram?

It’s a type of diagram used to represent the cardinality that’s found in a database visually. If you can get
across an ER diagram, it will be in the form of a flow chart with information regarding how different
database entities relate to each other.

Analysts can use ER diagrams to troubleshoot database problems, upgrade, or design the database system.

They enhance efficiency since they allow users to conveniently find data location and how the data connect.

What is a Cardinality Constraint?

It’s an important restriction normally established in database relationships. It’s important in limiting the
number of entity instances that are associated with relationships. It basically highlights the maximum
number or relationships that an entity can be involved in.

What are Some of the Factors that Determine Cardinality?

Your database’s cardinality is usually determined at the column. As a result, each column normally has a
different cardinality. This is because each column can have a different number of unique elements.

Will the Cardinality Rules Change in the Future?

Absolutely. Cardinality is now being used anywhere, including in object-oriented programming and
software engineering. But we are currently in the era of big data, which means that most organizations deal with large, complex data sets.

Therefore, the construction of efficient databases made with new advanced rules of cardinality is essential.
This will make it possible for database users to access and retrieve data faster. Besides that, it will make data storage more feasible.

Currently, there are systems created with sophisticated algorithms. They have made data accessibility easier as users can get cardinality estimation depending on the type of precision that’s more appealing from several data storage sites but without the need of introducing the data sets to one machine.

IBM has the Cognos software that uses relationship cardinality to:

• Eliminate the possibility of fact data double-counting
• Identifying query subjects that tend to behave as dimensions or facts
• Optimizing how users can access the underlying data-source system

IBM Cognos software can be used by different organizations to create and view business reports, monitor
metrics & events, and analyze data. This is important in making the best business decisions since it
integrates business intelligence activities.

Conclusion

The concept of cardinality in databases is usually complex for some people to understand. But from this
overview, it’s clear that cardinality can be used in the context of data models – to find the relationship
between two tables. This can result in the generation of one-to-one, one-to-many, many-to-one, and many-tomany relationships.

Additionally, it can be used in the context of database query optimization. This references the column’s
uniqueness in a database table. Columns with more unique values have higher cardinality, while those with more duplicate values lower cardinality. With such cardinality scores, you can optimize your database query.

What is next? This concept of cardinality goes hand to hand with Table relationships in RDBMS.