science

Unnormalized form

Unnormalized form is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Unnormalized form rather than just read about it. In short: In database normalization, unnormalized form (UNF or 0NF), also known as an unnormalized relation or non-first normal form (N1NF or NF2), is a database data model (organization of data in a database) which does not meet any of the conditions of database normalization defined by the relational model. Database systems which support unnormalized data are sometimes called non-relational or NoSQL databases.

Key takeaways

Unnormalized form belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Unnormalized form to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Unnormalized form from memory before moving on to harder problems.

Reference excerpt

In database normalization, unnormalized form (UNF or 0NF), also known as an unnormalized relation or non-first normal form (N1NF or NF2), is a database data model (organization of data in a database) which does not meet any of the conditions of database normalization defined by the relational model. Database systems which support unnormalized data are sometimes called non-relational or NoSQL databases. In the relational model, unnormalized relations can be considered the starting point for a process of normalization. "Unnormalized form" should not be confused with denormalization, where normalization is deliberately compromised for selected tables in a relational database.

History In 1970, E. F. Codd proposed the relational data model, now widely accepted as the standard data model. At that time, office automation was the major use of data storage systems, which resulted in the proposal of many UNF/NF2 data models like the Schek model, Jaeschke models (non-recursive and recursive algebra), and the nested table data model (NTD). IBM organized the first international workshop exclusively on this topic in 1987 which was held in Darmstadt, Germany. Moreover, a lot of research has been done and journals have been published to address the shortcomings of the relational model. Since the turn of the millennium, NoSQL databases have become popular owing to the demands of Web 2.0.

Relational form Normalization to first normal form requires the initial data to be viewed as relations. In database systems relations are represented as tables. The relation view implies some constraints on the tables:

No duplicate rows. In practice, this is ensured by defining one or more columns as primary keys. Rows do not have an intrinsic order. While tables have to be stored and presented in some order, this is unstable and implementation dependent. If a specific ordering needs to be represented, it has to be in the form of data, e.g. a "number" column. Columns have unique names within the same table. Each column has a domain (or data type) which defines the allowed values in the column. All rows in a table have the same set of columns. This definition does not preclude columns having sets or relations as values, e.g. nested tables. This is the major difference to first normal form. NoSQL databases like document databases typically do not conform to the relational view. For example, an JSON or XML database might support duplicate records and intrinsic ordering. Such database can be described as non-relational. But there are also database models which support the relational view, but do not embrace first normal form. Such models are called non-first normal form relations (abbreviated NFR, N1NF or NF2).

Example with a table valued column

This table represent a relation where one of the columns (Transactions) is itself relation-valued. This is a valid relation but does not conform to first normal form which does not allow nested relations. The table is therefore unnormalized.

Modern applications As of 2016, companies like Google, Amazon and Facebook deal with large amounts of data that are difficult to store efficiently. They use NoSQL databases, which are based on the principles of the unnormalized relational model, to deal with the storage issue. Some examples of NoSQL databases are MongoDB, Apache Cassandra and Redis.

See also Denormalization Normalization First normal form Second normal form Third normal form Boyce–Codd normal form NoSQL

References

Worked examples

Example 1 — a first encounter with Unnormalized form

Start with the simplest possible case. Write down what Unnormalized form claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Unnormalized form before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Unnormalized form ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Unnormalized form

In research: Unnormalized form appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Unnormalized form in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Unnormalized form is common in secondary-school and first-year university syllabi. It links to neighbouring topics Data modeling, Database normalization, so understanding it makes those chapters shorter.
In everyday life: Look for Unnormalized form outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Unnormalized form in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Unnormalized form means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Unnormalized form out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Unnormalized form in simple terms?

Why does Unnormalized form matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Unnormalized form?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Unnormalized form.