Tradeoffs between Normalization and Denormalization

4 minute read

TIL the difference between normalized and denormalized schemas for modeling data, and some of the tradeoffs with each.


Normalization is a way of defining your database schema in a way that is optimized for fast and high integrity writes by ensuring no redundant data across tables.

The standard for normalization is to shoot for 3rd normal form (3nf).

A brief recap of the criteria for achieving 3nf:

  • Records in a table should contain primary_keys that uniquely reference them (1nf)

  • There shouldn’t be any repeated columns in a table (1nf)

  • There shouldn’t be any functionally dependent keys (2nf)

  • There shouldn’t be any transitvely functionally dependent keys (3nf)

Since tables don’t contain any redundant data, the storage required for new records to any table is relatively small.

Since writes are small, they are also fast.

Writes are also guaranteed to leave database in a consistent state, due to referential integrity guarantees from foreign key constraints between related tables.


Denormalization is the act of adding redundancies or derived values in to your schema to optimize for reads that would otherwise be expensive in a normalized schema.

Think about a fully normalized database for a minute…

Everything is in its own table…

Everything has its own primary_key…

References between tables are maintained by foreign_key constraints…

… this is great from a storage and integrity perspective, but it can lead to pieces of a query being distributed across many tables, which leads to slow and complex joins to get the full picture for a query.

If you are able to anticipate the types of queries that your users might be making, it could make sense to store some values redundantly in your system to speed up query performance.

Adding denormalized values makes inserts and updates trickier: you need to ensure that denormalized values are properly maintained, since the integrity of these values are not automatically enforced by the schema.

The choice to denormalize should be made consciously. It should be documented, tested, and should be communicated across a team so all are aware of this additional consideration when writing to denormalized tables.

Normalized Example

I’m building an app called CashBackHero, which involves modeling relationships between credit_cards and users through an entity called a wallet. A 3nf normalized schema for these entities might look something like this:


id value
1 3
2 4
3 5


id name cash_back_id
1 Chase 2
2 Discover 1
3 Amex 3


id name
1 Brian’s Wallet
2 Alex’s Wallet
3 Lauren’s Wallet

CardWallets (associations between wallets and cards)

id wallet_id card_id
1 1 1
2 1 3
3 2 2
4 3 2
5 3 1

The tables above are normalized, since they do not contain any redundant data. Relationships between tables are maintained through foreign_key constraints to other tables. This stores data in its most compact form.

The lack of redundant data also optimizes for writes to the database. The most frequent writes are additions and removals to the CardWallets table, which just involves id columns which contain references to other information-containing tables.

Normalizing data also makes updates a breeze since each table is a single source of truth for the information contained within.

A normalized database also optimizes for some kinds of reads, like surfacing a list of all of the values in a particular table (like getting all of the cards).

Reads where data lies across multiple tables, though, become more challenging. A query to the normalized schema above to get the names and cash back values of cards for wallet 1 involves joining the Wallets and Cards tables to the CardWallets table, then joining the CashBackValue table to the Cards table. If these tables become large, queries could become slow when compared to a denormalized version where all of the data lives in one table.

Speaking of denormalized…

Denormalized Example

Check out what the normalized schema presented above would look like if it were fully denormalized:


id card_name wallet_name cash_back_value
1 Chase Brian’s Wallet 4
2 Amex Brian’s Wallet 5
3 Discover Alex’s Wallet 3
4 Discover Lauren’s Wallet 3
5 Chase Lauren’s Wallet 4

That’s one monster table!

Ok… it’s only 5 rows… but you get the idea that a denormalized table can often be easier to read from, since all of the data exists in one place.

Instead of needing to join 4 tables together to get the cards and cash_back_value for a user’s wallet, we can now just execute one select statement to the CashBackSchema table above.

Querying for unique cards becomes a bit slower, since the entire table needs to be read to determine unique card_names.

Updates are also a bit trickier: we need to ensure integrity of our data logically. For example, if we want to update the cash_back_value of the Chase card in Brian’s Wallet, we also need to consider if we need to update the cash_back_value of the Chase card in Lauren’s wallet as well. Updates need to be made in multiple places, which can be slow for large tables.

In a normalized schema, updates to something like “wallet_display_name” can just be made in one place (the Wallets table), instead of needing to comb through the entire CashBackSchema to ensure every row with the name “Brian’s Wallet” is updated appropriately.

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