Guide to PostgreSQL Database Design

Written by Team Timescale

The design of a database is more than just an architectural choice—it's a creative process that can make or break the efficiency and security of an organization's data systems. Engineers tasked with database design face a variety of challenges, from optimizing data storage to ensuring swift and secure access to critical information. 

By understanding fundamental concepts and best practices in database design, professionals can craft solutions that meet technical requirements and support the long-term scalability and reliability of a company’s data infrastructure.

PostgreSQL offers numerous features that help developers design efficient databases. In this guide, we will explore the core concepts of database design in PostgreSQL, walking through the principles of relational databases, table structures, partitioning, data compression, and optimizing time-series data. 

PostgreSQL: Relational Database Design

A relational database, like PostgreSQL, organizes data into tables where relationships between different data entities are established through keys. These keys help developers maintain data integrity, avoid redundancy, and create an efficient data structure that can support queries and analyses.

Relational database structure 

Relational databases store data using a table-based structure. Each table represents an entity, such as a customer, product, or transaction. Tables are connected via relationships, often established through primary and foreign keys. This setup ensures that data remains organized, eliminating redundancy and maintaining consistency.

Example

Consider two tables: a clients table and a contacts table. 

CREATE TABLE clients (     client_id SERIAL PRIMARY KEY,     name VARCHAR(100),     industry VARCHAR(50) );Copy

CREATE TABLE contacts (     contact_id SERIAL PRIMARY KEY,     client_id INT REFERENCES clients(client_id),     email VARCHAR(100),     phone VARCHAR(15) ); Copy

+------------+       +------------+ |  Clients   |       |  Contacts  | +------------+       +------------+ | client_id  |<------| client_id  | | name       |       | contact_id | | industry   |       | email      | |            |       | phone      | +------------+       +------------+

Each client in the clients table has a unique ID. The contacts table contains information on client contacts, and each contact entry is associated with a client through a client ID (foreign key). This allows for clear and structured relationships between the data, enabling efficient queries.

Key principles in relational structure design

• Clear table definitions: tables should represent real-world entities or concepts, and the data in each table should be relevant to that entity.

• Logical relationships: relationships between tables should reflect meaningful connections between entities, helping structure data logically.

• Well-defined purpose: when creating a new table or relationship, ensure it serves a defined purpose, either for queries or for future data organization.

Choosing Your Tables

One of the most critical decisions when designing a database is determining what tables to create and how to structure them.

Key considerations

Use case

What is the data being used for? What kinds of analyses will be performed on it? For example, an organization that needs to analyze customer behaviors may structure tables around customer demographics, purchase history, and interaction records.

Data source

Understanding how data enters the system is important for deciding table structures. If the data comes from different sources and needs transformation, it may be worth designing tables to capture raw data before transforming it into relational structures.

Update frequency

Tables that receive frequent updates, like log tables or real-time data, can degrade in performance over time, especially when they grow large. Choosing the right table size and partitioning strategy is essential for maintaining query performance.

Table size decisions

Should you break a large table into smaller ones?

Consider breaking up tables if smaller, more manageable sets of data can speed up query performance. However, the decomposition should create meaningful tables. Avoid unnecessary fragmentation, as it can complicate queries without significant performance gains.

Access patterns

Different types of queries work better with different table sizes. For instance:

• Complex searches may perform better on smaller tables.

• Frequent lookups through indexed queries may benefit from larger tables.

Optimize table design based on how critical speed and performance are for your application.

Table partitioning in PostgreSQL

For large tables, PostgreSQL offers partitioning as a solution to maintain performance while keeping tables manageable. Partitioning in PostgreSQL divides a table into smaller, more manageable pieces called partitions, each of which stores a subset of the data. This method significantly improves performance by ensuring queries only scan the relevant partition rather than the entire table.

Why partitioning?

• Query performance: partitioning improves query speed by limiting the scope of data scanned in any given query.

• Maintenance: smaller partitions are easier to maintain. Indexing, vacuuming, and backups are more efficient when data is divided into smaller sets.

However, partitioning is not a one-size-fits-all solution. Too many partitions can reduce performance, as it adds complexity to query planning.

Example

Suppose you have a table with time-series data that spreads over the course of a year. Your typical query might need to retrieve data from a particular month and might look like this: 

Now, notice that as the application tries to retrieve data for the month of August, the database needs to query the full table with all 12 months of the year. Depending on the amount of data in the table, this could be a very inefficient operation. 

Conversely, what if you partition this table into four quarters? Something like: 

    -- Step 1: Create the main partitioned table    CREATE TABLE IF NOT EXISTS transactions (         transaction_id SERIAL PRIMARY KEY,         transaction_date DATE NOT NULL,         amount NUMERIC(10, 2) NOT NULL,         description TEXT     ) PARTITION BY RANGE (transaction_date);     -- Step 2: Create partitions for each quarter of 2024     -- Q1 2024     CREATE TABLE IF NOT EXISTS txns_2024_q1     PARTITION OF transactions     FOR VALUES FROM (''2024-01-01'') TO (''2024-04-01'');     -- Q2 2024     CREATE TABLE IF NOT EXISTS txns_2024_q2     PARTITION OF transactions     FOR VALUES FROM (''2024-04-01'') TO (''2024-07-01'');Copy

    -- Q3 2024     CREATE TABLE IF NOT EXISTS txns_2024_q3     PARTITION OF transactions     FOR VALUES FROM (''2024-07-01'') TO (''2024-10-01'');     -- Q4 2024     CREATE TABLE IF NOT EXISTS txns_2024_q4     PARTITION OF transactions     FOR VALUES FROM (''2024-10-01'') TO (''2025-01-01''); Copy

With these partitions in place, the operation would look something like: 

With the partitioned table, the query is directed straight to the Q3 partition. With a much smaller data set to query, the retrieval is much more efficient. 

Choosing between partitioning and restructuring

• Partitioning is a great way to improve performance in large, frequently queried tables without overhauling the database’s overall design.

• Restructuring: This may be necessary when a table's underlying design doesn't align with usage patterns. Restructuring, however, requires a deeper redesign of how data is stored and accessed, which could be a more complex solution but offers scalability advantages.

PostgreSQL documentation has more details on partitioning. 

Normalization and denormalization

In database design, normalization is the process of organizing data to minimize redundancy and improve data integrity. This is achieved by structuring data across multiple tables and ensuring each table contains only relevant information. Normalization, typically guided by standard forms (e.g., 1NF, 2NF, 3NF), reduces anomalies during data insertion, updating, and deletion, ensuring consistency across the database.

1NF (First Normal Form)

A table is in 1NF if:

• All columns contain atomic (indivisible) values. No column should have sets, lists, or multiple values.

• Each column contains values of a single type.

• Each row is unique, typically enforced by a primary key.

Example

A table with a list of orders might look like this:

In this case, the Items column violates 1NF because it contains multiple values. To bring it to 1NF, you'd split these into separate rows:

2NF (Second Normal Form)

A table is in 2NF if:

• It is already in 1NF.

• All non-key attributes (columns not part of the primary key) are fully dependent on the primary key, not just part of it.

This usually becomes relevant when dealing with composite primary keys (keys made up of more than one column). If a column depends only on part of the composite key, it violates 2NF.

Example

Consider a table where the primary key consists of both OrderID and ItemID:

Here, Customer depends only on OrderID, not ItemID. To satisfy 2NF, you’d separate the customer information into its own table:

Orders Table:

Order Items Table:

3NF (Third Normal Form)

A table is in 3NF if:

• It is already in 2NF.

• There are no transitive dependencies—in other words, non-key columns must not depend on other non-key columns.

Example

Let’s say we have the following table:

Here, CustomerCity depends on CustomerName, which violates 3NF because CustomerCity is indirectly dependent on the OrderID through CustomerName. To fix this, separate customer details into a new table:

Customers Table:

Orders Table:

Denormalization

However, in some scenarios, especially when query performance is critical, denormalization can be applied. Denormalization reintroduces redundancy by combining data from related tables into a single table, reducing the need for complex joins. This approach can speed up read-heavy operations, especially in analytical databases or systems designed for real-time data retrieval.

When deciding between normalization and denormalization, it's essential to weigh the trade-offs between data integrity and query performance. While normalization ensures cleaner, more consistent data, denormalization can be beneficial for applications where speed is prioritized over strict data consistency.

Indexing strategies

Indexes are crucial for enhancing query performance in PostgreSQL, allowing the database to quickly locate the required data without scanning entire tables. Think of indexes like a table of contents for a book, allowing you to flip to the chapter you want to read quickly. 

Several indexing strategies can be employed depending on the query patterns and data structure.

• B-tree Indexes: Balanced tree indexes are the default and most commonly used indexes, and they are optimal for range queries.

• Hash Indexes: Suitable for equality comparisons, hash indexes can be beneficial in scenarios where B-tree indexes are overkill.

• GIN (Generalized Inverted Index): Perfect for columns containing multiple values like arrays or JSONB, GIN indexes excel at handling non-primitive data types.

• BRIN (Block Range INdexes): Designed for large, append-only tables (such as time-series data), BRIN indexes store a summary of data block ranges rather than individual rows, making them ideal for time-based queries.

The key to effective indexing lies in understanding your application's query patterns. Over-indexing can lead to performance degradation during insertions or updates, as each index must be maintained. Therefore, it's essential to apply indexes strategically to balance read and write performance.

Primary and foreign key contraints

Primary keys and foreign keys are essential for maintaining data integrity in relational databases. A primary key uniquely identifies each row in a table, ensuring that no two rows have the same key. This guarantees that the table’s data is well-structured and easily retrievable.

Foreign keys, on the other hand, establish relationships between tables. They enforce referential integrity by ensuring that a value in one table corresponds to a valid entry in another. 

Example

Let’s use the example of the clients table and the contacts table shared above. 

CREATE TABLE clients (     client_id SERIAL PRIMARY KEY,     name VARCHAR(100),     industry VARCHAR(50) ); Copy

CREATE TABLE contacts (     contact_id SERIAL PRIMARY KEY,     client_id INT REFERENCES clients(client_id),     email VARCHAR(100),     phone VARCHAR(15) ); Copy

+------------+       +------------+ |  Clients   |       |  Contacts  | +------------+       +------------+ | client_id  |<------| client_id  | | name       |       | contact_id | | industry   |       | email      | |            |       | phone      | +------------+       +------------+ Copy

A client_id in the contacts table must match an existing client_id in the clients table. This ensures that data remains consistent across related tables. client_id here is a foreign key. 

When designing a relational database, using primary and foreign key constraints effectively prevents orphaned records and maintains a structured, cohesive data architecture. PostgreSQL offers cascading options for foreign keys, allowing actions such as delete or update to propagate across related tables, further reinforcing integrity.

Data integrity and scalability

Maintaining data integrity while ensuring scalability is a fundamental challenge in database design. PostgreSQL provides several mechanisms to ensure that data remains accurate and consistent as databases grow.

• Constraints: These include not just primary and foreign keys but also unique, not null, and check constraints. Together, they enforce rules that prevent invalid data from being entered into the database.

• Transactions and ACID Compliance: PostgreSQL's support for atomic, consistent, isolated, and durable (ACID) transactions ensures that operations are either fully completed or rolled back, safeguarding data integrity during concurrent operations.

As databases scale, ensuring both performance and data integrity becomes increasingly important. Strategies such as sharding (distributing data across multiple nodes) and replication (creating copies of data across servers) are essential for supporting larger databases. However, these come with their own challenges, especially in maintaining consistency across distributed systems. PostgreSQL's logical replication and parallel query execution features help mitigate these challenges, ensuring databases can scale without compromising integrity.

Data Compression and Retention Policies

Efficient database design also requires considering how long it takes to retain data and whether or not it should be compressed. Data compression can save storage and optimize performance, especially for historical data that is no longer frequently accessed.

Data retention policy

Organizations often need to decide when to archive or delete data that is no longer relevant to their analyses. Simply keeping all data indefinitely can lead to increased storage costs and degraded performance.

Key principles in data retention

Data usage

How often is the data needed for analysis? For instance, historical sales data may be crucial for long-term trend analysis but not necessary for daily operations.

Data relevancy windows

Data should have defined periods of relevance. After that window closes, consider compressing or archiving it.

Automated data removal

For some systems, deleting data after a set period of inactivity or non-usage may be the best way to free up resources.

PostgreSQL data compression

PostgreSQL supports a variety of compression techniques, allowing developers to save space and maintain system performance. For example, when working with a table that stores millions of rows of sensor data, compression can significantly reduce the amount of disk space used.

Let’s say you have a table in PostgreSQL that stores temperature readings from different sensors, with one reading every minute:

CREATE TABLE sensor_data (     sensor_id INT,     timestamp TIMESTAMPTZ,     temperature DECIMAL(5, 2) );Copy

Over time, this table will accumulate millions of rows. Without compression, these rows take up a lot of space, which can slow down queries and increase storage costs. This is where PostgreSQL’s compression features and extensions like TimescaleDB come in.

For example, TimescaleDB allows you to compress this table. Here’s how it might look:

SELECT add_compression_policy('sensor_data', INTERVAL '7 days'); Copy

This command sets up a policy that automatically compresses data older than seven (7) days, reducing storage usage for historical data. Compression reduces disk space (between 90-95 % in most cases) while improving query performance.

To further optimize storage, Timescale Cloud, a mature PostgreSQL cloud platform with TimescaleDB at its core, also offers tiered storage, which allows you to move historical data to a cheaper, albeit slower, storage tier in Amazon S3. Tiering means you can keep recent data on faster, more expensive storage while archiving older data that remains fully queryable via SQL. Timescale recently introduced improvements to its tiered data storage architecture, boosting query performance on tiered data by 400x.

You can read more details about how data compression works in PostgreSQL and TimescaleDB in this guide from Timescale. 

Leveraging Data Structure for Performance

The structure of your data and how it’s stored plays a significant role in your database’s performance.

Time-series data optimization

Time-series data—data points tracked at successive times (such as stock prices or sensor data)—can benefit from specific design optimizations. This data is unique in that each record has a timestamp, allowing for the implementation of time-based partitioning and compression strategies.

PostgreSQL and Timescale

Timescale offers specific features to optimize time-series data, such as hypertables. These are automatically partitioned tables based on time intervals, allowing PostgreSQL to efficiently manage very large datasets. Additionally, Timescale's tiered storage and time-based retention policies are specifically designed to manage time-series data effectively.

Leveraging hypertables

Unlike traditional partitioning, hypertables dynamically manage partitions (or "chunks") in the background, ensuring efficient query execution across vast amounts of time-series data. When a query targets a specific time range, only the relevant chunks are accessed, dramatically reducing query time and resource usage.

By leveraging hypertables, developers can scale their PostgreSQL databases horizontally without worrying about manual partitioning. This is particularly beneficial in scenarios like IoT applications or financial systems where data accumulates rapidly.

Conclusion

PostgreSQL is a versatile and powerful database management system, and its flexibility makes it an excellent choice for application developers, even those new to database design. However, designing an efficient database requires a solid understanding of relational structures, table management, partitioning strategies, and data retention policies.

With the tools and principles discussed in this guide, you can now make key database design decisions in PostgreSQL. As your data needs evolve, you can further refine your design by leveraging advanced features such as table partitioning and data compression, ensuring your database remains scalable and high-performing.

If you’re looking for more detailed guidance or specific tools, Timescale offers a wealth of resources and extensions designed to help you manage your PostgreSQL databases effectively.

Further reading:

• Designing Your Database Schema: Wide vs. Narrow Postgres Tables

• An Intro to Data Modeling on PostgreSQL