6 Common Postgres Beginner Mistakes and Best Practices

“Anyone who has never made a mistake has never tried something new.” - Albert Einstein

Postgres' popularity is steadily increasing. It is the most popular open-source relational database available, and with almost 40 years of development, it is an excellent choice for applications of all sizes. However, starting with Postgres can feel like climbing a mountain, and just like learning anything new, you will undoubtedly make mistakes. Although mistakes are a normal part of the learning experience, they can be time-consuming and difficult to debug. So why not avoid them in the first place?

In this article, you'll learn about the most common mistakes beginners make when starting with Postgres. You'll see why these mistakes happen, how to avoid them, and techniques to help you write queries correctly. You'll also get actionable advice and practical tips to build confidence and develop better habits for database management. Learning from others' experiences can save you valuable time and frustration, so let's get started!

Common Beginner Mistakes

Here are the six common PostgreSQL mistakes beginners should avoid to maintain efficient and secure database environments.

1. Not Understanding What VACUUM Is or When to Use it

Understanding and using VACUUM accurately is important for maintaining a healthy and performant PostgreSQL database. VACUUM is a powerful command that reclaims storage occupied by dead tuples (dead rows). When a vacuum process runs, it marks the space occupied by dead tuples as reusable by other tuples.

When you delete records from a Postgres database table, Postgres does not immediately remove the rows from the database; these are just marked as deleted. The previous version of the record still remains in the data file. This is the same with updates; each update of a row creates a new version of that row. This is called table bloat. These dead spaces are empty rows that occupy unused disk space in the data file and remain present until a vacuum is done.

Many beginners are unaware that they need to run the VACUUM command to clean up dead tuples, which results in bloated databases and slower performance.

It is necessary to run the VACCUM command periodically, especially on frequently updated tables. Regularly reclaiming dead space can improve query performance, reduce disk space usage, decrease disk I/O by reducing table bloat, and ensure your database runs smoothly.

To identify tables requiring vacuuming, run the following code:

SELECT relname, n_dead_tup  
FROM pg_stat_all_tables  
WHERE n_dead_tup > 0;  

Note that despite being necessary for a healthy database, VACUUM can have damaging effects when misused. For instance, using the VACUUM FULL command on the production database can lock it for some time.

2. Forgetting to Close Connections

Every time you open a connection, either to fetch or update data, it takes time and utilizes resources like memory and CPU. If these connections are not closed properly after use, they remain open and idle, consuming system resources and eventually running out of the database’s connection limit. This is called a connection leak and can result in errors and downtimes.

One common cause of poor database performance is idle connections. Most developers new to Postgres think that open connections are just idle connections that are not doing anything, but this is incorrect because they’re consuming server resources.

When a connection is not closed, it can trigger an increase in resource consumption, lock tables or rows, and even stop the execution of other queries. This can lead to degradation in database performance over time or even crash your application.

To prevent connection leaks, you must ensure that every connection opened is correctly closed after use. You can do this manually in your code or by using connection pooling tools like PgBouncer, which manage connections efficiently and prevent resource exhaustion.

Here's a sample Python code snippet to properly handle PostgreSQL connections and avoid connection leaks:

import psycopg2

conn = psycopg2.connect("dbname=test user=postgres password=secret")
try:
    cur = conn.cursor()
    cur.execute("SELECT * FROM users")
    print(cur.fetchall())
finally:
    cur.close()
    conn.close()

3. Writing Inefficient Queries

If you’re working with a large number of rows from a big table, you need to be careful while writing queries because it can be tricky. An unoptimized query may scan the entire table or touch significantly more rows than necessary.

Inefficient queries are often the result of poorly written SQL, missing indexes, or a lack of understanding of how PostgreSQL executes queries. When a query hits too many rows, it drives up disk I/O and uses up more CPU and memory (thus decreasing available resources for other queries). Eventually, this will limit the overall performance of your database and your application.

Wrong queries are common among beginners. For example, SELECT * without WHERE or INNER or joining two large tables without proper indexes causes PostgreSQL to scan and process more data than necessary, leading to performance issues.

Here are some useful tips for writing better-optimized queries:

• Use Indexes: Indexes are one of the most effective ways to speed up queries. They allow PostgreSQL to quickly locate rows without scanning the entire table. For example, if you frequently query a user table by email, create an index on the email column:

CREATE INDEX idx_users_email ON users(email);

• Avoid SELECT *: Fetching all columns when you only need a few can increase the amount of data processed and transferred. This is called the wildcard frenzy. The wildcard might seem enticing, as it displays all data at once, but this approach is not recommended. It’s like going through a lot of things you don’t need to find what you’re looking for, and that’s not efficient.

The best way to query data is to be specific. Rather than returning all of the data from the database and filtering post-query, ask for the data columns you actually need. This makes queries faster and your results clearer.
Rather than writing:

SELECT * FROM users WHERE email = '[e-mail]';

Instead, specify only the columns you need:

SELECT id, name FROM users WHERE email = '[e-mail]';

• Use LIMIT: When working with small tables, you may not need to worry about the number of returned rows. However, when working with more oversized tables, LIMIT is beneficial for improving query performance. You can achieve that by using the LIMIT command.

SELECT * FROM users LIMIT 10

4. Forgetting to Add Primary Keys, Impacting Data Integrity

Whenever you create tables in your Postgres database, define primary keys. Without a primary key, the table lacks a unique identifier for each row, making it impossible to enforce data integrity and leading to problems such as duplicate rows. Over time, this can lead to inconsistent data, ruptured relationships, and unreliable query results.

Most novices skip creating a primary key for a table, either because they are unaware of its necessity or they mistakenly think Postgres will provide some unique enforcement on its own. However, this assumption can lead to serious problems, especially as your database grows and becomes more complex.

To avoid these problems, continually assign a primary key for each table. This will help you maintain the integrity of your data, ensure good performance in your queries, and have consistent relationships in your database.

Here are some best practices to prevent data integrity issues emerging from missing primary keys:

• Always Define a Primary Key: Include a primary key that uniquely identifies each row when defining a table. For example, in an orders table, you can use order_id as the primary key:

CREATE TABLE orders (
order_id SERIAL PRIMARY KEY,
customer_id INT NOT NULL,
order_date DATE NOT NULL
);

• Use Composite Keys: Sometimes, using a single column isn’t sufficient to uniquely identify a row. In such cases, use a composite key (a combination of columns) as the primary key. For example, in an order_items table, you might use both order_id and product_id:

CREATE TABLE order_items (
order_id INT NOT NULL,
product_id INT NOT NULL,
quantity INT NOT NULL,
PRIMARY KEY (order_id, product_id)
);

• Add Primary Keys to Existing Tables: If you have already created a table without a primary key, you can add one later using the ALTER TABLE command. For example:

ALTER TABLE orders ADD PRIMARY KEY (order_id);

5. Overcomplicating Schema Design

A database schema is any structure that you define around the data. This includes views, tables, relationships, fields, indexes, functions, and other elements. Without any of these, getting lost in a database is easy.

Most beginners are tempted to design a database schema with hundreds of tables, complex relationships, and constraints to guarantee flexibility and scalability. This practice often leads to an overcomplicated schema design that is difficult to maintain and inefficient to query. Overcomplicating schema design usually results in reduced performance and an increased developer learning curve.

When designing your schema, it is essential to maintain a balance between normalization and simplicity. Although data normalization reduces redundancy and improves data integrity, it can also increase complexity. For example, splitting a single logical entity into multiple tables or adding too many foreign key relationships can make queries harder to write and slower to execute.

To avoid complicating the schema too much, consider simplicity and practicality. Start with a base design that fulfills your immediate needs and expand it as your application grows. Keeping your schema clean and intuitive will make it easier to maintain and query your database.

Here are some helpful tips for creating less complex schemas:

• Start Simple: Begin with a minimal schema that addresses your immediate needs. Avoid adding tables or columns that you don’t currently require. For example, if you’re building a user table, start with basic fields like id, name, and email:

CREATE TABLE users (
id SERIAL PRIMARY KEY,
name TEXT NOT NULL,
email TEXT UNIQUE NOT NULL
);

• Normalize Thoughtfully: Normalization is important, but don’t overdo it. For example, splitting a user table into user_profiles, user_emails, and user_addresses might seem like a good idea, but it can lead to unnecessary complexity. Instead, keep related data together unless there’s a compelling reason to separate it.
• Avoid Overusing Foreign Keys: While foreign keys are essential for maintaining relationships between tables, using them excessively can complicate your schema. For example, creating a separate table for every one-to-many relationship can result in too many joins. Instead, consider embedding related data directly in a table when appropriate.

6. Overlooking the Importance of Backups

Data loss is a nightmare scenario, whether caused by a malicious attack, human error, or a software bug. One of the common mistakes that most developers new to Postgres or working with databases make is overlooking the importance of backups, and the consequences could be devastating.

The most critical aspects of database management are implementing and regularly maintaining a reliable backup strategy. When disaster hits, and it ultimately will, you'll be incredibly grateful that you took the time to plan ahead and back up your valuable data.

There are different ways to backup your data, including using:

• pg_dump, a command-line utility for backing up PostgreSQL databases. It can generate logical backups (SQL scripts) or physical backups (binary replicas of the data files). Logical backups are usually more flexible, while physical backups are faster to restore. Here’s an example of how to use it:

# Logical backup (plain SQL script)
pg_dump -U username -h hostname -p port database_name > backup.sql

# Physical backup (directory format)
pg_dump -U username -h hostname -p port -Fc database_name > backup.dump

• Third-party tools like Barman or pgBackups provide advanced backup and recovery capabilities for PostgreSQL. These tools make managing backups easier and offer granularity in the backup process.
• A managed PostgreSQL service (e.g., Amazon RDS, Google Cloud SQL, Azure Database for PostgreSQL, and Neon). One that is a beginner-friendly managed Postgres database is Neon. The following section will discuss how Neon simplifies database management for beginners, especially for backups and schema design.

Tackling Schema Changes, Autoscaling, and Backups the Smart Way

Managing a large database has a few drawbacks: schema upgrades will introduce downtime, backups will be problematic, and scaling will require a lot of hands-on effort. This can lead to insecurity, data loss, downtime, etc.

Neon helps mitigate such issues with a serverless PostgreSQL solution that prioritizes flexibility and resilience. One of the biggest challenges of working with databases is schema changes, especially with growing applications. Traditional database migrations may be risky and cause downtime or inconsistencies. Neon supports schema migration tools such as SQLAlchemy or Alembic, Flyway, Liquibase, and so on. This allows developers to perform structured migrations with minimal or no user interruption.

Another challenge of working with a database is ensuring the database scales efficiently as the workload fluctuates. Neon handles autoscaling to enable your database to withstand workload changes automatically without the need for manual intervention.

Additionally, manually maintaining data and recovering it quickly is a common database challenge. Neon has automatic backups and point-in-time recovery, so if anything goes wrong, like a failing migration or a mistake with a delete, you can quickly restore the data.

There are three main ways to perform backups in Neon:

1. Point-in-Time Restore (PITR): Point-in-time recovery allows you to restore your database to a specific moment in time. It requires backing up live database files and archiving the Write Ahead Log (WAL), which is the log of all the modifications happening on the database. Neon automatically backs up all of the data with Point-in-Time Restore (PITR), which helps users restore data to any specific point in time. Neon achieves this by retaining a history of all branches without explicitly taking backups. This is crucial for data integrity, guaranteeing your apps’ continuity in case of accidental data deletion and failed login attempts.
2. Manual Backup Using pg_dump: Like in regular PostgreSQL databases, Neon supports manual backup using pg_dump.
3. Automated Backups through GitHub Actions: Neon allows for planning automated remote backups (e.g., on Amazon S3) using GitHub Actions to back up sensitive data. You can read more about using GitHub Actions for automated backups here.

Each method serves different needs: PITR is best for quick recovery, pg_dump for full database snapshots, and GitHub Actions for automated external backups.

What’s next?

There are many mistakes that might make you feel discouraged when getting started with Postgres. Don't be; there are several resources and tools available to help you learn more about Postgres. One such tool is Neon, a serverless solution that helps reduce the performance impact of vacuuming, provides autoscaling, offers a branching feature for backups, and many more.

Remember that making mistakes is a major part of the learning process. Don’t be afraid to make mistakes; they help you improve.

Resources

For more information on the tools and concepts used in this guide, refer to the following resources:

• Entity Framework Core Documentation
• Neon Postgres
• Neon Branch Restore with Time Travel Assist