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Given the popularity of PostgreSQL, chances are that the role you’re applying for will require some PostgreSQL fluency. To help you during the interview process, this article will provide an overview of the most common PostgreSQL questions and answers for all levels.
Interviews normally go from more basic, general questions to more complex ones. The level of seniority also matters, meaning that interview questions will be overall more difficult if you’re applying for a more advanced role.
Finally, it’s important to keep it short when answering the questions. It’s better to just get to the point rather than divagate, for it may result in new questions from the interviewer where you may not feel as comfortable. Don’t worry: you will have plenty of time to demonstrate your skills throughout the interview as well as later stages of the application process.
Let’s get started!
PostgreSQL is a lightweight, free, and open-source relational database management system. PostgreSQL is used widely across regions and companies and can be used in most popular operating systems.
PostgreSQL excels among other SQL databases for several reasons, including:
PostgreSQL allows you to:
CRUD (Create, Read, Update, Delete) operations are the basic operations in any SQL database system, including PostgreSQL. CRUD operations are frequently used in database design and management.
As in other SQL dialects, the database is a structured storage space where the data is kept in many tables and organized so that the necessary information can be easily fetched, manipulated, and summarized.
pgAdmin is a handy utility that comes with the PostgreSQL installation, and it lets you do regular database-related tasks through a nice graphical interface.
PostgreSQL uses the standard CREATE DATABASE command to create new databases. But the devil is in the details. There is so much to consider when creating a database, that’s why we have prepared our Creating PostgreSQL Databases Course to get you covered.
PostgreSQL uses the standard INSERT INTO statement to add data to your SQL table.
To delete a database in PostgreSQL, use the DROP DATABASE command.
A database schema contains the logical and visual configuration of the entire relational database. In PostgreSQL, it includes the tables, along with the data types, views, indexes, sequences, constraints, and functions.
You can retrieve that data using the following query:
SELECT * FROM customers LIMIT 5;
Joins are used to combine and retrieve records from two or multiple tables. PostgreSQL uses standard SQL joins to perform these kinds of operations.
A primary key is used to identify a row uniquely in a table. Primary keys may be made of one column or multiple columns. A primary key can be classified as a type of constraint.
Yes, there are many packages available that allow you to use PostgreSQL through Python. Psycopg is one of the most popular Python libraries for PostgreSQL. Curious about how to use PostgreSQL through Python? Read our separate article Using PostgreSQL in Python Tutorial: Create, Connect, and Manage Databases.
A foreign key provides shared keys between two or more tables, whereas a primary key allows only unique and strictly non-null values. Both are considered types of constraints.
PostgreSQL providers for the following constraints:
As of January 2024, the latest version is PostgreSQL 16.
Yes, PostgreSQL is compatible and can be run on most popular cloud providers, including AWS, Azure, and Google Cloud.
PostgreSQL provides support to a procedural language known as PL/Python. This allows you to write SQL functions and procedures using Python.
Multi-version Concurrency Control or MVCC is an advanced technique in PostgreSQL. It enhances database performance in multi-user scenarios. This means that while querying a database each transaction sees a snapshot of data (a database version) as it was some time ago, regardless of the current state of the underlying data. This protects the transaction from viewing inconsistent data that could be caused by (other) concurrent transaction updates on the same data rows.
Put simply, MVCC creates multiple versions of a single database record, enabling various transactions to access different versions of one database record without conflicting with one another, thereby allowing simultaneous transitions.
While PostgreSQL provides unlimited database size, there is a maximum size for tables, which is set to 32 TB.
The main types of operators available in PostgreSQL are:
An index is a special data structure related to a table and used for storing its important parts and enabling faster data search and retrieval. This is achieved by creating a sorted data structure that allows PostgreSQL to locate rows more quickly compared to a full table scan. Indexes are especially efficient for large databases, where they significantly enhance query performance.
It’s the process of splitting a large table into smaller pieces. It can be done through several methods, including range partitioning, list partitioning, and hash partitioning.
Transactions in PostgreSQL are expected to be atomic, consistent, isolated, and durable. These properties are commonly referred to by the acronym, ACID.
Write-ahead logging is a technique used to ensure the data integrity of PostgreSQL databases. Write-ahead logging ensures that any changes and actions in the database are logged in a transaction log before the updating or modification of the database. In case of a database crash, this helps in providing the log of the database changes.
In PostgreSQL, the main joins are:
Functions, also known as Stored Procedures, allow you to wrap in a single procedure several operations that would normally take several queries. That can be reused anytime, thereby helping users save time. To know more about PostgreSQL functions, check out our Functions for Manipulating Data in PostgreSQL Course
Views represent the result of a query to one or more underlying tables. Views are used to simplify complex queries since these queries are defined once in the view, and can then be directly queried via the same.
Database normalization is a process by which databases and tables are created or modified to address inefficiencies associated with data storage, data modification, or querying processes. In simple terms, normalization involves multiple steps to reduce data redundancy and complexity.
A trigger, also known as a callback function, is a specification that the database should automatically execute a particular function whenever a certain type of operation is performed. Triggers can be defined to execute either before or after any INSERT, UPDATE, or DELETE operation, either once per modified row, or once per SQL statement. If a trigger event occurs, the trigger's function is called at the appropriate time to handle the event. Triggers help ensure data integrity during the modification of a database.
Making a backup of your database in PostgreSQL is fairly simple. There are various methods to perform a backup in PostgreSQL, including:
The pg_dump method allows you to create a text file with a set of SQL commands that, when run in a PostgreSQL server, will recreate the database in the same state as it was at the moment of the dump.
To delete a table in PostgreSQL, use the DROP TABLE command, followed by the name of the table. To ensure that any other object is associated with it, you will need to add the CASCADE command.
Partitioning allows to divide a table into smaller, more manageable partitions, which translate into increased query performance. It’s particularly suitable when dealing with big tables.
There are two main ways to remove the rows in a table: the DELETE command and the TRUNCATE command. The former is designed to remove rows more selectively and requires a full scan of the tables. The latter quickly removes all the rows and empty disk space without requiring a table scan. Therefore, the TRUNCATE command is the most suitable.
There are 3 main commands to control transactions in PostgreSQL:
PostgreSQL is one of the most secure SQL databases. Security is addressed on several levels:
When dealing with time-series data, the most effective strategy is range partitioning, which allows the breaking of tables based on a fixed range of values. In this case, the most likely partition would be based on days.
Parallel querying is a technique in PostgreSQL that allows you to create query plans that can leverage multiple CPUs to answer queries more efficiently. This technique is particularly well-suited when the query involves scanning a lot of data but returning a few rows, for example, aggregate calculations.
There are multiple strategies to increase query performance, including:
Especially when dealing with large databases, improving your query performance is crucial to avoid undesired bottlenecks. Check out our Improving Query Performance in PostgreSQL Course to become a query wizard.
The EXPLAIN command shows you the execution plan of a SQL statement. This includes the manner the table(s) referenced in your statement will be processed, the underlying algorithms that will be used for complex operations, such as joins, as well as the estimated execution time.
If, in addition to the estimated time, you want to know the actual time required, you can add the ANALYZE command, and the statement will be actually executed, not only planned.
Overall, The EXPLAIN ANALYZE command is particularly handy to spot bottleneck in complex queries so you can rewrite them to improve query performance
There are two main ways to address errors in PostgreSQL:
PostgreSQL logs are a valuable resource for troubleshooting problems, tracking performance, and auditing database activity. PostgreSQL comes with a wide variety of logs, including error logs. These logs can help you spot queries and statements that lead to errors during execution.
Having a sound knowledge of PostgreSQL will be crucial to pass the interview. But keep in mind that interviews also provide an opportunity to test other skills beyond the technical ones. In the end, the success of a data science project relies not only on the code, but also on the people that write the code.
That’s why proving solid soft skills, such as problem-solving approach, communication, and teamwork in technical roles, will significantly increase your chances to get the position.
Hence, be ready to face behavioral questions, like how to deal with deadlines, bad vibes in the team, and demanding clients. These scenarios are not only present in the everyday life of data professionals, but employees of any sector. That’s why your previous working experiences, even if they are not data-related, can also help you answer these questions. Just take the time to reply and be yourself: there’s no clear good-or-bad answer.
A thorough preparation of your PostgreSQL interview is crucial to pass this stage in the application process. We have already mentioned the importance of having a solid background in PostgreSQL, but other kinds of questions are likely to arise during the interview, including non-technical ones.
Because of the high degree of uncertainty associated with interviews, this stage can be stressful. To help you crack the interview, we have prepared a few tips and tricks.
Remember, industries differ. Working as a data professional in different domains may differ quite a lot. A biotech company is different from a cloud service provider. Spend some time understanding the specifics of the company’s domain and show the company that you want to learn. However, fundamentally, anyone works with the data, and the data is approachable in similar ways, no matter the industry.
As Andrew Reiskind, CDO at Mastercard, explained on a DataFramed podcast episode:
An interesting exercise we went through earlier last year was finding out where our priorities were. Our priorities are still very much about data quality. The AI basically consumes much more data, much faster, with less human intervention. And so, the quality of the data, the understandability of the data, has become that much more important. It’s this feedback loop that is just strengthening the need of just getting the basics right to feed the AI so that we know what the quality metrics are relative to completeness, to accuracy, that is mission critical.
Andrew Reiskind, CDO at Mastercard
Congratulations for making it to the end of this guide. We hope this list of frequently asked PostgreSQL questions will help you get ready for the interview and feel more confident.
If you feel that you need more PostgreSQL training to better prepare for an interview, DataCamp is here. Check out the following courses and dedicated materials:
I am a freelance data analyst, collaborating with companies and organisations worldwide in data science projects. I am also a data science instructor with 2+ experience. I regularly write data-science-related articles in English and Spanish, some of which have been published on established websites such as DataCamp, Towards Data Science and Analytics Vidhya As a data scientist with a background in political science and law, my goal is to work at the interplay of public policy, law and technology, leveraging the power of ideas to advance innovative solutions and narratives that can help us address urgent challenges, namely the climate crisis. I consider myself a self-taught person, a constant learner, and a firm supporter of multidisciplinary. It is never too late to learn new things.
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