The choice of a suitable database is essential for any organization, especially for data-intensive tasks, as it impacts the overall performance of different organizational workflows. Among the several available database options, developers prefer using DuckDB and PostgreSQL due to their notable features and applications.
DuckDB is an OLAP-oriented database optimized for analytical queries. It is lightweight, doesn’t have any external dependencies, and can be easily embedded in various web and mobile applications. In contrast, PostgreSQL is an ACID-compliant and highly functional database due to its support for numerous extensions.
A DuckDB vs Postgres comparison of performance, along with features such as storage model, query execution, and indexing, can help you make the right choice. Let’s get started!
What is DuckDB?
DuckDB is an open-source, embedded relational database management system. As an embedded database, you can directly integrate DuckDB into your applications. This facilitates high-speed data transfers between the database and the applications.
You can use DuckDB to effectively handle online analytical processing (OLAP) workloads, which typically involve processing large data volumes. The database system allows you to store data in a single database file and query it using SQL commands.
Among DuckDB’s main advantages are its columnar storage and vectorized query execution. Columnar storage facilitates organizing data in columns, while vectorized query execution allows data to be processed in batches, leading to faster query processing. As a result, DuckDB is often more efficient at handling analytical workloads than traditional row-format databases.
Key Features of DuckDB
- Simplicity: DuckDB is easy to install and has no external dependencies. Its source code is compiled into two files, a header and an implementation file, which simplifies the deployment process.
- SQL Support: DuckDB is used extensively by developers because of its support for SQL, an easy-to-use and versatile programming language.
- Portable: Due to the absence of dependencies, DuckDB is a highly portable database. You can deploy it on various operating systems, including Windows, macOS, and Linux. It is also suitable for various devices, such as resource-constrained edge devices and multi-terabyte memory servers.
- Extensible: DuckDB offers a flexible extension mechanism that helps you to expand the database’s capabilities. This allows you to define new data types, functions, file formats, and SQL syntaxes in DuckDB.
What is PostgreSQL?
PostgreSQL is an open-source and robust object-relational database system. It supports a wide range of data types (integer, boolean, and binary), as well as temporal types (time and date) and non-relational types (JSON). Postgres allows you to store such data in a row-oriented format.
For querying data in a Postgres table, you can use SQL functions. It supports type conversion, indexing, and full-text search to facilitate faster data retrieval and querying.
A notable feature of PostgreSQL is its support for parallel querying, which involves dividing tasks into smaller parts and executing them on multiple processors simultaneously. This increases the querying speed and scales up overall database performance.
Key Features of PostgreSQL
- Extensibility: You can enable various extensions in PostgreSQL to extend its functionality. Extensions, such as pgvector, allow you to use Postgres as a vector database, while PostGIS helps you manage geospatial data using a Postgres database.
- Data Replication: PostgreSQL supports both synchronous and asynchronous data replication to prevent data loss during system failures. Synchronous replication ensures low-latency data consistency. On the other hand, asynchronous replication is favorable for remote data replication where latency is not a concern.
- Robust Security: Postgres provides role-based access control and authentication mechanisms to protect your data from any unauthorized access. The authentication techniques in Postgres include trust-based, password-based, GSSAPI, and Kerberos.
Key Differences between DuckDB vs Postgres
Here are some key differences between DuckDB vs Postgres:
Data Storage
DuckDB supports a columnar data storage format, facilitating faster data retrieval. This also makes it efficient for managing analytical (OLAP) workloads that involve processing high-volume datasets.
On the other hand, PostgreSQL stores data in a row-based storage format, which is more useful in managing transactional (OLTP) workloads. Such operations involve frequent reading and writing of individual records.
Query Execution
You can query data in DuckDB through vectorized query execution. It is a query execution technique that enables you to divide your data into chunks called vectors for faster data processing.
Conversely, Postgres allows you to query data row-wise, which involves processing each row individually.
Index Types
DuckDB supports two built-in index types. One is the min-max or block range index, which is automatically created for columns of all general-purpose data types. The other is the adaptive radix tree (ART) index, which you can use for columns with UNIQUE or PRIMARY KEY constraints. You can also utilize the R-tree index for spatial data using the appropriate extensions.
In contrast, PostgreSQL supports several index types, such as B-tree, hash, GiST, SP-GiST, BRIN, and GIN.
Data Persistence
You can store data in the DuckDB database in either persistent mode on disk or in-memory mode, where you can store the complete dataset in the main memory.
Alternatively, PostgreSQL provides you with the Write-Ahead Logging (WAL) feature for data integrity. It facilitates the logging of all changes made to data records before these changes are applied to the data stored in the Postgres database. In the event of a system crash, you can recover the database using these logs.
Tabular Comparison Between Postgres vs DuckDB
Factors to Consider When Choosing DuckDB or PostgreSQL
From scalability and concurrency to use cases, here are some factors that can help you choose between DuckDB vs PostgreSQL:
Scalability
You can scale DuckDB vertically by increasing hardware resources to manage data volumes at a terabyte level.
Contrarily, you can scale Postgres vertically by adding more hardware resources. Horizontal scaling can be achieved in Postgres through partitioning and sharding methods.
Concurrency
DuckDB offers two configurable concurrency options. One is where a single process can both read and write into the database. For multiple user threads, DuckDB supports Multi-Version Concurrency Control (MVCC) and optimistic concurrency control. It is a single writer process and allows multiple users to make changes as long as there are no write conflicts. However, if two or more users try to edit the same data record simultaneously, optimistic concurrency control facilitates the execution of only one thread and generates errors for others. Another option is where multiple processes can read from the database while no processes can write.
Conversely, Postgres uses the MVCC model for concurrency. This enables multiple users to read and write simultaneously without locking data records. It is advantageous as read and write operations are not blocked because of concurrent access. You can remove the outdated copies using the VACUUM command.
Use Cases
As an embedded database, DuckDB is used in software applications for in-process analytics. You can also use it for feature engineering, exploratory data analysis, and building prototypes of machine learning models.
Alternatively, you can use the PostgreSQL database in business applications like ERP (Enterprise Resource Planning), CRM (Customer Relationship Management), and HR management systems. The PostGIS extension helps you to use Postgres in location-based services utilizing geospatial data. It is also used as a data warehousing solution to perform complex queries and analyze large-scale datasets.
Integrating Data in DuckDB and PostgreSQL Using Airbyte
After deciding whether you want to use DuckDB or PostgreSQL, you can integrate data from your source systems into these databases. For a simplified integration process, you can use Airbyte, a robust data integration platform.
Airbyte offers a vast library of 400+ connectors that help you extract data from the desired source systems and load it to DuckDB or PostgreSQL. Airbyte also supports PostgreSQL as a source connector, allowing you to load data from PostgreSQL to DuckDB.
Some of the important features of Airbyte are as follows:
- Flexibility to Develop Custom Connectors: You can build custom connectors in Airbyte with its offerings, such as Connector Builder, Low Code Connector Development Kit (CDK), Python CDK, and Java CDK.
- Change Data Capture (CDC): Airbyte’s CDC feature enables you to track incremental changes made to the source system and incorporate them into your target data system. This helps you to sync source and destination data systems to maintain data consistency.
- Automated Data Pipeline Setup with Terraform: Airbyte Terraform Provider facilitates management of Airbyte resources such as connections, sources, and destinations using Terraform. It is an IaC solution that enables you to define and provision infrastructure through code.
Conclusion
DuckDB and PostgreSQL are both high-performing databases with distinctive features and use cases. This blog highlights their capabilities through a detailed DuckDB vs PostgreSQL comparison.
The benefit of DuckDB is that you can embed it into various applications for in-process data analytics. On the contrary, you can utilize PostgreSQL in financial services, IoT devices, and geospatial applications.
The choice to use DuckDB or PostgreSQL depends on your specific use case and task objectives.