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Modern data management is built on relational databases. They structure information into tables, rows, and columns to establish clear relationships between different data points. This simplifies the process of accessing, managing, and analyzing data. From banking systems to e-commerce platforms and enterprise systems, relational databases power a vast array of applications.
With so many options available, choosing the right relational database for your project is crucial. The database you select can affect everything from performance to scalability and security. Different databases are fine-tuned for different needs, so you must understand the strengths and weaknesses of each to make an informed decision.
In this article, we'll compare two powerful relational databases, Postgres and VoltDB (aka Volt Active Data), to help you understand their key differences and choose the best fit for your data architecture.
Let’s start with brief overviews of both data platforms.
PostgreSQL, often referred to simply as Postgres, is an advanced open-source relational database management system (RDBMS). It was first developed in 1986 at the University of California, Berkeley, as part of the POSTGRES project led by Michael Stonebraker.
The goal was to create a database that could extend the relational model to support more complex data types, and Postgres has since evolved into one of the most feature-rich and reliable databases available today. PostgreSQL is released under the PostgreSQL License, a permissive open-source license that allows for both free and commercial use.
Volt Active Data (aka VoltDB) is an in-memory, distributed relational database designed for transactional workloads. It handles massive volumes of concurrent transactions with extremely low latency. It was co-founded by Michael Stonebraker, Sam Madden, and Daniel Abadi in 2009.
Unlike traditional disk-based databases, VoltDB stores data entirely in-memory, which leads to optimized read and write speeds. VoltDB is available under the GNU Affero General Public License (AGPL), an open-source license with specific requirements for commercial use.
In the following sections, we will compare the features of Postgres vs VoltDB across various categories.
Postgres follows a traditional disk-based architecture, where data is stored on disk and loaded into memory as needed. This approach allows Postgres to handle voluminous data, even when the available system memory is limited.
It uses a process-per-user connection model in which each client connection spawns a separate process on the server. This can be beneficial for isolating queries, but often requires extensive system resources as the number of connections increases.
Postgres supports a variety of indexing methods, including B-tree, hash, GiST, SP-GiST, GIN, and BRIN. It also uses a write-ahead logging (WAL) system to ensure data integrity and support recovery in case of a crash.
VoltDB, on the other hand, is designed with a modern in-memory architecture. All data in VoltDB is stored in RAM, which enables extremely fast data access and processing. This in-memory model allows VoltDB to handle millions of transactions per second with very low latency. However, it also requires orders of magnitude more RAM compared to traditional disk-based databases like Postgres.
VoltDB uses a single-threaded execution model, where all operations on a partitioned subset of the database are processed sequentially within a single thread. This eliminates the need for complex locking mechanisms and reduces contention.
When it comes to indexing, VoltDB supports only tree indexes. While its indexing capabilities are not as extensive as those of Postgres, the in-memory processing and partitioning help maintain high performance.
At its core, Postgres supports the traditional relational model with tables, rows, and columns, but it also goes beyond by offering extensive support for complex data types and structures. In addition to standard data types like integers, text, and dates, users can also store advanced types such as JSON, JSONB, XML, arrays, hstore (key-value pairs), and geometric data types.
The support for JSON and JSONB (a binary representation of JSON) is especially noteworthy, as it allows Postgres to handle semi-structured data, which increases its usability for applications that require both relational and document-based data models.
Postgres also offers native table inheritance, which allows developers to create tables that inherit columns from another table. This can be handy when you want to model hierarchical data or create specialized versions of a table without duplicating common attributes.
VoltDB's relational data model emphasizes simplicity and speed. It supports essential data types like integers, strings, timestamps, and geography points. However, its data type support is less comprehensive than Postgres.
For example, it doesn’t natively support the same level of JSON or semi-structured data handling as Postgres. With that said, it’s important to remember that VoltDB is designed to handle high-velocity data streams, so its data model is fine-tuned for scenarios where simple, high-speed transactions are more important than complex data structures.
Postgres scales vertically by default, meaning that you can increase its throughput by adding more resources (CPU, memory, storage) to a single server. Another way to boost Postgres performance is via optimization techniques like indexing, partitioning, and query tuning.
Postgres also offers horizontal scalability options, though they are relatively more complex to implement. For example, you can use the streaming replication feature to create read replicas that can handle read-heavy workloads, or you can use sharding to distribute portions of the database across multiple nodes.
VoltDB is designed from the ground up with horizontal scalability in mind. Its architecture naturally supports distributing data across multiple nodes, which makes it a breeze to handle high transaction volumes.
VoltDB also includes built-in support for fault tolerance and high availability. If a node fails, the system can continue operating using replicas on other nodes, which guarantees minimal downtime and data loss.
Postgres delivers solid performance for a wide range of workloads. With proper indexing, query optimization, configuration, and hardware resources, Postgres performs well in traditional OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing) scenarios.
Advanced features like parallel query execution play a part in speeding up the processing of large data sets. Additionally, the use of write-ahead logging (WAL) ensures that data integrity is maintained without sacrificing performance.
However, as a disk-based system, Postgres’s performance can be affected by disk I/O bottlenecks, particularly under heavy workloads. These issues can be mitigated via hardware improvements, like SSDs and RAID configurations.
VoltDB can process millions of transactions per second, far exceeding the performance capabilities of traditional disk-based databases like Postgres. In addition to transactions, VoltDB can also process analytical workloads in real time.
Its single-threaded execution model, combined with data partitioning, reduces the overhead associated with locking and concurrency control, which can slow down traditional databases. However, its in-memory nature means that it is more dependent on the available system memory. As the size of the data grows, more memory is required to maintain performance.
Additionally, while VoltDB’s performance is exceptional for transactional workloads, it is typically not as well-suited for traditional analytics or large-scale data warehousing compared to disk-based systems like Postgres.
PostgreSQL uses a robust concurrency control mechanism known as Multi-Version Concurrency Control (MVCC), which allows multiple transactions to read and write data concurrently without interfering with each other.
Postgres also supports ACID (Atomicity, Consistency, Isolation, Durability) transactions, which guarantees that either all database operations within a transaction are completed successfully, or none are.
VoltDB uses an optimistic concurrency control mechanism that relies on its single-threaded execution model within each partition. In other words, since transactions are processed sequentially within each partition, it eliminates the need for locking.
Like Postgres, VoltDB adheres to ACID principles to guarantee that transactions are processed reliably and consistently.
Postgres uses SQL (Structured Query Language) as its primary query language. It is packed with advanced SQL features such as Common Table Expressions (CTEs), window functions, recursive queries, and full-text search.
Additionally, it offers support for procedural languages like PL/pgSQL, PL/Tcl, PL/Perl, and PL/Python, which allow users to write custom functions and triggers within the database.
VoltDB supports a subset of SQL that is optimized for high-speed transaction processing. VoltDB’s SQL implementation includes support for basic SQL operations like SELECT, INSERT, UPDATE, and DELETE, as well as more advanced features like joins, aggregations, and groupings.
However, VoltDB’s SQL implementation is more streamlined compared to Postgres. For example, even though VoltDB supports basic joins and aggregations, it doesn’t offer the same level of complexity in query execution as Postgres.
Postgres offers robust security features, including roles and permissions, SSL/TLS encryption, and different authentication methods (e.g., password, Kerberos, LDAP). It also supports row-level security and data encryption at rest through third-party extensions.
Moreover, a Postgres installation includes built-in tools for backup and recovery, such as pg_dump, pg_restore, and point-in-time recovery (PITR) using WAL archives. It also supports third-party backup solutions.
VoltDB provides basic security features like role-based access control and SSL/TLS encryption. However, its security capabilities are relatively limited compared to Postgres, particularly in terms of fine-grained access control.
The voltadmin utility and the automated snapshot features can be leveraged to create and restore backups. However, compared to Postgres, VoltDB backup recovery can be more complex and may require coordination between nodes.
Postgres’s extensibility is one of its most significant strengths. Users can create custom data types to handle specific types of data that are not natively supported. They can also install extensions, which are packages that add new features or capabilities to the database. Popular extensions include PostGIS for spatial data, pg_trgm for trigram-based text search, hypoPG for hypothetical indexes, and pg_partman for automated partitioning.
VoltDB is also extensible, but its extensibility is limited as compared to Postgres. Users can create stored procedures in Java, which can be used to encapsulate complex operations and business logic. VoltDB also provides APIs for integration with external systems, allowing data to be imported into or exported from the database.
Next, let’s explore the different use cases in which each platform excels.
PostgreSQL is one of the most popular open-source databases, with a global user base contributing to its growth and improvement. The community actively participates in forums, mailing lists, and events such as PostgreSQL conferences and meetups. For professional or enterprise-level support, there are numerous commercial support providers and consulting services available.
Postgres has extensive and well-organized documentation that covers a wide range of topics, from basic installation to advanced features and configurations.
VoltDB has an active user base, but it is not as large or widespread as Postgres. The community includes users and developers who participate in forums, blogs, and occasional meetups, but the overall number of contributors and discussions is limited. The VoltDB company offers various levels of support packages, including technical support, training, and consulting services.
VoltDB’s documentation is solid. It covers installation, configuration, and usage. The official site is home to guides, reference materials, and tutorials to help users get started and make the most of the database. However, the depth and breadth of documentation is not as extensive as Postgres, given the smaller scope and focus of VoltDB’s features.
Finally, here’s a summary of the pros and cons of Postgres and VoltDB to help you determine the database that may be better suited for your use case.
Now that we have compared the two databases across different categories, and understood their pros and cons, it’s time to make an informed choice.
Go with Postgres if:
Go with VoltDB if:
Postgres and VoltDB are both dependable databases, each offering distinct features and catering to different use cases. This comparison has explored their architectural differences, performance characteristics, and suitability for various use cases. By understanding the nuances between the two databases, you can make an informed decision that aligns with your specific application requirements and infrastructure constraints.
Whichever platform you choose, remember to monitor its health and performance to ensure business continuity. Site24x7 offers dedicated monitoring tools for both Postgres and VoltDB.
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