Choosing the right database is a critical choice when building any software application. All databases have different strengths and weaknesses when it comes to performance, so deciding which database has the most benefits and the most minor downsides for your specific use case and data model is an important decision. Below you will find an overview of the key concepts, architecture, features, use cases, and pricing models of DuckDB and Rockset so you can quickly see how they compare against each other.

The primary purpose of this article is to compare how DuckDB and Rockset perform for workloads involving time series data, not for all possible use cases. Time series data typically presents a unique challenge in terms of database performance. This is due to the high volume of data being written and the query patterns to access that data. This article doesn’t intend to make the case for which database is better; it simply provides an overview of each database so you can make an informed decision.

DuckDB vs Rockset Breakdown


 
Database Model

Columnar database

Real time database

Architecture

DuckDB is intended for use as an embedded database and is primariliy focused on single node performance.

Rockset is a real-time analytics database built for modern cloud applications, designed to enable developers to create real-time, event-driven applications and run complex queries on structured, semi-structured, and unstructured data with low-latency. Rockset uses a cloud-native, distributed architecture that separates storage and compute, allowing for horizontal scalability and efficient resource utilization. Data is automatically indexed and served by a distributed, auto-scaled set of query processing nodes.

License

MIT

Closed source

Use Cases

Embedded analytics, Data Science, Data processing, ETL pipelines

Real-time analytics, event-driven applications, search and aggregations, personalized user experiences, IoT data analysis

Scalability

Embedded and single-node focused, with limited support for parallelism

Horizontally scalable with distributed storage and compute

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DuckDB Overview

DuckDB is an in-process SQL OLAP (Online Analytical Processing) database management system. It is designed to be simple, fast, and feature-rich. DuckDB can be used for processing and analyzing tabular datasets, such as CSV or Parquet files. It provides a rich SQL dialect with support for transactions, persistence, extensive SQL queries, and direct querying of Parquet and CSV files. DuckDB is built with a vectorized engine that is optimized for analytics and supports parallel query processing. It is designed to be easy to install and use, with no external dependencies and support for multiple programming languages.

Rockset Overview

Rockset is a real-time indexing database designed for fast, efficient querying of structured and semi-structured data. Founded in 2016 by former Facebook engineers, Rockset aims to provide a serverless search and analytics solution that enables users to build powerful applications and data-driven products without the complexities of traditional database management.


DuckDB for Time Series Data

DuckDB can be used effectively with time series data. It supports processing and analyzing tabular datasets, which can include time series data stored in CSV or Parquet files. With its optimized analytics engine and support for complex SQL queries, DuckDB can perform aggregations, joins, and other time series analysis operations efficiently. However, it’s important to note that DuckDB is not specifically designed for time series data management and may not have specialized features tailored for time series analysis like some dedicated time series databases.

Rockset for Time Series Data

Rockset’s real-time indexing and low-latency querying capabilities make it an excellent choice for time series data analysis. Its schemaless ingestion and support for complex data types enable effortless handling of time series data, while its Converged Index ensures efficient querying of both historical and real-time data. Rockset is particularly suitable for applications that demand real-time analytics, such as IoT monitoring and anomaly detection.


DuckDB Key Concepts

  • In-process: DuckDB operates in-process, meaning it runs within the same process as the application using it, without the need for a separate server.
  • OLAP: DuckDB is an OLAP database, which means it is optimized for analytical query processing.
  • Vectorized engine: DuckDB utilizes a vectorized engine that operates on batches of data, improving query performance.
  • Transactions: DuckDB supports transactional operations, ensuring the atomicity, consistency, isolation, and durability (ACID) properties of data operations.
  • SQL dialect: DuckDB provides a rich SQL dialect with advanced features such as arbitrary and nested correlated subqueries, window functions, collations, and support for complex types like arrays and structs

Rockset Key Concepts

  • Converged Index: Rockset uses a unique indexing approach that combines both an inverted index and a columnar index, allowing the database to optimize for both search and analytics use cases.
  • Schemaless Ingestion: Rockset automatically infers schema on ingestion, making it easy to work with semi-structured data formats like JSON.
  • Virtual Instances: Rockset uses the concept of virtual instances to provide isolation and resource allocation to different workloads, ensuring predictable performance.


DuckDB Architecture

DuckDB follows an in-process architecture, running within the same process as the application. It is a relational table-oriented database management system that supports SQL queries for producing analytical results. DuckDB is built using C++11 and is designed to have no external dependencies. It can be compiled as a single file, making it easy to install and integrate into applications.

Rockset Architecture

Rockset uses a cloud-native, serverless architecture that is built on top of a distributed, shared-nothing system. It is a NoSQL database, which allows for greater flexibility and scalability compared to traditional relational databases. The core components of Rockset’s architecture include the Ingestion Service, Storage Service, and Query Service. The Ingestion Service is responsible for ingesting data from various sources, while the Storage Service maintains the Converged Index. The Query Service processes queries and provides APIs for developers to interact with the database.

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DuckDB Features

Transactions and Persistence

DuckDB supports transactional operations, ensuring data integrity and durability. It allows for persistent storage of data between sessions.

Extensive SQL Support

DuckDB provides a rich SQL dialect with support for advanced query features, including correlated subqueries, window functions, and complex data types.

Direct Parquet & CSV Querying

DuckDB allows direct querying of Parquet and CSV files, enabling efficient analysis of data stored in these formats.

Fast Analytical Queries

DuckDB is designed to run analytical queries efficiently, thanks to its vectorized engine and optimization for analytics workloads.

Parallel Query Processing

DuckDB can process queries in parallel, taking advantage of multi-core processors to improve query performance.

Rockset Features

Serverless Scaling

Rockset automatically scales resources based on the workload, which means users don’t need to manage any infrastructure or capacity planning. ### Full-Text Search Rockset’s Converged Index supports full-text search, making it an ideal choice for applications that require advanced search capabilities. ### Integration with BI tools Rockset provides native integrations with popular business intelligence (BI) tools like Tableau, Looker, and Redash, allowing users to visualize and analyze their data without any additional setup.


DuckDB Use Cases

Processing and Storing Tabular Datasets

DuckDB is well-suited for scenarios where you need to process and store tabular datasets, such as data imported from CSV or Parquet files. It provides efficient storage and retrieval mechanisms for working with structured data.

Interactive Data Analysis

DuckDB is ideal for interactive data analysis tasks, particularly when dealing with large tables. It enables you to perform complex operations like joining and aggregating multiple large tables efficiently, allowing for rapid exploration and extraction of insights from your data.

Large Result Set Transfer to Client

When you need to transfer large result sets from the database to the client application, DuckDB can be a suitable choice. Its optimized query processing and efficient data transfer mechanisms enable fast and seamless retrieval of large amounts of data.

Rockset Use Cases

Real-Time Analytics

Rockset’s low-latency querying and real-time ingestion capabilities make it ideal for building real-time analytics dashboards for applications like IoT monitoring, social media analysis, and log analytics.

With its Converged Index and support for advanced search features, Rockset is an excellent choice for building full-text search applications, such as product catalogs or document search systems.

Machine Learning

Rockset’s ability to ingest and query large-scale, semi-structured data in real-time makes it a suitable choice for machine learning applications.


DuckDB Pricing Model

DuckDB is a free and open-source database management system released under the permissive MIT License. It can be freely used, modified, and distributed without any licensing costs.

Rockset Pricing Model

Rockset offers a usage-based pricing model that charges customers for the amount of data ingested, the number of virtual instances, and the volume of queries executed. The pricing model is designed to be transparent and flexible, allowing users to only pay for the resources they consume. Rockset also provides a free tier with limited resources for developers to explore the platform. Users can choose between on-demand and reserved instances, depending on their needs.