Kdb vs Rockset

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 Kdb and Rockset so you can quickly see how they compare against each other.

The primary purpose of this article is to compare how Kdb 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.

Kdb Overview

kdb+ is a high-performance columnar, time series database developed by Kx Systems. Released in 2003, kdb+ is designed to efficiently manage large volumes of data, with a primary focus on financial data, such as stock market trades and quotes. It is built on the principles of the q programming language, which is a descendant of APL and K. The database is known for its speed, scalability, and ability to process both real-time and historical data.

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.

Kdb for Time Series Data

kdb+ is designed to store time series data, making it a natural fit for applications that require high-speed querying and analysis of large volumes of data. Its columnar storage format allows for efficient compression and retrieval of time series data, while its q language provides a powerful and expressive means to manipulate and analyze the data. kdb+ is especially strong for financial data, though it can be used for other types of time series data as well.

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.

Kdb Key Concepts

• q language: A high-level, domain-specific programming language used for querying and manipulating data in kdb+. It combines SQL-like syntax with a functional programming style.
• Columnar storage: kdb+ stores data in columns, rather than rows, which allows for faster querying and analysis of time series data.
• Tables: kdb+ stores data in tables, which are similar to relational tables, but with a focus on columnar storage and time series data.
• Splayed tables: A table storage format where each column is stored in a separate file, further enhancing query performance.

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.

Kdb Architecture

kdb+ is a columnar, time series database that employs a custom data model tailored for efficient storage and querying of time series data. It does not use traditional SQL, but instead relies on the q language for querying and data manipulation. The architecture of kdb+ is designed for both in-memory and on-disk storage, with the ability to scale horizontally across multiple machines. The primary components of kdb+ are the database engine, the q language interpreter, and the built-in web server.

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.