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

The primary purpose of this article is to compare how Rockset and StarRocks 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.

Rockset vs StarRocks Breakdown


 
Database Model

Real time database

Data warehouse

Architecture

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.

StarRocks can be deployed on-premises, in the cloud, or in a hybrid environment, depending on your infrastructure preferences and requirements.

License

Closed source

Apache 2.0

Use Cases

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

Business intelligence, analytics, real-time data processing, large-scale data storage

Scalability

Horizontally scalable with distributed storage and compute

Horizontally scalable, with support for distributed storage and query processing

Looking for the most efficient way to get started?

Whether you are looking for cost savings, lower management overhead, or open source, InfluxDB can help.

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.

StarRocks Overview

StarRocks is an open source high-performance analytical data warehouse that enables real-time, multi-dimensional, and highly concurrent data analysis. It features an MPP (Massively Parallel Processing) architecture and is equipped with a fully vectorized execution engine and a columnar storage engine that supports real-time updates.


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.

StarRocks for Time Series Data

StarRocks is primarily focused on data warehousing workloads but can be used for time series data. StarRocks can be used for real time analytics and historical data analysis.


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.

StarRocks Key Concepts

  • MPP Architecture: StarRocks utilizes an MPP architecture, which enables parallel processing and distributed execution of queries, allowing for high-performance and scalability.
  • Vectorized Execution Engine: StarRocks employs a fully vectorized execution engine that leverages SIMD (Single Instruction, Multiple Data) instructions to process data in batches, resulting in optimized query performance.
  • Columnar Storage Engine: The columnar storage engine in StarRocks organizes data by column, which improves query performance by only accessing the necessary columns during query execution.
  • Cost-Based Optimizer (CBO): StarRocks includes a fully-customized cost-based optimizer that evaluates different query execution plans and selects the most efficient plan based on estimated costs.
  • Materialized View: StarRocks supports intelligent materialized views, which are precomputed summaries of data that accelerate query performance by providing faster access to aggregated data.


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.

StarRocks Architecture

StarRock’s architecture includes a fully vectorized execution engine and a columnar storage engine for efficient data processing and storage. It also incorporates features like a cost-based optimizer and materialized views for optimized query performance. StarRocks supports real-time and batch data ingestion from a variety of sources and enables direct analysis of data stored in data lakes without data migration

Free Time-Series Database Guide

Get a comprehensive review of alternatives and critical requirements for selecting yours.

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.

StarRocks Features

Multi-Dimensional Analysis

StarRocks supports multi-dimensional analysis, enabling users to explore data from different dimensions and perspectives.

High Concurrency

StarRocks is designed to handle high levels of concurrency, allowing multiple users to execute queries simultaneously.

Materialized View

StarRocks supports materialized views, which provide precomputed summaries of data for faster query performance.


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.

StarRocks Use Cases

Real-Time Analytics

StarRocks is well-suited for real-time analytics scenarios, where users need to analyze data as it arrives, enabling them to make timely and data-driven decisions.

Ad-Hoc Queries

With its high-performance and highly concurrent data analysis capabilities, StarRocks is ideal for ad-hoc querying, allowing users to explore and analyze data interactively.

Data Lake Analytics

StarRocks supports analyzing data directly from data lakes without the need for data migration. This makes it a valuable tool for organizations leveraging data lakes for storage and analysis.


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.

StarRocks Pricing Model

StarRocks can be deployed on your own hardware using the open source project. There are also a number of commercial vendors offering managed services to run StarRocks in the cloud.