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

The primary purpose of this article is to compare how Apache Cassandra 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.

Apache Cassandra vs Rockset Breakdown


 
Database Model

Distributed wide-column database

Real time database

Architecture

Apache Cassandra follows a masterless, peer-to-peer architecture, where each node in the cluster is functionally the same and communicates with other nodes using a gossip protocol. Data is distributed across nodes in the cluster using consistent hashing, and Cassandra supports tunable consistency levels for read and write operations. It can be deployed on-premises, in the cloud, or as a managed service

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

Apache 2.0

Closed source

Use Cases

High write throughput applications, time series data, messaging systems, recommendation engines, IoT

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

Scalability

Horizontally scalable with support for data partitioning, replication, and linear scalability as nodes are added

Horizontally scalable with distributed storage and compute

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.

Apache Cassandra Overview

Apache Cassandra is a highly scalable, distributed, and decentralized NoSQL database designed to handle large amounts of data across many commodity servers. Originally created by Facebook, Cassandra is now an Apache Software Foundation project. Its primary focus is on providing high availability, fault tolerance, and linear scalability, making it a popular choice for applications with demanding workloads and low-latency requirements.

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.


Apache Cassandra for Time Series Data

Cassandra can be used for handling time series data due to its distributed architecture and support for time-based partitioning. Time series data can be efficiently stored and retrieved using partition keys based on time ranges, ensuring quick access to data points.

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.


Apache Cassandra Key Concepts

  • Column Family: Similar to a table in a relational database, a column family is a collection of rows, each consisting of a key-value pair.
  • Partition Key: A unique identifier used to distribute data across multiple nodes in the cluster, ensuring even distribution and fast data retrieval.
  • Replication Factor: The number of copies of data stored across different nodes in the cluster to provide fault tolerance and high availability.
  • Consistency Level: A configurable parameter that determines the trade-off between read/write performance and data consistency across the cluster.

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.


Apache Cassandra Architecture

Cassandra uses a masterless, peer-to-peer architecture, in which all nodes are equal, and there is no single point of failure. This design ensures high availability and fault tolerance. Cassandra’s data model is a hybrid between a key-value and column-oriented system, where data is partitioned across nodes based on partition keys and stored in column families. Cassandra supports tunable consistency, allowing users to adjust the balance between data consistency and performance based on their specific needs.

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.

Free Time-Series Database Guide

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

Apache Cassandra Features

Linear Scalability

Cassandra can scale horizontally, adding nodes to the cluster to accommodate growing workloads and maintain consistent performance.

High Availability

With no single point of failure and support for data replication, Cassandra ensures data is always accessible, even in the event of node failures.

Tunable Consistency

Users can balance between data consistency and performance by adjusting consistency levels based on their application’s requirements.

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.


Apache Cassandra Use Cases

Messaging and Social Media Platforms

Cassandra’s high availability and low-latency make it suitable for messaging and social media applications that require fast, consistent access to user data.

IoT and Distributed Systems

With its ability to handle large amounts of data across distributed nodes, Cassandra is an excellent choice for IoT applications and other distributed systems that generate massive data streams.

E-commerce

Cassandra is a good fit for E-commerce use cases because it has the ability to support things like real-time inventory status and it’s architecture also allows for reduced latency by allowing region specific data to be closer to users.

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.


Apache Cassandra Pricing Model

Apache Cassandra is an open-source project, and there are no licensing fees associated with its use. However, costs can arise from hardware, hosting, and operational expenses when deploying a self-managed Cassandra cluster. Additionally, several managed Cassandra services, such as DataStax Astra and Amazon Keyspaces, offer different pricing models based on factors like data storage, request throughput, and support.

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.