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

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

Prometheus Overview

Prometheus is an open-source monitoring and alerting toolkit initially developed at SoundCloud in 2012. It has since become a widely adopted monitoring solution and a part of the Cloud Native Computing Foundation (CNCF) project. Prometheus focuses on providing real-time insights and alerts for containerized and microservices-based environments. Its primary use case is monitoring infrastructure and applications, with an emphasis on reliability and scalability.

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.

Prometheus for Time Series Data

Prometheus is specifically designed for time series data, as its primary focus is on monitoring and alerting based on the state of infrastructure and applications. It uses a pull-based model, where the Prometheus server scrapes metrics from the target systems at regular intervals. This model is suitable for monitoring dynamic environments, as it allows for automatic discovery and monitoring of new instances. However, Prometheus is not intended as a general-purpose time series database and might not be the best choice for high cardinality or long-term data storage.

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.

Prometheus Key Concepts

• Metric: A numeric representation of a particular aspect of a system, such as CPU usage or memory consumption.
• Time Series: A collection of data points for a metric, indexed by timestamp.
• Label: A key-value pair that provides metadata and context for a metric, enabling more granular querying and aggregation.
• PromQL: Prometheus uses its own query language called PromQL (Prometheus Query Language) for querying time series data and generating alerts.

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.

Prometheus Architecture

Prometheus is a single-server, standalone monitoring system that uses a pull-based approach to collect metrics from target systems. It stores time series data in a custom, highly compressed, on-disk format, optimized for fast querying and low resource usage. The architecture of Prometheus is modular and extensible, with components like exporters, service discovery mechanisms, and integrations with other monitoring systems. As a non-distributed system, it lacks built-in clustering or horizontal scalability, but it supports federation, allowing multiple Prometheus servers to share and aggregate 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.