Mimir vs StarRocks

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

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

Mimir Overview

Grafana Mimir is an open-source software project that provides a scalable long-term storage solution for Prometheus. Started at Grafana Labs and announced in 2022, Grafana Mimir aims to become the most scalable and performant open-source time series database for metrics. The project incorporates the knowledge and experience gained by Grafana Labs engineers from running Grafana Enterprise Metrics and Grafana Cloud Metrics at massive scale.

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.

Mimir for Time Series Data

Grafana Mimir is well-suited for handling time series data, making it a suitable choice for scenarios involving metric storage and analysis. It provides long-term storage capabilities for Prometheus, a popular open-source monitoring and alerting system. With Grafana Mimir, users can store and query time series metrics over extended periods, allowing for historical analysis and trend detection. It is especially useful for applications that require scalable and performant storage of time series data for metrics monitoring and observability purposes.

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.

Mimir Key Concepts

• Metrics: In Grafana Mimir, metrics represent the measurements or observations tracked over time. They can include various types of data, such as system metrics, application performance metrics, or sensor data.
• Long-term Storage: Grafana Mimir provides a storage solution specifically tailored for long-term retention of time series data, allowing users to store and query historical metrics over extended periods.
• Microservices: Grafana Mimir adopts a microservices-based architecture, where the system consists of multiple horizontally scalable microservices that can operate independently and in parallel.

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.

Mimir Architecture

Grafana Mimir adopts a microservices-based architecture, where the system comprises multiple horizontally scalable microservices. These microservices can operate independently and in parallel, allowing for efficient distribution of workload and scalability. Grafana Mimir’s components are compiled into a single binary, providing a unified and cohesive system. The architecture is designed to be highly available and multi-tenant, enabling multiple users and applications to utilize the database concurrently. This distributed architecture ensures scalability and resilience in handling large-scale metric storage and retrieval scenarios.

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