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

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

Snowflake Overview

Snowflake is a cloud-based data warehousing platform that was founded in 2012 and officially launched in 2014. It is designed to enable organizations to efficiently store, process, and analyze large volumes of structured and semi-structured data. Snowflake’s unique architecture separates storage, compute, and cloud services, allowing users to independently scale and optimize each component.

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.

Snowflake for Time Series Data

While Snowflake is not specifically designed for time series data, it can still effectively store, process, and analyze such data due to its scalable and flexible architecture. Snowflake’s columnar storage format, combined with its powerful query engine and support for SQL, makes it a suitable option for time series data analysis.

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.

Snowflake Key Concepts

• Virtual Warehouse: A compute resource in Snowflake that processes queries and performs data loading and unloading. Virtual Warehouses can be independently scaled up or down based on demand.
• Micro-Partition: A storage unit in Snowflake that contains a subset of the data in a table. Micro-partitions are automatically optimized for efficient querying.
• Time Travel: A feature in Snowflake that allows users to query historical data at specific points in time or within a specific time range.
• Data Sharing: The ability to securely share data between Snowflake accounts, without the need to copy or transfer the data.

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.

Snowflake Architecture

Snowflake’s architecture separates storage, compute, and cloud services, allowing users to scale and optimize each component independently. The platform uses a columnar storage format and supports ANSI SQL for querying and data manipulation. Snowflake is built on top of AWS, Azure, and GCP, providing a fully managed, elastic, and secure data warehouse solution. Key components of the Snowflake architecture include databases, tables, virtual warehouses, and micro-partitions.

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