Apache Doris vs StarRocks
A detailed comparison
Compare Apache Doris and StarRocks for time series and OLAP workloads
Learn About Time Series DatabasesChoosing 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 Doris and StarRocks so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how Apache Doris 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.
Apache Doris vs StarRocks Breakdown
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| Database Model | Data warehouse |
Data warehouse |
| Architecture | Doris can be deployed on-premises or in the cloud and is compatible with various data formats such as Parquet, ORC, and JSON. |
StarRocks can be deployed on-premises, in the cloud, or in a hybrid environment, depending on your infrastructure preferences and requirements. |
| License | Apache 2.0 |
Apache 2.0 |
| Use Cases | Interactive analytics, data warehousing, real-time data analysis, reporting, dashboarding |
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 |
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Apache Doris Overview
Apache Doris is an MPP-based interactive SQL data warehousing system designed for reporting and analysis. It is known for its high performance, real-time analytics capabilities, and ease of use. Apache Doris integrates technologies from Google Mesa and Apache Impala. Unlike other SQL-on-Hadoop systems, Doris is designed to be a simple and tightly coupled system that does not rely on external dependencies. It aims to provide a streamlined and efficient solution for data warehousing and analytics.
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.
Apache Doris for Time Series Data
Apache Doris can be effectively used with time series data for real-time analytics and reporting. With its high performance and sub-second response time, Doris can handle massive amounts of time-stamped data and provide timely query results. It supports both high-concurrent point query scenarios and high-throughput complex analysis scenarios, making it suitable for analyzing time series data with varying levels of complexity.
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.
Apache Doris Key Concepts
- MPP (Massively Parallel Processing): Apache Doris leverages MPP architecture, which allows it to distribute data processing across multiple nodes, enabling parallel execution and scalability.
- SQL: Apache Doris supports SQL as the query language, providing a familiar and powerful interface for data analysis and reporting.
- Point Query: Point query refers to retrieving a specific data point or a small subset of data from the database.
- Complex Analysis: Apache Doris can handle complex analysis scenarios that involve processing large volumes of data and performing advanced computations and aggregations.
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.
Apache Doris Architecture
Apache Doris is based on MPP architecture, which enables it to distribute data and processing across multiple nodes for parallel execution. It is a standalone system and does not depend on other systems or frameworks. Apache Doris combines the technology of Google Mesa and Apache Impala to provide a simple and tightly coupled system for data warehousing and analytics. It leverages SQL as the query language and supports efficient data processing and query optimization techniques to ensure high performance and scalability.
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
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Apache Doris Features
High Performance
Apache Doris is designed for high-performance data analytics, delivering sub-second query response times even with massive amounts of data.
Real-Time Analytics
Apache Doris enables real-time data analysis, allowing users to gain insights and make informed decisions based on up-to-date information.
Scalability
Apache Doris can scale horizontally by adding more nodes to the cluster, allowing for increased data storage and processing capacity.
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.
Apache Doris Use Cases
Real-Time Analytics
Apache Doris is well-suited for real-time analytics scenarios where timely insights and analysis of large volumes of data are crucial. It enables businesses to monitor and analyze real-time data streams, make data-driven decisions, and detect patterns or anomalies in real time.
Reporting and Business Intelligence
Apache Doris can be used for generating reports and conducting business intelligence activities. It supports fast and efficient querying of data, allowing users to extract meaningful insights and visualize data for reporting and analysis purposes.
Data Warehousing
Apache Doris is suitable for building data warehousing solutions that require high-performance analytics and querying capabilities. It provides a scalable and efficient platform for storing, managing, and analyzing large volumes of data for reporting and decision-making.
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.
Apache Doris Pricing Model
As an open-source project, Apache Doris is freely available for usage and does not require any licensing fees. Users can download the source code and set up Apache Doris on their own infrastructure without incurring any direct costs. However, it’s important to consider the operational costs associated with hosting and maintaining the database infrastructure.
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.
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