Apache Doris vs SQL Server
A detailed comparison
Compare Apache Doris and SQL Server 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 SQL Server so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how Apache Doris and SQL Server 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 SQL Server Breakdown
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| Database Model | Data warehouse |
Relational database |
| Architecture | Doris can be deployed on-premises or in the cloud and is compatible with various data formats such as Parquet, ORC, and JSON. |
SQL Server can be deployed on-premises, in virtual machines, or as a managed cloud service (Azure SQL Database) on Microsoft Azure. It is available in multiple editions tailored to different use cases, such as Express, Standard, and Enterprise. |
| License | Apache 2.0 |
Closed source |
| Use Cases | Interactive analytics, data warehousing, real-time data analysis, reporting, dashboarding |
Transaction processing, business intelligence, data warehousing, analytics, web applications, enterprise applications |
| Scalability | Horizontally scalable with distributed storage and compute |
Supports vertical and horizontal scaling, with features like partitioning, sharding, and replication for distributed environments |
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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.
SQL Server Overview
Microsoft SQL Server is a powerful and widely used relational database management system developed by Microsoft. Initially released in 1989, it has evolved over the years to become one of the most popular database systems for businesses of all sizes. SQL Server is known for its robust performance, security, and ease of use. It supports a variety of platforms, including Windows, Linux, and containers, providing flexibility for different deployment scenarios.
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.
SQL Server for Time Series Data
While Microsoft SQL Server is primarily a relational database, it does offer support for time series data through various features and optimizations. Temporal tables allow for tracking changes in data over time, providing an efficient way to store and query historical data. Indexing and partitioning can be leveraged to optimize time series data storage and retrieval. However, SQL Server may not be the best choice for applications requiring high write or query throughput specifically for time series data, as specialized time series databases offer more optimized solutions as well as a variety of developer productivity features that speed up development time for applications that heavily use time series data.
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.
SQL Server Key Concepts
- T-SQL: Transact-SQL, an extension of SQL that adds procedural programming elements, such as loops, conditional statements, and error handling, to the standard SQL language.
- SSMS: SQL Server Management Studio, an integrated environment for managing SQL Server instances, databases, and objects.
- Always On: A suite of high availability and disaster recovery features in SQL Server, including Always On Availability Groups and Always On Failover Cluster Instances.
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.
SQL Server Architecture
Microsoft SQL Server is a relational database that uses SQL for querying and manipulating data. It follows a client-server architecture, with the database server hosting the data and processing requests from clients. SQL Server supports both on-premises and cloud-based deployment through Azure SQL Database, a managed service offering in the Microsoft Azure cloud. SQL Server’s architecture includes components such as the Database Engine, which processes data storage and retrieval, and various services for reporting, integration, and analysis.
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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.
SQL Server Features
Security
SQL Server offers advanced security features, such as Transparent Data Encryption, Always Encrypted, and row-level security, to protect sensitive data.
Scalability
SQL Server supports scaling out through features like replication, distributed partitioned views, and Always On Availability Groups.
Integration Services
SQL Server Integration Services (SSIS) is a powerful platform for building high-performance data integration and transformation solutions.
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.
SQL Server Use Cases
Enterprise Applications
SQL Server is commonly used as the backend database for enterprise applications, providing a reliable and secure data storage solution.
Data Warehousing and Business Intelligence
SQL Server’s built-in analytical features, such as Analysis Services and Reporting Services, make it suitable for data warehousing and business intelligence applications.
E-commerce Platforms
SQL Server’s performance and scalability features enable it to support the demanding workloads of e-commerce platforms, handling high volumes of transactions and user data.
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.
SQL Server Pricing Model
Microsoft SQL Server offers a variety of licensing options, including per-core, server + CAL (Client Access License), and subscription-based models for cloud deployments. Costs depend on factors such as the edition (Standard, Enterprise, or Developer), the number of cores, and the required features. For cloud-based deployments, Azure SQL Database offers a pay-as-you-go model with various service tiers to accommodate different performance and resource requirements.
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