DataBend vs Apache Doris

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 DataBend and Apache Doris so you can quickly see how they compare against each other.

The primary purpose of this article is to compare how DataBend and Apache Doris 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.

DataBend Overview

DataBend is an open-source, cloud-native data processing and analytics platform designed to provide high-performance, cost-effective, and scalable solutions for big data workloads. The project is driven by a community of developers, researchers, and industry professionals aiming to create a unified data processing platform that combines batch and streaming processing capabilities with advanced analytical features. DataBend’s flexible architecture allows users to build a wide range of applications, from real-time analytics to large-scale data warehousing.

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.

DataBend for Time Series Data

DataBend’s architecture and processing capabilities make it a suitable choice for working with time series data. Its support for both batch and streaming data processing allows users to ingest, store, and analyze time series data at scale. Additionally, DataBend’s integration with Apache Arrow and its powerful query execution framework enable efficient querying and analytics on time series data, making it a versatile choice for applications that require real-time insights and analytics.

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.

DataBend Key Concepts

• DataFusion: DataFusion is a core component of DataBend, providing an extensible query execution framework that supports both SQL and DataFrame-based query APIs.
• Ballista: Ballista is a distributed compute platform within DataBend, built on top of DataFusion, that allows for efficient and scalable execution of large-scale data processing tasks.
• Arrow: DataBend leverages Apache Arrow, an in-memory columnar data format, to enable efficient data exchange between components and optimize query performance.

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.

DataBend Architecture

DataBend is built on a cloud-native, distributed architecture that supports both NoSQL and SQL-like querying capabilities. Its modular design allows users to choose and combine components based on their specific use case and requirements. The core components of DataBend’s architecture include DataFusion, Ballista, and the storage layer. DataFusion is responsible for query execution and optimization, while Ballista enables distributed computing for large-scale data processing tasks. The storage layer in DataBend can be configured to work with various storage backends, such as object storage or distributed file systems.

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.