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

The primary purpose of this article is to compare how Google BigQuery 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.

Google BigQuery Overview

Google BigQuery is a fully-managed, serverless data warehouse and analytics platform developed by Google Cloud. Launched in 2011, BigQuery is designed to handle large-scale data processing and querying, enabling users to analyze massive datasets in real-time. With a focus on performance, scalability, and ease of use, BigQuery is suitable for a wide range of data analytics use cases, including business intelligence, log analysis, and machine learning.

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.

Google BigQuery for Time Series Data

BigQuery can be used for storing and analyzing time series data, although it is more focused on traditional data warehouse use cases. BigQuery may struggle for use cases where low latency response times are required

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.

Google BigQuery Key Concepts

Some important concepts related to Google BigQuery include:

• Projects: A project in BigQuery represents a top-level container for resources such as datasets, tables, and views.
• Datasets: A dataset is a container for tables, views, and other data resources in BigQuery.
• Tables: Tables are the primary data storage structure in BigQuery and consist of rows and columns.
• Schema: A schema defines the structure of a table, including column names, data types, and constraints.

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.

Google BigQuery Architecture

Google BigQuery’s architecture is built on top of Google’s distributed infrastructure and is designed for high performance and scalability. At its core, BigQuery uses a columnar storage format called Capacitor, which enables efficient data compression and fast query performance. Data is automatically partitioned and distributed across multiple storage nodes, providing high availability and fault tolerance. BigQuery’s serverless architecture automatically allocates resources for queries and data storage, eliminating the need for users to manage infrastructure or capacity planning.

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.