Apache Drill vs. Baidu Doris: A Comparative Analysis

In the realm of big data analytics, selecting the right query engine is crucial for efficient data processing. Apache Drill and Baidu Doris (formerly known as Apache Doris, and originally developed by Baidu) are two notable systems designed for high-performance data querying. Below is a comparative analysis of both these systems across various dimensions.

Introduction

Apache Drill** is an open-source, low-latency query engine for large-scale datasets, including structured and semi-structured/nested data. It is schema-free, meaning it can query data without requiring metadata definitions in advance.
Baidu Doris, on the other hand, is an MPP (Massively Parallel Processing) analytical data warehouse for large-scale data, including real-time analytics. It is designed to provide high concurrency and low latency for OLAP (Online Analytical Processing) scenarios.

Architecture

• Apache Drill has a distributed architecture that can scale horizontally. It supports a variety of NoSQL databases and file systems, including HBase, MongoDB, Amazon S3, and HDFS. Drill’s architecture allows it to process data in-situ without the need for data transformation or schema definitions.
• Baidu Doris has a MPP-based architecture which is also scalable and can handle large datasets. It integrates well with the Hadoop ecosystem and is optimized for structured data. Doris is designed with columnar storage and vectorized query execution, making it highly efficient for OLAP tasks.

Query Language

• Both systems use SQL as their query language, making it easier for users familiar with traditional relational databases.
• Apache Drill supports ANSI SQL, which allows for complex queries, including JOINs and sub-queries.
• Baidu Doris also supports a subset of SQL and extends it with some analytical functions that are optimized for OLAP operations.

Performance

• Apache Drill is designed for fast data exploration and can handle complex queries on large datasets. Its performance is optimized by the Drill’s execution engine, which uses techniques such as columnar processing and predictive pipelining.
• Baidu Doris excels in OLAP scenarios, providing high throughput and low latency for concurrent query execution. Its columnar storage and MPP architecture contribute to its superior performance in analytical processing.

Use Cases

• Apache Drill is suitable for data exploration and discovery where schemas are not known in advance. It is ideal for organizations that deal with evolving data or a variety of data formats and sources.
• Baidu Doris is tailored for analytical workloads, real-time reporting, and complex OLAP queries. It serves businesses that require quick insights from their large-scale data warehouses.

Ecosystem Integration

• Apache Drill integrates with various BI tools and data sources, providing a flexible solution for data analysts and scientists.
• Baidu Doris is part of the broader big data ecosystem and can integrate with other data processing frameworks, making it a robust solution for data warehousing.

Conclusion

Apache Drill and Baidu Doris both offer robust solutions for data querying and analytics, but they cater to different needs. Apache Drill is a powerful tool for schema-free exploration of diverse data sources, making it ideal for scenarios where agility and flexibility are required. Baidu Doris, with its MPP architecture and OLAP optimizations, is well-suited for structured data analytics in a warehouse setting, where performance and concurrency are the priority.
Organizations must assess their specific use cases, data types, and performance requirements when choosing between Apache Drill and Baidu Doris. By understanding the strengths and limitations of each system, data professionals can make an informed decision that best supports their operational objectives.