Exploring the Architecture of BigQuery: Google’s Powerful Data Warehousing Solution

BigQuery is a robust data processing and analytics platform provided by Google, known for its unique architecture that separates storage and compute. This separation makes BigQuery a cost-effective and efficient solution for handling large datasets. In this blog we’ll delve into the key aspects of BigQuery’s architecture and explore the components that make it stand out.

The Foundation of BigQuery: Storage and Compute

At the core of any query engine, including BigQuery, are two main components: storage and compute. Storage is used to store data, while compute refers to the processing power required to run queries on that data.

Coupled vs. Decoupled Storage and Compute

In some query engines, storage and compute are coupled, meaning they cannot be separated. This often leads to higher infrastructure costs as scaling requires upgrading both storage and compute simultaneously. BigQuery, however, employs a decoupled architecture where storage and compute can be scaled independently. This flexibility allows users to pay only for the resources they use, making it a more cost-effective solution.

Key Points:

• Storage and Compute: Essential components for any query engine.
• Coupled Storage and Compute: More expensive as they cannot be scaled independently.
• Decoupled Storage and Compute in BigQuery: Allows for independent scaling and cost-effective usage.

BigQuery’s Storage Layer: Colossus

BigQuery uses Colossus, a distributed, cluster-level file system similar to the Google File System (GFS), to handle its storage needs. Colossus is designed for fault tolerance, ensuring data replication and reliability.

Comparison with Hadoop’s HDFS

Colossus can be compared to Hadoop’s HDFS, as both are distributed file systems that provide data replication. Colossus, however, is a successor to GFS and is used across various Google projects, including BigQuery.

Key Points:

• Colossus: Distributed storage layer providing data replication for fault tolerance.
• Comparison with HDFS: Both are distributed file systems; Colossus is a successor to GFS.

BigQuery’s Compute Engine: Dremel

The compute engine used by BigQuery is called Dremel, a framework that handles large-scale data processing. Dremel’s architecture consists of several key components, including root servers, mixers, and leaf nodes.

Components of Dremel:

• Leaf Nodes: Responsible for reading data from the Colossus storage system.
• Mixers: Handle data aggregation and processing, working with the results of SQL queries.
• Root Node: Coordinates the activities of the leaf nodes and mixers to ensure efficient data processing.

Key Points:

• Dremel: Compute engine used by BigQuery.
• Leaf Nodes, Mixers, Root Node: Components that work together for efficient data processing.

Connecting Compute and Storage: Jupiter

Jupiter, also known as the “Peta-bit Jupiter Network,” is a high-speed network protocol developed by Google. It connects Dremel (the compute component) and Colossus (the storage component), enabling efficient data processing capabilities within BigQuery.

Key Points:

• Jupiter: High-speed network protocol connecting Dremel and Colossus.
• Efficient Data Processing: Enabled by the connection between compute and storage components.

Orchestrating BigQuery: Bork

Bork is an in-house Google project that acts as a scheduler, orchestrating various internal systems within BigQuery. When a query is triggered, Bork communicates with other components such as Dremel, Jupiter, and Colossus to ensure the end-to-end execution of the query.

Key Points:

• Bork: Scheduler for internal systems in BigQuery.
• Orchestration: Ensures seamless communication between Dremel, Jupiter, and Colossus for query execution.

Column-Oriented Storage in BigQuery

BigQuery stores its data in a column-oriented format, known as Capacitor. This approach allows for efficient aggregation and processing of specific columns, significantly improving query performance compared to traditional row-oriented storage systems.

Key Points:

• Column-Oriented Storage: Data is stored in columns, allowing for efficient processing of specific columns.
• Capacitor: Column-oriented storage format used by BigQuery.

Benefits:

• Efficient Aggregation: Direct access to relevant column data without reading entire rows.
• Improved Query Performance: Faster processing of queries focusing on specific columns.

Conclusion

BigQuery’s architecture, with its decoupled storage and compute, powerful compute engine (Dremel), robust storage layer (Colossus), and high-speed network protocol (Jupiter), makes it a standout solution for data warehousing and analytics. The use of column-oriented storage further enhances its efficiency and performance, making BigQuery an ideal choice for handling large datasets.