BigQuery’s Architecture and Working Mechanism
Google BigQuery’s architecture is a marvel of modern data processing, embodying efficiency, scalability, and speed. It’s designed to handle petabytes of data with ease, providing users with fast access to insights without the operational overhead typically associated with traditional data warehouses. Understanding the architecture and working mechanism behind BigQuery not only reveals the source of its power but also how it achieves its remarkable performance. This deep dive into BigQuery’s architecture and mechanics will cover its serverless nature, storage solutions, the Dremel execution engine, and its approach to data security and compliance.
In this series of article we are focusing on below topics:
- Big query : An introduction
- BigQuery’s Architecture and Working Mechanism
- Getting Started with BigQuery
- Advanced Big Query concepts
- Authorised Views in Big Query
- Best Practices for Big Query
Under the hood, four main components operate and power BigQuery.
- Dremel : The Execution Engine
- Colossus : The Distributed Engine
- Jupiter : The Network
- Borg : The Compute
Dremel
Achieving fast query responses in BigQuery involves more than just extensive hardware; it relies on the Dremel query engine, introduced in a 2010 paper. Dremel dissects queries into manageable sections and integrates the outcomes. It transforms SQL queries into an execution tree, where ‘slots’ at the tree’s base handle data reading and computations, such as processing 100 billion rows for regex checks. The ‘mixers’ at the branches aggregate data, with the ‘shuffle’ phase facilitating swift data transfers via Google’s Jupiter network. Managed by Borg, which allocates hardware resources, Dremel dynamically assigns slots based on query demands, ensuring equitable resource distribution among users. With its widespread application across Google’s services, Dremel benefits from ongoing enhancements in performance and scalability, offering BigQuery users continuous improvements without the need for downtime or upgrades typical of traditional technologies.
Colossus
BigQuery utilizes Google’s advanced distributed file system, Colossus, across all its datacenters, providing users with thousands of disks each for unparalleled data handling and storage. Colossus ensures data replication, crash recovery, and distributed management to prevent any single point of failure. Its efficiency rivals in-memory databases with its cost-effective, scalable, and high-performance capabilities. Furthermore, BigQuery adopts the ColumnIO format and compression for optimal structured data storage, enabling effortless scaling up to dozens of Petabytes without the need for costly computing resources commonly associated with traditional databases.
Jupiter
Google’s Jupiter network significantly enhances Big Data operations by offering 1 Petabit/sec of bisection bandwidth, enabling efficient workload distribution. Distinguished as a key feature of Google Cloud Platform, Jupiter provides substantial bandwidth for 100,000 machines to communicate at 10 Gbps, using less than 0.1% of its total capacity. This full-duplex communication system makes physical data location within clusters irrelevant, allowing for seamless data exchange regardless of rack configurations. Unlike traditional storage-compute separation methods, Jupiter facilitates direct, rapid data access from storage, bypassing limitations of local VMs and object storage throughput, and allowing for the swift reading of terabytes of data for SQL queries, thereby streamlining operations.
Borg
Leveraging Google’s Borg, a vast cluster management system, BigQuery processes tasks with thousands of CPU cores across many machines, making individual queries a small part of its immense capacity. Borg ensures resilience against hardware failures and operational issues in Google’s data centers, automatically rerouting and abstracting software layers to maintain uninterrupted service. Thus, even daily server failures or disruptions like unplugging a rack go unnoticed by users.
Lets discuss different features of Big query which separates it from others.
Serverless Data Warehousing
At the heart of BigQuery’s architecture is its serverless design, which abstracts the complexities of infrastructure management from the user. This means that there is no need to manage hardware or configure database instances. Instead, BigQuery automatically allocates computing resources as needed to execute queries. This serverless approach ensures that users can focus on analyzing data rather than managing servers, making BigQuery highly accessible to organizations of all sizes.
Columnar Storage
BigQuery utilizes columnar storage for data organization, a key factor in its ability to quickly read and analyze large datasets. Unlike traditional row-oriented databases that store data in rows, columnar storage aligns data by column. This alignment is particularly beneficial for analytical queries that typically access only a subset of columns, as it allows for more efficient data compression and reduces the amount of data read from disk, significantly speeding up query execution times.
Massively Parallel Processing (MPP)
BigQuery’s ability to rapidly process queries over large datasets is also due to its use of Massively Parallel Processing (MPP). When a query is executed, it is distributed across thousands of servers, each working on a small portion of the task. This parallel processing capability ensures that even complex queries over vast datasets can be completed in seconds to minutes, showcasing BigQuery’s scalability and performance prowess.
Dremel Execution Engine
The core of BigQuery’s query processing capabilities is powered by Google’s Dremel technology. Dremel is an innovative execution engine designed to perform interactive analysis on large datasets. It achieves this through a combination of columnar storage, efficient compression algorithms, and a tree architecture for query execution that allows for the distribution of work across thousands of machines. This architecture not only speeds up query processing but also allows BigQuery to scale dynamically based on the complexity of the query and the size of the dataset.
Smart Data Caching
BigQuery improves performance through intelligent caching of query results. If a user submits a query that has been executed recently and the underlying data has not changed, BigQuery retrieves the result from the cache rather than re-executing the query. This caching mechanism reduces processing time and cost for frequently executed queries, making data analysis even more efficient.
Data Security and Compliance
Security is a cornerstone of BigQuery’s architecture. It encrypts data at rest and in transit, providing robust protection for sensitive information. BigQuery also integrates seamlessly with Google’s Identity and Access Management (IAM), allowing administrators to define fine-grained access controls to datasets, tables, and even columns. Compliance with major standards and regulations, such as GDPR, HIPAA, and ISO, ensures that BigQuery meets the stringent requirements of various industries and regions.
Storage and Query Optimization
BigQuery automatically optimizes data storage and query execution. It partitions tables based on time and automatically reorganizes data to improve query performance. Additionally, BigQuery’s query optimizer analyzes each query to determine the most efficient way to execute it, considering factors such as data distribution and the availability of cached results.
Integration with Machine Learning and AI
Beyond its core capabilities, BigQuery integrates with Google’s machine learning and artificial intelligence services, enabling users to apply predictive analytics and machine learning models directly to their datasets within BigQuery. This seamless integration opens up new possibilities for data analysis, allowing users to go beyond traditional analytics to predict future trends and outcomes based on their data.
BigQuery and Data Ecosystem
BigQuery does not operate in isolation; it’s a central component of a broader data ecosystem. It integrates with data ingestion tools like Google Cloud Dataflow, data processing services like Google Cloud Dataprep, and visualization tools like Google Data Studio and Tableau. This ecosystem approach ensures that users can easily move data into BigQuery, analyze it, and visualize insights, all within the Google Cloud Platform.
When a user executes a query, the following steps occur behind the scenes.
Conclusion
Google BigQuery’s architecture and working mechanism are the foundation of its power and appeal. By abstracting the complexities of data storage and processing, BigQuery allows users to focus on what truly matters: deriving insights from data. Its serverless nature, combined with advanced technologies like columnar storage, MPP, and the Dremel execution engine, enables it to handle vast amounts of data at incredible speeds. With robust security measures and compliance with major standards, BigQuery not only meets the needs of businesses today but also positions itself as a future-proof solution in the ever-evolving landscape of big data analytics. As organizations continue to generate and rely on large datasets for decision-making, BigQuery stands ready to transform those datasets into actionable insights with efficiency and ease.
