What Is the Data Structure That Stores Google’s Bigtable?

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Angela Bailey

What Is the Data Structure That Stores Google’s Bigtable?

In the world of big data, Google’s Bigtable stands as one of the most powerful and versatile data storage systems. It is a distributed, petabyte-scale NoSQL database designed to handle massive amounts of structured data across thousands of commodity servers.

But have you ever wondered what data structure lies at the heart of this robust system? In this article, we will explore the underlying data structure that powers Google’s Bigtable.

The Foundation: Distributed Sorted Map

At its core, Bigtable relies on a distributed sorted map to store and organize its vast amount of data. This data structure is similar to a traditional map or dictionary, but with two key differences: distribution and sorting.

  • Distribution: Bigtable partitions its data across multiple servers in a distributed manner. Each partition is called a “tablet” and is responsible for storing a subset of the overall data.
  • Sorting: The keys within each tablet are sorted lexicographically. This sorting allows for efficient retrieval and range scans over the keyspace.

This combination of distribution and sorting enables Bigtable to scale horizontally while maintaining efficient access patterns.

Underlying Storage: SSTables

To implement the distributed sorted map, Bigtable utilizes a disk-based storage structure called Sorted String Tables (SSTables). An SSTable is an immutable file that represents a sorted mapping from keys to values.

Each SSTable consists of multiple blocks:

  1. Data Blocks: These blocks contain compressed key-value pairs. They allow for efficient random access to individual entries within an SSTable.
  2. Index Block: This block contains an index that enables efficient lookup of a specific key within the SSTable.
  3. Filter Block: This block contains a compact data structure called a Bloom filter. The Bloom filter helps determine if a key exists in the SSTable, reducing unnecessary disk reads.

SSTables are stored on the file system and are periodically compacted to optimize storage utilization and query performance. This compaction process merges overlapping SSTables, discards deleted entries, and creates new compacted SSTables.

Metadata Management: Chubby

In addition to the distributed sorted map and SSTables, Bigtable relies on another Google technology called Chubby for metadata management. Chubby is a lock service that provides distributed coordination and consensus across multiple servers.

Chubby ensures consistency and availability of metadata such as tablet locations, schema information, and access control lists. It acts as a centralized repository for storing this critical information, allowing Bigtable to operate efficiently in a distributed environment.

In Conclusion

In summary, Google’s Bigtable utilizes a distributed sorted map as its underlying data structure. This structure enables scalable storage and efficient retrieval of vast amounts of structured data.

The use of SSTables as the storage format ensures durability and high-performance access patterns. With additional support from Chubby for metadata management, Bigtable stands as one of the most powerful data storage systems in the industry.

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