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Google File System: Introduction

Let’s explore Google File System and its use cases.


Design a distributed file system to store huge files (terabyte and larger). The system should be scalable, reliable, and highly available.

What is Google File System (GFS)?

GFS is a scalable distributed file system developed by Google for its large data-intensive applications.


GFS was built for handling batch processing on large data sets and is designed for system-to-system interaction, not user-to-system interaction.

Google built GFS keeping the following goals in mind:

  • Scalable: GFS should run reliably on a very large system built from commodity hardware.
  • Fault-tolerant: The design must be sufficiently tolerant of hardware and software failures to enable application-level services to continue their operation in the face of any likely combination of failure conditions.
  • Large files: Files stored in GFS will be huge. Multi-GB files are common.
  • Large sequential and small random reads: The workloads primarily consist of two kinds of reads: large, streaming reads and small, random reads.
  • Sequential writes: The workloads also have many large, sequential writes that append data to files. Typical operation sizes are similar to those for reads. Once written, files are seldom modified again.
  • Not optimized for small data: Small, random reads and writes do occur and are supported, but the system is not optimized for such cases.
  • Concurrent access: The level of concurrent access will also be high, with large numbers of concurrent appends being particularly prevalent, often accompanied by concurrent reads.
  • High throughput: GFS should be optimized for high and sustained throughput in reading the data, and this is prioritized over latency. This is not to say that latency is unimportant; rather, GFS needs to be optimized for high-performance reading and appending large volumes of data for the correct operation of the system.

GFS use cases

  • GFS is a distributed file system built for large, distributed data-intensive applications like Gmail or YouTube.
  • Originally, it was built to store data generated by Google's large crawling and indexing system.
  • Google's BigTable uses the distributed Google File System to store log and data files.


GFS does not provide standard POSIX-like APIs; instead, user-level APIs are provided. In GFS, files are organized hierarchically in directories and identified by their pathnames. GFS supports the usual file system operations:

create – To create a new instance of a file.
delete – To delete an instance of a file.
open – To open a named file and return a handle.
close – To close a given file specified by a handle.
read – To read data from a specified file and offset.
write – To write data to a specified file and offset.

In addition, GFS supports two special operations:

  • Snapshot: A snapshot is an efficient way of creating a copy of the current instance of a file or directory tree.
  • Append: An append operation allows multiple clients to append data to the same file concurrently while guaranteeing atomicity. It is useful for implementing multi-way merge results and producer-consumer queues that many clients can simultaneously append to without additional locking.
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