Google file system

Nov 16, 2013Download as PPTX, PDF0 likes1,575 views

The document summarizes Google File System (GFS), which was designed to provide reliable, scalable storage for Google's large data processing needs. GFS uses a master server to manage metadata and chunk servers to store file data in large chunks (64MB). It replicates chunks across multiple servers for reliability. The architecture supports high throughput by minimizing interaction between clients and the master, and allowing clients to read from the closest replica of a chunk.

Google File System
Dhan V Sagar
CB.EN.P2CSE13007
M.Tech CSE I

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Thousands of queries served per second.
One query reads 100's of MB of data.
One query consumes 10's of billions of CPU cycles.
Google Apps
Dozens of copies of the entire Web…!

DVS

INTRODUCTION

• TRADITIONAL ISSUES
• Need large, distributed, highly fault tolerant file system

2

DVS

• Single Master
• Multiple Chunk Servers
• Multiple Clients

4

Master Server
• Manages metadata

• Performs check pointing and logging of changes to metadata

DVS

• Manages chunk creation, replication, placement

• Garbage Collection
• Periodically communicate with chunkservers (Heart Beat
Message)

5

Master Server
• Single Point Failure..??
• Shadow Masters
DVS

• Least involvement of master

6

Chunk
• Files are divided into fixed-size chunks
• Chunk Servers store chunks on local disk as Linux files

• Replication for Reliability

DVS

• Unique 64 bit chunkhandle

• Chunk Size : 64MB - Much larger than typical file system block sizes

7

Large Chunk Size
• Reduce interaction between client and master

• Reduces network overhead by keeping persistent TCP connection
DVS

• Reduce size of metadata stored on the master

8

Client
• Control (metadata) requests to master server

• Caches metadata

DVS

• Data requests to chunkservers

• No caching of data

9

Client Read
• Client sends master:
• read(file name, chunk index)
• chunk ID, chunk version number, locations of replicas

DVS

• Master’s reply:
• Client sends “closest” chunkserver replica:
• read(chunk ID, byte range)
• “Closest” determined by IP address on simple rackbased network topology

• Chunkserver replies with data
10

DVS

Client Write

1. Application originates the request

2. GFS client translates request and sends it to master
3. Master responds with chunk handle and replica locations

13

DVS

Client Write

4. Client pushes write data to all locations. Data is stored

in chunkserver’s internal buffers
14

DVS

Client Write

5. Client sends write command to primary
6. Primary determines serial order for data instances in its buffer and writes the

instances in that order to the chunk
7. Primary sends the serial order to the secondaries and tells them to perform
the write

15

DVS

Client Write

8. Secondaries respond back to primary

9. Primary responds back to the client
16

File Deletion
• Master records deletion in its log
• File renamed to hidden name including deletion
timestamp

DVS

• When client deletes file:

• Master scans file namespace in background:
• In-memory metadata erased

• Master scans chunk namespace in background:
• Removes unreferenced chunks from chunkservers
17

References

DVS

• The Google File System - Sanjay Ghemawat, Howard
Gobioff, and Shun-Tak Leung

18

The Google File System is a distributed file system designed by Google to provide scalability, fault tolerance, and high performance on commodity hardware. It uses a master-slave architecture with one master and multiple chunkservers. Files are divided into 64MB chunks which are replicated across servers. The master maintains metadata and controls operations like replication and load balancing. Writes are replicated to replicas in order by the primary chunkserver holding the lease. The system provides high availability and reliability through replication and fast recovery from failures. It has been shown to achieve high throughput for Google's large-scale data processing workloads.

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GFS is a distributed file system designed by Google to store and manage large files on commodity hardware. It is optimized for large streaming reads and writes, with files divided into 64MB chunks that are replicated across multiple servers. The master node manages metadata like file mappings and chunk locations, while chunk servers store and serve data to clients. The system is designed to be fault-tolerant by detecting and recovering from frequent hardware failures.

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The document describes the Google File System (GFS), which was developed by Google to handle its large-scale distributed data and storage needs. GFS uses a master-slave architecture with the master managing metadata and chunk servers storing file data in 64MB chunks that are replicated across machines. It is designed for high reliability and scalability handling failures through replication and fast recovery. Measurements show it can deliver high throughput to many concurrent readers and writers.

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Want to learn practical tips for designing systems that can scale efficiently without compromising speed? Join us for a workshop where we’ll address these challenges head-on and explore how to architect low-latency systems using Rust. During this free interactive workshop oriented for developers, engineers, and architects, we’ll cover how Rust’s unique language features and the Tokio async runtime enable high-performance application development. As you explore key principles of designing low-latency systems with Rust, you will learn how to: - Create and compile a real-world app with Rust - Connect the application to ScyllaDB (NoSQL data store) - Negotiate tradeoffs related to data modeling and querying - Manage and monitor the database for consistently low latencies

Heap, Types of Heap, Insertion and DeletionJaydeep Kale

ThousandEyes Partner Innovation Updates for May 2025ThousandEyes

Massive Power Outage Hits Spain, Portugal, and France: Causes, Impact, and On...Aqusag Technologies

Role of Data Annotation Services in AI-Powered ManufacturingAndrew Leo

DevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptxJustin Reock

Building 10x Organizations with Modern Productivity Metrics 10x developers may be a myth, but 10x organizations are very real, as proven by the influential study performed in the 1980s, ‘The Coding War Games.’ Right now, here in early 2025, we seem to be experiencing YAPP (Yet Another Productivity Philosophy), and that philosophy is converging on developer experience. It seems that with every new method we invent for the delivery of products, whether physical or virtual, we reinvent productivity philosophies to go alongside them. But which of these approaches actually work? DORA? SPACE? DevEx? What should we invest in and create urgency behind today, so that we don’t find ourselves having the same discussion again in a decade?

TrsLabs - Fintech Product & Business ConsultingTrs Labs

Hybrid Growth Mandate Model with TrsLabs Strategic Investments, Inorganic Growth, Business Model Pivoting are critical activities that business don't do/change everyday. In cases like this, it may benefit your business to choose a temporary external consultant. An unbiased plan driven by clearcut deliverables, market dynamics and without the influence of your internal office equations empower business leaders to make right choices. Getting things done within a budget within a timeframe is key to Growing Business - No matter whether you are a start-up or a big company Talk to us & Unlock the competitive advantage

Increasing Retail Store Efficiency How can Planograms Save Time and Money.pptxAnoop Ashok

Manifest Pre-Seed Update | A Humanoid OEM Deeptech In Francechb3

Technology Trends in 2025: AI and Big Data AnalyticsInData Labs

At InData Labs, we have been keeping an ear to the ground, looking out for AI-enabled digital transformation trends coming our way in 2025. Our report will provide a look into the technology landscape of the future, including: -Artificial Intelligence Market Overview -Strategies for AI Adoption in 2025 -Anticipated drivers of AI adoption and transformative technologies -Benefits of AI and Big data for your business -Tips on how to prepare your business for innovation -AI and data privacy: Strategies for securing data privacy in AI models, etc. Download your free copy nowand implement the key findings to improve your business.