Cloud-File-Systems- - GFS HDFS file.pptx

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1. Cloud File Systems: Google File System (GFS) and Hadoop Distributed File System (HDFS).

2. Introduction to Cloud File Systems Old Ways Couldn't Keep Up Traditional file systems struggled to handle the massive amounts of data we started creating, especially when it was spread across many computers. Using Regular Computers GFS and HDFS changed the game. They were designed to use thousands of normal, inexpensive computers instead of a few costly, special ones. Made to Grow Big These systems are built to easily grow, handle problems if parts break, and process huge amounts of data smoothly across many connected computers.

3. Google File System (GFS): 01 Main Controller One main computer (the Master) keeps track of where all the files are, who can use them, and which parts (chunks) are stored where. 02 Storage Servers Hold Data Many other computers (Chunkservers) store the actual file parts, called 'chunks', which are typically 64MB each. These are spread out across many cheap computers to keep your data safe. 03 Users Get Data Directly When your computer (the Client) wants a file, it first asks the Master where it is. Then, it goes straight to the Chunkservers to get the file parts quickly. This system was built for Google to handle huge amounts of data, especially when it's constantly being used and updated.

4. Hadoop Distributed File System (HDFS): Based on GFS Ideas HDFS uses ideas from Google's file system, but it's made for handling and analyzing huge amounts of data in the larger Hadoop system. Main Parts: NameNode and DataNodes The NameNode keeps track of where files are stored. The DataNodes actually hold the pieces of data (called blocks, usually 128MB each) across many regular computers. Designed for One-Time Writing You write data to HDFS once, and then you can read it many times. This is perfect for analyzing big sets of data, like running reports or processing logs.

5.  Comparison: GFS vs. HDFS Feature GFS HDFS Data Block Size 64 MB 128 MB (standard) Number of Copies You can choose how many Usually 3 copies How Data is Added Added once, can add more to the end Added once, only new data can be added to the end What it Runs On Works on Linux systems Works on many systems (made with Java) Main Purpose Fast-moving data, live analysis Analyzing large amounts of data all at once

6. Applications . GFS  Application GFS helps Google Search find information on billions of web pages. It also stores YouTube's huge video library and handles live information for Gmail and Google Maps. HDFS Applications HDFS is the main system for analyzing huge amounts of data with Hadoop. It's used by companies like Yahoo and Facebook, and many others globally, to manage vast amounts of internet usage records and data for AI programs.

7. Conclusion. Their Unique Strengths GFS and HDFS helped create modern ways to store huge amounts of data across many computers. Each has special benefits for different big data tasks. How to Pick the Best One GFS is great for tasks that need data right away, like live streaming. HDFS is best for sorting and analyzing large batches of data, often used in big data warehouses. Building for the Future Knowing their different designs helps engineers build strong systems that can grow easily and keep working even if parts fail. This moves cloud computing and big data forward.