 | This article needs additional citations for verification .(July 2016) |
| Developer(s) | |
|---|---|
| Operating system | Linux kernel |
| Type | Distributed file system |
| License | Proprietary |
Google File System (GFS or GoogleFS, not to be confused with the GFS Linux file system) is a proprietary distributed file system developed by Google to provide efficient, reliable access to data using large clusters of commodity hardware. Google file system was replaced by Colossus in 2010. [1]
GFS is enhanced for Google's core data storage and usage needs (primarily the search engine), which can generate enormous amounts of data that must be retained; Google File System grew out of an earlier Google effort, "BigFiles", developed by Larry Page and Sergey Brin in the early days of Google, while it was still located in Stanford. Files are divided into fixed-size chunks of 64 megabytes, similar to clusters or sectors in regular file systems, which are only extremely rarely overwritten, or shrunk; files are usually appended to or read. It is also designed and optimized to run on Google's computing clusters, dense nodes which consist of cheap "commodity" computers, which means precautions must be taken against the high failure rate of individual nodes and the subsequent data loss. Other design decisions select for high data throughputs, even when it comes at the cost of latency.
A GFS cluster consists of multiple nodes. These nodes are divided into two types: one Master node and multiple Chunkservers. Each file is divided into fixed-size chunks. Chunkservers store these chunks. Each chunk is assigned a globally unique 64-bit label by the master node at the time of creation, and logical mappings of files to constituent chunks are maintained. Each chunk is replicated several times throughout the network. At default, it is replicated three times, but this is configurable. [2] Files which are in high demand may have a higher replication factor, while files for which the application client uses strict storage optimizations may be replicated less than three times - in order to cope with quick garbage cleaning policies. [2]
The Master server does not usually store the actual chunks, but rather all the metadata associated with the chunks, such as the tables mapping the 64-bit labels to chunk locations and the files they make up (mapping from files to chunks), the locations of the copies of the chunks, what processes are reading or writing to a particular chunk, or taking a "snapshot" of the chunk pursuant to replicate it (usually at the instigation of the Master server, when, due to node failures, the number of copies of a chunk has fallen beneath the set number). All this metadata is kept current by the Master server periodically receiving updates from each chunk server ("Heart-beat messages").
Permissions for modifications are handled by a system of time-limited, expiring "leases", where the Master server grants permission to a process for a finite period of time during which no other process will be granted permission by the Master server to modify the chunk. The modifying chunkserver, which is always the primary chunk holder, then propagates the changes to the chunkservers with the backup copies. The changes are not saved until all chunkservers acknowledge, thus guaranteeing the completion and atomicity of the operation.
Programs access the chunks by first querying the Master server for the locations of the desired chunks; if the chunks are not being operated on (i.e. no outstanding leases exist), the Master replies with the locations, and the program then contacts and receives the data from the chunkserver directly (similar to Kazaa and its supernodes).
Unlike most other file systems, GFS is not implemented in the kernel of an operating system, but is instead provided as a userspace library. [3]
The Google File System does not provide a POSIX interface. [4] Files are organized hierarchically in directories and identified by pathnames. The file operations such as create, delete, open, close, read, write are supported. It supports Record Append which allows multiple clients to append data to the same file concurrently and atomicity is guaranteed.
Deciding from benchmarking results, [2] when used with relatively small number of servers (15), the file system achieves reading performance comparable to that of a single disk (80–100 MB/s), but has a reduced write performance (30 MB/s), and is relatively slow (5 MB/s) in appending data to existing files. The authors present no results on random seek time. As the master node is not directly involved in data reading (the data are passed from the chunk server directly to the reading client), the read rate increases significantly with the number of chunk servers, achieving 583 MB/s for 342 nodes. Aggregating multiple servers also allows big capacity, while it is somewhat reduced by storing data in three independent locations (to provide redundancy).
In computing, the Global File System 2 or GFS2 is a shared-disk file system for Linux computer clusters. GFS2 allows all members of a cluster to have direct concurrent access to the same shared block storage, in contrast to distributed file systems which distribute data throughout the cluster. GFS2 can also be used as a local file system on a single computer.
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Sector/Sphere is an open source software suite for high-performance distributed data storage and processing. It can be broadly compared to Google's GFS and MapReduce technology. Sector is a distributed file system targeting data storage over a large number of commodity computers. Sphere is the programming architecture framework that supports in-storage parallel data processing for data stored in Sector. Sector/Sphere operates in a wide area network (WAN) setting.
Moose File System (MooseFS) is an open-source, POSIX-compliant distributed file system developed by Core Technology. MooseFS aims to be fault-tolerant, highly available, highly performing, scalable general-purpose network distributed file system for data centers. Initially proprietary software, it was released to the public as open source on May 30, 2008.
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Data-intensive computing is a class of parallel computing applications which use a data parallel approach to process large volumes of data typically terabytes or petabytes in size and typically referred to as big data. Computing applications that devote most of their execution time to computational requirements are deemed compute-intensive, whereas applications are deemed data-intensive require large volumes of data and devote most of their processing time to I/O and manipulation of data.
RozoFS is a free software distributed file system. It comes as a free software, licensed under the GNU GPL v2. RozoFS uses erasure coding for redundancy.
Oracle NoSQL Database is a NoSQL-type distributed key-value database from Oracle Corporation. It provides transactional semantics for data manipulation, horizontal scalability, and simple administration and monitoring.
A distributed file system for cloud is a file system that allows many clients to have access to data and supports operations on that data. Each data file may be partitioned into several parts called chunks. Each chunk may be stored on different remote machines, facilitating the parallel execution of applications. Typically, data is stored in files in a hierarchical tree, where the nodes represent directories. There are several ways to share files in a distributed architecture: each solution must be suitable for a certain type of application, depending on how complex the application is. Meanwhile, the security of the system must be ensured. Confidentiality, availability and integrity are the main keys for a secure system.
Howard Gobioff was a computer scientist. He graduated magna cum laude with a double major in computer science and mathematics from the University of Maryland, College Park. At Carnegie Mellon University, he worked on the network attached secure disks project, before he went on to earn his PhD in computer science. He died suddenly from lymphoma at the age of 36.
LizardFS is an open source distributed file system that is POSIX-compliant and licensed under GPLv3. It was released in 2013 as fork of MooseFS. LizardFS is also offering a paid technical support with possibility of configurating and setting up the cluster and active cluster monitoring.
The MapR File System is a clustered file system that supports both very large-scale and high-performance uses. MapR FS supports a variety of interfaces including conventional read/write file access via NFS and a FUSE interface, as well as via the HDFS interface used by many systems such as Apache Hadoop and Apache Spark. In addition to file-oriented access, MapR FS supports access to tables and message streams using the Apache HBase and Apache Kafka APIs, as well as via a document database interface.
Sanjay Ghemawat is an Indian American computer scientist and software engineer. He is currently a Senior Fellow at Google in the Systems Infrastructure Group. Ghemawat's work at Google, much of it in close collaboration with Jeff Dean, has included big data processing model MapReduce, the Google File System, and databases Bigtable and Spanner. Wired have described him as one of the "most important software engineers of the internet age".
