The evolution of linux file system
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The document summarizes the evolution of Linux file systems from local to cluster to distributed systems. It discusses Ext2, Ext3, and Ext4 local file systems and improvements made to support larger file systems and reduce filesystem check times. It introduces cluster file systems used with shared storage for high availability and scaling compute and storage. Distributed file systems are described as scaling to unified storage across commodity hardware, with examples like HDFS based on the Google File System model with separate metadata and data servers. Current trends include further scaling out, flash technology use, and unified object/block/file storage.
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Adobe Lightroom Classic is a desktop-based software application for editing and managing digital photos. It focuses on providing users with a powerful and comprehensive set of tools for organizing, editing, and processing their images on their computer. Unlike the newer Lightroom, which is cloud-based, Lightroom Classic stores photos locally on your computer and offers a more traditional workflow for professional photographers.
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The evolution of linux file system
- 1. The evolution of Linux file system Gang He [email protected]
- 2. 2 Agenda • Local file system (LFS) • Cluster file system (CFS) • Distributed file system (DFS)
- 3. Local file system (LFS)
- 5. 5 File system concepts • file descriptor (user space) • struct file, struct dentry, struct inode, struct address_space (kernel space) • struct super_block, meta data, file data, buffer/page cache
- 6. 6 Ext2 → Ext3 → Ext4 • Ext2 (1993), inspired from UFS, first popular and stable Linux file system, but design is plain. - File systems are getting bigger, how to look up a entry under a big directory, how to reduce fsck time after crash ... • Ext3 (2001), add journal, hash-tree directory indexing, etc. - File systems are getting bigger, have to eliminate these limitations - Various advanced file systems impact ext3 ... • Ext4 (2008), 48-bit addressing space, no limited directory entries, extents, multi-block allocation, delayed block allocation, online defragmentation, 256 bytes inode, persistent preallocation, barrier, etc.
- 7. 7 Nowadays • Ext4, will continue to be maintained due to stability and historical reasons • XFS, robust and scalable, good performance for large storage, will shine in handling big file area (e.g. virtual machine image) • BtrFS, new design (replace ext4), inspired from ZFS, contains many features of enterprise file system. for examples, copy on write, own internal RAID (manage volume ), snapshot/clone support, dynamically grow and shrink, SSD support, etc.
- 8. Cluster file system (CFS)
- 9. 9 Why CFS • Independent storage devices (e.g. SAN). • High availability requirement. • How to scale out file system in CPU, memory, even network bandwidth.
- 10. 10 CFS common points • File system POSIX semantics. • Shared disk. • Distributed lock manager (DLM). • Cluster manager stacks.
- 11. 11 CFS future • Scale out more nodes, provide higher aggregation IO bandwidth. • More high availability, support online fsck, online deframentation, online expand/shrink. • File system level snapshot, file level clone. • Tiered storage, SSD support. • Deduplication. • ...
- 12. Distributed file system (DFS)
- 13. 13 Background • Costs, storage array, fabric switches, HBA card, etc are expensive. • Unified storage space, linear expansion, commodity hardware. • Driven by Internet industry (e.g. search, picture share, big data, etc). • Google file system appeared (2003).
- 14. 14 GFS-like DFS (HDFS, MooseFS, KFS)
- 15. 15 DFS common points • Not strictly comply with File system POSIX semantics, most implementations are based on user-space. • Share nothing, meta-data/file data are stored separately, meta-data access/file data access are separated. • Have own local file system, a file represents a logical data block, a data block has several copy blocks. • Meta-data server usually load all meta data into memory at start-up, writing logs records incremental changes, then flushing memory to disk/merging log and previous meta-data file gets a new checkpoint of meta-data. • Other algorithms: heartbeat algorithm, rack-aware, block allocation policy, file lock management, etc.
- 16. 16 Scale out • Meta-data cluster server, e.g. GFS2, Ceph. • Fully symmetric, no central meta-data server, e.g. GlusterFS. • Improved cluster management mechanism. hearbeat/corosync → zookeeper cluster • IO Flow Control, reduce meta-data server dependence, costs control (ECC), etc. VS.
- 17. 17 Current trends • Linear scale out. • CompuStor hyper-converged systems. • Flash technology utilization. • High-speed network support. • Application-Aware (e.g. VM image). • Deduplication/Compression/Snapshot/Clone. • Object/Block/File unified storage.
- 18. 18