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Journaling file system

A journaling file system is a type of that maintains a dedicated log, called a , to record and sometimes changes before they are committed to the primary structures, drawing from logging principles to ensure atomicity and consistency. This approach prevents inconsistencies during system crashes by allowing through replaying the , which restores the to a known good state much faster than traditional methods that require exhaustive checks like . The origins of journaling trace back to the early 1990s in environments, with developing the Journaled File System (JFS) in 1990 for its AIX operating system to enhance reliability in high-availability servers. Silicon Graphics followed with in 1994, optimized for on , which later ported to in 2001. In the ecosystem, the file system, introduced in 2001 as a journaling extension to the widely used , became a standard for its compatibility and reduced recovery times, paving the way for successors like in 2008, in 2001 for efficient small-file handling. Microsoft's , deployed since in 1993, incorporates journaling through a ($LogFile) for changes, enabling recovery to a consistent state after system failures, and supports features like file compression and . Journaling systems vary in operation modes to trade off between performance, safety, and storage overhead: the writeback mode logs only changes for speed but risks inconsistency; ordered mode (common default, e.g., in /) writes blocks before their to prevent stale exposure; and data mode journals both and for full protection against corruption, at the cost of higher overhead. Benefits include against power failures or improper shutdowns, elimination of lengthy checks on boot, and support for large-scale storage, though they introduce minor from journaling. These systems underpin , enabling reliable in desktops, servers, and embedded devices.

Fundamentals

Definition

A journaling file system is a type of that records pending updates to its structures in a dedicated , known as a , before committing those changes to the main area on disk. This approach ensures that in the event of a or power failure, the can quickly recover by replaying the committed transactions from the , avoiding the need for extensive scanning of the entire disk. To understand journaling file systems, it is helpful to review basic file system components. A file system organizes on storage devices into fixed-size units called blocks, which serve as the fundamental allocation units for storing file contents and . Each file is represented by an inode, a that holds such as file size, permissions, timestamps, and pointers to the blocks containing the file's . The is a critical that describes the overall file system layout, including the number of blocks, inodes, and the location of other key structures like the free space . The journal itself functions as an append-only log or a , typically allocated as a contiguous or file-based region on disk, where —groups of changes—are written sequentially. Journaling systems distinguish between journaling, which logs only changes to structures like inodes and directories (while applying writes directly), and full journaling, which also logs file blocks to guarantee their . In ordered metadata-only mode, is written to its final location before the corresponding is committed, balancing and safety. Prominent examples of journaling file systems include:
  • ext3: Developed by Stephen Tweedie and released in 1999 for the Linux kernel 2.2, as a journaling extension of the ext2 file system.
  • ext4: Introduced in 2008 as an enhanced successor to ext3, led by Theodore Ts'o, with improvements in scalability and performance for larger volumes.
  • JFS: Created by IBM in 1990 for the AIX operating system and ported to Linux in 2001, emphasizing high throughput for enterprise workloads.
  • XFS: Originated by Silicon Graphics (SGI) in 1994 for the IRIX platform and ported to Linux in 2001, optimized for high-performance computing and large files.
  • NTFS: Developed by Microsoft starting in 1993 for Windows NT 3.1, serving as the default file system for modern Windows with support for security and quotas.
  • ReiserFS: Designed by Hans Reiser and Namesys, released in 2001 for Linux, focusing on efficient handling of small files through a B-tree structure (now deprecated and removed from the Linux kernel as of 2024).

Rationale

Non-journaling file systems, such as the original filesystem, face significant risks of corruption during system es or power failures because file operations typically require multiple asynchronous disk writes that may only be partially completed. If a interrupts these operations, the filesystem can enter an inconsistent state where structures like inodes, directories, or block allocation maps are left partially updated, potentially leading to , orphaned files, or allocation errors. Recovery in such cases relies on tools like , which must scan and repair the entire filesystem, often taking hours for large volumes due to the need to traverse all blocks and . Journaling file systems mitigate these issues by maintaining a of pending changes, ensuring that involves replaying only the rather than a full , which reduces boot times from hours to seconds or even sub-seconds. This approach minimizes the risk of filesystem inconsistencies by guaranteeing atomicity for updates and supports reliable execution of multi-step operations, such as file creation or deletion, even if interrupted. However, journaling introduces trade-offs, including write performance overhead from double-writing to the and main filesystem, which can slow writes in synchronous workloads. Additionally, the requires a small dedicated portion of the disk, often less than 1% for large filesystems depending on configuration, to hold logs without impacting the primary area. These benefits make journaling particularly valuable in use cases prone to unexpected interruptions, such as enterprise servers handling high volumes, requiring consistent state after failures, and mobile devices susceptible to sudden power loss during drain or removal.

Historical Development

Origins

The conceptual roots of journaling file systems trace back to transaction logging techniques in database systems during the 1970s and 1980s. Write-ahead logging (WAL), a method for ensuring atomicity and durability by recording transaction changes to a sequential log before applying them to the main data structures, emerged as a key innovation in late-1970s database research, such as IBM's System R project. This approach addressed consistency issues in multi-user environments prone to failures. The ARIES (Algorithms for Recovery and Isolation Exploiting Semantics) recovery algorithm, developed by C. Mohan and colleagues at IBM and published in 1992, built on WAL principles with three-phase recovery (analysis, redo, undo) and explicitly extended its applicability to recoverable file systems beyond databases. Pioneering file system research in the late 1980s at UC Berkeley produced the Sprite Log-Structured File System (LFS), a prototype that served as an influential precursor to journaling by appending all file system modifications—including data and metadata—sequentially to a disk log, rather than scattering updates randomly. Implemented by Mendel Rosenblum and John K. Ousterhout, Sprite LFS, detailed in their 1992 paper, achieved up to an order-of-magnitude improvement in write performance for small files on contemporary hardware by exploiting sequential disk access patterns, though it required complex cleaner processes to manage log space. The first production journaling file system appeared in 1990 with IBM's Journaled File System (JFS), integrated into AIX version 3.1 as a metadata-focused logging mechanism to enable rapid crash recovery without full file system scans. This development was motivated in part by the limitations of earlier Unix file systems, such as the Berkeley Fast File System (FFS) designed by Marshall Kirk McKusick, William Joy, Samuel Leffler, and Robert Fabry in 1983 for 4.2BSD, where post-crash consistency checks via on VAX minicomputers could require hours for large volumes due to exhaustive verification of inodes and blocks. Hardware constraints of the era, including disk times exceeding 30 milliseconds and limited to a few megabytes per second, posed significant early challenges for journaling, as full-data logging risked amplifying and reducing throughput. Consequently, initial designs like JFS prioritized metadata-only journaling to limit overhead, logging only structural changes (e.g., inode updates) while assuming writes were durable, thereby achieving sub-second times even on multi-gigabyte volumes.

Key Milestones

The development of journaling file systems gained momentum in the 1990s with the introduction of by alongside in 1993, marking one of the first widespread implementations of journaling to enhance in enterprise environments. Shortly thereafter, released in December 1994 for its operating system, emphasizing high-performance journaling for large-scale storage systems. These early systems laid the groundwork for journaling adoption beyond experimental prototypes. Entering the early 2000s, saw significant breakthroughs with the release of in November 2001 as part of version 2.4.15, building on with journaling capabilities developed since 1998 to provide crash recovery without full checks. Earlier in 2001, debuted with version 2.4.1, introducing B-tree-based structures for efficient handling of small files and directories in journaling contexts. open-sourced JFS in 1999, with integration following in 2001, bringing balanced tree optimizations from its AIX origins to open-source ecosystems. was ported to in 2001, enabling its high-throughput journaling features for broader use. The 2000s also featured widespread integration of these systems into the , solidifying journaling as a standard for reliability in distributions. By the late 2000s, emerged in 2008 with kernel 2.6.28, enhancing ext3's journaling through features like delayed allocation to reduce fragmentation and improve performance on larger volumes. released in 2005 under , influencing journaling hybrids with its mechanisms integrated into a pooled storage model. In the 2010s, entered the Linux kernel in 2009, combining journaling with for advanced snapshotting and , though it evolved toward hybrid approaches. Apple's APFS, introduced in 2016 with , optimized SSD performance through a scheme that achieves journaling-like crash protection via , eliminating traditional logs for faster recovery. Recent developments as of 2025 have focused on refining journaling for modern hardware, including improvements to ext4's operations and journaling in 6.x series. Notably, support was fully removed from the Linux kernel in version 6.13 (November 2024), marking the end of maintenance for this pioneering journaling file system due to lack of upstream development. Barriers such as Microsoft's patents on were addressed through licensing agreements, like the 2009 deal with Tuxera for the open-source NTFS-3G driver, enabling reliable cross-platform support without infringement risks.

Core Mechanisms

Journal Structures

A journaling file system's journal is structured as an log, where updates are written sequentially to ensure atomicity and ordered recovery. This log includes headers with transaction identifiers (TIDs) and sequence numbers to track the progress and ordering of operations, preventing replays of outdated entries during recovery. The log functions as a , allowing continuous overwriting of space from committed transactions once they are no longer needed. Key components of the journal include descriptor blocks, which detail changes such as inode modifications or entries; data blocks or tags, which either contain the updated content or reference block pointers for data writes; and commit blocks, which signal the successful completion of a . These elements are grouped into , with descriptors marking the start, followed by the , and commit blocks at the end. Journals vary between fixed-size formats, such as the contiguous 32 MB maximum in , and variable-size configurations that adapt to needs, as in where size is set during formatting. Space management in the journal relies on sizing it to buffer brief bursts of activity, with typically defaulting to around 32 MB for larger volumes to support recovery in approximately one second. Checkpointing reclaims space by verifying that committed updates have been applied to the main , advancing the log tail to free blocks for new transactions and preventing unbounded growth. Variations in journal implementation distinguish between fully on-disk storage for durability and in-memory buffering of dirty before commit, reducing while relying on the log for crash consistency. The journal's location is integrated with the , such as through a reserved inode in , which maintains with non-journaled layouts like ext2.

Logging Process

In journaling file systems, the logging process follows a transaction-based model to ensure atomicity and consistency of file system updates. A transaction begins by marking the start of a sequence of operations, such as metadata modifications or data writes, which are then logged as pending changes in the journal before any permanent commitment to the main file system structures. The transaction commits atomically once the log entries are synchronized to stable storage, typically via a flush operation that guarantees the journal contents are durable on disk. This write-ahead logging approach allows multiple file system operations to be batched into a single transaction, improving efficiency by reducing the frequency of disk synchronizations while maintaining the illusion of atomic updates. The write phases in the logging process emphasize ordered to prevent inconsistencies. Changes are first written to the in a dedicated area, often using a structure for sequential appends, before being applied to the main . Barriers or explicit flushes are then employed to enforce write ordering, ensuring that commits occur after preceding writes but before dependent updates, thereby preserving causal relationships across layers. After checkpointing, where committed transactions are replayed or discarded from the , the changes are propagated to their final locations in the , freeing space for new entries. Journaling file systems support configurable modes that balance consistency guarantees with performance, exemplified in implementations like ext3. In data=ordered mode, only metadata changes are logged in the journal, while file data is written directly to its final disk location before the corresponding metadata commit, relying on barriers to synchronize data writes and minimize corruption risks without full data journaling. The data=journal mode provides stronger guarantees by logging both file data and metadata in the journal prior to commitment, ensuring full atomicity but at the cost of writing data twice—once to the journal and again to the main file system. In contrast, data=writeback mode journals only metadata asynchronously, allowing data writes without immediate ordering, which offers the highest performance but exposes the system to potential data exposure or old data visibility after crashes. These processes introduce overheads inherent to the journaling mechanism, particularly the double-write penalty in full data journaling modes, where each modified block must be copied to the before its permanent location, doubling I/O operations and increasing for write-intensive workloads. Batching multiple operations into compound mitigates this by amortizing the fixed costs of journal commits across several changes, though transaction size is typically limited to a fraction of the journal capacity to bound recovery time.

Recovery Procedures

Upon system startup following a , journaling file systems first detect inconsistencies by examining the for a clean shutdown flag; if absent, the is scanned for uncommitted s marked by incomplete commit records or mismatched sequence numbers. In , for instance, the journal provides the sequence number of the last committed , allowing the process to identify and skip any partial updates. The core recovery mechanism employs a forward replay , where the system sequentially applies all committed transactions from the to the main structures, ensuring and, in data-journaling modes, contents are restored to a consistent state. Incomplete transactions—those lacking a valid commit block—are discarded, effectively rolling back any in-flight changes without altering already-committed data. This process relies on idempotency, achieved through redo that records the final outcomes of operations rather than procedural steps, preventing duplicate applications even if recovery replays overlapping transactions. Recovery is typically integrated into the mount process for the , with tools like e2fsck handling verification and repair for the ext family by replaying the and checking for broader inconsistencies. The time complexity is linear in the size, O(journal blocks), which remains efficient as journals are orders of magnitude smaller than the full disk volume, often completing in seconds even for large systems. Edge cases include power loss during a commit, where the absence of a commit record ensures the partial transaction is ignored, preserving prior consistency without data loss. Wrapped journals, which circularly overwrite old entries after reaching capacity (e.g., 2^32 blocks in ext4), are handled by starting the scan from the superblock's last sequence number, avoiding replay of checkpointed data. Additionally, checksum verification using algorithms like CRC32C on journal blocks detects corruption during replay, triggering abort or repair as needed.

Implementation Techniques

Physical Journaling

Physical journaling involves logging entire disk blocks, typically in fixed sizes such as 4 KB, encompassing both and as raw physical units before they are written to their final locations in the . This approach, also known as full or journaling, ensures that all changes are captured atomically in the , providing a complete for recovery. In systems like , this is implemented in the data=journal mode, where every modified block is first appended to the journal with tags indicating its type and destination. The primary advantage of physical journaling is its guarantee of full atomicity for both and , preventing inconsistencies even in the event of a crash during data writes, which makes it particularly suitable for applications requiring strict , such as . However, it incurs significant overhead due to the need to write each block twice—once to the and once to the final position—potentially doubling I/O operations and increasing space usage in the journal. This double-write penalty can lead to performance degradation, with benchmarks showing in data=journal mode achieving only 33-50% of the throughput of non-journaled under certain workloads. In implementation, the journal maintains tags for each block entry, including identifiers for the block's location and boundaries marked by commit headers, allowing for idempotent replay during . Upon system restart, the process scans the journal sequentially, copying valid, uncommitted blocks back to their intended positions in the main while skipping already-applied ones based on sequence numbers. This block-level tagging and copying mechanism contrasts with higher-level by treating data opaquely as full units, without file system semantics during journaling. Examples of physical journaling include the data=journal mode in ext3.

Logical Journaling

Logical journaling represents an approach in journaling file systems where metadata modifications are recorded as high-level, abstract operations—such as "allocate X to inode Y" or "update field Z in directory entry"—rather than logging the complete contents of affected data s. This method focuses exclusively on structures like inodes, directories, and allocation bitmaps, excluding user data s to minimize overhead. During , the file system replays these logged operations using specialized code to reconstruct the consistent state, ensuring atomicity without needing to store or replay full images. The key benefit of logical journaling lies in its reduced storage and I/O requirements, as metadata typically accounts for a small portion of file system activity—often less than 1% of total data volume—enabling faster write performance and quicker recovery times for metadata-intensive workloads compared to approaches that log user data. However, this efficiency comes at the cost of lower fault tolerance for user data integrity; if a crash occurs after writing data but before committing the corresponding metadata operation, the data may become inaccessible or appear corrupted, as the journal does not protect against such mismatches. Implementation of logical journaling relies on structured operation logs comprising descriptors that encode the intent and parameters of metadata changes, such as create, delete, or rename actions, often including support for reverse operations to enable or during partial . These logs are written to the journal in a compact format, with commit records marking boundaries and revoke entries preventing replay of superseded operations. In , the default ordered journaling mode employs descriptor blocks to succinctly capture multiple metadata updates, providing inherent compression and reducing journal traffic for operations like file allocations. implements metadata journaling through a write-ahead log that records individual structural changes to metadata objects, such as inode modifications, allowing for scalable in high-volume environments without the overhead of logging. These techniques yield substantial efficiency improvements, with logical approaches demonstrating lower journal write volumes than full block in comparable scenarios.

Write Hazard Mitigation

In journaling file systems, write hazards primarily arise from disk controllers and storage devices that reorder or buffer writes to optimize performance, potentially leading to inconsistent on-disk states such as updates committing before corresponding blocks are persisted—a phenomenon known as "tearing." This can result in scenarios where file pointers reference unwritten or garbage , causing upon . For instance, in metadata-only journaling, if a reorders operations, an inode modification might be journaled while the associated block remains unflushed, leaving the in an invalid state. To mitigate these hazards, journaling systems employ write barriers, which are explicit commands that ensure all preceding writes complete to stable storage before subsequent operations proceed, preserving the intended order despite device-level reordering. In implementations like and , barriers are integrated via system calls such as fdatasync() or mount options like barrier=1, forcing the disk to flush its write-back cache at critical points, such as before journal commits. Additionally, ordered journaling mode—common in journaling—addresses tearing by requiring blocks to be synchronously written to their final locations on disk prior to journaling the associated metadata, thus guaranteeing that pointers never reference uncommitted data. Advanced techniques further enhance reliability, including journal-specific barriers that delimit commits to prevent partial writes and checksums embedded in entries to verify integrity and detect incomplete operations. In , for example, checksums allow the to identify and skip corrupted journal blocks during replay, reducing the risk of propagating errors. For solid-state drives (SSDs), where barriers can exacerbate by triggering unnecessary erases and garbage collection, mitigations involve optimizing barrier usage—such as selective application in ordered mode—or leveraging SSD features for multi-block writes to minimize wear while maintaining order. These mitigations impose performance trade-offs, as barriers introduce by draining disk caches, typically adding 1-10 milliseconds per operation depending on , which can high-throughput workloads like . In or latency-sensitive systems, disabling barriers (e.g., via barrier=0 option) may be considered if the storage uses battery-backed caches or , though this increases risk from reordering. Overall, the balance favors enabled barriers in standard configurations to prioritize over raw speed.

Alternatives

Soft Updates

Soft updates represent a non-journaling for maintaining file system consistency by tracking and enforcing dependencies among metadata updates, allowing writes to be delayed and reordered in without requiring a dedicated log structure. The core mechanism involves maintaining a for operations such as block allocations, inode updates, and directory entries; for instance, an inode update that references a newly allocated block is not committed to disk until the allocation itself is persisted, preventing orphaned or dangling references during a . This dependency tracking occurs in the buffer , where updates are batched and flushed in an order that preserves atomicity, often using mechanisms to revert partial writes if prerequisites fail. A primary advantage of soft updates is the elimination of journal overhead, enabling faster write performance by avoiding extra disk I/O for and permitting most operations to proceed asynchronously, which can improve throughput in metadata-intensive workloads compared to synchronous alternatives. However, this approach is limited to and does not protect user data, and in the event of a , the may still require a full scan to resolve any residual inconsistencies, potentially taking several minutes on large volumes. In contrast, journaling systems achieve recovery in seconds through log replay, though at the cost of higher runtime . Soft updates were implemented in the (UFS) variant known as the Berkeley Fast File System, with integration into 's UFS2 during the late 1990s, building on earlier dependency-tracking concepts from the mid-1990s. The technique uses intent logging in memory for batches of related updates, such as directory modifications, to coordinate flushes efficiently without persistent storage. This implementation has been refined over time, including extensions like journaled soft updates in later versions, but the base form remains focused on dependency ordering for low-overhead consistency.

Log-Structured File Systems

Log-structured file systems (LFS) treat the entire as a single , where all modifications—including both and —are written sequentially to the disk without in-place updates. This approach eliminates the fragmentation typical of traditional file systems by organizing storage into fixed-size segments, typically 512 to 1 , which are filled sequentially before advancing to the next. Inodes and structures are also logged, with a fixed master inode map pointing to the most recent versions, ensuring that reads can locate current by following pointers backward through the . Unlike journaling systems that maintain a separate for transactional , LFS integrates the as the primary storage medium, simplifying the but requiring mechanisms to reclaim space from obsolete . The writing process in LFS buffers changes in and flushes them as complete segments to the , achieving high throughput by minimizing seek operations. To handle space reclamation, LFS employs garbage collection through segment cleaning: the cleaner scans segments to identify live (valid) and dead (obsolete) data, then copies live data to new clean segments and erases the old ones, effectively defragmenting the . Cleaning policies, such as or cost-benefit algorithms, prioritize segments with high ratios of to reduce overhead; in the original LFS implementation, this process runs in the background and separates files into generations similar to generational garbage collection in programming languages. For random writes, which could otherwise fragment the , segment cleaners consolidate updates, though this introduces where the total disk writes exceed user data writes by factors of 2 to 10 times, depending on workload and utilization. Modern variants optimize this for flash storage by separating hot and cold data into multi-head and using adaptive to switch modes based on free space availability. LFS excels in environments with sequential write patterns, such as SSDs, where it leverages the device's native efficiency to achieve up to 65-75% of disk utilization, compared to 5-10% in traditional Unix file systems like FFS. The absence of a separate reduces complexity and overhead, while crash recovery is rapid, involving a scan of only the recent log portion rather than the entire . However, the process can degrade under high utilization or random workloads, as it competes for I/O resources and amplifies writes, potentially shortening device lifespan on flash media. The seminal LFS prototype, developed in 1985 and detailed in 1991, demonstrated up to 10 times faster small-file write performance over FFS on disk-based systems. A prominent modern implementation is Linux's , introduced in 2012 and optimized for NAND flash, which outperforms by up to 3.1 times in benchmarks like iozone on mobile devices by mitigating the "wandering tree" problem in metadata and aligning with flash translation layer operations. In contrast to journaling file systems' hybrid approach—which logs metadata or data selectively before applying changes in-place—LFS's full-log structure provides inherent versioning and atomicity but demands proactive cleaning to manage patterns, making it particularly suited for write-heavy, append-dominant applications like or embedded systems. The concepts pioneered in LFS significantly influenced the development of journaling techniques by demonstrating the benefits of log-based storage for reliability and performance.

Copy-on-Write File Systems

Copy-on-write (COW) file systems represent an alternative approach to ensuring data consistency and crash recovery by avoiding in-place modifications entirely, instead creating new versions of modified blocks and updating filesystem pointers atomically. In this mechanism, when a write operation occurs, the filesystem allocates fresh space for the updated block, copies the modified data into it, and then adjusts the metadata pointers—such as those in the block tree—to reference the new block while leaving the original unchanged. This process propagates up the tree structure until the root pointer, often called the uberblock in systems like ZFS, is updated in a single atomic step, ensuring that the filesystem always presents a consistent view to the operating system either before or after the update, without partial states. Snapshots are a natural byproduct of COW, as they simply retain pointers to the unaltered original blocks, allowing instantaneous, space-efficient point-in-time copies that share data with the active filesystem until further divergence. The immutable nature of data blocks in COW systems facilitates easy backups and , as unchanged paths in the block tree can be referenced directly without duplication, promoting through verification on all blocks to detect . However, this approach incurs higher space usage due to the temporary retention of both old and new block versions, which can lead to overhead before garbage collection reclaims space, and it introduces read amplification because indirection may require traversing more pointers to access data. Additionally, while COW enhances by eliminating the need for post-crash replay, it can suffer from fragmentation over time, particularly in metadata-heavy operations, potentially degrading performance on spinning disks. Prominent implementations include , introduced by in 2005 as part of , which integrates COW with built-in volume management and RAID-like redundancy via to provide end-to-end and self-healing capabilities. , developed by Chris Mason at starting in 2007 and merged into the , extends COW with structures for efficient indexing, supporting features like subvolumes, , and multi-device levels for scalable storage pools. Unlike traditional journaling systems, COW achieves atomicity through block versioning and pointer indirection rather than logging changes, which eliminates journal overhead but enables superior deduplication by allowing shared references to identical immutable blocks across files.

References

  1. [1]
    OS Lecture #25 - NYU Computer Science
    A journaling file system prevents the problems by using an idea from database theory, namely transaction logs. To ensure that the multiple sub-actions are all ...4.4 File System Management... · 4.4. 1 Disk Space Management · 4.5 Example File Systems
  2. [2]
    [PDF] Anatomy of Linux journaling file systems - IBM
    Jun 4, 2008 · Learn about the various journaling file systems in use today, and peek into the next generation of journaling file systems. You can define ...
  3. [3]
    Change Journals - Win32 apps - Microsoft Learn
    Jan 7, 2021 · The NTFS file system associates an unsigned 64-bit identifier with each change journal. Creating, Modifying, and Deleting a Change JournalMissing: journaling | Show results with:journaling
  4. [4]
    [PDF] Analysis and Evolution of Journaling File Systems - USENIX
    We use SBA to analyze Linux ext3, ReiserFS, JFS, and Windows NTFS; in the process, we uncover many strengths and weaknesses of these journaling file systems. We ...
  5. [5]
    [PDF] JOURNALING FILE SYSTEMS
    6. Page 7. Filesystem Journaling (2) • Like the free-map, the filesystem journal is a separate region of the disk volume, devoted to journaling.Missing: definition | Show results with:definition
  6. [6]
    Ext3 Filesystem - The Linux Kernel documentation
    Ext3 was originally released in September 1999. Written by Stephen Tweedie for the 2.2 branch, and ported to 2.4 kernels by Peter Braam, Andreas Dilger, Andrew ...Missing: origin | Show results with:origin
  7. [7]
    ext4 General Information - The Linux Kernel documentation
    Ext4 is an advanced level of the ext3 filesystem which incorporates scalability and reliability enhancements for supporting large filesystems (64 bit)Missing: origin | Show results with:origin
  8. [8]
    The SGI XFS Filesystem - The Linux Kernel documentation
    XFS is a high performance journaling filesystem which originated on the SGI IRIX platform. It is completely multi-threaded, can support large files and large ...
  9. [9]
    NTFS overview - Microsoft Learn
    Jun 18, 2025 · NTFS is the default file system for modern Windows-based operating system (OS). It provides advanced features, including security descriptors, encryption, disk ...Increased Reliability · Support For Large Volumes · Maximum File Name And PathMissing: origin | Show results with:origin
  10. [10]
    [PDF] Journaling the Linux ext2fs Filesystem - Columbia CS
    This paper describes a work-in-progress to design and implement a transactional metadata journal for the Linux ext2fs filesystem. We review the problem of ...Missing: definition | Show results with:definition
  11. [11]
    IBM's Journaled Filesystem
    Non-journaling filesystems are subject to corruption in the event of a system failure. This is because a logical file operation often takes multiple media I/Os ...<|control11|><|separator|>
  12. [12]
    [PDF] Analysis and Evolution of Journaling File Systems - cs.wisc.edu
    In this section, we analyze the popular Linux filesystem, ext3. We begin by giving a brief overview of ext3, and then apply semantic block-level analysis and ...
  13. [13]
    Forensic Analysis of Android Phone Using Ext4 File System Journal ...
    Aug 6, 2025 · Ext4, the most widely used file system in Linux, not only assists large, but also provides fault tolerance through journaling function by ...
  14. [14]
    [PDF] ARIES: A Transaction Recovery Method Supporting Fine-Granularity ...
    ARIES is applicable not only to database management systems but also to persistent object-oriented languages, recoverable file systems and transaction-based.
  15. [15]
    [PDF] The design and implementation of a log-structured file system
    We have implemented a prototype logstructured file system called Sprite. LFS; it outperforms current Unix file systems by an order of magnitude for small-file.
  16. [16]
    [PDF] A Fast File System for UNIX* - Revised July 27, 1983 - Berkeley EECS
    Jul 27, 1983 · This paper describes the changes from the original 512 byte UNIX file system to the new one released with the 4.2 Berkeley Software Distribution ...<|separator|>
  17. [17]
    Analysis and Evolution of Journaling File Systems - USENIX
    We use SBA to analyze Linux ext3, ReiserFS, JFS, and Windows NTFS; in the process, we uncover many strengths and weaknesses of these journaling file systems. We ...Missing: ext4 | Show results with:ext4
  18. [18]
    [PDF] XFS: the big storage file system for Linux - USENIX
    XFS itself did not originate on Linux but was first released on IRIX, a UNIX vari- ant for SGI workstations and servers, in December 1994, almost 15 years ago.
  19. [19]
    What is ext3? -- introduction by The Linux Information Project (LINFO)
    Jan 22, 2007 · Ext3 (the third extended filesystem) is the most commonly used filesystem on Linux. It is basically an extension of ext2 to which a journaling capability has ...
  20. [20]
    ReiserFS Reaches Its Final Chapter - Linuxiac
    Nov 22, 2024 · Introduced in 2001 in kernel 2.4. 1, it was a groundbreaking journaling file system created by Hans Reiser and his team at Namesys. Designed ...
  21. [21]
    IBM's Journaled Filesystem
    Jan 1, 2003 · IBM introduced its UNIX filesystem as the Journaled Filesystem (JFS) with the initial release of AIX Version 3.1. This filesystem is now called ...Missing: origin | Show results with:origin
  22. [22]
    The Ext4 Filesystem - The Linux Information Project
    Mar 24, 2007 · Ext4 was announced on October 10, 2006 by Andrew Morton, one of the lead developers of the Linux kernel. It was included in 2.6.19 kernel, which ...
  23. [23]
    History - - OpenZFS
    May 25, 2014 · 2001 – Development of ZFS started with two engineers at Sun Microsystems. 2005 – Source code was released as part of OpenSolaris. 2006 ...
  24. [24]
    Introduction — BTRFS documentation - Read the Docs
    BTRFS is a modern copy on write (COW) filesystem for Linux aimed at implementing advanced features while also focusing on fault tolerance, repair and easy ...
  25. [25]
    LWN.net Weekly Edition for June 22, 2023
    Jun 21, 2023 · Reports from OSPM 2023, part 2: discussions from the second day of the 2023 Conference on Power Management and Scheduling in the Linux Kernel.Missing: journaling | Show results with:journaling
  26. [26]
    Microsoft signs confidential IP deal over NTFS - ZDNET
    Aug 26, 2009 · Tuxera, an open-source NTFS company, has signed a confidential deal with Microsoft to co-operate on exFAT, a proprietary alternative to the NTFS file system.
  27. [27]
    [PDF] Crash Consistency: FSCK and Journaling - cs.wisc.edu
    An early paper we wrote analyzing how journaling file systems work. [R+11] “Coerced Cache Eviction and Discreet-Mode Journaling” by Abhishek Rajimwale ...
  28. [28]
    [PDF] State of the Art: Where we are with the Ext3 filesystem
    It is clear that since ordered mode is the default journaling mode, any delayed allocation imple- mentation must be able to support it. 4.3 Efficiently ...
  29. [29]
    3.6. Journal (jbd2) — The Linux Kernel documentation
    Should the system crash during the second slow write, the journal can be replayed all the way to the latest commit record, guaranteeing the atomicity of ...
  30. [30]
    https://www.eecs.harvard.edu/~cs161/notes/journaling.pdf
  31. [31]
    [PDF] Journaling - CS 161
    Apr 4, 2017 · • ext3 is a journaling file system that does physical redo logging. • To make a file system update in ordered mode, ext3 does the following:.
  32. [32]
    [PDF] Out-of-Place Journaling - USENIX
    A file system should accomplish atomic write in two steps. The first step, called backup, is to augment the on-disk state with a backup of updates in stable ...
  33. [33]
    [PDF] EXT3, Journaling Filesystem - cs.wisc.edu
    Jul 20, 2000 · The ext3 filesystem is a journaling extension to the standard ext2 filesystem on Linux. Journaling results in massively reduced time spent.Missing: definition | Show results with:definition
  34. [34]
    [PDF] Journaling - File Systems
    • Journaling file systems treat the log as a circular data structure, re ... Prabhakaran, “Analysis and evolution of journaling file systems,” USENIX ATC'05.
  35. [35]
    Improving ext4: bigalloc, inline data, and metadata checksums
    Nov 29, 2011 · Block allocation times drop significantly, but file I/O performance also improves in general as the result of reduced on-disk fragmentation.
  36. [36]
    [PDF] Atomic Writes to Unleash Pivotal Fault-Tolerance in SSDs - USENIX
    Feb 27, 2025 · File systems and DBMSs use journaling or write-ahead logging (WAL) to maintain on-disk consistency even upon power failures or crashes ...
  37. [37]
    Barriers and journaling filesystems - LWN.net
    May 21, 2008 · Barriers ensure all journal data is written to the media before a commit record, preventing corruption. They are disabled by default due to ...<|control11|><|separator|>
  38. [38]
    Soft Updates: A Technique for Eliminating Most Synchronous Writes ...
    This paper describes an implementation of soft updates and its incorporation into the 4.4BSD fast filesystem. It details the changes that were needed, both to ...
  39. [39]
    [PDF] Journaling versus Soft Updates: Asynchronous Meta-data Protection ...
    The final difference between journaling systems and Soft Updates is the ability to provide atomicity of updates. Since a journaling system records a logical ...
  40. [40]
    [PDF] Journaled Soft-updates - Marshall Kirk McKusick
    After a crash, a variant of the venerable fsck program runs through the journal to identify and free the lost resources. Only if an inconsistency between the ...Missing: motivation VAX
  41. [41]
    [PDF] F2FS: A New File System for Flash Storage - USENIX
    In this paper, we present the design and implemen- tation of F2FS, a new file system optimized for mod- ern flash storage devices. As far as we ...
  42. [42]
    None
    ### Summary of Copy-on-Write Mechanism in ZFS
  43. [43]
    [PDF] The Btrfs Filesystem
    ... by the filesystem. • Btrfs works very well overall on low end flash. Page 20. Thank You! • Chris Mason <chris.mason@oracle.com>. • http://btrfs.wiki.kernel.org.
  44. [44]
    [PDF] Copy On Write Based File Systems Performance Analysis And ...
    This thesis is divided into two main parts. The first part focuses on the design and performance of Copy On Write based file systems.<|control11|><|separator|>
  45. [45]
    [PDF] SYSTEMS How to Not Copy Files - USENIX
    In this article, we explore the performance characteristics of an ideal cloning implementation; we show why copy-on-write induces a trade-off that prevents ...