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This article is about the now proprietary filesystem. For its open-source successor, see OpenZFS. For other uses, see Oracle ZFS (disambiguation).

ZFS
Developer(s)Sun Microsystems originally, Oracle Corporation since 2010.
Full nameZFS
IntroducedNovember 2005 with OpenSolaris
Structures
Directory contentsExtensible hash table
Limits
Max volume size256 trillion yobibytes (2128 bytes)[1]
Max file size16 exbibytes (264 bytes)
Max no. of files
  • Per directory: 248
  • Per file system: unlimited[1]
Max filename length255 ASCII characters (fewer for multibyte character standards such as Unicode)
Features
ForksYes (called "extended attributes", but they are full-fledged streams)
AttributesPOSIX
File system
permissions		POSIX, NFSv4 ACLs
Transparent
compression		Yes
Transparent
encryption		Yes[2]
Data deduplicationYes
Copy-on-writeYes
Other
Supported
operating systems		Solaris, OpenSolaris, illumos distributions, OpenIndiana, FreeBSD, Mac OS X Server 10.5 (limited to read-only), NetBSD, Linux via third-party kernel module ("ZFS on Linux")[3] or ZFS-FUSE, OSv
Repositoryhttps://github.com/zfsonlinux/zfs

ZFS is a combined proprietary file system and logical volume manager. Its open-source successor OpenZFS and the proprietary implementation by Oracle are extremely similar, making ZFS widely available within Unix-like systems. ZFS is a registered trademark belonging to Oracle.[4]

History

Main article: History and implementations of ZFS

Implementations

Main article: History and implementations of ZFS § Implementations

Overview and design goals

ZFS compared to other file systems

The management of stored data generally involves two aspects: the physical volume management of one or more block storage devices such as hard drives and SD cards and their organization into logical block devices as seen by the operating system (often involving a volume manager, RAID controller, array manager, or suitable device driver), and the management of data and files that are stored on these logical block devices (a file system or other data storage).

Example: A RAID array of 2 hard drives and an SSD caching disk is controlled by Intel's RST system, part of the chipset and firmware built into a desktop computer. The Windows user sees this as a single volume, containing an NTFS-formatted drive of their data, and NTFS is not necessarily aware of the manipulations that may be required (such as reading from/writing to the cache drive or rebuilding the RAID array if a disk fails). The management of the individual devices and their presentation as a single device is distinct from the management of the files held on that apparent device.

ZFS is unusual because, unlike most other storage systems, it unifies both of these roles and acts as both the volume manager and the file system. Therefore, it has complete knowledge of both the physical disks and volumes (including their condition and status, their logical arrangement into volumes, and also of all the files stored on them). ZFS is designed to ensure (subject to suitable hardware) that data stored on disks cannot be lost due to physical errors or misprocessing by the hardware or operating system, or bit rot events and data corruption which may happen over time, and its complete control of the storage system is used to ensure that every step, whether related to file management or disk management, is verified, confirmed, corrected if needed, and optimized, in a way that storage controller cards and separate volume and file managers cannot achieve.

ZFS also includes a mechanism for dataset and pool-level snapshots and replication, including snapshot cloning which is described by the FreeBSD documentation as one of its "most powerful features", having features that "even other file systems with snapshot functionality lack".[5] Very large numbers of snapshots can be taken, without degrading performance, allowing snapshots to be used prior to risky system operations and software changes, or an entire production ("live") file system to be fully snapshotted several times an hour, in order to mitigate data loss due to user error or malicious activity. Snapshots can be rolled back "live" or previous file system states can be viewed, even on very large file systems, leading to savings in comparison to formal backup and restore processes.[5] Snapshots can also be cloned to form new independent file systems. A pool level snapshot (known as a "checkpoint") is available which allows rollback of operations that may affect the entire pool's structure, or which add or remove entire datasets.

Features

Summary

ZFS is scalable, and includes extensive protection against data corruption, support for high storage capacities, efficient data compression, integration of the concepts of filesystem and volume management, snapshots and copy-on-write clones, continuous integrity checking and automatic repair, RAID-Z, native NFSv4 ACLs, and can be very precisely configured.

Examples of features specific to ZFS include:

  • Designed for long-term storage of data, and indefinitely scaled datastore sizes with zero data loss, and high configurability.
  • Hierarchical checksumming of all data and metadata, ensuring that the entire storage system can be verified on use, and confirmed to be correctly stored, or remedied if corrupt. Checksums are stored with a block's parent block, rather than with the block itself. This contrasts with many file systems where checksums (if held) are stored with the data so that if the data is lost or corrupt, the checksum is also likely to be lost or incorrect.
  • Can store a user-specified number of copies of data or metadata, or selected types of data, to improve the ability to recover from data corruption of important files and structures.
  • Automatic rollback of recent changes to the file system and data, in some circumstances, in the event of an error or inconsistency.
  • Automated and (usually) silent self-healing of data inconsistencies and write failure when detected, for all errors where the data is capable of reconstruction. Data can be reconstructed using all of the following: error detection and correction checksums stored in each block's parent block; multiple copies of data (including checksums) held on the disk; write intentions logged on the SLOG (ZIL) for writes that should have occurred but did not occur (after a power failure); parity data from RAID/RAIDZ disks and volumes; copies of data from mirrored disks and volumes.
  • Native handling of standard RAID levels and additional ZFS RAID layouts ("RAIDZ"). The RAIDZ levels stripe data across only the disks required, for efficiency (many RAID systems stripe indiscriminately across all devices), and checksumming allows rebuilding of inconsistent or corrupted data to be minimised to those blocks with defects;
  • Native handling of tiered storage and caching devices, which is usually a volume related task. Because ZFS also understands the file system, it can use file-related knowledge to inform, integrate and optimize its tiered storage handling which a separate device cannot;
  • Native handling of snapshots and backup/replication which can be made efficient by integrating the volume and file handling. Relevant tools are provided at a low level and require external scripts and software for utilization.
  • Native data compression and deduplication, although the latter is largely handled in RAM and is memory hungry.
  • Efficient rebuilding of RAID arrays—a RAID controller often has to rebuild an entire disk, but ZFS can combine disk and file knowledge to limit any rebuilding to data which is actually missing or corrupt, greatly speeding up rebuilding;
  • Unaffected by RAID hardware changes which affect many other systems. On many systems, if self-contained RAID hardware such as a RAID card fails, or the data is moved to another RAID system, the file system will lack information that was on the original RAID hardware, which is needed to manage data on the RAID array. This can lead to a total loss of data unless near-identical hardware can be acquired and used as a "stepping stone". Since ZFS manages RAID itself, a ZFS pool can be migrated to other hardware, or the operating system can be reinstalled, and the RAIDZ structures and data will be recognized and immediately accessible by ZFS again.
  • Ability to identify data that would have been found in a cache but has been discarded recently instead; this allows ZFS to reassess its caching decisions in light of later use and facilitates very high cache-hit levels (ZFS cache hit rates are typically over 80%);
  • Alternative caching strategies can be used for data that would otherwise cause delays in data handling. For example, synchronous writes which are capable of slowing down the storage system can be converted to asynchronous writes by being written to a fast separate caching device, known as the SLOG (sometimes called the ZIL – ZFS Intent Log).
  • Highly tunable—many internal parameters can be configured for optimal functionality.
  • Can be used for high availability clusters and computing, although not fully designed for this use.

Data integrity

See also: Hard disk error rates and handling and Silent data corruption

One major feature that distinguishes ZFS from other file systems is that it is designed with a focus on data integrity by protecting the user's data on disk against silent data corruption caused by data degradation, current spikes, bugs in disk firmware, phantom writes (the previous write did not make it to disk), misdirected reads/writes (the disk accesses the wrong block), DMA parity errors between the array and server memory or from the driver (since the checksum validates data inside the array), driver errors (data winds up in the wrong buffer inside the kernel), accidental overwrites (such as swapping to a live file system), etc.

A 1999 study showed that neither any of the then-major and widespread filesystems (such as UFS, Ext,[6] XFS, JFS, or NTFS), nor hardware RAID (which has some issues with data integrity) provided sufficient protection against data corruption problems.[7][8][9][10] Initial research indicates that ZFS protects data better than earlier efforts.[11][12] It is also faster than UFS[13][14] and can be seen as its replacement.

Within ZFS, data integrity is achieved by using a Fletcher-based checksum or a SHA-256 hash throughout the file system tree.[15] Each block of data is checksummed and the checksum value is then saved in the pointer to that block—rather than at the actual block itself. Next, the block pointer is checksummed, with the value being saved at its pointer. This checksumming continues all the way up the file system's data hierarchy to the root node, which is also checksummed, thus creating a Merkle tree.[15] In-flight data corruption or phantom reads/writes (the data written/read checksums correctly but is actually wrong) are undetectable by most filesystems as they store the checksum with the data. ZFS stores the checksum of each block in its parent block pointer so the entire pool self-validates.[15]

When a block is accessed, regardless of whether it is data or meta-data, its checksum is calculated and compared with the stored checksum value of what it "should" be. If the checksums match, the data are passed up the programming stack to the process that asked for it; if the values do not match, then ZFS can heal the data if the storage pool provides data redundancy (such as with internal mirroring), assuming that the copy of data is undamaged and with matching checksums.[16] It is optionally possible to provide additional in-pool redundancy by specifying copies=2 (or copies=3 or more), which means that data will be stored twice (or three times) on the disk, effectively halving (or, for copies=3, reducing to one third) the storage capacity of the disk.[17] Additionally some kinds of data used by ZFS to manage the pool are stored multiple times by default for safety, even with the default copies=1 setting.

If other copies of the damaged data exist or can be reconstructed from checksums and parity data, ZFS will use a copy of the data (or recreate it via a RAID recovery mechanism), and recalculate the checksum—ideally resulting in the reproduction of the originally expected value. If the data passes this integrity check, the system can then update all faulty copies with known-good data and redundancy will be restored.

Consistency of data held in memory, such as cached data in the ARC, is not checked by default, as ZFS is expected to run on enterprise-quality hardware with error correcting RAM, but the capability to check in-memory data exists and can be enabled using "debug flags".

RAID ("RaidZ")

For ZFS to be able to guarantee data integrity, it needs multiple copies of the data, usually spread across multiple disks. Typically this is achieved by using either a RAID controller or so-called "soft" RAID (built into a file system).

Avoidance of hardware RAID controllers

While ZFS can work with hardware RAID devices, ZFS will usually work more efficiently and with greater protection of data, if it has raw access to all storage devices, and disks are not connected to the system using a hardware, firmware or other "soft" RAID, or any other controller which modifies the usual ZFS-to-disk I/O path. This is because ZFS relies on the disk for an honest view, to determine the moment data is confirmed as safely written, and it has numerous algorithms designed to optimize its use of caching, cache flushing, and disk handling.

If a third-party device performs caching or presents drives to ZFS as a single system, or without the low level view ZFS relies upon, there is a much greater chance that the system will perform less optimally, and that a failure will not be preventable by ZFS or as quickly or fully recovered by ZFS. For example, if a hardware RAID card is used, ZFS may not be able to determine the condition of disks or whether the RAID array is degraded or rebuilding, it may not know of all data corruption, and it cannot place data optimally across the disks, make selective repairs only, control how repairs are balanced with ongoing use, and may not be able to make repairs even if it could usually do so, as the hardware RAID card will interfere. RAID controllers also usually add controller-dependent data to the drives which prevents software RAID from accessing the user data. While it is possible to read the data with a compatible hardware RAID controller, this isn't always possible, and if the controller card develops a fault then a replacement may not be available, and other cards may not understand the manufacturer's custom data which is needed to manage and restore an array on a new card.

Therefore, unlike most other systems, where RAID cards or similar are used to offload resources and processing and enhance performance and reliability, with ZFS it is strongly recommended these methods not be used as they typically reduce the system's performance and reliability.

If disks must be connected through a RAID or other controller, it is recommended to use a plain HBA (host adapter) or fanout card, or configure the card in JBOD mode (i.e. turn off RAID and caching functions), to allow devices to be attached but the ZFS-to-disk I/O pathway to be unchanged. A RAID card in JBOD mode may still interfere, if it has a cache or depending upon its design, and may detach drives that do not respond in time (as has been seen with many energy-efficient consumer-grade hard drives), and as such, may require Time-Limited Error Recovery (TLER)/CCTL/ERC-enabled drives to prevent drive dropouts, so not all cards are suitable even with RAID functions disabled.[18]

ZFS' approach: RAID-Z and mirroring

Instead of hardware RAID, ZFS employs "soft" RAID, offering RAID-Z (parity based like RAID 5 and similar) and disk mirroring (similar to RAID 1). The schemes are highly flexible.

RAID-Z is a data/parity distribution scheme like RAID-5, but uses dynamic stripe width: every block is its own RAID stripe, regardless of blocksize, resulting in every RAID-Z write being a full-stripe write. This, when combined with the copy-on-write transactional semantics of ZFS, eliminates the write hole error. RAID-Z is also faster than traditional RAID 5 because it does not need to perform the usual read-modify-write sequence.[19]

As all stripes are of different sizes, RAID-Z reconstruction has to traverse the filesystem metadata to determine the actual RAID-Z geometry. This would be impossible if the filesystem and the RAID array were separate products, whereas it becomes feasible when there is an integrated view of the logical and physical structure of the data. Going through the metadata means that ZFS can validate every block against its 256-bit checksum as it goes, whereas traditional RAID products usually cannot do this.[19]

In addition to handling whole-disk failures, RAID-Z can also detect and correct silent data corruption, offering "self-healing data": when reading a RAID-Z block, ZFS compares it against its checksum, and if the data disks did not return the right answer, ZFS reads the parity and then figures out which disk returned bad data. Then, it repairs the damaged data and returns good data to the requestor.[19]

RAID-Z and mirroring do not require any special hardware: they do not need NVRAM for reliability, and they do not need write buffering for good performance or data protection. With RAID-Z, ZFS provides fast, reliable storage using cheap, commodity disks.[19]

There are five different RAID-Z modes: RAID-Z0 (similar to RAID 0, offers no redundancy), RAID-Z1 (similar to RAID 5, allows one disk to fail), RAID-Z2 (similar to RAID 6, allows two disks to fail), RAID-Z3 (a RAID 7[a] configuration, allows three disks to fail), and mirroring (similar to RAID 1, allows all but one disk to fail).[21]

The need for RAID-Z3 arose in the early 2000s as muti-terabyte capacity drives became more common. This increase in capacity—without a corresponding increase in throughput speeds—meant that rebuilding an array due to a failed drive could take "weeks or even months" to complete.[20] During this time, the older disks in the array will be stressed by the additional workload, which could result data corruption or drive failure. By increasing parity, RAID-Z3 reduces the chance of data loss by simply increasing redundancy.[22]

Resilvering and scrub (array syncing and integrity checking)

ZFS has no tool equivalent to fsck (the standard Unix and Linux data checking and repair tool for file systems).[23] Instead, ZFS has a built-in scrub function which regularly examines all data and repairs silent corruption and other problems. Some differences are:

  • fsck must be run on an offline filesystem, which means the filesystem must be unmounted and is not usable while being repaired, while scrub is designed to be used on a mounted, live filesystem, and does not need the ZFS filesystem to be taken offline.
  • fsck usually only checks metadata (such as the journal log) but never checks the data itself. This means, after an fsck, the data might still not match the original data as stored.
  • fsck cannot always validate and repair data when checksums are stored with data (often the case in many file systems), because the checksums may also be corrupted or unreadable. ZFS always stores checksums separately from the data they verify, improving reliability and the ability of scrub to repair the volume. ZFS also stores multiple copies of data—metadata, in particular, may have upwards of 4 or 6 copies (multiple copies per disk and multiple disk mirrors per volume), greatly improving the ability of scrub to detect and repair extensive damage to the volume, compared to fsck.
  • scrub checks everything, including metadata and the data. The effect can be observed by comparing fsck to scrub times—sometimes a fsck on a large RAID completes in a few minutes, which means only the metadata was checked. Traversing all metadata and data on a large RAID takes many hours, which is exactly what scrub does.

The official recommendation from Sun/Oracle is to scrub enterprise-level disks once a month, and cheaper commodity disks once a week.[24][25]

Capacity

ZFS is a 128-bit file system,[26][27] so it can address 1.84 × 1019 times more data than 64-bit systems such as Btrfs. The maximum limits of ZFS are designed to be so large that they should never be encountered in practice. For instance, fully populating a single zpool with 2128 bits of data would require 3×1024 TB hard disk drives.[28]

Some theoretical limits in ZFS are:

  • 248: number of entries in any individual directory[29]
  • 16 exbibytes (264 bytes): maximum size of a single file
  • 16 exbibytes: maximum size of any attribute
  • 256 quadrillion zebibytes (2128 bytes): maximum size of any zpool
  • 256: number of attributes of a file (actually constrained to 248 for the number of files in a directory)
  • 264: number of devices in any zpool
  • 264: number of zpools in a system
  • 264: number of file systems in a zpool

Encryption

With Oracle Solaris, the encryption capability in ZFS[30] is embedded into the I/O pipeline. During writes, a block may be compressed, encrypted, checksummed and then deduplicated, in that order. The policy for encryption is set at the dataset level when datasets (file systems or ZVOLs) are created. The wrapping keys provided by the user/administrator can be changed at any time without taking the file system offline. The default behaviour is for the wrapping key to be inherited by any child data sets. The data encryption keys are randomly generated at dataset creation time. Only descendant datasets (snapshots and clones) share data encryption keys.[31] A command to switch to a new data encryption key for the clone or at any time is provided—this does not re-encrypt already existing data, instead utilising an encrypted master-key mechanism.

As of 2019 the encryption feature is also fully integrated into OpenZFS 0.8.0 available for Debian and Ubuntu Linux distributions.[32]

Read/write efficiency

ZFS will automatically allocate data storage across all vdevs in a pool (and all devices in each vdev) in a way that generally maximises the performance of the pool. ZFS will also update its write strategy to take account of new disks added to a pool, when they are added.

As a general rule, ZFS allocates writes across vdevs based on the free space in each vdev. This ensures that vdevs which have proportionately less data already, are given more writes when new data is to be stored. This helps to ensure that as the pool becomes more used, the situation does not develop that some vdevs become full, forcing writes to occur on a limited number of devices. It also means that when data is read (and reads are much more frequent than writes in most uses), different parts of the data can be read from as many disks as possible at the same time, giving much higher read performance. Therefore, as a general rule, pools and vdevs should be managed and new storage added, so that the situation does not arise that some vdevs in a pool are almost full and others almost empty, as this will make the pool less efficient.

Other features

Storage devices, spares, and quotas

Pools can have hot spares to compensate for failing disks. When mirroring, block devices can be grouped according to physical chassis, so that the filesystem can continue in the case of the failure of an entire chassis.

Storage pool composition is not limited to similar devices, but can consist of ad-hoc, heterogeneous collections of devices, which ZFS seamlessly pools together, subsequently doling out space to diverse filesystems[clarification needed] as needed. Arbitrary storage device types can be added to existing pools to expand their size.[33]

The storage capacity of all vdevs is available to all of the file system instances in the zpool. A quota can be set to limit the amount of space a file system instance can occupy, and a reservation can be set to guarantee that space will be available to a file system instance.

Caching mechanisms: ARC, L2ARC, Transaction groups, ZIL, SLOG, Special VDEV

ZFS uses different layers of disk cache to speed up read and write operations. Ideally, all data should be stored in RAM, but that is usually too expensive. Therefore, data is automatically cached in a hierarchy to optimize performance versus cost;[34] these are often called "hybrid storage pools".[35] Frequently accessed data will be stored in RAM, and less frequently accessed data can be stored on slower media, such as solid state drives (SSDs). Data that is not often accessed is not cached and left on the slow hard drives. If old data is suddenly read a lot, ZFS will automatically move it to SSDs or to RAM.

ZFS caching mechanisms include one each for reads and writes, and in each case, two levels of caching can exist, one in computer memory (RAM) and one on fast storage (usually solid state drives (SSDs)), for a total of four caches.

  Where stored Read cache Write cache
First level cache In RAM Known as ARC, due to its use of a variant of the adaptive replacement cache (ARC) algorithm. RAM will always be used for caching, thus this level is always present. The efficiency of the ARC algorithm means that disks will often not need to be accessed, provided the ARC size is sufficiently large. If RAM is too small there will hardly be any ARC at all; in this case, ZFS always needs to access the underlying disks which impacts performance considerably. Handled by means of "transaction groups" – writes are collated over a short period (typically 5 – 30 seconds) up to a given limit, with each group being written to disk ideally while the next group is being collated. This allows writes to be organized more efficiently for the underlying disks at the risk of minor data loss of the most recent transactions upon power interruption or hardware fault. In practice the power loss risk is avoided by ZFS write journaling and by the SLOG/ZIL second tier write cache pool (see below), so writes will only be lost if a write failure happens at the same time as a total loss of the second tier SLOG pool, and then only when settings related to synchronous writing and SLOG use are set in a way that would allow such a situation to arise. If data is received faster than it can be written, data receipt is paused until the disks can catch up.
Second level cache On fast storage devices (which can be added or removed from a "live" system without disruption in current versions of ZFS, although not always in older versions) Known as L2ARC ("Level 2 ARC"), optional. ZFS will cache as much data in L2ARC as it can, which can be tens or hundreds of gigabytes in many cases. L2ARC will also considerably speed up deduplication if the entire deduplication table can be cached in L2ARC. It can take several hours to fully populate the L2ARC from empty (before ZFS has decided which data are "hot" and should be cached). If the L2ARC device is lost, all reads will go out to the disks which slows down performance, but nothing else will happen (no data will be lost). Known as SLOG or ZIL ("ZFS Intent Log") – the terms are often used incorrectly. A SLOG (secondary log device) is an optional dedicated cache on a separate device, for recording writes, in the event of a system issue. If an SLOG device exists, it will be used for the ZFS Intent Log as a second level log, and if no separate cache device is provided, the ZIL will be created on the main storage devices instead. The SLOG thus, technically, refes to the dedicated disk to which the ZIL is offloaded, in order to speed up the pool. Strictly speaking, ZFS does not use the SLOG device to cache its disk writes. Rather, it uses SLOG to ensure writes are captured to a permanent storage medium as quickly as possible, so that in the event of power loss or write failure, no data which was acknowledged as written, will be lost. The SLOG device allows ZFS to speedily store writes and quickly report them as written, even for storage devices such as HDDs that are much slower. In the normal course of activity, the SLOG is never referred to or read, and it does not act as a cache; its purpose is to safeguard data in flight during the few seconds taken for collation and "writing out", in case the eventual write were to fail. If all goes well, then the storage pool will be updated at some point within the next 5 to 60 seconds, when the current transaction group is written out to disk (see above), at which point the saved writes on the SLOG will simply be ignored and overwritten. If the write eventually fails, or the system suffers a crash or fault preventing its writing, then ZFS can identify all the writes that it has confirmed were written, by reading back the SLOG (the only time it is read from), and use this to completely repair the data loss.

This becomes crucial if a large number of synchronous writes take place (such as with ESXi, NFS and some databases),[36] where the client requires confirmation of successful writing before continuing its activity; the SLOG allows ZFS to confirm writing is successful much more quickly than if it had to write to the main store every time, without the risk involved in misleading the client as to the state of data storage. If there is no SLOG device then part of the main data pool will be used for the same purpose, although this is slower.

If the log device itself is lost, it is possible to lose the latest writes, therefore the log device should be mirrored. In earlier versions of ZFS, loss of the log device could result in loss of the entire zpool, although this is no longer the case. Therefore, one should upgrade ZFS if planning to use a separate log device.

A number of other caches, cache divisions, and queues also exist within ZFS. For example, each VDEV has its own data cache, and the ARC cache is divided between data stored by the user and metadata used by ZFS, with control over the balance between these.

Special VDEV Class

In ZFS 0.8 and later, it is possible to configure a Special VDEV class to preferentially store filesystem metadata, and optionally the Data Deduplication Table (DDT), and small filesystem blocks. This allows, for example, to create a Special VDEV on fast solid-state storage to store the metadata, while the regular file data is stored on spinning disks. This speeds up metadata-intensive operations such as filesystem traversal, scrub, and resilver, without the expense of storing the entire filesystem on solid-state storage.

Copy-on-write transactional model

ZFS uses a copy-on-write transactional object model. All block pointers within the filesystem contain a 256-bit checksum or 256-bit hash (currently a choice between Fletcher-2, Fletcher-4, or SHA-256)[37] of the target block, which is verified when the block is read. Blocks containing active data are never overwritten in place; instead, a new block is allocated, modified data is written to it, then any metadata blocks referencing it are similarly read, reallocated, and written. To reduce the overhead of this process, multiple updates are grouped into transaction groups, and ZIL (intent log) write cache is used when synchronous write semantics are required. The blocks are arranged in a tree, as are their checksums (see Merkle signature scheme).

Snapshots and clones

An advantage of copy-on-write is that, when ZFS writes new data, the blocks containing the old data can be retained, allowing a snapshot version of the file system to be maintained. ZFS snapshots are consistent (they reflect the entire data as it existed at a single point in time), and can be created extremely quickly, since all the data composing the snapshot is already stored, with the entire storage pool often snapshotted several times per hour. They are also space efficient, since any unchanged data is shared among the file system and its snapshots. Snapshots are inherently read-only, ensuring they will not be modified after creation, although they should not be relied on as a sole means of backup. Entire snapshots can be restored and also files and directories within snapshots.

Writeable snapshots ("clones") can also be created, resulting in two independent file systems that share a set of blocks. As changes are made to any of the clone file systems, new data blocks are created to reflect those changes, but any unchanged blocks continue to be shared, no matter how many clones exist. This is an implementation of the Copy-on-write principle.

Sending and receiving snapshots

ZFS file systems can be moved to other pools, also on remote hosts over the network, as the send command creates a stream representation of the file system's state. This stream can either describe complete contents of the file system at a given snapshot, or it can be a delta between snapshots. Computing the delta stream is very efficient, and its size depends on the number of blocks changed between the snapshots. This provides an efficient strategy, e.g., for synchronizing offsite backups or high availability mirrors of a pool.

Dynamic striping

Dynamic striping across all devices to maximize throughput means that as additional devices are added to the zpool, the stripe width automatically expands to include them; thus, all disks in a pool are used, which balances the write load across them.[citation needed]

Variable block sizes

ZFS uses variable-sized blocks, with 128 KB as the default size. Available features allow the administrator to tune the maximum block size which is used, as certain workloads do not perform well with large blocks. If data compression is enabled, variable block sizes are used. If a block can be compressed to fit into a smaller block size, the smaller size is used on the disk to use less storage and improve IO throughput (though at the cost of increased CPU use for the compression and decompression operations).[38]

Lightweight filesystem creation

In ZFS, filesystem manipulation within a storage pool is easier than volume manipulation within a traditional filesystem; the time and effort required to create or expand a ZFS filesystem is closer to that of making a new directory than it is to volume manipulation in some other systems.[citation needed]

Adaptive endianness

Pools and their associated ZFS file systems can be moved between different platform architectures, including systems implementing different byte orders. The ZFS block pointer format stores filesystem metadata in an endian-adaptive way; individual metadata blocks are written with the native byte order of the system writing the block. When reading, if the stored endianness does not match the endianness of the system, the metadata is byte-swapped in memory.

This does not affect the stored data; as is usual in POSIX systems, files appear to applications as simple arrays of bytes, so applications creating and reading data remain responsible for doing so in a way independent of the underlying system's endianness.

Deduplication

Data deduplication capabilities were added to the ZFS source repository at the end of October 2009,[39] and relevant OpenSolaris ZFS development packages have been available since December 3, 2009 (build 128).

Effective use of deduplication may require large RAM capacity; recommendations range between 1 and 5 GB of RAM for every TB of storage.[40][41][42] An accurate assessment of the memory required for deduplication is made by referring to the number of unique blocks in the pool, and the number of bytes on disk and in RAM ("core") required to store each record—these figures are reported by inbuilt commands such as zpool and zdb. Insufficient physical memory or lack of ZFS cache can result in virtual memory thrashing when using deduplication, which can cause performance to plummet, or result in complete memory starvation.[citation needed] Because deduplication occurs at write-time, it is also very CPU-intensive and this can also significantly slow down a system.

Other storage vendors use modified versions of ZFS to achieve very high data compression ratios. Two examples in 2012 were GreenBytes[43] and Tegile.[44] In May 2014, Oracle bought GreenBytes for its ZFS deduplication and replication technology.[45]

As described above, deduplication is usually not recommended due to its heavy resource requirements (especially RAM) and impact on performance (especially when writing), other than in specific circumstances where the system and data are well-suited to this space-saving technique.

Additional capabilities

  • Explicit I/O priority with deadline scheduling.[citation needed]
  • Claimed globally optimal I/O sorting and aggregation.[citation needed]
  • Multiple independent prefetch streams with automatic length and stride detection.[citation needed]
  • Parallel, constant-time directory operations.[citation needed]
  • End-to-end checksumming, using a kind of "Data Integrity Field", allowing data corruption detection (and recovery if you have redundancy in the pool). A choice of 3 hashes can be used, optimized for speed (fletcher), standardization and security (SHA256) and salted hashes (Skein).[46]
  • Transparent filesystem compression. Supports LZJB, gzip[47] and LZ4.
  • Intelligent scrubbing and resilvering (resyncing).[48]
  • Load and space usage sharing among disks in the pool.[49]
  • Ditto blocks: Configurable data replication per filesystem, with zero, one or two extra copies requested per write for user data, and with that same base number of copies plus one or two for metadata (according to metadata importance).[50] If the pool has several devices, ZFS tries to replicate over different devices. Ditto blocks are primarily an additional protection against corrupted sectors, not against total disk failure.[51]
  • ZFS design (copy-on-write + superblocks) is safe when using disks with write cache enabled, if they honor the write barriers.[citation needed] This feature provides safety and a performance boost compared with some other filesystems.[according to whom?]
  • On Solaris, when entire disks are added to a ZFS pool, ZFS automatically enables their write cache. This is not done when ZFS only manages discrete slices of the disk, since it does not know if other slices are managed by non-write-cache safe filesystems, like UFS.[citation needed] The FreeBSD implementation can handle disk flushes for partitions thanks to its GEOM framework, and therefore does not suffer from this limitation.[citation needed]
  • Per-user, per-group, per-project, and per-dataset quota limits.[52]
  • Filesystem encryption since Solaris 11 Express[2] (on some other systems ZFS can utilize encrypted disks for a similar effect; GELI on FreeBSD can be used this way to create fully encrypted ZFS storage).
  • Pools can be imported in read-only mode.
  • It is possible to recover data by rolling back entire transactions at the time of importing the zpool.[citation needed]
  • ZFS is not a clustered filesystem; however, clustered ZFS is available from third parties.[citation needed]
  • Snapshots can be taken manually or automatically. The older versions of the stored data that they contain can be exposed as full read-only file systems. They can also be exposed as historic versions of files and folders when used with CIFS (also known as SMB, Samba or file shares); this is known as "Previous versions", "VSS shadow copies", or "File history" on Windows, or AFP and "Apple Time Machine" on Apple devices.[53]
  • Disks can be marked as 'spare'. A data pool can be set to automatically and transparently handle disk faults by activating a spare disk and beginning to resilver the data that was on the suspect disk onto it, when needed.

Limitations

Limitations in preventing data corruption

The authors of a 2010 study that examined the ability of file systems to detect and prevent data corruption, with particular focus on ZFS, observed that ZFS itself is effective in detecting and correcting data errors on storage devices, but that it assumes data in RAM is "safe", and not prone to error. The study comments that "a single bit flip in memory causes a small but non-negligible percentage of runs to experience a failure", with the probability of committing bad data to disk varying from 0% to 3.6% (according to the workload)," and that when ZFS caches pages or stores copies of metadata in RAM, or holds data in its "dirty" cache for writing to disk, no test is made whether the checksums still match the data at the point of use.[54] Much of this risk can be mitigated in one of two ways:

  • According to the authors, by using ECC RAM; however, the authors considered that adding error detection related to the page cache and heap would allow ZFS to handle certain classes of error more robustly.[54]
  • One of the main architects of ZFS, Matt Ahrens, explains there is an option to enable checksumming of data in memory by using the ZFS_DEBUG_MODIFY flag (zfs_flags=0x10) which addresses these concerns.[55]

Other limitations specific to ZFS

  • Capacity expansion is normally achieved by adding groups of disks as a top-level vdev: simple device, RAID-Z, RAID Z2, RAID Z3, or mirrored. Newly written data will dynamically start to use all available vdevs. It is also possible to expand the array by iteratively swapping each drive in the array with a bigger drive and waiting for ZFS to self-heal; the heal time will depend on the amount of stored information, not the disk size.
  • As of Solaris 10 Update 11 and Solaris 11.2, it was neither possible to reduce the number of top-level vdevs in a pool, nor to otherwise reduce pool capacity.[56] This functionality was said to be in development in 2007.[57] Enhancements to allow reduction of vdevs is under development in OpenZFS.[58]
  • As of 2008 it was not possible to add a disk as a column to a RAID Z, RAID Z2 or RAID Z3 vdev. However, a new RAID Z vdev can be created instead and added to the zpool.[59]
  • Some traditional nested RAID configurations, such as RAID 51 (a mirror of RAID 5 groups), are not configurable in ZFS. Vdevs can only be composed of raw disks or files, not other vdevs. However, a ZFS pool effectively creates a stripe (RAID 0) across its vdevs, so the equivalent of a RAID 50 or RAID 60 is common.
  • Reconfiguring the number of devices in a top-level vdev requires copying data offline, destroying the pool, and recreating the pool with the new top-level vdev configuration, except for adding extra redundancy to an existing mirror, which can be done at any time or if all top level vdevs are mirrors with sufficient redundancy the zpool split[60] command can be used to remove a vdev from each top level vdev in the pool, creating a 2nd pool with identical data.
  • IOPS performance of a ZFS storage pool can suffer if the ZFS raid is not appropriately configured. This applies to all types of RAID, in one way or another. If the zpool consists of only one group of disks configured as, say, eight disks in RAID Z2, then the IOPS performance will be that of a single disk (write speed will be equivalent to 6 disks, but random read speed will be similar to a single disk). However, there are ways to mitigate this IOPS performance problem, for instance add SSDs as L2ARC cache—which can boost IOPS into 100.000s.[61] In short, a zpool should consist of several groups of vdevs, each vdev consisting of 8–12 disks, if using RAID Z. It is not recommended to create a zpool with a single large vdev, say 20 disks, because IOPS performance will be that of a single disk, which also means that resilver time will be very long (possibly weeks with future large drives).
  • Online shrink zpool remove was not supported until Solaris 11.4 released in August 2018[62]
  • Resilver (repair) of a crashed disk in a ZFS RAID can take a long time which is not unique to ZFS, it applies to all types of RAID, in one way or another. This means that very large volumes can take several days to repair or to being back to full redundancy after severe data corruption or failure, and during this time a second disk failure may occur, especially as the repair puts additional stress on the system as a whole. In turn this means that configurations that only allow for recovery of a single disk failure, such as RAID Z1 (similar to RAID 5) should be avoided. Therefore, with large disks, one should use RAID Z2 (allow two disks to crash) or RAID Z3 (allow three disks to crash).[63] ZFS RAID differs from conventional RAID by only reconstructing live data and metadata when replacing a disk, not the entirety of the disk including blank and garbage blocks, which means that replacing a member disk on a ZFS pool that is only partially full will take proportionally less time compared to conventional RAID.[48]

Data recovery

Historically, ZFS has not shipped with tools such as fsck to repair damaged file systems, because the file system itself was designed to self-repair, so long as it had been built with sufficient attention to the design of storage and redundancy of data. If the pool was compromised because of poor hardware, inadequate design or redundancy, or unfortunate mishap, to the point that ZFS was unable to mount the pool, traditionally there were no tools which allowed an end-user to attempt partial salvage of the stored data. This led to threads in online forums where ZFS developers sometimes tried to provide ad-hoc help to home and other small scale users, facing loss of data due to their inadequate design or poor system management.[64]

Modern ZFS has improved considerably on this situation over time, and continues to do so:

  • Removal or abrupt failure of caching devices no longer causes pool loss. (At worst, loss of the ZIL may lose very recent transactions, but the ZIL does not usually store more than a few seconds' worth of recent transactions. Loss of the L2ARC cache does not affect data.)
  • If the pool is unmountable, modern versions of ZFS will attempt to identify the most recent consistent point at which the pool which can be recovered, at the cost of losing some of the most recent changes to the contents. Copy on write means that older versions of data, including top-level records and metadata, may still exist even though they are superseded, and if so, the pool can be wound back to a consistent state based on them. The older the data, the more likely it is that at least some blocks have been overwritten and that some data will be irrecoverable, so there is a limit at some point, on the ability of the pool to be wound back.
  • Informally, tools exist to probe the reason why ZFS is unable to mount a pool, and guide the user or a developer as to manual changes required to force the pool to mount. These include using zdb (ZFS debug) to find a valid importable point in the pool, using dtrace or similar to identify the issue causing mount failure, or manually bypassing health checks that cause the mount process to abort, and allow mounting of the damaged pool.
  • As of March 2018, a range of significantly enhanced methods are gradually being rolled out within OpenZFS. These include:[64]
  • Code refactoring, and more detailed diagnostic and debug information on mount failures, to simplify diagnosis and fixing of corrupt pool issues;
  • The ability to trust or distrust the stored pool configuration. This is particularly powerful, as it allows a pool to be mounted even when top-level vdevs are missing or faulty, when top level data is suspect, and also to rewind beyond a pool configuration change if that change was connected to the problem. Once the corrupt pool is mounted, readable files can be copied for safety, and it may turn out that data can be rebuilt even for missing vdevs, by using copies stored elsewhere in the pool.
  • The ability to fix the situation where a disk needed in one pool, was accidentally removed and added to a different pool, causing it to lose metadata related to the first pool, which becomes unreadable.

See also

Notes

  1. ^ While RAID 7 is not a standard RAID level, it has been proposed as a catch-all term for any >3 parity RAID configuration[20]

References

  1. ^ a b "What Is ZFS?". Oracle Solaris ZFS Administration Guide. Oracle. Archived from the original on March 4, 2016. Retrieved December 29, 2015.
  2. ^ a b "What's new in Solaris 11 Express 2010.11" (PDF). Oracle. Archived (PDF) from the original on November 16, 2010. Retrieved November 17, 2010.
  3. ^ "1.1 What about the licensing issue?". Archived from the original on September 26, 2010. Retrieved November 18, 2010.
  4. ^ "Status Information for Serial Number 85901629 (ZFS)". United States Patent and Trademark Office. Archived from the original on October 21, 2013. Retrieved October 21, 2013.
  5. ^ a b "19.4. zfs Administration". www.freebsd.org. Archived from the original on February 23, 2017. Retrieved February 22, 2017.
  6. ^ The Extended file system (Ext) has metadata structure copied from UFS. "Rémy Card (Interview, April 1998)". April Association. April 19, 1999. Archived from the original on February 4, 2012. Retrieved February 8, 2012. (In French)
  7. ^ Vijayan Prabhakaran (2006). "IRON FILE SYSTEMS" (PDF). Doctor of Philosophy in Computer Sciences. University of Wisconsin-Madison. Archived (PDF) from the original on April 29, 2011. Retrieved June 9, 2012.
  8. ^ "Parity Lost and Parity Regained". Archived from the original on June 15, 2010. Retrieved November 29, 2010.
  9. ^ "An Analysis of Data Corruption in the Storage Stack" (PDF). Archived (PDF) from the original on June 15, 2010. Retrieved November 29, 2010.
  10. ^ "Impact of Disk Corruption on Open-Source DBMS" (PDF). Archived (PDF) from the original on June 15, 2010. Retrieved November 29, 2010.
  11. ^ Kadav, Asim; Rajimwale, Abhishek. "Reliability Analysis of ZFS" (PDF). Archived (PDF) from the original on September 21, 2013. Retrieved September 19, 2013.
  12. ^ Yupu Zhang; Abhishek Rajimwale; Andrea C. Arpaci-Dusseau; Remzi H. Arpaci-Dusseau. "End-to-end Data Integrity for File Systems: A ZFS Case Study" (PDF). Madison: Computer Sciences Department, University of Wisconsin. p. 14. Archived (PDF) from the original on June 26, 2011. Retrieved December 6, 2010.
  13. ^ Larabel, Michael. "Benchmarking ZFS and UFS On FreeBSD vs. EXT4 & Btrfs On Linux". Phoronix Media 2012. Archived from the original on November 29, 2016. Retrieved November 21, 2012.
  14. ^ Larabel, Michael. "Can DragonFlyBSD's HAMMER Compete With Btrfs, ZFS?". Phoronix Media 2012. Archived from the original on November 29, 2016. Retrieved November 21, 2012.
  15. ^ a b c Bonwick, Jeff (December 8, 2005). "ZFS End-to-End Data Integrity". blogs.oracle.com. Archived from the original on April 3, 2012. Retrieved September 19, 2013.
  16. ^ Cook, Tim (November 16, 2009). "Demonstrating ZFS Self-Healing". blogs.oracle.com. Archived from the original on August 12, 2011. Retrieved February 1, 2015.
  17. ^ Ranch, Richard (May 4, 2007). "ZFS, copies, and data protection". blogs.oracle.com. Archived from the original on August 18, 2016. Retrieved February 2, 2015.
  18. ^ wdc.custhelp.com. "Difference between Desktop edition and RAID (Enterprise) edition drives". Archived from the original on January 5, 2015. Retrieved September 8, 2011.
  19. ^ a b c d Bonwick, Jeff (November 17, 2005). "RAID-Z". Jeff Bonwick's Blog. Oracle Blogs. Archived from the original on December 16, 2014. Retrieved February 1, 2015.
  20. ^ a b Leventhal, Adam (December 17, 2009). "Triple-Parity RAID and Beyond". Queue. 7 (11): 30. doi:10.1145/1661785.1670144 (inactive January 16, 2020). Archived from the original on March 15, 2019. Retrieved April 12, 2019.{{cite journal}}: CS1 maint: DOI inactive as of January 2020 (link)
  21. ^ "ZFS Raidz Performance, Capacity and integrity". calomel.org. Archived from the original on November 27, 2017. Retrieved June 23, 2017.
  22. ^ "Why RAID 6 stops working in 2019". ZDNet. February 22, 2010. Archived from the original on October 31, 2014. Retrieved October 26, 2014.
  23. ^ "No fsck utility equivalent exists for ZFS. This utility has traditionally served two purposes, those of file system repair and file system validation." "Checking ZFS File System Integrity". Oracle. Archived from the original on January 31, 2013. Retrieved November 25, 2012.
  24. ^ "ZFS Scrubs". freenas.org. Archived from the original on November 27, 2012. Retrieved November 25, 2012.
  25. ^ "You should also run a scrub prior to replacing devices or temporarily reducing a pool's redundancy to ensure that all devices are currently operational." "ZFS Best Practices Guide". solarisinternals.com. Archived from the original on September 5, 2015. Retrieved 25 November 2012.
  26. ^ Jeff Bonwick. "128-bit storage: are you high?". oracle.com. Archived from the original on May 29, 2015. Retrieved May 29, 2015.
  27. ^ Bonwick, Jeff (October 31, 2005). "ZFS: The Last Word in Filesystems". blogs.oracle.com. Archived from the original on June 19, 2013. Retrieved June 22, 2013.
  28. ^ "ZFS: Boils the Ocean, Consumes the Moon (Dave Brillhart's Blog)". Archived from the original on December 8, 2015. Retrieved December 19, 2015.
  29. ^ "Solaris ZFS Administration Guide". Oracle Corporation. Retrieved February 11, 2011.
  30. ^ "Encrypting ZFS File Systems". Archived from the original on June 23, 2011. Retrieved May 2, 2011.
  31. ^ "Having my secured cake and Cloning it too (aka Encryption + Dedup with ZFS)". Archived from the original on May 29, 2013. Retrieved October 9, 2012.
  32. ^ "ZFS - Debian Wiki". wiki.debian.org. Archived from the original on September 8, 2019. Retrieved December 10, 2019.
  33. ^ "Solaris ZFS Enables Hybrid Storage Pools—Shatters Economic and Performance Barriers" (PDF). Sun.com. September 7, 2010. Archived (PDF) from the original on October 17, 2011. Retrieved November 4, 2011.
  34. ^ Gregg, Brendan. "ZFS L2ARC". Brendan's blog. Dtrace.org. Archived from the original on November 6, 2011. Retrieved October 5, 2012.
  35. ^ Gregg, Brendan (October 8, 2009). "Hybrid Storage Pool: Top Speeds". Brendan's blog. Dtrace.org. Archived from the original on April 5, 2016. Retrieved August 15, 2017.
  36. ^ "Solaris ZFS Performance Tuning: Synchronous Writes and the ZIL". Constantin.glez.de. July 20, 2010. Archived from the original on June 23, 2012. Retrieved October 5, 2012.
  37. ^ "ZFS On-Disk Specification" (PDF). Sun Microsystems, Inc. 2006. Archived from the original (PDF) on December 30, 2008. See section 2.4.
  38. ^ Eric Sproul (May 21, 2009). "ZFS Nuts and Bolts". slideshare.net. pp. 30–31. Archived from the original on June 22, 2014. Retrieved June 8, 2014.
  39. ^ "ZFS Deduplication". blogs.oracle.com. Archived from the original on December 24, 2019. Retrieved November 25, 2019.
  40. ^ Gary Sims (January 4, 2012). "Building ZFS Based Network Attached Storage Using FreeNAS 8". TrainSignal Training. TrainSignal, Inc. Archived from the original (Blog) on May 7, 2012. Retrieved June 9, 2012.
  41. ^ Ray Van Dolson (May 2011). "[zfs-discuss] Summary: Deduplication Memory Requirements". zfs-discuss mailing list. Archived from the original on April 25, 2012.
  42. ^ "ZFSTuningGuide". Archived from the original on January 16, 2012. Retrieved January 3, 2012.
  43. ^ Chris Mellor (October 12, 2012). "GreenBytes brandishes full-fat clone VDI pumper". The Register. Archived from the original on March 24, 2013. Retrieved August 29, 2013.
  44. ^ Chris Mellor (June 1, 2012). "Newcomer gets out its box, plans to sell it cheaply to all comers". The Register. Archived from the original on August 12, 2013. Retrieved August 29, 2013.
  45. ^ Chris Mellor (December 11, 2014). "Dedupe, dedupe... dedupe, dedupe, dedupe: Oracle polishes ZFS diamond". The Register. Archived from the original on July 7, 2017. Retrieved December 17, 2014.
  46. ^ "Checksums and Their Use in ZFS". github.com. September 2, 2018. Archived from the original on July 19, 2019. Retrieved July 11, 2019.
  47. ^ "Solaris ZFS Administration Guide". Chapter 6 Managing ZFS File Systems. Archived from the original on February 5, 2011. Retrieved March 17, 2009.
  48. ^ a b "Smokin' Mirrors". blogs.oracle.com. May 2, 2006. Archived from the original on December 16, 2011. Retrieved February 13, 2012.
  49. ^ "ZFS Block Allocation". Jeff Bonwick's Weblog. November 4, 2006. Archived from the original on November 2, 2012. Retrieved February 23, 2007.
  50. ^ "Ditto Blocks — The Amazing Tape Repellent". Flippin' off bits Weblog. May 12, 2006. Archived from the original on May 26, 2013. Retrieved March 1, 2007.
  51. ^ "Adding new disks and ditto block behaviour". Archived from the original on August 23, 2011. Retrieved October 19, 2009.
  52. ^ "OpenSolaris.org". Sun Microsystems. Archived from the original on May 8, 2009. Retrieved May 22, 2009.
  53. ^ "10. Sharing — FreeNAS User Guide 9.3 Table of Contents". doc.freenas.org. Archived from the original on January 7, 2017. Retrieved February 23, 2017.
  54. ^ a b Zhang, Yupu; Rajimwale, Abhishek; Arpaci-Dusseau, Andrea C.; Arpaci-Dusseau, Remzi H. (January 2, 2018). "End-to-end Data Integrity for File Systems: A ZFS Case Study". USENIX Association. p. 3 – via ACM Digital Library.
  55. ^ "Ars walkthrough: Using the ZFS next-gen filesystem on Linux". arstechnica.com. Archived from the original on February 10, 2017. Retrieved June 19, 2017.
  56. ^ "Bug ID 4852783: reduce pool capacity". OpenSolaris Project. Archived from the original on June 29, 2009. Retrieved March 28, 2009.
  57. ^ Goebbels, Mario (April 19, 2007). "Permanently removing vdevs from a pool". zfs-discuss (Mailing list). {{cite mailing list}}: Unknown parameter |mailinglist= ignored (|mailing-list= suggested) (help)[permanent dead link] archive link
  58. ^ Chris Siebenmann Information on future vdev removal Archived August 11, 2016, at the Wayback Machine, Univ Toronto, blog, quote: informal Twitter announcement by Alex Reece Archived August 11, 2016, at the Wayback Machine
  59. ^ "Expand-O-Matic RAID Z". Adam Leventhal. April 7, 2008. Archived from the original on December 28, 2011. Retrieved April 16, 2012.
  60. ^ "zpool(1M)". Download.oracle.com. June 11, 2010. Retrieved November 4, 2011.
  61. ^ brendan (December 2, 2008). "A quarter million NFS IOPS". Oracle Sun. Archived from the original on December 17, 2011. Retrieved January 28, 2012.
  62. ^ "Data Management Features - What's New in Oracle® Solaris 11.4". Archived from the original on September 24, 2019. Retrieved October 9, 2019.
  63. ^ Leventhal, Adam. "Triple-Parity RAID Z". Adam Leventhal's blog. Archived from the original on April 16, 2011. Retrieved December 19, 2013.
  64. ^ a b "Turbocharging ZFS Data Recovery". Archived from the original on November 29, 2018. Retrieved November 29, 2018.

Bibliography

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