19.8. ZFS Features and Terminology

19.8. ZFS Features and Terminology
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ZFS is a fundamentally different file system because it is more than just a file system. ZFS combines the roles of file system and volume manager, enabling additional storage devices to be added to a live system and having the new space available on all of the existing file systems in that pool immediately. By combining the traditionally separate roles, ZFS is able to overcome previous limitations that prevented RAID groups being able to grow. Each top level device in a pool is called a vdev, which can be a simple disk or a RAID transformation such as a mirror or RAID-Z array. ZFS file systems (called datasets) each have access to the combined free space of the entire pool. As blocks are allocated from the pool, the space available to each file system decreases. This approach avoids the common pitfall with extensive partitioning where free space becomes fragmented across the partitions.

pool	A storage pool is the most basic building block of ZFS. A pool is made up of one or more vdevs, the underlying devices that store the data. A pool is then used to create one or more file systems (datasets) or block devices (volumes). These datasets and volumes share the pool of remaining free space. Each pool is uniquely identified by a name and a GUID. The features available are determined by the ZFS version number on the pool.
vdev Types	A pool is made up of one or more vdevs, which themselves can be a single disk or a group of disks, in the case of a RAID transform. When multiple vdevs are used, ZFS spreads data across the vdevs to increase performance and maximize usable space. Disk - The most basic type of vdev is a standard block device. This can be an entire disk (such as `/dev/ada0` or `/dev/da0`) or a partition (`/dev/ada0p3`). On FreeBSD, there is no performance penalty for using a partition rather than the entire disk. This differs from recommendations made by the Solaris documentation. File - In addition to disks, ZFS pools can be backed by regular files, this is especially useful for testing and experimentation. Use the full path to the file as the device path in `zpool create`. All vdevs must be at least 128 MB in size. Mirror - When creating a mirror, specify the `mirror` keyword followed by the list of member devices for the mirror. A mirror consists of two or more devices, all data will be written to all member devices. A mirror vdev will only hold as much data as its smallest member. A mirror vdev can withstand the failure of all but one of its members without losing any data. Note: A regular single disk vdev can be upgraded to a mirror vdev at any time with `zpool attach`. RAID-Z - ZFS implements RAID-Z, a variation on standard RAID-5 that offers better distribution of parity and eliminates the “RAID-5 write hole” in which the data and parity information become inconsistent after an unexpected restart. ZFS supports three levels of RAID-Z which provide varying levels of redundancy in exchange for decreasing levels of usable storage. The types are named RAID-Z1 through RAID-Z3 based on the number of parity devices in the array and the number of disks which can fail while the pool remains operational. In a RAID-Z1 configuration with four disks, each 1 TB, usable storage is 3 TB and the pool will still be able to operate in degraded mode with one faulted disk. If an additional disk goes offline before the faulted disk is replaced and resilvered, all data in the pool can be lost. In a RAID-Z3 configuration with eight disks of 1 TB, the volume will provide 5 TB of usable space and still be able to operate with three faulted disks. Sun™ recommends no more than nine disks in a single vdev. If the configuration has more disks, it is recommended to divide them into separate vdevs and the pool data will be striped across them. A configuration of two RAID-Z2 vdevs consisting of 8 disks each would create something similar to a RAID-60 array. A RAID-Z group's storage capacity is approximately the size of the smallest disk multiplied by the number of non-parity disks. Four 1 TB disks in RAID-Z1 has an effective size of approximately 3 TB, and an array of eight 1 TB disks in RAID-Z3 will yield 5 TB of usable space. Spare - ZFS has a special pseudo-vdev type for keeping track of available hot spares. Note that installed hot spares are not deployed automatically; they must manually be configured to replace the failed device using `zfs replace`. Log - ZFS Log Devices, also known as ZFS Intent Log (ZIL) move the intent log from the regular pool devices to a dedicated device, typically an SSD. Having a dedicated log device can significantly improve the performance of applications with a high volume of synchronous writes, especially databases. Log devices can be mirrored, but RAID-Z is not supported. If multiple log devices are used, writes will be load balanced across them. Cache - Adding a cache vdev to a pool will add the storage of the cache to the L2ARC. Cache devices cannot be mirrored. Since a cache device only stores additional copies of existing data, there is no risk of data loss.
Transaction Group (TXG)	Transaction Groups are the way changed blocks are grouped together and eventually written to the pool. Transaction groups are the atomic unit that ZFS uses to assert consistency. Each transaction group is assigned a unique 64-bit consecutive identifier. There can be up to three active transaction groups at a time, one in each of these three states: Open - When a new transaction group is created, it is in the open state, and accepts new writes. There is always a transaction group in the open state, however the transaction group may refuse new writes if it has reached a limit. Once the open transaction group has reached a limit, or the `vfs.zfs.txg.timeout` has been reached, the transaction group advances to the next state. Quiescing - A short state that allows any pending operations to finish while not blocking the creation of a new open transaction group. Once all of the transactions in the group have completed, the transaction group advances to the final state. Syncing - All of the data in the transaction group is written to stable storage. This process will in turn modify other data, such as metadata and space maps, that will also need to be written to stable storage. The process of syncing involves multiple passes. The first, all of the changed data blocks, is the biggest, followed by the metadata, which may take multiple passes to complete. Since allocating space for the data blocks generates new metadata, the syncing state cannot finish until a pass completes that does not allocate any additional space. The syncing state is also where synctasks are completed. Synctasks are administrative operations, such as creating or destroying snapshots and datasets, that modify the uberblock are completed. Once the sync state is complete, the transaction group in the quiescing state is advanced to the syncing state. All administrative functions, such as `snapshot` are written as part of the transaction group. When a synctask is created, it is added to the currently open transaction group, and that group is advanced as quickly as possible to the syncing state to reduce the latency of administrative commands.
Adaptive Replacement Cache (ARC)	ZFS uses an Adaptive Replacement Cache (ARC), rather than a more traditional Least Recently Used (LRU) cache. An LRU cache is a simple list of items in the cache, sorted by when each object was most recently used. New items are added to the top of the list. When the cache is full, items from the bottom of the list are evicted to make room for more active objects. An ARC consists of four lists; the Most Recently Used (MRU) and Most Frequently Used (MFU) objects, plus a ghost list for each. These ghost lists track recently evicted objects to prevent them from being added back to the cache. This increases the cache hit ratio by avoiding objects that have a history of only being used occasionally. Another advantage of using both an MRU and MFU is that scanning an entire file system would normally evict all data from an MRU or LRU cache in favor of this freshly accessed content. With ZFS, there is also an MFU that only tracks the most frequently used objects, and the cache of the most commonly accessed blocks remains.
L2ARC	L2ARC is the second level of the ZFS caching system. The primary ARC is stored in RAM. Since the amount of available RAM is often limited, ZFS can also use cache vdevs. Solid State Disks (SSDs) are often used as these cache devices due to their higher speed and lower latency compared to traditional spinning disks. L2ARC is entirely optional, but having one will significantly increase read speeds for files that are cached on the SSD instead of having to be read from the regular disks. L2ARC can also speed up deduplication because a DDT that does not fit in RAM but does fit in the L2ARC will be much faster than a DDT that must be read from disk. The rate at which data is added to the cache devices is limited to prevent prematurely wearing out SSDs with too many writes. Until the cache is full (the first block has been evicted to make room), writing to the L2ARC is limited to the sum of the write limit and the boost limit, and afterwards limited to the write limit. A pair of sysctl(8) values control these rate limits. `vfs.zfs.l2arc_write_max` controls how many bytes are written to the cache per second, while `vfs.zfs.l2arc_write_boost` adds to this limit during the “Turbo Warmup Phase” (Write Boost).
ZIL	ZIL accelerates synchronous transactions by using storage devices like SSDs that are faster than those used in the main storage pool. When an application requests a synchronous write (a guarantee that the data has been safely stored to disk rather than merely cached to be written later), the data is written to the faster ZIL storage, then later flushed out to the regular disks. This greatly reduces latency and improves performance. Only synchronous workloads like databases will benefit from a ZIL. Regular asynchronous writes such as copying files will not use the ZIL at all.
Copy-On-Write	Unlike a traditional file system, when data is overwritten on ZFS, the new data is written to a different block rather than overwriting the old data in place. Only when this write is complete is the metadata then updated to point to the new location. In the event of a shorn write (a system crash or power loss in the middle of writing a file), the entire original contents of the file are still available and the incomplete write is discarded. This also means that ZFS does not require a fsck(8) after an unexpected shutdown.
Dataset	Dataset is the generic term for a ZFS file system, volume, snapshot or clone. Each dataset has a unique name in the format `poolname/path@snapshot`. The root of the pool is technically a dataset as well. Child datasets are named hierarchically like directories. For example, `mypool/home`, the home dataset, is a child of `mypool` and inherits properties from it. This can be expanded further by creating `mypool/home/user`. This grandchild dataset will inherit properties from the parent and grandparent. Properties on a child can be set to override the defaults inherited from the parents and grandparents. Administration of datasets and their children can be delegated.
File system	A ZFS dataset is most often used as a file system. Like most other file systems, a ZFS file system is mounted somewhere in the systems directory hierarchy and contains files and directories of its own with permissions, flags, and other metadata.
Volume	In additional to regular file system datasets, ZFS can also create volumes, which are block devices. Volumes have many of the same features, including copy-on-write, snapshots, clones, and checksumming. Volumes can be useful for running other file system formats on top of ZFS, such as UFS virtualization, or exporting iSCSI extents.
Snapshot	The copy-on-write (COW) design of ZFS allows for nearly instantaneous, consistent snapshots with arbitrary names. After taking a snapshot of a dataset, or a recursive snapshot of a parent dataset that will include all child datasets, new data is written to new blocks, but the old blocks are not reclaimed as free space. The snapshot contains the original version of the file system, and the live file system contains any changes made since the snapshot was taken. No additional space is used. As new data is written to the live file system, new blocks are allocated to store this data. The apparent size of the snapshot will grow as the blocks are no longer used in the live file system, but only in the snapshot. These snapshots can be mounted read only to allow for the recovery of previous versions of files. It is also possible to rollback a live file system to a specific snapshot, undoing any changes that took place after the snapshot was taken. Each block in the pool has a reference counter which keeps track of how many snapshots, clones, datasets, or volumes make use of that block. As files and snapshots are deleted, the reference count is decremented. When a block is no longer referenced, it is reclaimed as free space. Snapshots can also be marked with a hold. When a snapshot is held, any attempt to destroy it will return an `EBUSY` error. Each snapshot can have multiple holds, each with a unique name. The release command removes the hold so the snapshot can deleted. Snapshots can be taken on volumes, but they can only be cloned or rolled back, not mounted independently.
Clone	Snapshots can also be cloned. A clone is a writable version of a snapshot, allowing the file system to be forked as a new dataset. As with a snapshot, a clone initially consumes no additional space. As new data is written to a clone and new blocks are allocated, the apparent size of the clone grows. When blocks are overwritten in the cloned file system or volume, the reference count on the previous block is decremented. The snapshot upon which a clone is based cannot be deleted because the clone depends on it. The snapshot is the parent, and the clone is the child. Clones can be promoted, reversing this dependency and making the clone the parent and the previous parent the child. This operation requires no additional space. Because the amount of space used by the parent and child is reversed, existing quotas and reservations might be affected.
Checksum	Every block that is allocated is also checksummed. The checksum algorithm used is a per-dataset property, see `set`. The checksum of each block is transparently validated as it is read, allowing ZFS to detect silent corruption. If the data that is read does not match the expected checksum, ZFS will attempt to recover the data from any available redundancy, like mirrors or RAID-Z). Validation of all checksums can be triggered with `scrub`. Checksum algorithms include: `fletcher2` `fletcher4` `sha256` The `fletcher` algorithms are faster, but `sha256` is a strong cryptographic hash and has a much lower chance of collisions at the cost of some performance. Checksums can be disabled, but it is not recommended.
Compression	Each dataset has a compression property, which defaults to off. This property can be set to one of a number of compression algorithms. This will cause all new data that is written to the dataset to be compressed. Beyond a reduction in space used, read and write throughput often increases because fewer blocks are read or written. LZ4 - Added in ZFS pool version 5000 (feature flags), LZ4 is now the recommended compression algorithm. LZ4 compresses approximately 50% faster than LZJB when operating on compressible data, and is over three times faster when operating on uncompressible data. LZ4 also decompresses approximately 80% faster than LZJB. On modern CPUs, LZ4 can often compress at over 500 MB/s, and decompress at over 1.5 GB/s (per single CPU core). LZJB - The default compression algorithm. Created by Jeff Bonwick (one of the original creators of ZFS). LZJB offers good compression with less CPU overhead compared to GZIP. In the future, the default compression algorithm will likely change to LZ4. GZIP - A popular stream compression algorithm available in ZFS. One of the main advantages of using GZIP is its configurable level of compression. When setting the `compress` property, the administrator can choose the level of compression, ranging from `gzip1`, the lowest level of compression, to `gzip9`, the highest level of compression. This gives the administrator control over how much CPU time to trade for saved disk space. ZLE - Zero Length Encoding is a special compression algorithm that only compresses continuous runs of zeros. This compression algorithm is only useful when the dataset contains large blocks of zeros.
Copies	When set to a value greater than 1, the `copies` property instructs ZFS to maintain multiple copies of each block in the File System or Volume. Setting this property on important datasets provides additional redundancy from which to recover a block that does not match its checksum. In pools without redundancy, the copies feature is the only form of redundancy. The copies feature can recover from a single bad sector or other forms of minor corruption, but it does not protect the pool from the loss of an entire disk.
Deduplication	Checksums make it possible to detect duplicate blocks of data as they are written. With deduplication, the reference count of an existing, identical block is increased, saving storage space. To detect duplicate blocks, a deduplication table (DDT) is kept in memory. The table contains a list of unique checksums, the location of those blocks, and a reference count. When new data is written, the checksum is calculated and compared to the list. If a match is found, the existing block is used. The SHA256 checksum algorithm is used with deduplication to provide a secure cryptographic hash. Deduplication is tunable. If `dedup` is `on`, then a matching checksum is assumed to mean that the data is identical. If `dedup` is set to `verify`, then the data in the two blocks will be checked byte-for-byte to ensure it is actually identical. If the data is not identical, the hash collision will be noted and the two blocks will be stored separately. Because DDT must store the hash of each unique block, it consumes a very large amount of memory. A general rule of thumb is 5-6 GB of ram per 1 TB of deduplicated data). In situations where it is not practical to have enough RAM to keep the entire DDT in memory, performance will suffer greatly as the DDT must be read from disk before each new block is written. Deduplication can use L2ARC to store the DDT, providing a middle ground between fast system memory and slower disks. Consider using compression instead, which often provides nearly as much space savings without the additional memory requirement.
Scrub	Instead of a consistency check like fsck(8), ZFS has `scrub`. `scrub` reads all data blocks stored on the pool and verifies their checksums against the known good checksums stored in the metadata. A periodic check of all the data stored on the pool ensures the recovery of any corrupted blocks before they are needed. A scrub is not required after an unclean shutdown, but is recommended at least once every three months. The checksum of each block is verified as blocks are read during normal use, but a scrub makes certain that even infrequently used blocks are checked for silent corruption. Data security is improved, especially in archival storage situations. The relative priority of `scrub` can be adjusted with `vfs.zfs.scrub_delay` to prevent the scrub from degrading the performance of other workloads on the pool.
Dataset Quota	ZFS provides very fast and accurate dataset, user, and group space accounting in addition to quotas and space reservations. This gives the administrator fine grained control over how space is allocated and allows space to be reserved for critical file systems. ZFS supports different types of quotas: the dataset quota, the reference quota (refquota), the user quota, and the group quota. Quotas limit the amount of space that a dataset and all of its descendants, including snapshots of the dataset, child datasets, and the snapshots of those datasets, can consume. Note: Quotas cannot be set on volumes, as the `volsize` property acts as an implicit quota.
Reference Quota	A reference quota limits the amount of space a dataset can consume by enforcing a hard limit. However, this hard limit includes only space that the dataset references and does not include space used by descendants, such as file systems or snapshots.
User Quota	User quotas are useful to limit the amount of space that can be used by the specified user.
Group Quota	The group quota limits the amount of space that a specified group can consume.
Dataset Reservation	The `reservation` property makes it possible to guarantee a minimum amount of space for a specific dataset and its descendants. If a 10 GB reservation is set on `storage/home/bob`, and another dataset tries to use all of the free space, at least 10 GB of space is reserved for this dataset. If a snapshot is taken of `storage/home/bob`, the space used by that snapshot is counted against the reservation. The `refreservation` property works in a similar way, but it excludes descendants like snapshots. Reservations of any sort are useful in many situations, such as planning and testing the suitability of disk space allocation in a new system, or ensuring that enough space is available on file systems for audio logs or system recovery procedures and files.
Reference Reservation	The `refreservation` property makes it possible to guarantee a minimum amount of space for the use of a specific dataset excluding its descendants. This means that if a 10 GB reservation is set on `storage/home/bob`, and another dataset tries to use all of the free space, at least 10 GB of space is reserved for this dataset. In contrast to a regular reservation, space used by snapshots and descendant datasets is not counted against the reservation. For example, if a snapshot is taken of `storage/home/bob`, enough disk space must exist outside of the `refreservation` amount for the operation to succeed. Descendants of the main data set are not counted in the `refreservation` amount and so do not encroach on the space set.
Resilver	When a disk fails and is replaced, the new disk must be filled with the data that was lost. The process of using the parity information distributed across the remaining drives to calculate and write the missing data to the new drive is called resilvering.
Online	A pool or vdev in the `Online` state has all of its member devices connected and fully operational. Individual devices in the `Online` state are functioning normally.
Offline	Individual devices can be put in an `Offline` state by the administrator if there is sufficient redundancy to avoid putting the pool or vdev into a Faulted state. An administrator may choose to offline a disk in preparation for replacing it, or to make it easier to identify.
Degraded	A pool or vdev in the `Degraded` state has one or more disks that have been disconnected or have failed. The pool is still usable, but if additional devices fail, the pool could become unrecoverable. Reconnecting the missing devices or replacing the failed disks will return the pool to an Online state after the reconnected or new device has completed the Resilver process.
Faulted	A pool or vdev in the `Faulted` state is no longer operational. The data on it can no longer be accessed. A pool or vdev enters the `Faulted` state when the number of missing or failed devices exceeds the level of redundancy in the vdev. If missing devices can be reconnected, the pool will return to a Online state. If there is insufficient redundancy to compensate for the number of failed disks, then the contents of the pool are lost and must be restored from backups.

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19.7. Additional Resources	Home	Chapter 20. Other File Systems

19.8. ZFS Features and Terminology

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