Replication (computing)

Sharing information to ensure consistency in computing

Replication in computing involves sharing information so as to ensure consistency between redundant resources, such as software or hardware components, to improve reliability, fault tolerance, or accessibility.

Database replication can be used on many database management systems (DBMS), usually with a primary/replica relationship between the original and the copies. The primary logs the updates, which then ripple through to the replicas. Each replica outputs a message stating that it has received the update successfully, thus allowing the sending of subsequent updates. In multi master replication, updates can be submitted to any database node, and then ripple through to other servers. This is often desired but introduces substantially increased costs and complexity which may make it impractical in some situations. The most common challenge that exists in multi master replication is transactional conflict prevention or resolution. Most synchronous (or eager) replication solutions perform conflict prevention, while asynchronous (or lazy) solutions have to perform conflict resolution. For instance, if the same record is changed on two nodes simultaneously, an eager replication system would detect the conflict before confirming the commit and abort one of the transactions. A lazy replication system would allow both transactions to commit and run a conflict resolution during re synchronization. The resolution of such a conflict may be based on a timestamp of the transaction, on the hierarchy of the origin nodes or on much more complex logic, which decides consistently across all nodes. Database replication becomes more complex when it scales up horizontally and vertically. Horizontal scale up has more data replicas, while vertical scale up has data replicas located at greater physical distances. Problems raised by horizontal scale up can be alleviated by a multi layer, multi view access protocol. The early problems of vertical scale up have largely been addressed by improving Internet reliability and performance. When data is replicated between database servers, so that the information remains consistent throughout the database system and users cannot tell or even know which server in the DBMS they are using, the system is said to exhibit replication transparency. However, replication transparency can not always be achieved. When data is replicated in a database, they will be constrained by CAP theorem or PACELC theorem. In the NoSQL movement, data consistency is usually sacrificed in exchange for other more desired properties, such as availability (A), partition tolerance (P), etc. Various data consistency models have also been developed to serve as Service Level Agreement (SLA) between service providers and the users.

Active (real time) storage replication is usually implemented by distributing updates of a block device to several physical hard disks. This way, any file system supported by the operating system can be replicated without modification, as the file system code works on a level above the block device driver layer. It is implemented either in hardware (in a disk array controller) or in software (in a device driver). The most basic method is disk mirroring, which is typical for locally connected disks. The storage industry narrows the definitions, so mirroring is a local (short distance) operation. A replication is extendable across a computer network, so that the disks can be located in physically distant locations, and the primary/replica database replication model is usually applied. The purpose of replication is to prevent damage from failures or disasters that may occur in one location – or in case such events do occur, to improve the ability to recover data. For replication, latency is the key factor because it determines either how far apart the sites can be or the type of replication that can be employed. The main characteristic of such cross site replication is how write operations are handled, through either asynchronous or synchronous replication; synchronous replication needs to wait for the destination server's response in any write operation whereas asynchronous replication does not. Synchronous replication guarantees "zero data loss" by the means of atomic write operations, where the write operation is not considered complete until acknowledged by both the local and remote storage. Most applications wait for a write transaction to complete before proceeding with further work, hence overall performance decreases considerably. Inherently, performance drops proportionally to distance, as minimum latency is dictated by the speed of light. For 10 km distance, the fastest possible roundtrip takes 67 μs, whereas an entire local cached write completes in about 10–20 μs. In asynchronous replication, the write operation is considered complete as soon as local storage acknowledges it. Remote storage is updated with a small lag. Performance is greatly increased, but in case of a local storage failure, the remote storage is not guaranteed to have the current copy of data (the most recent data may be lost). Semi synchronous replication typically considers a write operation complete when acknowledged by local storage and received or logged by the remote server. The actual remote write is performed asynchronously, resulting in better performance but remote storage will lag behind the local storage, so that there is no guarantee of durability (i. e., seamless transparency) in the case of local storage failure. Point in time replication produces periodic snapshots which are replicated instead of primary storage. This is intended to replicate only the changed data instead of the entire volume. As less information is replicated using this method, replication can occur over less expensive bandwidth links such as iSCSI or T1 instead of fiberoptic lines.

Many distributed filesystems use replication to ensure fault tolerance and avoid a single point of failure. Many commercial synchronous replication systems do not freeze when the remote replica fails or loses connection – behaviour which guarantees zero data loss – but proceed to operate locally, losing the desired zero recovery point objective. Techniques of wide area network (WAN) optimization can be applied to address the limits imposed by latency.

File based replication conducts data replication at the logical level (i. e., individual data files) rather than at the storage block level. There are many different ways of performing this, which almost exclusively rely on software.

A kernel driver (specifically a filter driver) can be used to intercept calls to the filesystem functions, capturing any activity as it occurs. This uses the same type of technology that real time active virus checkers employ. At this level, logical file operations are captured like file open, write, delete, etc. The kernel driver transmits these commands to another process, generally over a network to a different machine, which will mimic the operations of the source machine. Like block level storage replication, the file level replication allows both synchronous and asynchronous modes. In synchronous mode, write operations on the source machine are held and not allowed to occur until the destination machine has acknowledged the successful replication. Synchronous mode is less common with file replication products although a few solutions exist. File level replication solutions allow for informed decisions about replication based on the location and type of the file. For example, temporary files or parts of a filesystem that hold no business value could be excluded. The data transmitted can also be more granular; if an application writes 100 bytes, only the 100 bytes are transmitted instead of a complete disk block (generally 4,096 bytes). This substantially reduces the amount of data sent from the source machine and the storage burden on the destination machine. Drawbacks of this software only solution include the requirement for implementation and maintenance on the operating system level, and an increased burden on the machine's processing power.

Similarly to database transaction logs, many file systems have the ability to journal their activity. The journal can be sent to another machine, either periodically or in real time by streaming. On the replica side, the journal can be used to play back file system modifications. One of the notable implementations is Microsoft's System Center Data Protection Manager (DPM), released in 2005, which performs periodic updates but does not offer real time replication.

This is the process of comparing the source and destination file systems and ensuring that the destination matches the source. The key benefit is that such solutions are generally free or inexpensive. The downside is that the process of synchronizing them is quite system intensive, and consequently this process generally runs infrequently. One of the notable implementations is rsync.

In a paging operating system, pages in a paging file are sometimes replicated within a track to reduce rotational latency. In IBM's VSAM, index data are sometimes replicated within a track to reduce rotational latency.

Another example of using replication appears in distributed shared memory systems, where many nodes of the system share the same page of memory. This usually means that each node has a separate copy (replica) of this page.

Many classical approaches to replication are based on a primary backup model where one device or process has unilateral control over one or more other processes or devices. For example, the primary might perform some computation, streaming a log of updates to a backup (standby) process, which can then take over if the primary fails. This approach is common for replicating databases, despite the risk that if a portion of the log is lost during a failure, the backup might not be in a state identical to the primary, and transactions could then be lost. A weakness of primary backup schemes is that only one is actually performing operations. Fault tolerance is gained, but the identical backup system doubles the costs. For this reason, starting c. 1985, the distributed systems research community began to explore alternative methods of replicating data. An outgrowth of this work was the emergence of schemes in which a group of replicas could cooperate, with each process acting as a backup while also handling a share of the workload. Computer scientist Jim Gray analyzed multi primary replication schemes under the transactional model and published a widely cited paper skeptical of the approach "The Dangers of Replication and a Solution". He argued that unless the data splits in some natural way so that the database can be treated as n n disjoint sub databases, concurrency control conflicts will result in seriously degraded performance and the group of replicas will probably slow as a function of n. Gray suggested that the most common approaches are likely to result in degradation that scales as O(n³). His solution, which is to partition the data, is only viable in situations where data actually has a natural partitioning key. In the 1985–1987, the virtual synchrony model was proposed and emerged as a widely adopted standard (it was used in the Isis Toolkit, Horus, Transis, Ensemble, Totem, Spread, C Ensemble, Phoenix and Quicksilver systems, and is the basis for the CORBA fault tolerant computing standard). Virtual synchrony permits a multi primary approach in which a group of processes cooperates to parallelize some aspects of request processing. The scheme can only be used for some forms of in memory data, but can provide linear speedups in the size of the group. A number of modern products support similar schemes. For example, the Spread Toolkit supports this same virtual synchrony model and can be used to implement a multi primary replication scheme; it would also be possible to use C Ensemble or Quicksilver in this manner. WANdisco permits active replication where every node on a network is an exact copy or replica and hence every node on the network is active at one time; this scheme is optimized for use in a wide area network (WAN). Modern multi primary replication protocols optimize for the common failure free operation. Chain replication is a  popular family of such protocols. State of the art protocol variants of chain replication offer high throughput and strong consistency by arranging replicas in a chain for writes. This approach enables local reads on all replica nodes but has high latency for writes that must traverse multiple nodes sequentially. A more recent multi primary protocol, Hermes, combines cache coherent inspired invalidations and logical timestamps to achieve strong consistency with local reads and high performance writes from all replicas. During fault free operation, its broadcast based writes are non conflicting and commit after just one multicast round trip to replica nodes. This design results in high throughput and low latency for both reads and writes.