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Eventual Consistency
Eventual consistency is a consistency model used in distributed computing to achieve high availability. Put simply: if no new updates are made to a given data item, ''eventually'' all accesses to that item will return the last updated value. Eventual consistency, also called optimistic replication, is widely deployed in distributed systems and has origins in early mobile computing projects. A system that has achieved eventual consistency is often said to have converged, or achieved replica convergence. Eventual consistency is a weak guarantee – most stronger models, like linearizability, are trivially eventually consistent. Eventually-consistent services are often classified as providing BASE semantics (basically-available, soft-state, eventual consistency), in contrast to traditional ACID (atomicity, consistency, isolation, durability). In chemistry, a base is the opposite of an acid, which helps in remembering the acronym. According to the same resource, these are the rou ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Consistency Model
In computer science, a consistency model specifies a contract between the programmer and a system, wherein the system guarantees that if the programmer follows the rules for operations on memory, memory will be data consistency, consistent and the results of reading, writing, or updating memory will be predictable. Consistency models are used in Distributed computing, distributed systems like distributed shared memory systems or distributed data stores (such as filesystems, databases, optimistic replication systems or web caching). Consistency is different from coherence, which occurs in systems that are cache coherence, cached or cache-less, and is consistency of data with respect to all processors. Coherence deals with maintaining a global order in which writes to a single location or single variable are seen by all processors. Consistency deals with the ordering of operations to multiple locations with respect to all processors. High level languages, such as C++ and Java (progr ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Safety (distributed Computing)
Properties of an execution of a computer program—particularly for concurrent and distributed systems—have long been formulated by giving safety properties ("bad things don't happen") and liveness properties ("good things do happen"). A program is totally correct with respect to a precondition P and postcondition Q if any execution started in a state satisfying P terminates in a state satisfying Q. Total correctness is a conjunction of a safety property and a liveness property: * The safety property prohibits these "bad things": executions that start in a state satisfying P and terminate in a final state that does not satisfy Q. For a program C, this safety property is usually written using the Hoare triple \ C \. * The liveness property, the "good thing", is that execution that starts in a state satisfying P terminates. Note that a ''bad thing'' is discrete, since it happens at a particular place during execution. A "good thing" need not be discrete, but the liveness property ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
CAP Theorem
In database theory, the CAP theorem, also named Brewer's theorem after computer scientist Eric Brewer (scientist), Eric Brewer, states that any distributed data store can provide at most Inconsistent triad, two of the following three guarantees: ; Consistency model, Consistency: Every read receives the most recent write or an error. Note that consistency as defined in the CAP theorem is quite different from the consistency guaranteed in ACID database transactions. ; Availability: Every request received by a non-failing node in the system must result in a response. This is the definition of availability in CAP theorem as defined by Gilbert and Lynch. Note that availability as defined in CAP theorem is different from high availability in software architecture. ; Network partitioning, Partition tolerance: The system continues to operate despite an arbitrary number of messages being dropped (or delayed) by the network between nodes. When a network partition failure happens, it must be ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Conflict-free Replicated Data Type
In distributed computing, a conflict-free replicated data type (CRDT) is a data structure that is replicated across multiple computers in a network, with the following features: # The application can update any replica independently, concurrently and without coordinating with other replicas. # An algorithm (itself part of the data type) automatically resolves any inconsistencies that might occur. # Although replicas may have different state at any particular point in time, they are guaranteed to eventually converge. The CRDT concept was formally defined in 2011 by Marc Shapiro, Nuno Preguiça, Carlos Baquero and Marek Zawirski. Development was initially motivated by collaborative text editing and mobile computing. CRDTs have also been used in online chat systems, online gambling, and in the SoundCloud audio distribution platform. The NoSQL distributed databases Redis, Riak and Cosmos DB have CRDT data types. Background Concurrent updates to multiple replicas of the same d ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Monotonic
In mathematics, a monotonic function (or monotone function) is a function between ordered sets that preserves or reverses the given order. This concept first arose in calculus, and was later generalized to the more abstract setting of order theory. In calculus and analysis In calculus, a function f defined on a subset of the real numbers with real values is called ''monotonic'' if it is either entirely non-decreasing, or entirely non-increasing. That is, as per Fig. 1, a function that increases monotonically does not exclusively have to increase, it simply must not decrease. A function is termed ''monotonically increasing'' (also ''increasing'' or ''non-decreasing'') if for all x and y such that x \leq y one has f\!\left(x\right) \leq f\!\left(y\right), so f preserves the order (see Figure 1). Likewise, a function is called ''monotonically decreasing'' (also ''decreasing'' or ''non-increasing'') if, whenever x \leq y, then f\!\left(x\right) \geq f\!\left(y\right), so ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Safety (distributed Computing)
Properties of an execution of a computer program—particularly for concurrent and distributed systems—have long been formulated by giving safety properties ("bad things don't happen") and liveness properties ("good things do happen"). A program is totally correct with respect to a precondition P and postcondition Q if any execution started in a state satisfying P terminates in a state satisfying Q. Total correctness is a conjunction of a safety property and a liveness property: * The safety property prohibits these "bad things": executions that start in a state satisfying P and terminate in a final state that does not satisfy Q. For a program C, this safety property is usually written using the Hoare triple \ C \. * The liveness property, the "good thing", is that execution that starts in a state satisfying P terminates. Note that a ''bad thing'' is discrete, since it happens at a particular place during execution. A "good thing" need not be discrete, but the liveness property ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Vector Clock
A vector clock is a data structure used for determining the partial ordering of events in a distributed system and detecting causality violations. Just as in Lamport timestamps, inter-process messages contain the state of the sending process's logical clock. A vector clock of a system of ''N'' processes is an array/vector of ''N'' logical clocks, one clock per process; a local "largest possible values" copy of the global clock-array is kept in each process. Denote VC_i as the vector clock maintained by process i, the clock updates proceed as follows: * Initially all clocks are zero. * Each time a process experiences an internal event, it increments its own logical clock in the vector by one. For instance, upon an event at process i, it updates VC_ \leftarrow VC_ + 1. * Each time a process sends a message, it increments its own logical clock in the vector by one (as in the bullet above, but not twice for the same event) then it pairs the message with a copy of its own vector ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Lamport Timestamps
The Lamport timestamp algorithm is a simple logical clock algorithm used to determine the order of events in a distributed computer system. As different nodes or processes will typically not be perfectly synchronized, this algorithm is used to provide a partial ordering of events with minimal overhead, and conceptually provide a starting point for the more advanced vector clock method. The algorithm is named after its creator, Leslie Lamport. Distributed algorithms such as resource synchronization often depend on some method of ordering events to function. For example, consider a system with two processes and a disk. The processes send messages to each other, and also send messages to the disk requesting access. The disk grants access in the order the messages were ''received''. For example process A sends a message to the disk requesting write access, and then sends a read instruction message to process B. Process B receives the message, and as a result sends its own read reques ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Liveness
Properties of an execution of a computer program—particularly for concurrent and distributed systems—have long been formulated by giving safety properties ("bad things don't happen") and liveness properties ("good things do happen"). A program is totally correct with respect to a precondition P and postcondition Q if any execution started in a state satisfying P terminates in a state satisfying Q. Total correctness is a conjunction of a safety property and a liveness property: * The safety property prohibits these "bad things": executions that start in a state satisfying P and terminate in a final state that does not satisfy Q. For a program C, this safety property is usually written using the Hoare triple \ C \. * The liveness property, the "good thing", is that execution that starts in a state satisfying P terminates. Note that a ''bad thing'' is discrete, since it happens at a particular place during execution. A "good thing" need not be discrete, but the liveness property ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Distributed Computing
Distributed computing is a field of computer science that studies distributed systems, defined as computer systems whose inter-communicating components are located on different networked computers. The components of a distributed system communicate and coordinate their actions by passing messages to one another in order to achieve a common goal. Three significant challenges of distributed systems are: maintaining concurrency of components, overcoming the lack of a global clock, and managing the independent failure of components. When a component of one system fails, the entire system does not fail. Examples of distributed systems vary from SOA-based systems to microservices to massively multiplayer online games to peer-to-peer applications. Distributed systems cost significantly more than monolithic architectures, primarily due to increased needs for additional hardware, servers, gateways, firewalls, new subnets, proxies, and so on. Also, distributed systems are prone to ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Acid
An acid is a molecule or ion capable of either donating a proton (i.e. Hydron, hydrogen cation, H+), known as a Brønsted–Lowry acid–base theory, Brønsted–Lowry acid, or forming a covalent bond with an electron pair, known as a Lewis acid. The first category of acids are the proton donors, or Brønsted–Lowry acid–base theory, Brønsted–Lowry acids. In the special case of aqueous solutions, proton donors form the hydronium ion H3O+ and are known as Acid–base reaction#Arrhenius theory, Arrhenius acids. Johannes Nicolaus Brønsted, Brønsted and Martin Lowry, Lowry generalized the Arrhenius theory to include non-aqueous solvents. A Brønsted–Lowry or Arrhenius acid usually contains a hydrogen atom bonded to a chemical structure that is still energetically favorable after loss of H+. Aqueous Arrhenius acids have characteristic properties that provide a practical description of an acid. Acids form aqueous solutions with a sour taste, can turn blue litmus red, and ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
