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Apache Spark has its architectural foundation in the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. [2] The Dataframe API was released as an abstraction on top of the RDD, followed by the Dataset API.
Checkpointing is a technique that provides fault tolerance for computing systems. It involves saving a snapshot of an application's state, so that it can restart from that point in case of failure. This is particularly important for long-running applications that are executed in failure-prone computing systems.
Determinism is an ideal characteristic for providing fault-tolerance. Intuitively, if multiple copies of a system exist, a fault in one would be noticeable as a difference in the State or Output from the others. The minimum number of copies needed for fault-tolerance is three; one which has a fault, and two others to whom we compare State and ...
There is a difference between fault tolerance and systems that rarely have problems. For instance, the Western Electric crossbar systems had failure rates of two hours per forty years, and therefore were highly fault resistant. But when a fault did occur they still stopped operating completely, and therefore were not fault tolerant.
In a fault-tolerant system, the failure might go undetected, whereas in a system that is neither fault-tolerant nor fail-fast, the failure might be temporarily hidden until it causes some seemingly unrelated problem later.
Fault-tolerant computer systems (6 C, 72 P) Pages in category "Fault tolerance" The following 21 pages are in this category, out of 21 total.
Fault-tolerant computers (e.g., see Tandem Computers and Stratus Technologies), which tend to have duplicate components running in lock-step for reliability, have become less popular, due to their high cost. High availability systems, using distributed computing techniques like computer clusters, are often used as cheaper alternatives ...
A fault-tolerant computer system can be achieved at the internal component level, at the system level (multiple machines), or site level (replication).. One would normally deploy a load balancer to ensure high availability for a server cluster at the system level. [3]