FLP impossibility result
E467812
The FLP impossibility result is a foundational theorem in distributed computing showing that in an asynchronous system, no deterministic consensus protocol can guarantee both safety and liveness in the presence of even a single crash failure.
All labels observed (1)
| Label | Occurrences |
|---|---|
| FLP impossibility result canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4765359 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: FLP impossibility result Context triple: [Byzantine Generals Problem, relatedConcept, FLP impossibility result]
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A.
"Reaching Agreement in the Presence of Faults"
"Reaching Agreement in the Presence of Faults" is a seminal paper in distributed computing that introduced the Byzantine Generals Problem and laid the foundations for understanding consensus in unreliable, fault-prone systems.
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B.
"Time, Clocks, and the Ordering of Events in a Distributed System"
"Time, Clocks, and the Ordering of Events in a Distributed System" is a seminal 1978 paper that introduced logical clocks and the happened-before relation, fundamentally shaping the theory and practice of distributed computing.
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C.
Byzantine Generals Problem
The Byzantine Generals Problem is a classic computer science and distributed systems thought experiment that illustrates the difficulty of achieving reliable consensus among participants in the presence of faulty or malicious actors.
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D.
Paxos
Paxos is a small Greek island in the Ionian Sea, known for its clear turquoise waters, olive groves, and tranquil, less-touristed atmosphere.
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E.
Paxos consensus algorithm
The Paxos consensus algorithm is a fault-tolerant protocol for achieving agreement among distributed systems, widely used as a foundation for reliable, replicated state machines and modern distributed databases.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: FLP impossibility result Target entity description: The FLP impossibility result is a foundational theorem in distributed computing showing that in an asynchronous system, no deterministic consensus protocol can guarantee both safety and liveness in the presence of even a single crash failure.
-
A.
"Reaching Agreement in the Presence of Faults"
"Reaching Agreement in the Presence of Faults" is a seminal paper in distributed computing that introduced the Byzantine Generals Problem and laid the foundations for understanding consensus in unreliable, fault-prone systems.
-
B.
"Time, Clocks, and the Ordering of Events in a Distributed System"
"Time, Clocks, and the Ordering of Events in a Distributed System" is a seminal 1978 paper that introduced logical clocks and the happened-before relation, fundamentally shaping the theory and practice of distributed computing.
-
C.
Byzantine Generals Problem
The Byzantine Generals Problem is a classic computer science and distributed systems thought experiment that illustrates the difficulty of achieving reliable consensus among participants in the presence of faulty or malicious actors.
-
D.
Paxos
Paxos is a small Greek island in the Ionian Sea, known for its clear turquoise waters, olive groves, and tranquil, less-touristed atmosphere.
-
E.
Paxos consensus algorithm
The Paxos consensus algorithm is a fault-tolerant protocol for achieving agreement among distributed systems, widely used as a foundation for reliable, replicated state machines and modern distributed databases.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
impossibility result
ⓘ
theorem in distributed computing ⓘ |
| alsoKnownAs |
FLP theorem
NERFINISHED
ⓘ
Fischer–Lynch–Paterson impossibility result NERFINISHED ⓘ |
| appliesTo | asynchronous distributed systems ⓘ |
| assumes |
asynchronous message-passing model
ⓘ
deterministic consensus protocols ⓘ messages experience arbitrary but finite delays ⓘ possibility of at least one crash failure ⓘ processes execute steps at arbitrary but finite speeds ⓘ reliable message delivery without loss or corruption ⓘ |
| basedOn | bivalence argument ⓘ |
| centralTo | theory of fault-tolerant distributed systems ⓘ |
| citationCountCategory | highly cited result in distributed computing ⓘ |
| clarifies | trade-off between safety and liveness in asynchronous consensus ⓘ |
| concernsProblem | distributed consensus ⓘ |
| coreInsight | asynchrony plus even a single crash failure prevents guaranteed deterministic consensus termination ⓘ |
| doesNotApplyTo |
randomized consensus protocols that may terminate with probability 1
ⓘ
synchronous systems with known time bounds ⓘ |
| doesNotAssume |
Byzantine failures
ⓘ
synchrony assumptions such as known message delay bounds ⓘ |
| failureModel | crash failures ⓘ |
| field |
distributed computing
ⓘ
theoretical computer science ⓘ |
| implies |
any deterministic consensus protocol in a purely asynchronous system is vulnerable to non-terminating executions
ⓘ
no deterministic consensus algorithm can guarantee termination in a purely asynchronous system with one possible crash failure ⓘ |
| influenced |
design of consensus algorithms such as Paxos
ⓘ
design of consensus algorithms such as Raft ⓘ |
| livenessProperty | every correct process eventually decides ⓘ |
| motivated |
failure detectors abstraction in distributed systems
ⓘ
introduction of partial synchrony models ⓘ use of randomization in consensus protocols ⓘ |
| namedAfter |
Michael J. Fischer
NERFINISHED
ⓘ
Michael S. Paterson NERFINISHED ⓘ Nancy A. Lynch NERFINISHED ⓘ |
| originalPaperTitle | Impossibility of Distributed Consensus with One Faulty Process NERFINISHED ⓘ |
| publicationYear | 1985 ⓘ |
| publishedIn | Journal of the ACM NERFINISHED ⓘ |
| relatedTo |
Byzantine agreement problem
ⓘ
CAP theorem NERFINISHED ⓘ Fischer–Lynch–Merritt impossibility results NERFINISHED ⓘ |
| safetyProperty | no two correct processes decide differently ⓘ |
| showsImpossibilityOf |
deterministic consensus with guaranteed termination in asynchronous systems with one crash failure
ⓘ
simultaneous guarantee of safety and liveness for deterministic consensus in fully asynchronous systems with one crash failure ⓘ |
| taughtIn | graduate courses on distributed algorithms ⓘ |
| usesConcept |
adversarial message scheduling
ⓘ
bivalent configuration ⓘ univalent configuration ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: FLP impossibility result Description of subject: The FLP impossibility result is a foundational theorem in distributed computing showing that in an asynchronous system, no deterministic consensus protocol can guarantee both safety and liveness in the presence of even a single crash failure.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.