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unti 1 blockchain, types of fault, CAP - Coggle Diagram
unti 1 blockchain
Distributed sytems
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A distributed system is a computing paradigm whereby two or more nodes work with each other in a coordinated fashion in order to achieve a common outcome.
Distributed systems are modeled in such a way that end users see them as a single logical platform, even though multiple machines operate behind the scenes.
A node can be defined as an individual player in a distributed system. Each node has its own memory and processor and operates independently.
All nodes are capable of sending and receiving messages to and from each other through communication channels. Hence, distributed systems are often referred to as message-passing systems.
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This arbitrary behavior can be intentionally malicious, which is detrimental to the operation of the network. Generally, any unexpected behavior of a node on the network can be categorized as Byzantine behavior.
example of distributed system: A common real-world example is a search engine like Google, which is powered by a large distributed system but appears as a single unified system to users.anothe example is distrubutec ledger system
Even if some nodes fail or communication links break, the system should:
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The concept of Byzantine faults originates from the Byzantine Generals Problem, proposed by Leslie Lamport (1982).
This illustrates the difficulty of achieving consensus in the presence of faults, which is a core challenge in distributed systems.
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types of fault
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Motivation
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Non-malicious: Software bugs, hardware failures, power failures
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A consensus mechanism is a set of steps that are taken by all, or
most, nodes in order to agree on a proposed state or value.
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CAP
In Bitcoin:
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Eventually, all nodes agree on transaction history
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👉 Eventual Consistency
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Over time, all nodes agree on a single state
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At first glance, it may seem that blockchain achieves all three properties, but in reality:
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Blockchain achieves consistency over time, called:
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👉 Therefore, either consistency or availability must be sacrificed
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Thus, mining plays a key role in achieving state machine replication and eventual consistency.
When a network partition occurs, a distributed system must choose between:
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The CAP theorem is often visualized using a Venn diagram, showing that:
👉 In practice, CA does not truly exist, because network failures (partitions) are unavoidable.
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Even under such conditions, the system should continue functioning.
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In simple terms, every request gets a response, even if it may not be the latest data
Consistency ensures that all nodes in a distributed system have a single, latest, and identical copy of data.
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Consistency is implemented through state machine replication (SMR), where all nodes maintain the same state.
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The CAP Theorem, also known as Brewer’s Theorem, was originally introduced by
Eric Brewer in 1998 as a conjecture.
Later, in 2002, it was formally proved as a theorem by
Seth Gilbert and
Nancy Lynch.
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