In recent years, blockchain has gained attention as a new way to store and transfer data without relying on a central authority.

But what exactly is blockchain? Before we dive into that, it’s important to understand the concept of a Distributed Ledger System (DLS).

A distributed ledger system is a network where records are shared among multiple nodes. Each participant maintains a copy of the ledger, and any new record can only be added once the network reaches an agreement, called consensus. This decentralization provides transparency and resilience, but it also introduces complex challenges which we will explore below.

What is Blockchain?

Blockchain is a type of decentralized distributed ledger that securely stores data across networks of computers in a way that is immutable, transparent, and resistant to tampering.

Each block contains data linked to the previous block, forming a chain like DNA, where each block carries a trait showing its place in the lineage of the network.

For a distributed ledger to be effective, it must achieve consensus. This ensures that all participants agree on the same records. But here’s the challenge:

How can all nodes agree on the same data, stay available for users, and handle network failures at the same time?

To answer this, we need to understand the CAP theorem.

CAP Theorem

The CAP theorem is a principle in distributed systems which states that a network cannot simultaneously guarantee:

• Consistency (C): Every node sees the same data at the same time.

• Availability (A): Every request receives a response, even if some nodes fail.

• Partition Tolerance (P): The system continues to operate despite network failures or splits.

In other words, a system can only fully guarantee two of these three properties, and must make trade-offs.

Byzantine Nodes

A Byzantine node is a node in a distributed system that behaves unpredictably or maliciously.

Consensus becomes tricky when some participants are unreliable or actively trying to disrupt the network. This is called the Byzantine Fault problem.

So how can a distributed system continue to operate safely even when some nodes act maliciously?

How Blockchain Handles Byzantine Nodes

Satoshi Nakamoto solved this problem with Proof-of-Work (PoW) in Bitcoin.

Bitcoin sacrifices immediate consistency in favor of Availability and Partition Tolerance, using probabilistic finality.

Probabilistic Finality

Probabilistic finality means that a transaction is not instantly final. Instead, the chance of reversal decreases over time.

• When a new block is added, malicious actors may try to fork the chain.

• The system does not halt, it continues processing new blocks.

• Over time, the longest chain, representing the most work, is accepted, while shorter or malicious forks are discarded.

In practice:

• 1 confirmation → transaction could be reversed

• 6 confirmations → reversal is extremely unlikely

• 100 confirmations → practically impossible

This is why blockchains like Bitcoin are considered secure but probabilistically final.

PoW vs PBFT: Two Approaches to Consensus

Key point:

• PoW blockchains trade consistency for availability and partition tolerance.

• PBFT systems trade availability for strong consistency and deterministic finality.

Real-World Analogy

• PoW Blockchain: Think of a busy highway during rush hour. Cars may temporarily take different routes (forks), but eventually, most cars end up on the same main highway (longest chain). The road never closes, but some routes might be abandoned.

• PBFT System: Imagine a courtroom. Everyone must agree on the verdict before proceeding. The trial pauses if communication breaks, but once a decision is made, it is final and cannot be changed.

Conclusion & Takeaways

• Blockchain is a decentralized distributed ledger designed to handle failures and malicious nodes.

• The CAP theorem explains why distributed systems must make trade-offs between consistency, availability, and partition tolerance.

• PoW blockchains prioritize availability and partition tolerance over immediate consistency, using probabilistic finality to eventually reach agreement.

• PBFT systems prioritize strong consistency and deterministic finality, sacrificing availability during network partitions.

• Understanding these trade-offs is key for anyone learning about blockchain or distributed systems.

Learning blockchain is not just about code; it’s about understanding the fundamental trade-offs that make decentralized systems possible.

github: https://github.com/zuru122/article-on-blockchain-and-distributed-system

In recent years, blockchain has gained attention as a new way to store and transfer data without relying on a central authority.

But what exactly is blockchain? Before we dive into that, it’s important to understand the concept of a Distributed Ledger System (DLS).

A distributed ledger system is a network where records are shared among multiple nodes. Each participant maintains a copy of the ledger, and any new record can only be added once the network reaches an agreement, called consensus. This decentralization provides transparency and resilience, but it also introduces complex challenges which we will explore below.

What is Blockchain?

Blockchain is a type of decentralized distributed ledger that securely stores data across networks of computers in a way that is immutable, transparent, and resistant to tampering.

Each block contains data linked to the previous block, forming a chain like DNA, where each block carries a trait showing its place in the lineage of the network.

For a distributed ledger to be effective, it must achieve consensus. This ensures that all participants agree on the same records. But here’s the challenge:

How can all nodes agree on the same data, stay available for users, and handle network failures at the same time?

To answer this, we need to understand the CAP theorem.

CAP Theorem

The CAP theorem is a principle in distributed systems which states that a network cannot simultaneously guarantee:

• Consistency (C): Every node sees the same data at the same time.

• Availability (A): Every request receives a response, even if some nodes fail.

• Partition Tolerance (P): The system continues to operate despite network failures or splits.

In other words, a system can only fully guarantee two of these three properties, and must make trade-offs.

Byzantine Nodes

A Byzantine node is a node in a distributed system that behaves unpredictably or maliciously.

Consensus becomes tricky when some participants are unreliable or actively trying to disrupt the network. This is called the Byzantine Fault problem.

So how can a distributed system continue to operate safely even when some nodes act maliciously?

How Blockchain Handles Byzantine Nodes

Satoshi Nakamoto solved this problem with Proof-of-Work (PoW) in Bitcoin.

Bitcoin sacrifices immediate consistency in favor of Availability and Partition Tolerance, using probabilistic finality.

Probabilistic Finality

Probabilistic finality means that a transaction is not instantly final. Instead, the chance of reversal decreases over time.

• When a new block is added, malicious actors may try to fork the chain.

• The system does not halt, it continues processing new blocks.

• Over time, the longest chain, representing the most work, is accepted, while shorter or malicious forks are discarded.

In practice:

• 1 confirmation → transaction could be reversed

• 6 confirmations → reversal is extremely unlikely

• 100 confirmations → practically impossible

This is why blockchains like Bitcoin are considered secure but probabilistically final.

PoW vs PBFT: Two Approaches to Consensus

Key point:

• PoW blockchains trade consistency for availability and partition tolerance.

• PBFT systems trade availability for strong consistency and deterministic finality.

Real-World Analogy

• PoW Blockchain: Think of a busy highway during rush hour. Cars may temporarily take different routes (forks), but eventually, most cars end up on the same main highway (longest chain). The road never closes, but some routes might be abandoned.

• PBFT System: Imagine a courtroom. Everyone must agree on the verdict before proceeding. The trial pauses if communication breaks, but once a decision is made, it is final and cannot be changed.

Conclusion & Takeaways

• Blockchain is a decentralized distributed ledger designed to handle failures and malicious nodes.

• The CAP theorem explains why distributed systems must make trade-offs between consistency, availability, and partition tolerance.

• PoW blockchains prioritize availability and partition tolerance over immediate consistency, using probabilistic finality to eventually reach agreement.

• PBFT systems prioritize strong consistency and deterministic finality, sacrificing availability during network partitions.

• Understanding these trade-offs is key for anyone learning about blockchain or distributed systems.

Learning blockchain is not just about code; it’s about understanding the fundamental trade-offs that make decentralized systems possible.

github: https://github.com/zuru122/article-on-blockchain-and-distributed-system