分布式计算视角下理解区块链基础概念

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本文《分布式计算视角下的区块链》由马修·赫利希(Maurice Herlihy)撰写，发表于2018年的ACM期刊，从分布式计算的角度深入探讨了区块链的基本概念和原理。作者以比特币的诞生为起点，指出尽管区块链和加密货币在当今社会备受瞩目，但媒体报道往往充斥着夸大、煽情和争议，如价格波动、非法交易以及对未来的过度乐观或悲观预测。文章并非技术手册，也不是对现有文献的全面综述，而是旨在通过对比区块链世界与分布式计算世界的相似性和差异性，提供一种理解和分析的基础。作者强调，理解区块链机制需要将它们置于分布式计算的框架下，这包括但不限于共识算法、去中心化特性、交易验证和数据存储等核心元素。 1. **共识机制**：区块链的核心是共识算法，如比特币的工作量证明(Proof-of-Work, PoW)，以及以太坊的权益证明(Proof-of-Stake, PoS)，这些机制确保了网络中的所有节点就交易历史达成一致，防止了单点故障和数据篡改。 2. **分布式账本**：区块链是一种分布式数据库，所有参与者共同维护一个公开透明的账本，每个新区块都包含前一个区块的哈希值，形成一条不可篡改的数据链，确保了数据的一致性和完整性。 3. **去中心化**：与传统的中心化系统不同，区块链没有单一的控制中心，而是通过网络中的众多节点共同协作，这降低了集中攻击的风险，并赋予用户更大的隐私和控制权。 4. **交易验证**：在分布式计算视角下，交易在被确认并添加到区块链之前，需要经过多轮节点间的验证，这保证了交易的真实性和有效性。 5. **挑战与局限**：文章也指出了区块链面临的挑战，如扩展性问题（随着交易量的增长，如何保持处理速度）、能源消耗（如PoW的高能耗）以及监管问题，这些都是从分布式计算角度探讨区块链时不能忽视的方面。这篇教程为读者揭示了区块链技术的分布式计算本质，帮助他们更深入地理解这一新兴技术，并批判性地思考其背后的理论基础和实际应用前景。

Blockchains from a Distribute d Computing Perspective

MAURICE HERLIHY, Brown University

ACM Reference Format:

Maurice Herlihy. 2018. Blockchains from a Distributed Computing Perspec-

tive. 1, 1 (January 2018), 6 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn

1 INTRODUCTION

Bitcoin rst appeared in a 2008 white paper authored by someone

called Satoshi Nakamoto [

], the mysterious deus abscondidus of the

blockchain world. Today, cryptocurrencies and blockchains are very

much in the news. Much of this coverage is lurid, sensationalistic,

and irresistible: roller-coaster prices and instant riches, vast sums of

money stolen or inexplicably lost, underground markets for drugs

and weapons, and promises of libertarian utopias just around the

corner.

This article is a tutorial on the basic notions and mechanisms

underlying blockchains, colored by the perspective that much of the

blockchain world is a disguised, sometimes distorted, mirror-image

of the distributed computing world.

This article is not a technical manual, nor is it a broad survey of

the literature (both widely available elsewhere). Instead, it attempts

to explain blockchain research in terms of the many similarities, par-

allels, semi-reinventions, and lessons not learned from distributed

computing. This article is intended mostly to appeal to blockchain

novices, but perhaps it will provide some insights to those familiar

with blockchain research but less familiar with its precursors.

2 THE LEDGER ABSTRACTION

The abstraction at the heart of blockchain systems is the notion

of a le dger, an invention of the Italian Renaissance originally de-

veloped to support double-entry bookkeeping, a distant precursor

of modern cryptocurrencies. For our purposes, a ledger is just an

indelible, append-only log of transactions that take place between

various parties. A ledger establishes which transactions happened

(“Alice transferred 10 coins to Bob”), and the order in which those

transactions happened (“Alice transferred 10 coins to Bob, and then

Bob transferred title to his car to Alice”). Ledgers are public, accessi-

ble to all parties, and they must be tamper-proof: no party can add,

delete, or modify ledger entries once they have been recorded. In

short, the algorithms that maintain ledgers must be fault-tolerant,

ensuring the ledger remains secure even if some parties misbehave,

whether accidentally or maliciously .

Author’s address: Maurice HerlihyBrown University, maurice.herlihy@gmail.com.

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https://doi.org/10.1145/nnnnnnn.nnnnnnn

2.1 Blockchain Ledger Precursors

It is helpful to start by reviewing a blockchain precursor, the so-

called universal construction for lock-free data structures [12].

Alice runs an on-line news service. Articles that arrive concur-

rently on multiple channels are placed in an in-memory table where

they are indexed for retrieval. At rst, Alice used a lock to synchro-

nize concurrent access to the table, but every now and then, the

thread holding the lock would take a page fault or a scheduling

interrupt, leaving the articles inaccessible for too long. Despite the

availability of excellent textbooks on the subject [

], Alice was

uninterested in customized lock-free algorithms, so she was in need

of a simple way to eliminate lock-based vulnerabilities.

She decided to implement her data structure in two parts. To

record articles as they arrive, she created a ledger implemented as

a simple linked list, where each list entry includes the article and

a link to the entry before it. When an article arrives, it is placed

in a shared pool, and a set of dedicated threads, called miners (for

reasons to be explained later), collectively run a repeated protocol,

called consensus, to select which article to append to the ledger.

Here, Alice’s consensus protocol can be simple: each thread creates

a list entry, then calls a compare-and-swap instruction to attempt

to make that entry the new head of the list.

Glossing over some technical details, to query for a recent article,

a thread scans the linked-list ledger. To add a new article, a thread

adds the article to the pool, and waits for for a miner to append it

to the ledger.

This structure may seem cumbersome, but it has two compelling

advantages. First, it is universal: it can implement any type of data

structure, no matter how complex. Second, all questions of concur-

rency and fault-tolerance are compartmentalized in the consensus

protocol.

A consensus protocol involves a collection of parties, some of whom

are honest, and follow the protocol, and some of whom are dishonest,

and may depart from the protocol for any reason. Consensus is

a notion that applies to a broad range of computational models.

In some contexts, dishonest parties might simply halt arbitrarily

(so-called crash failures), while in other contexts, they may behave

maliciously (so-called Byzantine failures). In some contexts, parties

communicate through objects in a shared memory, and in others,

they exchange messages. Some contexts restrict how many parties

may be dishonest, some do not.

In consensus, each party proposes a transaction to append to the

ledger, and one of these proposed transaction is chosen. Consensus

ensures: (1) agreement: all honest parties agree on which transaction

was selected„ (2) termination: all honest parties eventually learn the

selected transaction, and (3) validity: the selected transaction was

actually proposed by some party.

Consensus protocols have been the focus of decades of research in

the distributed computing community. The literature contains many

algorithms and impossibility results for many dierent models of

computation (see surveys in [1, 13]).

, Vol. 1, No. 1, Article . Publication date: January 2018.

Draft 16 Jan 18

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