分布式事务的21种武器 - 5

2023-05-02  本文已影响0人  DeepNoMind

在分布式系统中，事务的处理分布在不同组件、服务中，因此分布式事务的ACID保障面临着一些特殊难点。本系列文章介绍了21种分布式事务设计模式，并分析其实现原理和优缺点，在面对具体分布式事务问题时，可以选择合适的模式进行处理。原文: Exploring Solutions for Distributed Transactions (5)

Duncan Meyer @Unsplash

在不同业务场景下，可以有不同的解决方案，常见方法有:

  1. 阻塞重试(Blocking Retry)
  2. 二阶段和三阶段提交(Two-Phase Commit (2PC) and Three-Phase Commit (3PC))
  3. 基于后台队列的异步处理(Using Queues to Process Asynchronously in the Background)
  4. TCC补偿(TCC Compensation Matters)
  5. 本地消息表(异步保证)/发件箱模式(Local Message Table (Asynchronously Ensured)/Outbox Pattern)
  6. MQ事务(MQ Transaction)
  7. Saga模式(Saga Pattern)
  8. 事件驱动(Event Sourcing)
  9. 命令查询职责分离(Command Query Responsibility Segregation, CQRS)
  10. 原子提交(Atomic Commitment)
  11. 并行提交(Parallel Commits)
  12. 事务复制(Transactional Replication)
  13. 一致性算法(Consensus Algorithms)
  14. 时间戳排序(Timestamp Ordering)
  15. 乐观并发控制(Optimistic Concurrency Control)
  16. 拜占庭容错(Byzantine Fault Tolerance, BFT)
  17. 分布式锁(Distributed Locking)
  18. 分片(Sharding)
  19. 多版本并发控制(Multi-Version Concurrency Control, MVCC)
  20. 分布式快照(Distributed Snapshots)
  21. 主从复制(Leader-Follower Replication)

本文将介绍一致性算法、时间戳排序以及乐观并发控制三种模式。

13. 一致性算法(Consensus Algorithms)
图片来源: https://www.baeldung.com/cs/consensus-algorithms-distributed-systems 
from typing import List, Tuple

class PaxosNode:
    def __init__(self, node_id: int, nodes: List[int]):
        self.node_id = node_id
        self.nodes = nodes
        self.state = "proposed"
        self.proposed_value = None
        self.accepted_value = None
        self.accepted_round = -1
    
    def run_paxos(self, value: int) -> int:
        while True:
            if self.state == "proposed":
                self.proposed_value = value
                self.state = "prepare"
            
            if self.state == "prepare":
                max_round, max_val = self.prepare()
                if max_val is None:
                    self.state = "accept"
                else:
                    self.state = "proposed"
            
            if self.state == "accept":
                self.accepted_value = self.proposed_value
                self.accepted_round = max_round
                self.send_accept()
                self.state = "decided"
            
            if self.state == "decided":
                return self.accepted_value
    
    def prepare(self) -> Tuple[int, int]:
        max_round = -1
        max_val = None
        for node in self.nodes:
            round, val = node.receive_prepare()
            if round > max_round:
                max_round = round
                max_val = val
        
        return max_round, max_val
    
    def send_prepare(self, round: int):
        for node in self.nodes:
            node.receive_prepare_request(round, self.node_id)
    
    def receive_prepare_request(self, round: int, sender_id: int):
        if round > self.accepted_round:
            self.accepted_round = round
            self.send_prepare(round)
    
    def receive_prepare(self) -> Tuple[int, int]:
        return self.accepted_round, self.accepted_value
    
    def send_accept(self):
        for node in self.nodes:
            node.receive_accept_request(self.accepted_round, self.accepted_value)
    
    def receive_accept_request(self, round: int, value: int):
        if round >= self.accepted_round:
            self.accepted_round = round
            self.accepted_value = value
            self.send_accepted()
    
    def send_accepted(self):
        for node in self.nodes:
            node.receive_accepted(self.accepted_round, self.accepted_value)
    
    def receive_accepted(self, round: int, value: int):
        if round == self.accepted_round:
            self.proposed_value = value

示例代码

优点

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适用场景

14. 时间戳排序(Timestamp Ordering)
图片来源: https://www.geeksforgeeks.org/multiversion-timestamp-ordering/ 
from typing import List, Tuple

class Timestamp:
    def __init__(self, node_id: int):
        self.node_id = node_id
        self.counter = 0

    def increment(self):
        self.counter += 1

    def __str__(self):
        return f"{self.node_id}:{self.counter}"

class Event:
    def __init__(self, node_id: int, timestamp: Timestamp, data: str):
        self.node_id = node_id
        self.timestamp = timestamp
        self.data = data

    def __str__(self):
        return f"{self.node_id} {self.timestamp} {self.data}"

class Network:
    def __init__(self, nodes: List[int]):
        self.nodes = nodes
        self.message_queues = {node: [] for node in nodes}

    def send(self, sender: int, receiver: int, message: str):
        self.message_queues[receiver].append((sender, message))

    def receive(self, node_id: int) -> Tuple[int, str]:
        if len(self.message_queues[node_id]) > 0:
            return self.message_queues[node_id].pop(0)
        else:
            return None

class Node:
    def __init__(self, node_id: int, network: Network, initial_data: List[str]):
        self.node_id = node_id
        self.network = network
        self.clock = Timestamp(node_id)
        self.queue = []
        for data in initial_data:
            self.queue.append(Event(node_id, self.clock, data))
            self.clock.increment()

    def run(self):
        while True:
            event = self.queue.pop(0)
            print(f"Node {self.node_id} executing event {event}")
            self.clock = max(self.clock, event.timestamp)  # Update local clock

示例代码

优点

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适用场景

15. 乐观并发控制(Optimistic Concurrency Control)
from typing import List

class Account:
    def __init__(self, id: int, balance: float):
        self.id = id
        self.balance = balance
        self.version = 0

    def withdraw(self, amount: float):
        self.balance -= amount
        self.version += 1

    def deposit(self, amount: float):
        self.balance += amount
        self.version += 1

class OptimisticConcurrencyControl:
    def __init__(self, accounts: List[Account]):
        self.accounts = accounts

    def transfer(self, sender_id: int, receiver_id: int, amount: float):
        # Find sender and receiver accounts
        sender = next(acc for acc in self.accounts if acc.id == sender_id)
        receiver = next(acc for acc in self.accounts if acc.id == receiver_id)

        # Create copies of the accounts to modify
        sender_copy = Account(sender.id, sender.balance)
        receiver_copy = Account(receiver.id, receiver.balance)

        # Withdraw from sender and deposit to receiver
        sender_copy.withdraw(amount)
        receiver_copy.deposit(amount)

        # Update the global accounts list if there are no conflicts
        for i, acc in enumerate(self.accounts):
            if acc.id == sender_id:
                if acc.version != sender.version:
                    raise Exception("Optimistic Concurrency Control failed")
                self.accounts[i] = sender_copy
            elif acc.id == receiver_id:
                if acc.version != receiver.version:
                    raise Exception("Optimistic Concurrency Control failed")
                self.accounts[i] = receiver_copy

示例代码

优点

缺点

适用场景

挑战

参考文献

What is a consensus algorithm?

Consensus Algorithms in Blockchain

How Many Consensus Algorithms Are There? An Overview

Analysis of the Blockchain Consensus Algorithms

Consensus Algorithms Distributed Systems

Multiversion Timestamp Ordering

DBMS Timestamp Ordering Protocol

Timestamp-based Concurrency Control

Timestamp Ordering Protocol in DBMS

Timestamp-based Ordering Protocol in DBMS

What is an optimistic concurrency control in DBMS

Optimistic vs Pessimistic Concurrency: What Every Developer Should Know

Dealing with Optimistic Concurrency Control Collisions

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