分布式事务的21种武器 - 6

2023-05-26  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 (6)

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)

本文将介绍拜占庭容错、分布式锁以及分片三种模式。

16. 拜占庭容错(Byzantine Fault Tolerance, BFT)
class Message:
    def __init__(self, sender: int, content: str):
        self.sender = sender
        self.content = content

class ByzantineNode:
    def __init__(self, id: int, network: Dict[int, List[Message]], threshold: int):
        self.id = id
        self.network = network
        self.threshold = threshold
        self.decisions = {}

    def send_message(self, receiver: int, content: str):
        message = Message(self.id, content)
        self.network[receiver].append(message)

    def receive_messages(self) -> List[Message]:
        messages = self.network[self.id]
        self.network[self.id] = []
        return messages

    def generate_vote(self, messages: List[Message]) -> bool:
        count = 0
        for message in messages:
            if message.content == 'True':
                count += 1
            elif message.content == 'False':
                count -= 1
        return count >= self.threshold

    def run_bft(self, decision_content: str):
        # Phase 1: Broadcast proposal to all nodes
        proposal = Message(self.id, decision_content)
        for node_id in self.network:
            self.send_message(node_id, str(proposal))

        # Phase 2: Receive messages and generate votes
        messages = self.receive_messages()
        vote = self.generate_vote(messages)

        # Phase 3: Broadcast decision to all nodes
        decision = Message(self.id, str(vote))
        for node_id in self.network:
            self.send_message(node_id, str(decision))

        # Phase 4: Receive decisions and count votes
        decisions = [m.content for m in self.receive_messages()]
        count_true = decisions.count('True')
        count_false = decisions.count('False')

        # Record decision if it meets threshold, else record failure
        if count_true >= self.threshold:
            self.decisions[decision_content] = True
        elif count_false >= self.threshold:
            self.decisions[decision_content] = False
        else:
            self.decisions[decision_content] = None`

示例代码

优点

缺点

适用场景

挑战

17. 分布式锁(Distributed Locking)
from kazoo.exceptions import LockTimeout
import time

class DistributedLock:
    def __init__(self, zk_address, lock_path):
        self.zk = KazooClient(hosts=zk_address)
        self.lock_path = lock_path
        self.lock = None

    def __enter__(self):
        self.zk.start()
        self.lock = self.zk.Lock(self.lock_path)
        try:
            self.lock.acquire(timeout=10)
        except LockTimeout:
            self.zk.stop()
            raise Exception("Timeout while waiting for lock")

    def __exit__(self, exc_type, exc_val, exc_tb):
        self.lock.release()
        self.zk.stop()

if __name__ == '__main__':
    zk_address = 'localhost:2181'
    lock_path = '/my_lock'
    with DistributedLock(zk_address, lock_path):
        print("Acquired lock!")
        time.sleep(10)
    print("Released lock!")

示例代码

优点

缺点

适用场景

挑战

18. 分片(Sharding)
# Connect to MySQL database
mydb = mysql.connector.connect(
  host="localhost",
  user="yourusername",
  password="yourpassword",
  database="mydatabase"
)

# Define sharding rules
shard_key = "user_id"
num_shards = 4

# Create sharded tables
for i in range(num_shards):
    cursor = mydb.cursor()
    cursor.execute(f"CREATE TABLE users_{i} (id INT AUTO_INCREMENT PRIMARY KEY, name VARCHAR(255), email VARCHAR(255))")

# Insert data into sharded tables
users = [
    {"id": 1, "name": "John", "email": "john@example.com"},
    {"id": 2, "name": "Jane", "email": "jane@example.com"},
    {"id": 3, "name": "Bob", "email": "bob@example.com"},
    # ...
]

for user in users:
    shard_id = user[shard_key] % num_shards
    cursor = mydb.cursor()
    cursor.execute(f"INSERT INTO users_{shard_id} (id, name, email) VALUES (%s, %s, %s)", (user["id"], user["name"], user["email"]))

# Query data from sharded tables
cursor = mydb.cursor()
cursor.execute("SELECT * FROM users_0 UNION SELECT * FROM users_1 UNION SELECT * FROM users_2 UNION SELECT * FROM users_3")
users = cursor.fetchall()

print(users)

示例代码

优点

缺点

适用场景

参考文献

Byzantine Fault Tolerance (BFT) | River Glossary

What Is Byzantine Fault Tolerance?[1]

[Byzantine Fault Tolerance (BFT) Explained](https://beincrypto.com/learn/byzantine-fault-tolerance "Byzantine Fault Tolerance (BFT "Byzantine Fault Tolerance (BFT) Explained") Explained")

Distributed Locks with Redis[2]

How to do distributed locking[3]

Distributed Locking[4]

Sharding[5]

What is Database Sharding?[6]

What is Sharding?[7]

Understanding Database Sharding[8]

你好，我是俞凡，在Motorola做过研发，现在在Mavenir做技术工作，对通信、网络、后端架构、云原生、DevOps、CICD、区块链、AI等技术始终保持着浓厚的兴趣，平时喜欢阅读、思考，相信持续学习、终身成长，欢迎一起交流学习。微信公众号：DeepNoMind

参考资料

[1] What Is Byzantine Fault Tolerance?: https://www.fool.com/investing/stock-market/market-sectors/financials/cryptocurrency-stocks/byzantine-fault-tolerance
[2] Distributed Locks with Redis: https://redis.io/docs/manual/patterns/distributed-locks
[3] How to do distributed locking: https://martin.kleppmann.com/2016/02/08/how-to-do-distributed-locking.html
[4] Distributed Locking: https://awesome-architecture.com/distributed-locking
[5] Sharding: https://www.mongodb.com/docs/manual/sharding
[6] What is Database Sharding?: https://aws.amazon.com/what-is/database-sharding
[7] What is Sharding?: https://www.geeksforgeeks.org/what-is-sharding
[8] Understanding Database Sharding: https://www.digitalocean.com/community/tutorials/understanding-database-sharding

你好，我是俞凡，在Motorola做过研发，现在在Mavenir做技术工作，对通信、网络、后端架构、云原生、DevOps、CICD、区块链、AI等技术始终保持着浓厚的兴趣，平时喜欢阅读、思考，相信持续学习、终身成长，欢迎一起交流学习。微信公众号：DeepNoMind

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