Optimization strategies for distributed transaction processing in high concurrency scenarios

Strategies for optimizing distributed transactions under high concurrency include: 1. Using a distributed transaction coordinator (such as ZooKeeper); 2. Optimizing data sharding; 3. Using asynchronous processing; 4. Optimizing the lock mechanism; 5. Shrinking Scope of affairs. These optimization strategies help improve concurrent processing capabilities, reduce transaction failure rates, and ensure the stability of distributed systems.

Optimization strategy of distributed transaction processing in high concurrency scenarios

1. Use distributed transaction coordinator

• Distributed transaction coordinators, such as Apache ZooKeeper and etcd, are responsible for managing and coordinating various services participating in distributed transactions.
• It provides features such as transaction consistency, isolation and durability.

// 使用 ZooKeeper 实现分布式事务协调器
ZooKeeper zk = new ZooKeeper("localhost:2181", 60000, new Watcher() {
    public void process(WatchedEvent watchedEvent) {
        // 处理事务协调事件
    }
});

2. Optimize data sharding

• Split the large data set into smaller shards and store them in different on the database or server.
• This can reduce the number of transactions processed simultaneously on a single server and reduce the load in high concurrency scenarios.

-- 创建分片表
CREATE TABLE orders (id INT NOT NULL, product_id INT NOT NULL, quantity INT NOT NULL)
PARTITION BY LIST(product_id) (
PARTITION p1 VALUES IN (1),
PARTITION p2 VALUES IN (2)
);

3. Use asynchronous processing

• Asynchronousize some transaction processing tasks to avoid blocking the main thread.
• Message queues can be used to deliver transaction messages and be executed asynchronously by specialized handlers.

// 使用 Kafka 异步处理事务
KafkaProducer<String, String> producer = new KafkaProducer<String, String>(props);
producer.send(new ProducerRecord<String, String>("tx-topic", jsonPayload));

4. Optimize the lock mechanism

• Use distributed lock mechanisms, such as Redis and Memcached, to coordinate access to shared resources.
• This can prevent data inconsistency when concurrent transactions access the same resource.

// 使用 Redis 加锁
SETNX lock-key "locked"

5. Reduce transaction scope

• Split larger transactions into smaller sub-transactions and, where possible, Localized transaction processing.
• This can reduce the overhead of distributed coordination and improve concurrent processing capabilities.

Practical case:

An e-commerce system encountered high concurrent access during the Double Eleven promotion period, and the order generation failure rate continued to rise. Through the above optimization strategy, the system splits the order generation transaction into multiple sub-transactions and uses ZooKeeper as the distributed transaction coordinator. After optimization, the order generation failure rate has been greatly reduced, and system stability has been effectively guaranteed.

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