How to optimize a high-load balance table with tens of thousands of requests per second?

Optimizing a high-load balance table with tens of thousands of requests per second requires a comprehensive approach:

1. Indexing — ensure that there are effective indexes on key fields (e.g., user_id).
2. Caching — use Redis or Memcached to store frequently requested data to reduce database load.
3. Sharding — split the table into parts based on a key (e.g., user_id) to distribute the load across multiple servers.
4. Query optimization — minimize the number and complexity of queries, use prepared statements.
5. Using CQRS — separate read and write operations so that reads can be scaled independently.
6. Batch processing — if possible, combine multiple operations into a single transaction.
7. Vertical and horizontal scaling — increase server resources or add new nodes.

Example of caching a balance in Node.js with Redis:

const redis = require('redis');
const client = redis.createClient();

async function getBalance(userId) {
  const cacheKey = `balance:${userId}`;
  const cached = await client.get(cacheKey);
  if (cached) {
    return JSON.parse(cached);
  }
  const balance = await db.query('SELECT balance FROM balances WHERE user_id = ?', [userId]);
  await client.set(cacheKey, JSON.stringify(balance), 'EX', 60); // cache for 60 seconds
  return balance;
}

This approach reduces the load on the database during frequent requests.