What needs to be done to prevent the database from slowing down as data volume increases?

• Indexing: Create indexes for frequently used fields in queries WHERE, JOIN, ORDER BY.
• Normalization: Ensure proper normalization of the database to avoid redundancy and anomalies.
• Denormalization (with caution): In some cases, denormalize certain parts of the schema to improve read performance, but this increases the risk of anomalies during writes.
• Query optimization: Analyze and optimize slow queries. Use EXPLAIN to understand the query execution plan.

  EXPLAIN SELECT * FROM users WHERE age > 30;
  

• Partitioning (Sharding): Divide large tables into smaller ones based on a specific criterion (e.g., date or ID range). This improves query performance on subsets of data and simplifies management (backups, archiving).
• Connection Pooling: Use connection pools to reduce the overhead of establishing new database connections.
• Caching: Cache frequently requested data in application memory or with specialized caching systems (Redis, Memcached).
• Monitoring: Regularly monitor database performance (CPU load, memory usage, slow query count).
• Hardware: Scale or upgrade server hardware if necessary.
• Store only necessary data: Regularly archive or delete outdated or irrelevant data.
• Use appropriate data types: Choose the most efficient data types for storing information (e.g., integer instead of string for IDs).
• Limit the amount of data returned: Use LIMIT in queries if all data is not needed.
• Deferred joins: In complex queries with sorting, deferred joins can sometimes help.

-- Example of deferred join
SELECT t1.*
FROM large_table t1
JOIN (SELECT id FROM large_table ORDER BY some_column LIMIT 100) t2 ON t1.id = t2.id;