Each “Technique” optimizes database performance in distinct ways
๐ญ. ๐ฅ๐ฒ๐ฎ๐ฑ ๐ฅ๐ฒ๐ฝ๐น๐ถ๐ฐ๐ฎ๐ ๐ณ๐ผ๐ฟ ๐ฅ๐ฒ๐ฝ๐ผ๐ฟ๐๐ถ๐ป๐ด:
– Used to offload read traffic from the main database instance
– Beneficial for heavy read queries like reporting and analytics
๐ฎ. ๐๐ผ๐บ๐ฝ๐ฟ๐ฒ๐๐๐ถ๐ผ๐ป ๐ฎ๐ป๐ฑ ๐๐ผ๐น๐๐บ๐ป๐ฎ๐ฟ ๐ฆ๐๐ผ๐ฟ๐ฎ๐ด๐ฒ:
– Compression reduces storage costs and improves read performance
– Columnar storage is well-suited for analytics workloads
๐ฏ. ๐๐ป-๐บ๐ฒ๐บ๐ผ๐ฟ๐ ๐๐ฎ๐๐ฎ ๐ฆ๐๐ผ๐ฟ๐ฒ๐:
Like Redis or Memcached reduces the need to retrieve frequently accessed data from the database repeatedly, improving response times
๐ฐ. ๐ข๐ฝ๐๐ถ๐บ๐ถ๐๐ฒ๐ฑ ๐๐ฎ๐๐ฎ ๐ง๐๐ฝ๐ฒ๐:
– Choose appropriate data types for columns
– Avoid using excessively large data types, as they consume more storage and memory
๐ฑ. ๐ฃ๐ฎ๐ฟ๐๐ถ๐๐ถ๐ผ๐ป๐ถ๐ป๐ด:
For large datasets, consider partitioning tables based on certain criteria (e.g., date ranges)
๐ฒ. ๐ฆ๐ต๐ฎ๐ฟ๐ฑ๐ถ๐ป๐ด:
Distributes data across instances for better query performance
DB optimization is vast and deep, this is just a tip of it.
It’s worth to consider other viable options like:
– Indexing Strategies
– Query Optimization
– Performance Tuning
– Denormalization etc.,
