Consider increasing RAM, CPU cores, or using faster storage (e.g., SSDs) if hardware is the limiting factor.
Track CPU, memory, and disk I/O during the workload execution to identify resource bottlenecks.
Consider creating indexes that cover multiple columns used together in query predicates.
Use database performance monitoring tools or query execution plans to find columns that would benefit from indexing.
Use `EXPLAIN` or `EXPLAIN ANALYZE` to identify bottlenecks in the query execution and optimize accordingly.
Refactor queries to use more efficient join strategies, avoid `SELECT *`, and utilize window functions where appropriate.
Create indexes on columns frequently used in WHERE clauses, JOIN conditions, and ORDER BY clauses.
Ensure efficient connection management to reduce overhead for frequent query executions.
Adjust parameters like `work_mem` (PostgreSQL) or `sort_buffer_size` (MySQL) to allow for larger sorts and hash joins in memory.
Increase shared_buffers (PostgreSQL) or innodb_buffer_pool_size (MySQL) to cache more data in memory.
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Missing or inefficient indexes on frequently queried columns force the database to perform full table scans, significantly slowing down read operations as data grows.
Over-normalization can lead to excessive JOINs, increasing query complexity and execution time. Conversely, excessive denormalization can lead to data redundancy and update anomalies.
Using inappropriate data types (e.g., storing large text blobs in primary tables) or inefficient storage mechanisms can bloat tables and slow down operations.
Circular references, overly complex relationships, or incorrect foreign key constraints can lead to performance bottlenecks and data integrity issues.
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