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Query Optimization Strategies
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Indexing
Techniques: B-trees, Bitmap Indexes, Hash Indexes Advantages: Reduces data access time, efficient search
Join Algorithms
Techniques: Nested Loop Join, Hash Join, Merge Join Advantages: Optimizes join operations, reduces complexity
Partitioning
Techniques: Horizontal Partitioning, Vertical Partitioning Advantages: Parallel processing, manageability, performance
Query Caching
Techniques: Result-set caching, Prepared statements Advantages: Faster query execution for repeated queries, less load
Materialized Views
Techniques: Precomputed joins/aggregations, Incremental updates Advantages: Reduced computation, query acceleration
Subquery Flattening
Techniques: Merging subqueries into the main query Advantages: Simplifies execution plan, can improve performance
Predicate Pushdown
Techniques: Moving predicates closer to the data Advantages: Reduces data movement, improves efficiency
Heuristic Optimization
Techniques: Rule-based optimization, Query rewriting Advantages: Quick plan generation, simple to implement
Parallel Query Execution
Techniques: Data partitioning, Parallel join/aggregate Advantages: Uses multi-processor systems, reduces response time
Cost-Based Optimization
Techniques: Statistics-based planning, Cost models Advantages: Chooses lowest cost plan, improves performance
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