Table of contents
Indexing:
Definition: Indexing is a technique used in databases to improve the performance of queries by creating data structures that allow for efficient data retrieval.
Purpose: Indexes help speed up data retrieval operations by reducing the number of disk accesses required to find specific data.
Types of Indexes:
B-Tree Index: Commonly used index structure that organizes data in a balanced tree-like structure.
Hash Index: Uses a hash function to map keys to specific locations in memory.
Bitmap Index: Stores bitmaps for each distinct value in a column, allowing for quick data retrieval based on multiple conditions.
Clustered Index: Determines the physical order of rows in a table based on the indexed column(s).
Non-clustered Index: Creates a separate data structure that points to the physical location of rows in a table.
Index Creation Considerations:
Selectivity: Aim for high selectivity to reduce the number of records returned by a query.
Size and Disk Space: Indexes require additional disk space, so consider the trade-off between performance and storage requirements.
Maintenance Overhead: Indexes need to be updated when data is inserted, updated, or deleted, which can impact system performance.
Indexing Strategies: Choose appropriate columns for indexing based on query patterns, data distribution, and system requirements.
Query Optimization:
Definition: Query optimization is the process of improving the performance of database queries by selecting the most efficient execution plan.
Purpose: Optimized queries reduce execution time, resource utilization, and overall system load.
Techniques for Query Optimization:
Query Rewriting: Rewriting queries to an equivalent but more efficient form.
Cost-Based Optimization: Evaluating different execution plans and selecting the one with the lowest estimated cost.
Use of Indexes: Utilizing appropriate indexes to speed up data retrieval.
Table Partitioning: Splitting large tables into smaller, more manageable partitions to improve query performance.
Join Optimization: Optimizing join operations by choosing the most efficient join algorithms and order.
Query Caching: Storing the results of frequently executed queries to avoid redundant computations.
Denormalization: Introducing redundancy in the database schema to improve query performance.
Query Statistics and Profiling: Analyzing query statistics and performance profiles to identify bottlenecks and areas for improvement.
Query Optimization Tools: Database management systems provide tools and features to analyze query performance and suggest optimizations.
Regular Performance Monitoring: Continuous monitoring and analysis of query performance can help identify areas for optimization and maintain efficient query execution.