In Oracle SQL, the optimizer is responsible for determining the most efficient way to execute a SQL query. The Oracle Optimizer primarily uses three approaches to make decisions:
- Rule-Based Optimizer (RBO) (Deprecated)
How it works:
The Rule-Based Optimizer relies on a set of predefined rules and a fixed ranking system to determine the execution plan. The order of operations and indexes used is based on these static rules.
Characteristics:
Does not consider the size of the data or table statistics.
Uses hard-coded priorities (e.g., always prefers indexed access over full table scans).
Status:
Deprecated and replaced by the Cost-Based Optimizer. No longer recommended or used in modern Oracle databases.
- Cost-Based Optimizer (CBO) (Default and Recommended)
How it works:
The Cost-Based Optimizer evaluates the cost of different execution plans and chooses the plan with the lowest cost. The cost is an estimate of the resources (CPU, memory, I/O) required to execute the query.
Factors Considered:
Statistics: Table, index, and column statistics (e.g., row counts, data distribution, etc.).
System Resources: CPU, memory, and disk I/O capabilities.
Indexes: Availability and selectivity of indexes.
Parallelism: Degree of parallelism and system load.
Join Methods: Nested loops, hash joins, or merge joins.
Access Paths: Full table scans, index scans, or unique scans.
Advantages:
Dynamically adapts to data and system conditions.
Optimizes complex queries and large datasets effectively.
Supports advanced features like partitioning, parallelism, and materialized views.
Example Optimizations:
Choosing between a full table scan and an index scan based on table size and filter conditions.
Deciding whether to sort data in memory or use temporary tables on disk.
- Adaptive Optimizer (Introduced in Oracle 12c)
How it works:
The Adaptive Optimizer enhances the Cost-Based Optimizer by allowing the execution plan to adjust dynamically during query execution, based on real-time observations.
Key Features:
Adaptive Plans: Adjusts join methods or access paths during execution based on runtime statistics.
Adaptive Statistics: Uses feedback from previous executions to refine future query plans.
Automatic Reoptimization: Modifies execution plans if performance issues are detected.
Use Case:
Queries with unpredictable data distribution or cardinality estimates.
Dynamic systems where workloads vary significantly.
Optimizer Decisions and Execution Plan Components
- Access Paths:
Full Table Scan: Scans all rows in the table (preferred for small tables or when no suitable indexes exist).
Index Scan: Uses an index to locate rows (preferred when filters or joins involve indexed columns).
Index Skip Scan: Skips parts of a composite index when not all columns in the index are used.
- Join Methods:
Nested Loops Join: Efficient for small datasets or highly selective joins.
Hash Join: Used for large datasets with non-selective joins.
Merge Join: Efficient for sorted data or pre-sorted datasets.
- Sort Operations:
The optimizer decides whether sorting should be performed in memory or using temporary disk space.
- Parallelism:
Determines if the query should be executed in parallel and the degree of parallelism.
- Transformations:
Predicate Pushdown: Moves filters closer to the data source to minimize data retrieval.
Query Rewrite: Rewrites queries to use materialized views or equivalent structures.
- Statistics Feedback:
The optimizer adjusts execution plans if runtime statistics differ significantly from estimates.
Influencing the Optimizer
You can influence the optimizer's decisions using hints or by configuring database settings:
Hints: Direct the optimizer to use specific methods (e.g., FULL, INDEX, USE_HASH, PARALLEL).
Statistics: Regularly gather accurate table, column, and index statistics.
Optimizer Parameters:
OPTIMIZER_MODE: Controls the optimizer's behavior (e.g., ALL_ROWS, FIRST_ROWS, RULE).
OPTIMIZER_DYNAMIC_SAMPLING: Controls runtime sampling to improve cardinality estimates.
PARALLEL_DEGREE_POLICY: Determines the automatic use of parallelism.
Key Optimizer Modes
ALL_ROWS:
Optimizes for throughput, minimizing total resource consumption (ideal for batch processing).FIRST_ROWS:
Optimizes for response time, returning the first rows as quickly as possible (ideal for interactive applications).CHOOSE:
Automatically selects the mode based on whether table statistics are available.
Conclusion
The Oracle Optimizer is a powerful engine that adapts to the query context, data distribution, and system conditions. It typically defaults to the Cost-Based Optimizer, but you can fine-tune its behavior with hints, statistics, and parameters to meet specific performance requirements.
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