DATABASE PERFORMANCE TUNING.pptx

DATABASE PERFORMANCE TUNING AND MONITORING STEPS TO PERFORMANCE TUNING BUSINESS LOGIC DATA DESIGN – NORMALIZATION APPLICATION DESIGN CHANGE DB STRUCTURE(INDEXES) SQL TUNING ACCESS PATH –Oracle defines multiple ways to access your data and choose the best one. Memor...

DATABASE PERFORMANCE TUNING AND MONITORING STEPS TO PERFORMANCE TUNING BUSINESS LOGIC DATA DESIGN – NORMALIZATION APPLICATION DESIGN CHANGE DB STRUCTURE(INDEXES) SQL TUNING ACCESS PATH –Oracle defines multiple ways to access your data and choose the best one. Memory Allocation - Instance turning (RAM) TUNE THE I/0 (PHYSICAL STORAGE) TUNE RESOURCE – LOCKS TUNE THE SERVER DATABASE PERFORMANCE TUNING Database performance tuning involves optimizing the performance of your database to ensure efficient and timely retrieval and storage of data. Performance tuning is the process of optimizing the database and applications interacting with it to reduce resource usage (CPU, memory, I/O), minimize response times, and increase throughput. KEY PERFORMANCE TUNING AREAS: a) SQL Query Tuning Poorly written SQL can lead to excessive resource usage. Oracle provides tools and features to optimize SQL queries. EXPLAIN PLAN: A tool to examine the execution path of a query and determine if Oracle is using the optimal method (e.g., index scans vs. full table scans). SQL Trace and TKPROF: These tools are used to trace SQL execution and analyze its performance. Indexes: Proper indexing can drastically reduce the time it takes to query large tables. Poor indexing, on the other hand, can cause performance bottlenecks. b) Instance Turning The database instance, consisting of memory structures and background processes, plays a key role in performance. Shared Pool: Contains parsed SQL statements, and the data dictionary. Tuning the shared pool ensures that SQL parsing and execution are efficient. Buffer Cache: Stores copies of data blocks. Tuning this ensures that Oracle reads data from memory (which is fast) instead of disk (which is slow). Redo Log Buffer: Tuning this buffer is essential for performance, especially in write-heavy environments. Dynamic Initialization Parameters: Adjustments in parameters like SGA_TARGET, PGA_AGGREGATE_TARGET, and DB_CACHE_SIZE can significantly improve instance performance. c) I/O Turning Disk I/O is often the bottleneck in many systems. Reducing disk access times can drastically improve performance. Tablespace Management: Spread data across multiple disks to avoid bottlenecks on a single disk (e.g., use RAID configurations). Use of ASM (Automatic Storage Management): ASM simplifies storage management and ensures better performance through disk striping and mirroring. Partitioning: Large tables can be split into partitions to improve I/O performance, especially for read-heavy operations. d) Memory Turning Oracle dynamically allocates memory, but manual tuning may be required in specific scenarios. SGA (System Global Area): Contains data shared by all sessions. Tuning the SGA is critical for instance-level performance. PGA (Program Global Area): Allocated per user session. Tuning the PGA is essential for managing sorts, hash joins, and other session-level activities. e) Concurrency Turning When multiple users access the database simultaneously, concurrency issues can arise, leading to locking or contention problems Locking and Latching: Excessive locking or latching can degrade performance. Oracle uses multi-version read consistency to reduce locking but monitoring locks is still necessary. Oracle Wait Events: By analyzing wait events (e.g., db file sequential read, log file sync), you can identify and resolve concurrency issues. f) Network Turning. If your application or users are interacting with Oracle over a network, optimizing network throughput and minimizing latency are key. Oracle Net: Monitoring and tuning Oracle Net (the network layer) can improve data transfer rates between the database and clients. Distributed Database Tuning: For distributed databases, tuning the network interconnect between databases can significantly reduce performance overhead. 3. Performance Tuning Methodology Oracle provides a structured methodology to approach performance tuning. Identify Bottlenecks: Use monitoring tools like AWR, ADDM, and V$ views to identify where performance degradation occurs (SQL, I/O, memory, etc.). Quantify the Impact: Understand how the bottleneck affects overall performance (e.g., does it cause long query times, excessive CPU usage?). Analyze Solutions: Identify potential solutions (e.g., optimize SQL, adjust initialization parameters, add indexes, tune memory allocations).  Implement Changes: Apply the solutions iteratively, monitoring the impact of each change. Verify Improvements: Continuously monitor the database after making changes to ensure performance has improved without creating new bottlenecks.  Performance Tuning Methodology Oracle provides a structured methodology to approach performance tuning. 1. Identify Bottlenecks: Use monitoring tools like AWR, ADDM, and V$ views to identify where performance degradation occurs (SQL, I/O, memory, etc.). 2. Quantify the Impact: Understand how the bottleneck affects overall performance (e.g., does it cause long query times, excessive CPU usage?). 3. Analyze Solutions: Identify potential solutions (e.g., optimize SQL, adjust initialization parameters, add indexes, tune memory allocations). Performance Tuning Methodology 4. Implement Changes: Apply the solutions iteratively, monitoring the impact of each change. 5. Verify Improvements: Continuously monitor the database after making changes to ensure performance has improved without creating new bottlenecks.  Automatic Diagnostic Tools Oracle 19c and 21c offer built-in tools to simplify performance tuning: 1. ADDM (Automatic Database Diagnostic Monitor): Automatically detects performance bottlenecks and provides recommendations for tuning. 2. AWR (Automatic Workload Repository): Automatically collects and stores performance statistics and provides insights into database health. 3. SQL Tuning Advisor: Provides recommendations for optimizing poorly performing SQL statements. 4. SQL Access Advisor: Helps improve the design of the database schema by advising on indexes, materialized views, and partitions.  Best Practices Regularly monitor the database and address any warning signs. Collect and analyze performance data (using AWR or Statspack reports). Keep the database and application well-indexed. Optimize memory settings for both the SGA and PGA. Regularly tune SQL queries and execution plans. Maintain the latest patches and updates to Oracle software. Perform stress testing and load testing to simulate real- world performance. concepts and techniques for database performance tuning: 1. Database Design: Normalization: Ensure your database is properly normalized to minimize redundancy and improve data integrity. Indexing: Create appropriate indexes on columns frequently used in queries to speed up data retrieval. 2. Monitoring and Profiling Regularly monitor database performance using tools like SQL Server Profiler, Oracle Enterprise Manager, or MySQL Performance Schema. Identify and analyze slow queries using query profiling tools. 3. Query Optimization Optimize SQL queries to reduce execution time. Use EXPLAIN or equivalent statements to understand how the database executes queries. Consider rewriting queries, adding indexes, or denormalizing data for performance gains. 4. Indexing Strategies Understand different types of indexes (B-tree, bitmap, etc.) and choose the appropriate ones. Regularly review and update index statistics. Avoid over-indexing, as it can slow down write operations. 5. Database Configuration Tune database configuration parameters such as cache sizes, connection limits, and buffer pools. Adjust the transaction isolation level based on the application's requirements. 6. Table Partitioning Implement table partitioning for large tables to improve query performance. Distribute data across multiple filegroups or tablespaces. 7. Caching Mechanisms Utilize caching mechanisms at the database, application, and network levels to reduce the load on the database server. Implement query result caching where applicable. 8. Regular Maintenance Schedule regular database maintenance tasks like index rebuilding, updating statistics, and database reorganization. Clean up unnecessary data and maintain an appropriate level of free space. 9. Hardware Considerations Ensure the server hardware meets the performance requirements. Consider SSDs for I/O-intensive workloads. 10. Concurrency Control Optimize transaction isolation levels to balance consistency and performance. Minimize the use of long-running transactions. 11. Connection Pooling Implement connection pooling to reuse database connections and reduce the overhead of connection establishment. 12. Backup and Recovery Planning Develop a robust backup and recovery strategy to minimize downtime. Test and optimize the restore process. 13. Database Sharding Consider database sharding for distributing data across multiple servers, especially in large-scale applications. 14. Regular Performance Testing Conduct regular performance testing to identify and address potential bottlenecks before they impact production. 15. Documentation and Monitoring Document your tuning efforts and changes made to the database. Implement continuous monitoring to detect performance issues early. 16. Stay Informed Keep up with the latest developments in database management systems and performance optimization techniques. WHAT IS THROUGHPUT This refers to the rate at which a database system can process a certain amount of work within a given time period. It is a measure of the system's capacity to handle transactions, queries, or data operations. Throughput is often expressed in terms of transactions per second (TPS) or queries per second (QPS). Transaction Throughput: For transactional databases, throughput is commonly measured in terms of the number of transactions processed per second. A transaction is a logical unit of work that may involve multiple database operations (reads or writes). Query Throughput: In analytical or reporting databases, throughput may be measured in terms of the number of queries processed per second. These queries can be complex and involve aggregations, joins, and other operations. Factors Affecting Throughput: Several factors can influence database throughput, including hardware resources (CPU, memory, disk I/O), database design, indexing, query optimization, and the efficiency of the underlying database management system (DBMS). Optimizing Throughput Database administrators and developers often work to optimize throughput by fine-tuning the database configuration, improving query performance, and ensuring that hardware resources are appropriately allocated. Concurrency and Throughput Throughput is closely related to concurrency, which refers to the ability of the database system to handle multiple transactions or queries simultaneously. Higher levels of concurrency can lead to increased throughput, but it also requires effective management of locks, isolation levels, and other factors to maintain data consistency. Benchmarking: Throughput is a common metric used in benchmarking and performance testing. By simulating real-world scenarios, organizations can assess how well a database system performs under different loads and identify potential bottlenecks. Monitoring and Tuning: Monitoring tools are used to track and analyze throughput in real-time or over specific time intervals. Database administrators can use this information to identify performance issues and implement tuning strategies to improve throughput. Scalability: Scalability is an important consideration for throughput. As the workload on a database increases, the system should be able to scale horizontally (adding more servers) or vertically (upgrading hardware) to maintain or improve throughput. Tuning of SQL is driving in less record into your database through your query The goal of tuning is to write an effective SQL that reduce number of record that goes through your query