Boosting Relational Database Efficiency: Strategies for MS SQL Server and PostgreSQL

Database optimization enhances the efficiency of your database, improving performance and resource utilization. This process involves tasks such as speeding up queries, reclaiming storage space, managing indexes, and ensuring smooth database operations.

Relational databases like MS SQL Server and PostgreSQL can be optimized through multiple strategies, some of which we will discuss in this blog. Importantly, effective optimization begins at the design stage of application development, not only when performance issues arise. A well-structured schema forms the foundation of an optimized database; when designed with a robust relational structure aligned with business requirements and query patterns, it sets the foundation for high performance. However, if this foundation is weak, even advanced optimization techniques may have limited impact. This is why focusing on database optimization early in the application development process is essential.

Insights for Designing an Efficient Database

Before designing a database, it is essential to thoroughly understand the business requirements it will address. This includes considering how the application may grow over time and what outputs, such as analyses and reports, will be needed. Avoid designing core entities based on incomplete or ambiguous information. Instead, aim to anticipate future requirements whenever possible before finalizing the structure of your core entities. This proactive approach helps establish a solid foundation for your database schema.

Creating a conceptual data model for each functionality and reviewing it with the client can significantly reduce the gap between business requirements and database design.

In this blog, I will walk you through three key stages of database optimization that are applicable to both MS SQL and PostgreSQL, focusing on maintenance tasks, indexing strategies, and query optimizations.

1.Maintenance Operations

Maintenance operations in relational databases are essential to ensure consistent performance, data integrity, and efficient storage management. By proactively maintaining databases, organizations can avoid performance bottlenecks, enhance data accessibility, and support scalability. Following three maintenance operations we should maintain in MS SQL and Postgres to achieve optimum performance.

a. Data Pages Maintenance

Data pages are the fundamental units of I/O in most relational databases, including MS SQL Server and PostgreSQL. Proper maintenance of data pages is essential to optimize database performance and efficiency. Frequent updates and deletions can fragment data pages, leading to wasted space and increased I/O and CPU usage. Reducing fragmentation can significantly enhance query performance and minimize I/O overhead.

In MS SQL Server, data page fragmentation is managed by rebuilding the clustered index using the ALTER INDEX REBUILD command, which reorganizes data pages and improves data organization. The SHRINK command can reclaim unused space but should not be routine, as it may lead to performance issues.

In PostgreSQL , the VACUUM command helps reclaim storage and reduce page fragmentation by cleaning up deleted/dead tuples and reorganizing pages. VACUUM FULL provides deeper cleanup but locks the table during execution.

Before executing these commands in a production environment, always review the official documentation and take necessary precautions to avoid unintended impacts.

b. Index Maintenance

Indexes are essential database features that enhance the performance of read operations. Maintaining index health is crucial in database optimization, as indexes can become fragmented over time due to frequent DML operations. This fragmentation can degrade query performance, making it essential to regularly assess and address index fragmentation.

In MS SQL Server, fragmentation can be addressed using the REORGANIZE or REBUILD commands, based on fragmentation levels.

In PostgreSQL, the REINDEX command rebuilds B-tree indexes, reclaiming space and restoring index efficiency.

Regular monitoring using tools like sys.dm_db_index_physical_stats (SQL Server) or extensions and views such as pg_stat_all_indexes and pgstattuple (PostgreSQL) helps ensure index health and performance.

c. Statistics Update

Keeping statistics up to date is crucial for the query optimizer in relational databases. Outdated statistics can lead to suboptimal query execution plans, slowing down performance. Therefore, maintaining updated statistics is essential for efficient database operation.

In MS SQL Server, the UPDATE STATISTICS command refreshes statistics, ensuring the optimizer makes informed decisions.

In PostgreSQL, the ANALYZE command performs a similar function.

Regular updates can be automated or triggered after significant data changes to maintain performance.

These three maintenance operations should be performed periodically based on your workload, data characteristics, and fragmentation levels. The frequency of these operations may vary depending on factors such as the volume of DML activities, the size of the database, and specific performance requirements.

2.Indexing Strategies

As mentioned earlier, indexes are a key feature for improving read performance. However, selecting the appropriate columns and the number of indexes for a table should be carefully considered. Over-indexing can negatively impact the performance of DML operations. Therefore, it is essential to maintain a balance in the number of indexes based on DML and read operations.

To optimize indexing strategies, analyse the most common queries executed against your database to identify frequently accessed columns that could benefit from indexing. Pay particular attention to columns that appear in WHERE clauses, JOIN conditions, and ORDER BY statements. If certain queries consistently filter on multiple columns, consider creating composite indexes, which can significantly improve query performance by allowing the database to access the relevant data more efficiently. By strategically planning your indexing approach, you can enhance overall database performance.

In MS SQL Server, most indexes are B-tree-based, including clustered and non-clustered indexes. However, specific types like full-text, XML, and spatial indexes use different structures tailored to their unique use cases.

In PostgreSQL, multiple index types are supported beyond the general B-tree index, such as GIN (Generalized Inverted Index) and GiST (Generalized Search Tree). Selecting the appropriate index type is crucial for achieving optimal performance.

3. Query Optimizations

When optimizing individual queries, it is essential to review the execution plan, as it provides insights into how the database optimizer retrieves data. This post does not go into detail about the components of the execution plan. The main aim of query optimization is to minimize disk reads and maximize cache usage, thereby reducing I/O operations. Achieving this often involves strategic indexing, restructuring queries, or breaking down complex queries into simpler parts to enhance performance.

In MS SQL Server, use the Execution Plan and STATISTICS IO/TIME commands to analyse performance.

In PostgreSQL, the EXPLAIN ANALYZE command identifies bottlenecks and inefficient operations.

Key Differences in Database Optimization: PostgreSQL vs. MS SQL Server

While both PostgreSQL and MS SQL Server follow similar core optimization principles, they differ significantly in their implementation, indexing strategies, and available tools.

MS SQL Server:

• B-tree structures are used for indexing, including clustered and non-clustered indexes. Specialized indexes, such as full-text and spatial indexes, cater to specific use cases like text searching and geospatial data.

• Relies on locking mechanisms to manage concurrency, but features like Read Committed Snapshot Isolation (RCSI) minimize blocking by using row versioning, which improves concurrency for certain workloads.

• Space management is largely automated, reducing the need for manual intervention in everyday operations.

• A key feature, the Query Store, captures execution statistics, query plans, and resource usage over time, allowing database administrators to proactively monitor query performance and resolve performance bottlenecks.

• Offers diagnostic tools like Extended Events and sys.dm_exec_query_stats, enabling comprehensive query and server performance analysis, allowing administrators to troubleshoot and optimize database operations.

PostgreSQL:

• Provides a variety of indexing options, including B-trees, GIN (Generalized Inverted Index), and GiST (Generalized Search Tree), each optimized for specific types of queries, such as full-text search, JSON data indexing, and geospatial queries.

• Utilizes Multi-Version Concurrency Control (MVCC) to enhance concurrency by maintaining multiple versions of a row, thus avoiding locking conflicts and allowing for higher concurrency.

• Space management relies on the VACUUM command, which reclaims storage by removing dead tuples from tables, helping to maintain optimal table performance.

• The pgstattuple extension provides detailed statistics about the physical storage of tables and indexes, including information on table and index fragmentation.

• Built-in tools like EXPLAIN and pg_stat_statements assist in analysing query execution plans and tracking performance metrics, enabling developers to identify bottlenecks and optimize queries.

Conclusion

Optimizing relational databases is a complex process that requires a proactive approach. By focusing on purposeful database design, effective indexing, efficient query writing, and regular maintenance you can significantly improve performance and scalability. Remember, the key to successful optimization lies not only in advanced optimization techniques but also in establishing a solid foundation from the start.