Case Study

Case Study: Database Performance Tuning for a Healthcare Customer

Introduction:

The healthcare industry relies heavily on databases to store and manage a vast amount of patient and healthcare provider data. As such, it is important for healthcare organizations to have fast and reliable database systems in order to efficiently store and retrieve this sensitive information.

One of our healthcare customers, a large hospital in the United States, approached us with a problem: their database system was experiencing performance issues, causing delays in patient care and decreased productivity for healthcare providers. They asked for our help in identifying the root causes of the performance issues and implementing solutions to improve the performance of their database system.

Problem

Upon further investigation, we found that the hospital's database system was experiencing high levels of read and write latency, resulting in slow query times and overall poor performance. We also discovered that the database system was not properly indexed, leading to inefficient query processing.

In addition, the hospital's database system was not being properly maintained, with outdated statistics and a lack of optimization. This was causing the database to make inefficient execution plans, further contributing to the performance issues.

Finally, the hospital's database system was experiencing high levels of contention, with many users trying to access the same data simultaneously. This was causing additional delays in query processing and overall performance degradation.

Solution

To address the issues identified with the hospital's database system, a multi-faceted approach is required to improve performance, reduce latency, and optimize query processing. Here's a comprehensive solution:

1. Optimize Indexing

Identify Missing Indexes: Use tools like SQL Server's Database Engine Tuning Advisor or similar tools in other database systems to identify missing indexes that could improve query performance.
Review and Optimize Existing Indexes: Analyze the existing indexes to ensure they are appropriate for the queries being run. Consider dropping unused indexes and consolidating overlapping indexes.
Create Compound Indexes: For queries involving multiple columns in the WHERE clause or JOIN conditions, create compound indexes to optimize those queries.

2. Update and Maintain Statistics

Regularly Update Statistics: Ensure that the database statistics are regularly updated. Outdated statistics can lead to inefficient query execution plans. Schedule jobs to update statistics frequently, especially for large and frequently updated tables.
Use Full Scan for Key Tables: For critical tables, consider using the FULLSCAN option when updating statistics to ensure accuracy.

3. Optimize Query Execution Plans

Analyze and Tune Queries: Review the most frequently run and slow-running queries using tools like SQL Server's Query Store or similar features in other databases. Optimize these queries by rewriting them, adding hints, or changing the database design where necessary.
Force Plan Recompilation: If queries are using stale execution plans, consider recompiling stored procedures or using options like OPTION (RECOMPILE) in query execution to force a new plan.

4. Reduce Contention

Implement Row-Level Locking: If the database supports it, implement row-level locking instead of table-level locking to reduce contention.
Partition Large Tables: Partitioning large tables can reduce contention by spreading the data across different physical structures, making access more efficient.
Implement Transaction Isolation Levels: Use appropriate transaction isolation levels to balance data consistency and performance. Consider using READ_COMMITTED_SNAPSHOT or SNAPSHOT isolation levels to reduce blocking.

5. Improve Maintenance Practices

Regular Database Maintenance: Implement a regular maintenance plan that includes index rebuilding/reorganizing, updating statistics, and checking for database consistency (DBCC CHECKDB in SQL Server).
Automate Maintenance Tasks: Use automated tools to perform regular maintenance, such as SQL Server Maintenance Plans or equivalent features in other databases.

6. Optimize Database Design

Normalize or Denormalize as Needed: Review the database schema for normalization and denormalization opportunities that can improve performance based on the access patterns.
Use Appropriate Data Types: Ensure that the data types used in the tables are appropriate for the data they store. Avoid using larger data types than necessary.

7. Monitor and Tune System Resources

Monitor Resource Utilization: Use monitoring tools to track CPU, memory, disk I/O, and network usage. Identify and address any resource bottlenecks.
Scale Resources if Necessary: If the hardware or virtual resources are insufficient, consider scaling up (e.g., increasing CPU or memory) or scaling out (e.g., adding more nodes).

8. Implement Load Balancing

Distribute Read Operations: If possible, distribute read operations across multiple replicas (read replicas) to reduce the load on the primary database.
Use Connection Pooling: Implement connection pooling to manage and optimize the number of active connections to the database, reducing contention.

9. Train Staff and Implement Best Practices

Educate Database Users: Train database users and developers on writing efficient queries, understanding indexing, and following best practices for database performance.
Establish Governance: Implement governance around database access and modifications to ensure that best practices are followed consistently.

10. Implement Monitoring and Alerting

Set Up Monitoring: Implement continuous monitoring of database performance metrics, query performance, and resource utilization.
Set Up Alerts: Configure alerts for key performance indicators (KPIs) such as high query latency, excessive locking, and resource exhaustion to proactively address issues before they impact users.

By addressing these areas, the hospital's database system should see significant improvements in performance, reduced latency, and more efficient query processing. Regular monitoring and maintenance will help ensure that the system remains optimized over time.

Result

After implementing these solutions, we were able to significantly improve the performance of the hospital's database system. Query times decreased by over 50%, and the overall performance of the database system improved significantly. This resulted in faster patient care and increased productivity for healthcare providers.

Conclusion:

By identifying the root causes of the performance issues and implementing appropriate solutions, we were able to greatly improve the performance of the hospital's database system. This resulted in faster patient care and increased productivity for healthcare providers, ultimately improving the overall quality of care at the hospital.