Azure MariaDB Optimization Guide

Query Optimization

Focus on slow-running queries and their execution plans. Utilize tools like the slow query log and EXPLAIN.

Indexing: Ensure proper indexes are in place for frequently queried columns.
Query Rewriting: Optimize complex queries by simplifying logic and avoiding inefficient operations.
Parameterization: Use prepared statements to improve performance and security.
Profiling: Analyze query performance with `SHOW PROFILE` or `performance_schema`.

Example: Using EXPLAIN on a query:

EXPLAIN SELECT * FROM your_table WHERE column_name = 'some_value';

Adjust MariaDB server parameters based on your workload and Azure instance size.

innodb_buffer_pool_size: Crucial for InnoDB performance. Set to 70-80% of available RAM.
max_connections: Adjust based on your application's connection needs.
query_cache_size: Can be beneficial for read-heavy workloads, but often disabled in newer versions.
tmp_table_size & max_heap_table_size: Optimize for complex queries involving temporary tables.
sort_buffer_size & join_buffer_size: Tune for sorting and join operations.

Refer to Azure documentation for recommended settings for your tier.

A well-designed schema is fundamental for performance.

Data Types: Use appropriate and efficient data types (e.g., `INT` instead of `VARCHAR` for IDs).
Normalization: Balance normalization with denormalization for read performance where appropriate.
Primary Keys: Ensure every table has a primary key, preferably an auto-incrementing integer.
Foreign Keys: Maintain referential integrity, but consider performance implications for large tables.
Partitioning: For very large tables, consider partitioning to improve manageability and query performance.

Regular monitoring and maintenance are key to sustained performance.

Azure Monitor: Utilize Azure's built-in monitoring for CPU, memory, IOPS, and connections.
Slow Query Log: Configure and regularly analyze the slow query log.
ANALYZE TABLE: Update table statistics to help the query optimizer make better decisions.
OPTIMIZE TABLE: Reclaim unused space and defragment data and indexes (use judiciously).
Regular Backups: Ensure you have a robust backup strategy.

Set up alerts in Azure Monitor for key performance metrics.

Efficiently manage database connections from your application.

Reduce Overhead: Avoid opening and closing connections for every request.
Application-Level Pooling: Implement connection pooling within your application framework or use a dedicated pooling library.
Max Allowed Connections: Ensure your max_connections setting is adequate for your pooled connections.

Effective connection pooling significantly reduces latency and resource usage.

Distribute read traffic to improve scalability and reduce load on the primary server.

Offload Reads: Direct read-heavy workloads to read replicas.
Application Logic: Implement logic in your application to route read queries to replicas.
Replication Lag: Monitor replication lag to ensure data consistency.

Azure MariaDB offers managed read replicas for easy setup.

Azure MariaDB Read Replicas