Tools: Determine High-Performing Database Solutions

🎯 Determining High-Performing Database Solutions is about picking and designing databases that meet: ## Knowledge ## 1 | AWS Global Infrastructure ## Availability Zones And Regions ## 2 | Caching Strategies And Services ## Amazon ElastiCache ## 3 | Data Access Patterns ## Read-Intensive vs Write-Intensive ## 4 | Database Capacity Planning ## Capacity Units, Instance Types And Provisioned IOPS ## 5 | Database Connections And Proxies ## 6 | Database Engines With Appropriate Use Cases ## Homogeneous vs Heterogeneous Migrations ## 7 | Database Replication ## Read Replicas ## 8 | Database Types And Services ## Relational (SQL) ## Non-relational (NoSQL) ## In-memory ## Serverless Database Patterns ## Skills ## A | Configure Read Replicas To Meet Business Requirements ## You Should Know When And Why ## B | Design Database Architectures ## C | Determine An Appropriate Database Engine ## MySQL vs PostgreSQL, etc. ## D | Determine An Appropriate Database Type ## Aurora vs DynamoDB ## E |Integrate Caching To Meet Business Requirements ## Caching Options ## Cheat Sheet ## Recap Checklist ✅ ## AWS Whitepapers and Official Documentation ## Core database services ## Read scaling, HA, and connections ## Caching ## Migration ## Capacity planning references Exam Guide: Solutions Architect - Associate ⚡ Domain 3: Design High-Performing Architectures 📘 Task Statement 3.3 1 Performance goals 2 Scale requirements 3 Availability expectations 4 Operational constraints Start with the data model + access pattern: relational vs key-value vs document, then choose the service, then add performance boosters: read replicas, caching, connection pooling. “Must survive AZ outage” → Multi-AZ “Global users with low latency” → global DB patterns Caching reduces database load and improves latency. “Reduce read load / hot keys / repeated queries” → ElastiCache. This is one of the most important drivers of database design: 1 Read-heavy → add caching, read replicas, or purpose-built read scaling 2 Write-heavy → consider partitioning/sharding patterns, or DynamoDB if it fits 3 Spiky traffic → serverless options or buffering with queues 1 RDS/Aurora performance depends on instance size, storage type, and sometimes Provisioned IOPS 2 DynamoDB uses RCUs/WCUs (or on-demand) and partition design affects performance 3 High-performance workloads often need correct sizing plus monitoring Connection limits are a common real-world and exam bottleneck. Amazon RDS Proxy pools connections and helps with spiky connection patterns (especially Lambda) and helps reduce failover impact and connection storms. “Serverless app is exhausting DB connections” → RDS Proxy. Read replicas are mainly for: 1 Scaling reads 2 Offloading reporting/analytics queries 3 Cross-region read performance (depending on engine) Amazon RDS: MySQL, PostgreSQL, MariaDB, Oracle, SQL Server Amazon Aurora MySQL/PostgreSQL-compatible, high performance, managed Amazon DynamoDB: key-value/document, massive scale, low latency ElastiCache: Redis/Memcached (cache, sessions) Aurora Serverless v2: elastic relational capacity 1 Add replicas to scale reads and isolate reporting workloads 2 Place replicas in other AZs or Regions if needed (engine-dependent) 3 Monitor replication lag and route read traffic appropriately Typical high-performing patterns: 1 App → (optional cache) → DB 2 Multi-AZ for HA 3 Read replicas for scaling reads 4 Shard/partition when required (more advanced, usually not primary SAA topic) 5 Offload analytics to separate systems when needed Expectation: pick based on compatibility/features/organization standards rather than arguing favorites. 1 Choose MySQL/Aurora MySQL when compatibility with MySQL ecosystem is needed. 2 Choose PostgreSQL/Aurora PostgreSQL when advanced SQL features/extensions are needed. 3 Choose commercial engines (Oracle/SQL Server) when required by licensing/app constraints. Fast rules: 1 Need joins/transactions/relational schema → RDS/Aurora 2 Need massive scale + low latency key-value/document → DynamoDB 3 Need sub-millisecond repeated reads → add ElastiCache DynamoDB vs RDS is a frequent exam decision point. “Microsecond reads for DynamoDB queries” → DAX (if DynamoDB is the DB). 1. [ ] Database choice matches data model (relational vs non-relational) 2. [ ] Read-heavy workloads use read scaling (read replicas) and/or caching 3. [ ] Write scaling is considered (correct service + partition design if DynamoDB) 4. [ ] Connection spikes are handled (RDS Proxy when appropriate) 5. [ ] Capacity planning is understood at a high level (instance types, IOPS, RCUs/WCUs) 6. [ ] Multi-AZ is used for availability; read replicas are used for read scaling 7. [ ] Caching is integrated appropriately (ElastiCache/DAX) These are the primary AWS documents behind Task Statement 3.3 You do not need to memorize them, use them to understand how to Design High-Performing Database Solutions 1. Amazon RDS 2. Amazon Aurora 3. Aurora Serverless v2 4. Amazon DynamoDB 1. RDS Read Replicas 2. RDS Multi-AZ (concepts) 3. Amazon RDS Proxy 1. ElastiCache (Redis/Memcached) 2. DynamoDB Accelerator (DAX) AWS Database Migration Service (DMS) 1. DynamoDB Capacity Modes 2. RDS storage options Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse - Multi-AZ deployments improve availability and can improve performance under failure. - Multi-region designs support disaster recovery and global performance. - ElastiCache for Redis: caching + sessions + pub/sub + sorted sets - ElastiCache for Memcached: simple, distributed cache, no persistence - Homogeneous migration: same engine to same engine (e.g., MySQL → MySQL) - Heterogeneous migration: different engines (e.g., Oracle → PostgreSQL) _ AWS DMS is commonly used for migrations (especially minimal downtime)._ - Read replicas are usually asynchronous - Multi-AZ is for availability, not for read scaling - ElastiCache for app-side caching of hot data - DAX (DynamoDB Accelerator) for DynamoDB read caching (in-memory, managed)