DAY-12 AWS-CSA

1] Amazon Aurora

Amazon Aurora is a relational database service provided by AWS (Amazon Web Services). It is designed for high performance, scalability, and reliability while being fully managed. Aurora is compatible with MySQL and PostgreSQL, meaning applications designed for these databases can work with Aurora with minimal modifications.

Key Features of Amazon Aurora:

1. High Performance – Aurora offers up to 5 times the performance of standard MySQL and 3 times the performance of PostgreSQL due to its unique architecture.
2. Scalability – It can automatically scale storage from 10GB to 128TB, depending on the workload.
3. High Availability – It replicates data across multiple AWS Availability Zones (AZs) and provides automatic failover.
4. Managed Service – AWS handles database maintenance, backups, software patching, and security updates.
5. Serverless Option – Amazon Aurora Serverless allows you to run databases without managing infrastructure, scaling capacity automatically based on demand.
6. Security – It supports encryption at rest and in transit, as well as AWS Identity and Access Management (IAM) for access control.
7. Global Database – Allows replication across multiple AWS regions with low-latency reads.

Use Cases:

• Applications requiring high availability and fault tolerance.
• SaaS applications needing scalable databases.
• Enterprise applications needing fast and secure database solutions.
• E-commerce and financial applications with high transaction rates.

2] Aurora High Availability and Read Scaling

Amazon Aurora is designed to provide high availability (HA) and read scaling to ensure minimal downtime and handle high query loads efficiently.

1. High Availability in Amazon Aurora

Aurora ensures 99.99% availability by utilizing multi-AZ (Availability Zone) architecture and automatic failover mechanisms.

Key Features of High Availability:

? Multi-AZ Replication – Aurora stores six copies of data across three AWS Availability Zones (AZs).

? Automatic Failover – If the primary database fails, Aurora promotes a standby replica automatically.

? Continuous Backup & Point-in-Time Recovery – Aurora backs up data continuously to Amazon S3.

? Self-Healing Storage – Automatically detects and repairs data corruption without downtime.

? Cluster Endpoints for Failover – Aurora uses a cluster endpoint to automatically redirect connections to the active writer node in case of failure.

How Failover Works?

• If the primary instance fails, Aurora promotes the most up-to-date replica as the new primary.
• Applications using the cluster endpoint automatically reconnect to the new writer instance.

2. Read Scaling in Amazon Aurora

Aurora can handle high read workloads by allowing multiple read replicas.

Key Features of Read Scaling:

? Read Replicas (Up to 15) – Aurora supports up to 15 read replicas within an Aurora cluster. ? Aurora Auto Scaling – Automatically adds or removes replicas based on traffic load.

? Cluster Endpoint for Reads – Applications can distribute read traffic using the reader endpoint, which balances the load across all replicas.

? Global Database for Multi-Region Reads – Aurora Global Database allows low-latency cross-region reads and disaster recovery.

? Parallel Query Execution – Aurora’s Parallel Query feature speeds up analytical queries by processing them across multiple storage nodes.

How to Distribute Read Traffic?

• Use Reader Endpoint (your-cluster.cluster-ro-xyz.us-east-1.rds.amazonaws.com) for read queries.
• Configure Aurora Auto Scaling to adjust the number of replicas dynamically.
• Use Application Load Balancers or custom routing logic to optimize read queries.

3. Best Practices for Aurora HA & Read Scaling

?? Use Multi-AZ deployment for automatic failover.

?? Enable Aurora Auto Scaling to adjust replicas based on workload.

?? Route writes through the cluster endpoint and reads through the reader endpoint.

?? Use Global Database for low-latency cross-region reads.

?? Monitor with Amazon CloudWatch for performance insights.

3] Aurora DB Cluster

Amazon Aurora uses a DB cluster architecture that consists of a writer instance (primary) and multiple reader instances (replicas) to enhance high availability and read scalability.

1. Aurora DB Cluster Overview

An Aurora DB cluster consists of:

• 1 Primary Instance (Writer) – Handles read & write operations.
• Up to 15 Read Replicas (Readers) – Handle only read queries to scale performance.
• Shared Cluster Storage – All instances (writer & readers) share the same distributed storage, reducing replication lag.

2. Aurora Endpoints

Aurora provides different endpoints to optimize database connections for various use cases.

?? Writer Endpoint (your-cluster.cluster-xyz.us-east-1.rds.amazonaws.com)

• Points to the primary (writer) instance in the cluster.
• Handles both read & write queries.
• If the writer fails, Aurora automatically promotes a replica and updates the writer endpoint.
• Used for transactions that require writing data.

?? Example Use Case:

• Insert, update, and delete operations.

?? Reader Endpoint (your-cluster.cluster-ro-xyz.us-east-1.rds.amazonaws.com)

• Distributes traffic among all read replicas in the cluster.
• Handles only read queries (SELECT statements).
• Improves performance by offloading read queries from the writer instance.
• Automatically adjusts when new replicas are added or removed.

?? Example Use Case:

• Reporting and analytics applications.
• Read-heavy workloads like dashboards.

3. Other Aurora Endpoints

?? Cluster Endpoint (your-cluster.cluster-xyz.us-east-1.rds.amazonaws.com)

• Same as writer endpoint but used for connection pooling.

?? Instance Endpoints (your-instance-1.abcdefgh.us-east-1.rds.amazonaws.com)

• Each instance (writer or reader) has a unique endpoint for direct connections.

4. Best Practices for Using Aurora Endpoints

? Use the Writer Endpoint for all write operations.

? Use the Reader Endpoint to distribute read queries and improve performance.

? Enable Auto Scaling for replicas to handle variable read workloads.

? Monitor Aurora with Amazon CloudWatch to track database performance.

4]Features of Aurora

- Automatic fail-over

- Backup and Recovery

- Isolation and security

- Industry compliance

- Push-button Scaling

- Automated Patching with zero downtime

- Advance monitoring

- Routine Maintenance

5] Aurora Replicas-Auto Scaling

Amazon Aurora provides read replicas to scale read traffic and enhance high availability. Aurora Auto Scaling automatically adjusts the number of read replicas based on demand, ensuring efficient resource utilization.

1. Aurora Read Replicas

Aurora supports up to 15 read replicas in a DB cluster, which:

? Share the same cluster storage (no need for full replication).

? Improve performance by offloading read queries from the primary instance.

? Can be promoted to a writer instance if the primary fails.

? Sync with the primary in milliseconds (low replication lag).

2. Aurora Auto Scaling

Aurora Auto Scaling automatically adds or removes read replicas based on CPU utilization, connections, or other metrics.

How Aurora Auto Scaling Works?

1. Define Scaling Policies – Set conditions (e.g., CPU usage above 70%) to trigger scaling.
2. Add or Remove Read Replicas – Aurora creates or terminates replicas dynamically.
3. Balance Read Traffic – The reader endpoint (your-cluster.cluster-ro-xyz.rds.amazonaws.com) automatically distributes queries across replicas.

3. Best Practices for Aurora Auto Scaling

? Set a reasonable max replica limit to avoid over-scaling.

? Use multiple AZs to improve fault tolerance.

? Monitor Aurora Auto Scaling metrics using Amazon CloudWatch. ? Use read replicas for analytics & reporting workloads.

6] Aurora-Custom endpoints

Custom endpoints in Amazon Aurora allow you to define a specific subset of instances in your DB cluster for customized traffic routing. This is useful when you want to control how queries are distributed among different database instances.

1. Why Use Aurora Custom Endpoints?

? Dedicated Query Processing – Assign specific read replicas for analytics, reporting, or specific applications.

? Better Load Balancing – Distribute traffic efficiently among a subset of instances.

? Isolation of Workloads – Separate OLTP (Online Transaction Processing) from OLAP (Online Analytical Processing).

? Improved Performance – Avoid overloading the primary writer or general reader endpoint.

7] Aurora Serverless

Amazon Aurora Serverless is an on-demand, auto-scaling configuration for Aurora that automatically adjusts capacity based on application demand. It allows you to pay only for the database resources you use, making it ideal for unpredictable or intermittent workloads.

When to Use Aurora Serverless?

? Infrequent or unpredictable workloads (e.g., Dev/Test environments).

? Event-driven applications that don’t need a continuously running database.

? Variable workloads where database demand spikes at different times.

? New applications that are not yet at full production scale.

Aurora Serverless Scaling (ACUs)

Aurora Serverless scales in Aurora Capacity Units (ACUs).

• 1 ACU = Compute + Memory + Network & Storage I/O.
• Aurora scales between 0.5 ACU to 256 ACUs automatically.
• Cold start delay may occur when resuming from 0 ACUs.

8] Global Aurora

Amazon Aurora Global Database is designed for low-latency global applications by enabling multi-region replication. It allows a single Aurora database to span multiple AWS regions, providing faster read performance and disaster recovery capabilities.

Key Features of Aurora Global Database

? Low-Latency Reads – Global replicas provide read access with latency as low as 1 second. ? Fast Disaster Recovery – Promote a secondary region to a primary in under a minute.

? Automatic Replication – Data is replicated across regions with minimal lag.

? Multi-Region Scaling – Scale read workloads across multiple regions.

? Cost Optimization – Pay only for storage and replication in secondary regions.

Aurora Global Database Architecture

A Global Aurora Database consists of:

1?? Primary Region – The writer instance that handles all read & write requests.

2?? Secondary Regions – Read-only replicas that provide low-latency reads and disaster recovery.

3?? Replication Layer – Uses Amazon Aurora storage replication to sync data across regions.

Use Cases for Aurora Global Database

? Global Applications – Low-latency access for users across continents.

? Disaster Recovery – Failover to a secondary region in case of failure.

? Multi-Region Analytics – Read replicas help distribute analytical workloads.

? Regulatory Compliance – Store data closer to users for compliance requirements.