Concepts
Capacity planning involves predicting future database resource requirements to ensure that the database can handle upcoming data loads and performance demands. Resource requirements can include storage, memory, CPU, and IOPS (Input/Output Operations Per Second).
RDS Capacity Planning
When using Amazon Relational Database Service (RDS), capacity planning revolves around choosing the right instance type and storage capacity. AWS RDS supports multiple database engines like MySQL, PostgreSQL, Oracle, and SQL Server.
Key Considerations:
- Instance Size: Select an instance based on expected CPU and memory usage.
- Storage: Choose between General Purpose SSD, Provisioned IOPS SSD, or Magnetic storage based on performance needs.
- IOPS: If using Provisioned IOPS SSD, specify the IOPS rate based on expected throughput.
- Read Replicas: Deploy read replicas to handle heavy read traffic and increase application scalability.
DynamoDB Capacity Planning
Amazon DynamoDB requires a different approach. It uses the concept of read and write capacity units to handle throughput capacity.
Key Considerations:
- Capacity Mode: Choose between Provisioned and On-Demand capacity modes.
- Provisioned Capacity: Specify the number of read and write capacity units (RCUs and WCUs) based on expected workload.
- Auto Scaling: Enable DynamoDB Auto Scaling to adjust provisioned throughput automatically.
- Partitions: Understand how data is partitioned and how additional partitions are allocated as you increase capacity units or storage.
| Feature | Provisioned Capacity | On-Demand Capacity |
|---|---|---|
| Throughput | Must configure read and write capacity units. | Automatically adapts to workload without management. |
| Cost | Less expensive for predictable workloads. | Higher cost but simplified planning for variable loads. |
| Scalability | Requires manual adjustment or auto-scaling setup. | Scales seamlessly without manual intervention. |
| Management Overhead | Higher due to monitoring and adjusting capacities. | Lower as AWS manages scaling. |
Aurora Capacity Planning
For Amazon Aurora, you must consider how Aurora separates compute and storage scaling.
Key Considerations:
- Aurora Instances: Select among the different types like R3, R4, or R5 based on the workload.
- Replicas: Create Aurora Replicas for read scaling.
- Storage: Aurora scales storage automatically up to 64TB, so focus more on instance capacity.
- IOPS: Aurora can scale up IOPS to match workload, no need to pre-provision.
Monitoring and Autoscaling
No matter the database service, monitoring is essential for successful capacity planning. AWS CloudWatch provides metrics for monitoring database performance. Setting alarms and enabling autoscaling can help manage capacity effectively.
Autoscaling Example:
For an RDS instance with auto-scaling, you might define a target metric like CPU utilization and set minimum and maximum capacity limits:
aws application-autoscaling register-scalable-target \
--service-namespace rds \
--resource-id "cluster:my-rds-cluster" \
--scalable-dimension "rds:cluster:ReadReplicaCount" \
--min-capacity 1 \
--max-capacity 15
aws application-autoscaling put-scaling-policy \
--service-namespace rds \
--resource-id "cluster:my-rds-cluster" \
--scalable-dimension "rds:cluster:ReadReplicaCount" \
--policy-name "my-target-tracking-policy" \
--policy-type "TargetTrackingScaling" \
--target-tracking-scaling-policy-configuration "{
\"TargetValue\": 70.0,
\"PredefinedMetricSpecification\": {
\"PredefinedMetricType\": \"RDSReaderAverageCPUUtilization\"
}
}"
The example above sets up auto-scaling for an RDS cluster using AWS CLI with a policy to maintain the CPU utilization at 70%.
Conclusion
Effective database capacity planning on AWS involves understanding service-specific nuances, estimating future demands, and incorporating monitoring and autoscaling solutions. By diligently assessing database requirements and utilizing the right AWS features, you can ensure that the database layer remains performant and cost-effective, adhering to best practices for the AWS Certified Solutions Architect – Associate exam.
Answer the Questions in Comment Section
True/False: Database capacity planning is only concerned with the amount of storage required for a database.
Answer: False
Database capacity planning involves not only storage but also considerations of memory, compute power, IOPS (input/output operations per second), and the scalability to meet future demands.
In the context of Amazon RDS, which AWS service provides automated capacity scaling?
- A) AWS Auto Scaling
- B) Amazon Elastic Compute Cloud (EC2) Auto Scaling
- C) Amazon RDS Auto Scaling
- D) AWS Elastic Beanstalk
Answer: A) AWS Auto Scaling
AWS Auto Scaling monitors your applications and automatically adjusts capacity to maintain steady, predictable performance at the lowest possible cost.
True/False: When using Amazon DynamoDB, you must manually provision read and write throughput.
Answer: False
Amazon DynamoDB offers two capacity modes for processing reads and writes on your tables: On-Demand and Provisioned. In On-Demand mode, capacity is managed automatically.
Which of the following is NOT a common factor to consider in database capacity planning?
- A) Networking throughput
- B) Number of concurrent users
- C) The color scheme of the database management system interface
- D) Data backup and retention policies
Answer: C) The color scheme of the database management system interface
The color scheme of the interface is related to user interface design and not to database capacity planning.
True/False: In Amazon RDS, the storage size automatically scales with the increase in database usage.
Answer: False
Amazon RDS storage does not automatically scale. You need to manually modify the instance to either increase the storage capacity or to implement a scaling strategy like sharding.
Provisioned IOPS in Amazon RDS is best suited for which type of workload?
- A) Unpredictable workloads
- B) Workloads that don’t require fast IO
- C) IO-intensive workloads that require sustained IOPS performance
- D) Small, infrequent database operations
Answer: C) IO-intensive workloads that require sustained IOPS performance
Provisioned IOPS are designed to meet the needs of IO-intensive workloads, particularly database workloads, that are sensitive to storage performance and consistency.
True/False: Amazon Aurora automatically scales database instance memory and compute resources up to 128 TB.
Answer: False
Amazon Aurora scales storage automatically, growing in increments up to 128 TB, but scaling compute resources requires a change in the DB instance class.
Which AWS service enables asynchronous replication of your data across different AWS Regions for disaster recovery?
- A) Amazon RDS Read Replicas
- B) Amazon ElastiCache
- C) AWS DataSync
- D) Amazon DynamoDB Global Tables
Answer: D) Amazon DynamoDB Global Tables
Amazon DynamoDB Global Tables provide a fully managed, multi-region, and multi-master database that enables asynchronous replication across multiple AWS Regions.
True/False: AWS’s Database Migration Service (DMS) can be used for ongoing replication to synchronize databases.
Answer: True
AWS DMS supports ongoing replication to keep your source and target databases synchronized.
When planning the capacity of a database, which AWS tool can help forecast the future demands based on historical data?
- A) AWS Cost Explorer
- B) AWS Trusted Advisor
- C) Amazon CloudWatch
- D) AWS Simple Monthly Calculator
Answer: C) Amazon CloudWatch
Amazon CloudWatch provides detailed monitoring of resources, which helps in understanding and predicting future capacity requirements based on historical data.
True/False: It’s important to consider the maximum number of allowed connections when capacity planning for a relational database.
Answer: True
The maximum number of allowed connections is a critical aspect to consider since it can limit the number of concurrent users and operations, affecting the overall performance and capacity of the database.
In Amazon DynamoDB, how is provisioned throughput affected when you enable Auto Scaling?
- A) Auto Scaling doesn’t affect provisioned throughput
- B) Provisioned throughput remains constant regardless of usage
- C) Auto Scaling adjusts provisioned throughput based on pre-defined utilization thresholds
- D) Provisioned throughput is decreased, and you cannot increase it manually
Answer: C) Auto Scaling adjusts provisioned throughput based on pre-defined utilization thresholds
With Amazon DynamoDB Auto Scaling, you can setup utilization thresholds that trigger automatic adjustments to your table’s provisioned throughput settings, ensuring efficiency and performance.
Great post on database capacity planning!
How do capacity units in AWS differ from traditional metrics like IOPS?
I’m still confused about how to estimate the required capacity units for my DynamoDB tables.
Thanks for the clear explanation!
I highly appreciate this post. Very informative!
One of the best articles I’ve read on capacity planning!
For exam prep, understanding these concepts is crucial. Thanks for the information!
Does anyone have a practical example of how they estimated capacity units and their outcome?