AWS, Cloud Computing

4 Mins Read

Strategic Guidelines for Right-sizing Amazon ElastiCache for Redis Clusters


Generally, organizations that deal with applications experience fluctuations in application usage patterns, leading to performance bottlenecks in their Amazon ElastiCache for Redis clusters. The challenge lies in finding a scalable solution that can dynamically adjust to varying workloads and maintain consistent performance without compromising reliability.

This blog addresses the practical challenges organizations face in optimizing Amazon ElastiCache for Redis clusters. It offers strategic guidelines for right-sizing, emphasizing real-world scenarios where performance and scalability are critical. By exploring common challenges and providing actionable insights, the blog becomes a valuable resource for organizations seeking to enhance their Redis experience, and it helps readers relate to their challenges and apply the provided strategies to optimize their Amazon ElastiCache for Redis clusters.


Amazon ElastiCache for Redis is a fully managed service, compatible with Redis and Memcached, engineered to provide real-time, cost-optimized performance for contemporary applications. Boasting the capability to scale to hundreds of millions of operations per second with microsecond response times, this service ensures enterprise-grade security and reliability.

Embarking on a journey with Amazon ElastiCache for Redis involves making critical sizing decisions. The type of instance, the number of shards, and the balance between instance size and shard quantity all play pivotal roles. But how do you make the right choices? The key lies in understanding your workload. Is it memory-bound, CPU-bound, or network-bound? Let’s consider the below topics to make these decisions effectively and strike the ideal balance between price and performance.

  1. Workload Identification:

Is your workload memory-bound, CPU-bound, or network-bound?

  1. Instance Type Selection:

What instance type aligns best with your workload’s resource requirements?

How can the instance type be modified to optimize performance?

  1. Shard Considerations:

How many shards does your workload demand for efficient scaling?

Should you prioritize larger instances with fewer shards or vice versa?

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Determining the Nature of Your Workload

Identifying the type of workload is the initial step in making effective decisions for your Amazon ElastiCache for Redis cluster:

a. Memory-Bound Workload:

  • The size of your dataset plays a crucial role in cluster design.
  • Amazon ElastiCache for Redis handles datasets ranging from megabytes to terabytes.
  • A few gigabytes can fit in a single instance with one shard, while larger datasets may require distribution across multiple shards.
  • Workloads with large datasets that aren’t accessed concurrently can be classified as memory-bound.

b. CPU-Bound Workload:

  • Workloads demanding high concurrency and scaling due to intensive CPU utilization are CPU-bound.
  • A single core typically supports around 100,000 requests per second (RPS) for basic GET/SET commands.
  • Consideration of command types and factors like TLS influence RPS capacity.
  • Observing the XXXCmdLatency metric in Amazon CloudWatch provides insights into command performance.
  • Awareness of computational complexity (O(1), O(N), etc.) for each command is available in the official Redis command reference.
  • Monitoring slow commands can be achieved through the SLOWLOG feature.

c. Network-Bound Workload:

  • Storing and retrieving large objects in Redis can result in substantial network responses.
  • This may indicate a network-bound workload, risking breaches of instance network bandwidth limits if using insufficient shards or small instance types.
  • Metrics like NetworkBandwidthOutAllowanceExceeded and NetworkBandwidthInAllowanceExceeded in Amazon CloudWatch signal potential issues.
  • Network-bound workloads typically involve thousands of RPS with responses sized 10 KB or more.

Amazon ElasticCache: Redis Instance types

Amazon ElastiCache for Redis offers various instance types tailored to specific performance and resource requirements. The available instance families include:

  • General Purpose Instances (M-family):

Versatile instances are suitable for various workloads.

  • Memory-Optimized Instances (R-family):

Higher memory-to-core ratio, optimal for memory-intensive applications.

  • Data Tiering Instances (R6gd family):

Combines memory and SSD storage for efficient data tiering.

  • Network-Optimized Instances (C7gn):

Higher network bandwidth limits are ideal for network-intensive tasks.

  • Burstable Instances (T-family):

Generally recommended for non-production use cases.

Each instance family has different instance sizes, each offering varying core counts, total RAM, and network bandwidth. This diversity allows for precise selection based on the unique demands of different workloads.

Leveraging Multiple vCPUs in Amazon ElastiCache for Redis:

The Amazon ElastiCache team consistently enhances performance for multi-CPU instances. The implementation of features like Enhanced I/O (Redis 5.0.3+) and Enhanced I/O Multiplexing (Redis 7+) ensures that users with instances having multiple CPUs experience increased Requests Per Second (RPS) capacity and reduced latency. These performance-enhancing features are accessible in instances with 4 vCPUs or more. Refer to the Supported node types documentation for details on feature compatibility.

Mastering Auto Scaling for Redis Clusters in Amazon ElastiCache: shard classification

Auto Scaling Amazon ElastiCache for Redis clusters ensures optimal performance by dynamically adjusting resources based on demand. Leveraging features like Enhanced I/O and TLS offloading enhances scalability and connection management for varying workloads. Careful consideration of instance types and sizes is vital to efficiently handling concurrency and connections in diverse scenarios.

Auto Scaling ElastiCache for Redis clusters involves sizing workloads based on their type:

  1. Memory-Bound Workloads:

Consider dataset size; smaller datasets may fit in a single instance, while larger ones require distribution across multiple shards.

  • Workloads with Elevated Requests Per Second (RPS):

These workloads involve executing numerous swift commands, like GET and SET, characterized by high-speed operations.

  • Workloads with Intensive Computational Complexity:

These workloads entail executing slower commands, including EVAL, HGETALL, LRANGE, and similar operations, distinguished by their intricate computational processes.



2. CPU-Bound Workloads:

Evaluate command complexity; high CPU utilization workloads benefit from larger instances with increased core count.


3. Network-Bound Workloads:

Focus on large object storage; store and retrieve large objects efficiently to prevent breaches of network bandwidth limits.



In conclusion, this blog has unraveled the complexities of Auto Scaling for Amazon ElastiCache Redis clusters, providing a roadmap for optimal performance. By understanding and sizing workloads based on their types, be it memory-bound, CPU-bound, or network-bound, we’ve explored strategies for efficient scaling, connection management, and leveraging enhanced features.

Drop a query if you have any questions regarding Amazon ElastiCache and we will get back to you quickly.

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1. How does Auto Scaling in ElastiCache for Redis handle memory-bound workloads?

ANS: – Auto Scaling addresses memory-bound workloads by offering seamless scaling based on dataset sizes. Smaller datasets may fit within a single instance, while larger datasets requiring distribution across multiple shards can be efficiently managed.

2. How does Auto Scaling in ElastiCache for Redis cater to workloads with slow commands and high computational complexity?

ANS: – Workloads involving slow commands, such as EVAL, HGETALL, and LRANGE, benefit from Auto Scaling’s ability to handle computational complexity. Choosing larger instance types with enhanced features ensures efficient execution of these commands and optimal performance for intricate workloads.

WRITTEN BY Bhanu Prakash K

K Bhanu Prakash is working as a Subject Matter Expert in CloudThat. He is proficient in Managing and configuring AWS Infrastructure as well as on Kubernetes and DevOps tools like Terraform, ansible, Jenkins, and Git. He is very keen on learning new technologies and publishing blogs for the tech community.



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