A Deep Dive into ElastiCache Node & Instance Types

May 22, 2023

A Deep Dive into ElastiCache Node & Instance Types

Introduction to ElastiCache Node & Instance Types

Welcome to this deep dive where we'll be exploring ElastiCache node and instance types. In order to help you optimize your applications, it's essential to understand the various options available and how they can impact performance. So let's dive in!

Brief Overview of ElastiCache

ElastiCache is a managed caching service provided by Amazon Web Services (AWS) that simplifies the process of deploying, operating, and scaling cache environments in the cloud. It supports two popular open-source caching engines: Redis and Memcached. By utilizing ElastiCache, you can improve the performance of data-intensive applications, reduce latency, increase throughput, and enable real-time processing.

Importance of Choosing the Right Node Type

An ElastiCache node is the fundamental building block for both Redis and Memcached. As the primary unit of organization, nodes store and retrieve data from memory, which helps reduce the need for repeated, expensive database queries. AWS offers several different node types for ElastiCache, each with varying levels of performance, capacity, and cost. Choosing the correct node type for your specific use case is critical for maximizing performance while minimizing costs.

By selecting an appropriate node type, you can ensure a balance between cost-effectiveness, performance requirements, and scalability capabilities. Therefore, understanding the differences between these options will enable you to make more informed decisions and optimize your application infrastructure effectively.

In the upcoming sections, we'll be diving into the various ElastiCache node and instance types, discussing their features, and providing guidance on how to choose the best fit for your needs.

Node in ElastiCache - What You Need to Know

Understanding the concepts of ElastiCache nodes and instance types is crucial for building efficient, scalable caching solutions. Without further ado, let's delve deeper.

Definition and Role of Nodes

A node in ElastiCache refers to an individual computing unit within your cache cluster. Each node runs an instance of the selected caching engine (Redis or Memcached) and contains a specific amount of memory and CPU resources, depending on the chosen instance type.

Nodes are essential for distributing your data across multiple computing units, ensuring redundancy, and achieving horizontal scalability for increased performance. When you create an ElastiCache cluster, you can specify the number of nodes you would like it to contain, enabling you to balance cost, performance, and reliability according to your application's requirements.

Primary and Replica Nodes

In ElastiCache, there are two types of nodes: primary and replica nodes.

Primary nodes are responsible for handling write operations from clients and act as the source of truth for your cached data. They store a copy of your data in memory and can serve read requests just like replica nodes. There can only be one primary node per Redis cluster or partition.

Replica nodes are responsible for storing a copy of the data from the primary node, providing redundancy and allowing read-heavy workloads to be distributed across multiple replicas. Replica nodes continuously synchronize with their associated primary node, ensuring that they maintain an up-to-date copy of the data. You can have multiple replica nodes per primary node, depending on your application's needs.

Here is an example of creating an ElastiCache cluster with one primary node and two replicas using the aws CLI:

aws elasticache create-replication-group \
--replication-group-id my-replication-group \
--replication-group-description "Example replication group with one primary and two replica nodes" \
--cache-node-type cache.t3.micro \
--engine redis \
--num-cache-clusters 3 \
--cache-parameter-group default.redis6.x \

ElastiCache Node Types

ElastiCache Node Families

Amazon ElastiCache supports four different node families, each designed to cater to specific use case scenarios:

  1. T-type (burstable)
  2. M-type (general purpose)
  3. R-type (memory optimized)

Let's examine each of these node families in more detail.

T-Type (Burstable)

T-type instances are designed for workloads that require occasional bursts of computational power. These instances provide a baseline level of CPU performance with the ability to burst above the baseline when needed. They are suitable for development, testing environments, or applications that can tolerate variable performance.

Example use cases include caching session data, small websites, or relatively low-demand applications.

Here's an example of creating a cache cluster with burstable T-type nodes using AWS CLI:

aws elasticache create-cache-cluster \
--cache-cluster-id my-test-cluster \
--engine redis \
--cache-node-type cache.t3.small \
--num-cache-nodes 3 \
--region us-west-2

M-Type (General Purpose)

M-type instances provide a balance between compute, memory, and network resources. They are ideal for applications that need a stable and consistent level of performance. M-types offer more consistent CPU and network performance compared to T-type instances.

These instances are suitable for most web apps, content management systems, database caches, and other general-purpose applications.

Example code for creating an M-type cache cluster in Python using Boto3:

import boto3

elasticache = boto3.client("elasticache")

response = elasticache.create_cache_cluster(
    ReplicationGroupId="your-replication-group",  # replace with your replication group ID


R-Type (Memory Optimized)

R-type instances are designed for memory-intensive applications that require low-latency access to large amounts of data. These instances offer higher memory capacity and enhanced network performance, making them ideal for caching larger datasets, real-time analytics, or in-memory databases.

Use cases include recommendation engines, high-performance caches, and distributed in-memory processing systems.

Here's an example of creating an R-type cache cluster using AWS CLI:

aws elasticache create-cache-cluster \
--cache-cluster-id my-memory-cluster \
--engine redis \
--cache-node-type cache.r6g.large \
--num-cache-nodes 4 \
--region us-east-1

Comparing ElastiCache Node Types

Use Cases

  • Low-latency applications: For web and mobile applications that require low latency, selecting a node type with good network performance is crucial. The T-type (T2 and T3) and M-type (M5) nodes are a good fit for this use case, as they deliver a decent balance between compute, memory, and network resources.

  • High-throughput applications: Applications that demand high throughput need a node type with ample CPU, memory, and network bandwidth. R-type (R5 and R6g) nodes are designed for such use cases, providing memory-optimized performance and larger cache sizes.

  • Memory-intensive operations: Some applications may perform memory-intensive operations like sorting and searching. The R-type (memory-optimized) node family in Amazon ElastiCache provides high memory capacity and is optimized for these kinds of tasks.


Each ElastiCache node type has distinct performance characteristics:

  • T-type Nodes: These are designed for development and testing environments, offering burstable CPU performance. However, they have limited performance capabilities and may not be well-suited for demanding production workloads.

  • M-type Nodes: These general-purpose nodes provide a balance of CPU, memory, and network resources. They deliver consistent performance, making them a good choice for a wide variety of use cases.

  • R-type Nodes: The R-type nodes (such as R5 and R6g) are designed for memory-intensive workloads. They offer higher memory capacity and better performance compared to M-type nodes, making them ideal for applications with demanding cache workloads.

Factors to Consider when Choosing a Node Type


Performance is crucial when selecting a node type, as it directly impacts your application's response time, throughput, and overall user experience. Consider the following factors:

  1. Memory capacity: Choose a node type with enough memory to hold your entire dataset while leaving room for future growth.
  2. vCPU: Select a node type with multiple vCPUs if your workload requires high levels of parallelism or computational power.
  3. Network performance: Opt for a node type with higher network bandwidth to ensure quick data transfer between client and server.

For instance, if you anticipate a high read/write workload, you may want to choose a node type like cache.r6g.large, which offers 13.07 GB of memory, 2 vCPUs, and up to 10 Gbps of network bandwidth.


Scalability is another critical factor when choosing a node type. ElastiCache offers three main ways to scale:

  1. Vertical scaling: Increase the memory or CPU resources of your existing nodes by upgrading to a larger node type.
  2. Horizontal scaling: Add more nodes to your cluster to distribute traffic and increase capacity without modifying existing nodes.
  3. Data partitioning: Divide your dataset across multiple shards to improve performance and redundancy.

When considering scalability, evaluate your application's growth requirements and select a node type that will allow for seamless scaling without compromising performance.


Last but not least, consider the cost of running your ElastiCache nodes. AWS charges for ElastiCache based on:

  1. Node type: Larger node types with more memory, vCPUs, and network bandwidth come at a higher price.
  2. Number of nodes: The total cost also depends on the number of nodes in your cluster.
  3. Data transfer: You'll be billed for data transfer between your nodes and other AWS services or the internet.

To minimize costs while still meeting your performance and scalability requirements, compare different node types and choose one that offers an optimal balance between resources and pricing.

To summarize, when selecting an ElastiCache node type, consider aspects such as performance, scalability, and cost to ensure your application runs smoothly and efficiently while minimizing expenses. Keep in mind that you can always adjust your chosen node type later on to accommodate changing needs.

Scaling ElastiCache Nodes

Vertical Scaling

Vertical scaling, also known as "scaling up," is the process of increasing the capacity of a single node or instance by upgrading its memory, CPU, and network resources. In the context of ElastiCache, vertical scaling means moving from a smaller instance type to a larger one. To do this, you can modify your cache cluster configuration in the AWS Management Console, AWS CLI, or SDKs.

For example, if you want to scale an existing ElastiCache Redis cluster from cache.t2.micro to cache.t3.medium, run the following command using AWS CLI:

aws elasticache modify-cache-cluster --cache-cluster-id my-cache-cluster --cache-node-type cache.t3.medium --apply-immediately

Keep in mind that the cluster might experience downtime during the resizing process. Plan accordingly and schedule maintenance windows to minimize impact on your application's performance.

Horizontal Scaling

Horizontal scaling, or "scaling out," refers to adding more nodes or instances to your ElastiCache cluster to increase its overall capacity and throughput. This approach helps you distribute data and workload across multiple nodes, providing better fault tolerance and availability.

Amazon ElastiCache supports horizontal scaling by allowing you to add or remove nodes to/from a cluster. For Redis, you can use sharding with Redis Cluster. To add a new shard with two replicas per shard, you can use the following AWS CLI command:

aws elasticache modify-replication-group --replication-group-id my-replication-group --num-node-groups 2 --replicas-per-node-group 2 --apply-immediately

For Memcached, you can scale your cluster horizontally by adding or removing nodes:

aws elasticache modify-cache-cluster --cache-cluster-id my-memcached-cluster --num-cache-nodes 4 --apply-immediately

Cluster Resizing Strategies and Best Practices

When resizing your ElastiCache cluster, consider the following best practices to maximize performance and minimize downtime:

  1. Monitor the metrics: Keep an eye on key performance indicators (KPIs) like cache hit ratio, evictions, latency, and CPU utilization to identify when it's time to scale up or out.
  2. Scale gradually: Gradual scaling helps you test and assess the impact of each scaling step on your application performance.
  3. Optimize data structures: Before scaling, ensure that you're using appropriate data structures and caching patterns for your use case to make the most of available resources.
  4. Backup before resizing: Take snapshots of your ElastiCache clusters before initiating any resizing operation. This will allow you to recover your data in case of issues during the process.
  5. Test failover mechanisms: Regularly test your cluster's failover mechanisms to ensure they can handle node failures or other unexpected events.

By following these guidelines and understanding the different scaling options available in ElastiCache, you'll have a well-performing and resilient caching infrastructure tailored to your application's needs.

Monitoring and Managing ElastiCache Nodes

Key Performance Metrics

To monitor and manage ElastiCache nodes, it's essential to keep track of a few key performance metrics:

  1. CPU Utilization: This metric monitors the percentage of the CPU being used by your node. High CPU utilization may indicate increased data processing or cache operations, which could lead to slower response times.

  2. Memory Usage: This measures the amount of memory used by your node. A sudden increase in memory usage could signify a potential bottleneck, leading to slow response times or even cache eviction if the available memory is insufficient.

  3. Cache Hits and Misses: These metrics show the number of cache hits (successful lookups) and misses (unsuccessful lookups). A higher cache hit ratio generally means your caching strategy is working well, while a low hit ratio indicates that you may need to optimize your application's caching policy.

  4. Network Throughput: This measures the amount of data transferred in and out of your node. High network throughput could mean high data processing demands, which could impact the node's performance.

  5. Latency: Latency refers to the time taken for an operation to complete, such as reading data from or writing data to the cache. Low latency is desirable for good application performance.

Monitoring Options

Monitoring your ElastiCache nodes can be done using various tools, including:

  1. Amazon CloudWatch: By default, ElastiCache integrates with CloudWatch, providing multiple preconfigured metrics for monitoring performance. You can create custom dashboards and set alarms based on these metrics to ensure that you receive timely notifications for any performance issues.
import boto3
from datetime import datetime, timedelta

client = boto3.client('cloudwatch')

# Change these strings into datetime objects
start_time = datetime.strptime('2023-05-20T00:00:00Z', '%Y-%m-%dT%H:%M:%SZ')
end_time = datetime.strptime('2023-05-20T23:59:59Z', '%Y-%m-%dT%H:%M:%SZ')

response = client.get_metric_data(
            'Id': 'm1',
            'MetricStat': {
                'Metric': {
                    'Namespace': 'AWS/ElastiCache',
                    'MetricName': 'CPUUtilization',
                    'Dimensions': [
                            'Name': 'CacheClusterId',
                            'Value': 'YourCacheClusterId'
                'Period': 300,
                'Stat': 'Average',
            'ReturnData': True,

  1. Custom Monitoring Solutions: While CloudWatch provides comprehensive monitoring capabilities, you may want to integrate ElastiCache with third-party monitoring solutions or build your own custom monitoring tools using AWS SDKs and APIs.

Alerts and Notifications

Setting up alerts and notifications for your ElastiCache nodes allows you to proactively respond to potential issues. Using Amazon CloudWatch Alarms, you can define specific thresholds for key performance metrics and configure notifications through various channels like email, SMS, or webhook.

To create a CloudWatch Alarm for high CPU utilization, you can use the following code:

import boto3

client = boto3.client('cloudwatch')

response = client.put_metric_alarm(
    Statistic='Average', # Correct Statistic
    AlarmDescription='Alarm for high CPU utilization in ElastiCache node',
            'Name': 'CacheClusterId',
            'Value': 'YourCacheClusterId' # replace with your actual CacheClusterId


By monitoring and managing your ElastiCache nodes effectively, you can ensure smooth operation and maintain the high performance of your applications. Implementing these best practices will help you optimize resource usage, detect potential bottlenecks early, and take corrective actions to address any issues.

ElastiCache Node Cost Optimization Strategies

ElastiCache Node Pricing - How It Works

ElastiCache node pricing varies depending on the chosen node type and region. The factors affecting the price include:

  • Instance Size: Larger instances usually have more vCPU, memory, and network bandwidth, which makes them costlier.

  • Usage Duration: ElastiCache follows a pay-as-you-go model, meaning you only pay for the time your instance is running. For long-term projects, opting for reserved instances may result in significant cost savings.

  • Data Transfer: While data transfer within the same region is free, transferring data between regions or out of AWS can incur additional charges.

To get an accurate estimate of pricing, consult the AWS ElastiCache Pricing page for up-to-date information.

Analyzing Your Workload

First, evaluate your application's caching needs by understanding your workload characteristics. Monitor key performance indicators (KPIs) such as cache hit ratio, memory usage, and CPU utilization with Amazon CloudWatch or third-party tools. This will help you understand how efficiently your cache is running and identify areas for improvement.

import boto3
from datetime import datetime

cloudwatch = boto3.client('cloudwatch')

# Define Metric Names and Dimensions
metric_names = ['CPUUtilization', 'CacheHits', 'CacheMisses']

dimensions = [
        'Name': 'CacheClusterId',
        'Value': 'your_cache_cluster_id'  # replace with your actual CacheClusterId

# Change these strings into datetime objects
start_time = datetime.strptime('2023-05-01T00:00:00Z', '%Y-%m-%dT%H:%M:%SZ')
end_time = datetime.strptime('2023-05-30T23:59:59Z', '%Y-%m-%dT%H:%M:%SZ')

# Get Statistics for Each Metric
for metric_name in metric_names:
    response = cloudwatch.get_metric_statistics(
        Statistics=['SampleCount', 'Average']

    print(f"{metric_name}: {response['Datapoints']}")

Right-Sizing Instances

Selecting the appropriate instance type directly impacts your caching performance and costs. ElastiCache supports various node and instance types, which differ in memory, CPU, and networking capabilities. Ensure you choose an instance that aligns with your application's requirements without over-provisioning resources.

To right-size instances, analyze your workload and determine the amount of memory and CPU needed. Use the AWS Price List API or console to compare prices across instance types, considering both cost-per-hour and performance.

Utilizing Reserved Instances

Reserved Instances (RIs) offer significant savings for long-term workloads compared to on-demand pricing. By committing to a one- or three-year term up front, you can save up to 45% on standard node hourly rates. ElastiCache offers several RI options, like All Upfront, Partial Upfront, and No Upfront, allowing you to choose the best payment plan for your needs.

To purchase a reserved instance via AWS CLI, use the following command:

aws elasticache-purchase-reserved-cache-nodes-offering \
--reserved-cache-nodes-offering-id \

Scheduled Scaling

If your application experiences predictable traffic patterns, consider using scheduled scaling to balance performance and cost. This strategy allows you to automatically scale your cache resources during peak hours and downscale when demand is lower. You can implement scheduled scaling with AWS Lambda and Amazon EventBridge.

Here's an example Lambda function for scaling your ElastiCache cluster:

import boto3

def lambda_handler(event, context):
    elasticache = boto3.client('elasticache')

    # Modify the Cache Cluster Parameters as Needed
    response = elasticache.modify_cache_cluster(
        CacheClusterId='your_cache_cluster_id',  # replace with your actual CacheClusterId

    return {
        'statusCode': 200,
        'body': "ElastiCache cluster updated successfully."

Set up an EventBridge rule to trigger this function at defined intervals based on your application's usage patterns.

By employing these cost optimization strategies, you can efficiently manage your ElastiCache resources and reduce overall expenses without sacrificing performance.


Understanding ElastiCache node and instance types is crucial to optimizing your cache performance and resource utilization. Throughout this deep dive, we've discussed the various node types available, their configurations, and how they can impact your caching strategy. To make informed decisions on choosing the right instance type for your needs, always refer to the official AWS documentation on ElastiCache instance types: AWS ElastiCache Instance Types.

By considering factors such as memory capacity, vCPU, network performance, and cost, you can efficiently allocate resources to suit your application's requirements. As with any AWS service, staying up-to-date with new developments and features is essential for maximizing the value of your infrastructure.

Frequently Asked Questions

How many nodes do I need for ElastiCache?

The optimal number of ElastiCache nodes depends on your application's performance needs, traffic, and data size. Consider factors such as cache hit ratios, latency, throughput, and failover support. Begin with a few nodes and adjust based on performance metrics to meet your use case and goals. Continuously evaluate and adapt your deployment for optimal performance.

How many nodes are in ElastiCache?

The specific number of nodes in any given ElastiCache deployment will depend on user requirements and configurations set during the creation process. Users can choose to create clusters with multiple nodes for increased performance, scalability, and availability.

How do I change the instance type in ElastiCache?

To change the instance type in ElastiCache, follow these steps:

  1. Sign in to the AWS Management Console and navigate to the ElastiCache dashboard.
  2. Locate the desired ElastiCache cluster by searching or browsing the list of clusters.
  3. Choose the specific node whose instance type you want to modify.
  4. Click the 'Actions' dropdown menu and select 'Modify.'
  5. In the 'Modify Cache Node' dialog, update the 'Node Type' field to the desired new instance type.
  6. Choose whether to apply the change immediately or during your next maintenance window.
  7. Click 'Modify' to confirm and initiate the process.

Keep in mind that this process may cause temporary unavailability as the node is replaced with the new instance type. Make a backup before proceeding if necessary.

What is the smallest instance of ElastiCache?

The smallest instance of Amazon ElastiCache is the cache.t2.micro node type. This instance is designed for low-traffic workloads and testing purposes, offering 555 megabytes of available memory. It provides a cost-effective option for developers who want to explore or experiment with caching strategies before scaling up to larger instances as their application requirements grow.

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