Amazon Web Services
Compute
- AWS EC2
- EC2 Instance Types
- EC2 Pricing Models
- EC2 Auto Scaling
- Elastic Load Balancing-ELB
- AWS Lambda β Serverless Computing
- Amazon Lightsail
- AWS Elastic Beanstalk
- AWS Fargate
- Amazon ECS (Elastic Container Service)
- Amazon EKS (Elastic Kubernetes Service)
DynamoDB
- DynamoDB Global Table vs Regular DynamoDB Table
- DynamoDB Streams
- Athena query data to DynamoDB
- Athena Query Results with DynamoDB
- PySpark DataFrame to DynamoDB
Redshift
Lambda
Glue
Lambda
Storage
- S3 vs. EBS vs. EFS
- Amazon S3 (Simple Storage Service)
- Amazon S3 Storage Classes
- Amazon EBS (Elastic Block Store)
- Amazon EFS (Elastic File System)
- AWS Storage Gateway
- AWS Snowball
- Amazon FSx
- AWS Backup
Security
π₯οΈ AWS EC2 Instance Types Explained: General Purpose, Compute, Memory & Storage Optimized
Amazon Elastic Compute Cloud (EC2) is the backbone of AWSβs cloud infrastructure. It allows developers and businesses to rent virtual servers (instances) to run their applications. But hereβs the catch: not all EC2 instances are created equal.
To optimize performance and costs, AWS groups instances into families designed for different use cases:
- General Purpose Instances β balanced compute, memory, and networking.
- Compute Optimized Instances β for compute-intensive workloads.
- Memory Optimized Instances β for workloads needing high memory.
- Storage Optimized Instances β for high disk I/O performance.
If you pick the wrong instance, you may end up with poor performance or unnecessary costs. Thatβs why mastering EC2 instance types is crucial.
π 1. General Purpose Instances
π What Are They?
General Purpose instances offer a balanced ratio of CPU, memory, and networking. Theyβre versatile and fit a wide range of workloads.
Common families: t3, t4g, m5, m6i.
β When to Use
- Web servers and application servers.
- Development and testing environments.
- Small-to-medium databases.
π₯οΈ Example Programs for General Purpose
Example 1: Launch General Purpose Instance via AWS CLI
aws ec2 run-instances \ --image-id ami-1234567890abcdef0 \ --count 1 \ --instance-type t3.micro \ --key-name MyKeyPair \ --security-groups my-sg
Example 2: Python Flask App Running on General Purpose Instance
from flask import Flaskapp = Flask(__name__)
@app.route('/')def home(): return "Running on General Purpose EC2 Instance (t3.micro)"
if __name__ == '__main__': app.run(host='0.0.0.0', port=80)
Upload this app to a t3.micro instance β perfectly balanced for lightweight apps.
Example 3: Terraform Script for General Purpose Instance
resource "aws_instance" "general" { ami = "ami-12345678" instance_type = "m5.large" tags = { Name = "general-purpose-instance" }}
π 2. Compute Optimized Instances
π What Are They?
Compute Optimized instances are designed for high-performance processing tasks. They provide more CPU per dollar compared to other families.
Common families: c5, c6g, c7g.
β When to Use
- High-performance web servers.
- Machine learning inference.
- Gaming servers.
- Scientific modeling.
π₯οΈ Example Programs for Compute Optimized
Example 1: Launch Compute Optimized Instance via AWS CLI
aws ec2 run-instances \ --image-id ami-0abcdef1234567890 \ --count 1 \ --instance-type c5.large \ --key-name MyKeyPair \ --security-groups compute-sg
Example 2: Python Program for Parallel Processing (CPU-bound Task)
from multiprocessing import Poolimport math
def intensive_task(x): return math.factorial(5000)
if __name__ == "__main__": with Pool(8) as p: print(p.map(intensive_task, range(1000)))
Running on c5.large ensures faster computation.
Example 3: Compile & Benchmark C++ Code on Compute Instance
g++ -O3 compute_heavy.cpp -o compute_heavytime ./compute_heavy
This shows compute instances excel at CPU-intensive workloads.
π 3. Memory Optimized Instances
π What Are They?
Memory Optimized instances are built for applications that process large data sets in memory. They offer high memory-to-vCPU ratio.
Common families: r5, r6g, x1e, z1d.
β When to Use
- High-performance databases (Redis, MySQL).
- Real-time big data analytics.
- In-memory caching.
- High-performance computing apps.
π₯οΈ Example Programs for Memory Optimized
Example 1: Launch Memory Optimized Instance via CLI
aws ec2 run-instances \ --image-id ami-0abcdef1234567890 \ --count 1 \ --instance-type r5.large \ --key-name MyKeyPair \ --security-groups memory-sg
Example 2: Python In-Memory Data Processing (Pandas)
import pandas as pdimport numpy as np
data = pd.DataFrame(np.random.randint(0, 100, size=(10000000, 10)), columns=list("ABCDEFGHIJ"))print(data.describe())
This is memory-heavy and runs best on r5 family instances.
Example 3: Run Redis In-Memory Cache on Memory Optimized Instance
sudo yum install redis -yredis-server --daemonize yesredis-cli set testkey "Hello Memory Optimized"redis-cli get testkey
π 4. Storage Optimized Instances
π What Are They?
Storage Optimized instances are designed for workloads requiring high sequential read/write throughput and low-latency access to large data sets.
Common families: i3, i4i, d2, h1.
β When to Use
- Big Data workloads (Hadoop, Spark).
- High-frequency OLTP systems.
- Data warehouses.
- Log processing and search engines.
π₯οΈ Example Programs for Storage Optimized
Example 1: Launch Storage Optimized Instance via CLI
aws ec2 run-instances \ --image-id ami-0abcdef1234567890 \ --count 1 \ --instance-type i3.large \ --key-name MyKeyPair \ --security-groups storage-sg
Example 2: Benchmark Disk Performance with fio
sudo yum install -y fiofio --name=write_test --filename=/data/testfile --size=1G --time_based --runtime=60s --rw=write --bs=1M --ioengine=libaio --direct=1
Example 3: Setup Hadoop Cluster on Storage Optimized Instance
hdfs dfs -mkdir /bigdatahdfs dfs -put large_dataset.csv /bigdatahadoop jar wordcount.jar input output
π§ How to Remember for Interview & Exam
- General Purpose β Balanced. (Think: Swiss Army knife)
- Compute Optimized β CPU power. (Think: Math-heavy tasks)
- Memory Optimized β RAM-heavy. (Think: Databases, caching)
- Storage Optimized β Disk-heavy. (Think: Big Data, I/O)
π Trick: Remember acronym GCMS (General, Compute, Memory, Storage).
π― Why Itβs Important to Learn This Concept
- Cost Optimization β Right instance = no wasted money.
- Performance Optimization β Match workloads to the right hardware.
- Scalability β Build architectures that scale efficiently.
- Certifications & Interviews β Common AWS Solutions Architect exam topic.
- Real-World Impact β Choosing wrong instance can cause outages or unnecessary bills.
π Conclusion
AWS EC2 instances are categorized to match workload requirements. Choosing the right type ensures high performance, cost-efficiency, and reliability.
- General Purpose β balanced workloads.
- Compute Optimized β CPU-intensive tasks.
- Memory Optimized β in-memory processing & databases.
- Storage Optimized β heavy I/O workloads.
Mastering this concept is essential for interviews, AWS exams, and real-world projects.