This week’s system design refresher:
Scalability Simply Explained in 10 Minutes (Youtube video)
What do Amazon, Netflix, and Uber have in common?
What are the differences between paging and segmentation?
How does Git Work?
What tech stack is commonly used for microservices?
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Scalability Simply Explained in 10 Minutes
What do Amazon, Netflix, and Uber have in common?
They are extremely good at scaling their system whenever needed.
Here are 8 must-know strategies to scale your system.
Stateless Services
Design stateless services because they don’t rely on server-specific data and are easier to scale.
Horizontal Scaling
Add more servers so that the workload can be shared.
Load Balancing
Use a load balancer to distribute incoming requests evenly across multiple servers.
Auto Scaling
Implement auto-scaling policies to adjust resources based on real-time traffic.
Caching
Use caching to reduce the load on the database and handle repetitive requests at scale.
Database Replication
Replicate data across multiple nodes to scale the read operations while improving redundancy.
Database Sharding
Distribute data across multiple instances to scale the writes as well as reads.
Async Processing
Move time-consuming and resource-intensive tasks to background workers using async processing to scale out new requests.
Over to you: Which other strategies have you used?
Latest articles
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Data Sharing Between Microservices
CAP, PACELC, ACID, BASE - Essential Concepts for an Architect’s Toolkit
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What are the differences between paging and segmentation?
Paging
Paging is a memory management scheme that eliminates the need for contiguous allocation of physical memory. The process's address space is divided into fixed-size blocks called pages, while physical memory is divided into fixed-size blocks called frames.
The address translation process works in 3 steps:
Logical Address Space: The logical address (generated by the CPU) is divided into a page number and a page offset.
Page Table Lookup: The page number is used as an index into the page table to find the corresponding frame number.
Physical Address Formation: The frame number is combined with the page offset to form the physical address in memory.
Advantages:
Eliminates external fragmentation.
Simplifies memory allocation.
Supports efficient swapping and virtual memory.
Segmentation
Segmentation is a memory management technique where the memory is divided into variable-sized segments based on the logical divisions of a program, such as functions, objects, or data arrays.
The address tranlation process works in 3 steps:
Logical Address Space: The logical address consists of a segment number and an offset within that segment.
Segment Table Lookup: The segment number is used as an index into the segment table to find the base address of the segment.
Physical Address Formation: The base address is added to the offset to form the physical address in memory.
Advantages:
Provides logical separation of different parts of a program.
Facilitates protection and sharing of segments.
Simplifies management of growing data structures.
How does Git Work?
The diagram below shows the Git workflow.
Git is a distributed version control system.
Every developer maintains a local copy of the main repository and edits and commits to the local copy.
The commit is very fast because the operation doesn’t interact with the remote repository.
If the remote repository crashes, the files can be recovered from the local repositories.
Over to you: Which Git command do you use to resolve conflicting changes?
What tech stack is commonly used for microservices?
Below you will find a diagram showing the microservice tech stack, both for the development phase and for production.
Pre-Production
Define API - This establishes a contract between frontend and backend. We can use Postman or OpenAPI for this.
Development - Node.js or react is popular for frontend development, and java/python/go for backend development. Also, we need to change the configurations in the API gateway according to API definitions.
Continuous Integration - JUnit and Jenkins for automated testing. The code is packaged into a Docker image and deployed as microservices.
Production
NGinx is a common choice for load balancers. Cloudflare provides CDN (Content Delivery Network).
API Gateway - We can use spring boot for the gateway, and use Eureka/Zookeeper for service discovery.
The microservices are deployed on clouds. We have options among AWS, Microsoft Azure, or Google GCP.
Cache and Full-text Search - Redis is a common choice for caching key-value pairs. ElasticSearch is used for full-text search.
Communications - For services to talk to each other, we can use messaging infra Kafka or RPC.
Persistence - We can use MySQL or PostgreSQL for a relational database, and Amazon S3 for object store. We can also use Cassandra for the wide-column store if necessary.
Management & Monitoring - To manage so many microservices, the common Ops tools include Prometheus, Elastic Stack, and Kubernetes.
Over to you: Did I miss anything? Please comment on what you think is necessary to learn microservices.
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