EP164: JWT Simply Explained

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This week’s system design refresher:

• JWT Simply Explained

• The 5 Pillars of API Design

• How Computer Memory Works?

• Top Kubernetes Scaling Strategies You Must Know

• Hiring Now: Top AI Startups and AI Roles

• SPONSOR US

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JWT Simply Explained

JWT or JSON Web Tokens is an open standard for securely transmitting information between two parties. They are widely used for authentication and authorization.

A JWT consists of three main components:

1. Header
   Every JWT carries a header specifying the algorithms for signing the JWT. It’s written in JSON format.

2. Payload
   The payload consists of the claims and the user data. There are different types of claims such as registered, public, and private claims.

3. Signature
   The signature is what makes the JWT secure. It is created by taking the encoded header, encoded payload, secret key, and the algorithm and signing it.

JWTs can be signed in two different ways:

1. Symmetric Signatures
   It uses a single secret key for both signing the token and verifying it. The same key must be shared between the server that signs the JWT and the system that verifies it.

2. Asymmetric Signatures
   In this case, a private key is used to sign the token, and a public key to verify it. The private key is kept secure on the server, while the public key can be distributed to anyone who needs to verify the token.

Over to you: Do you use JWTs for authentication?

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The 5 Pillars of API Design

APIs are the backbone of modern systems. But it is also important to design them in the right way.

Here are a few things that a developer should consider while designing APIs

1. The Interface
   API Design is concerned with defining the inputs and outputs of an API. For example, defining how the CRUD operations may be exposed to the user or the client.

2. API Paradigms
   APIs can be built following different paradigms, each with its own set of protocols and standards. Some options are REST, GraphQL, and gRPC.

3. Relationships in API
   APIs often need to establish relationships between the various entities. For example, a user might have multiple orders related to their account. The API endpoints should reflect these relationships for a better client experience.

4. Versioning
   When modifying API endpoints, proper versioning and supporting backward compatibility are important.

5. Rate Limiting
   Rate limiting is used to control the number of requests a user can make to an API within a certain timeframe. This is crucial for maintaining the reliability and availability of the API.

Over to you: Which other API Design principle will you add to the list?

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How Computer Memory Works?

Here’s a simple breakdown that shows how data moves through a system from input to processing to storage.

1. Data enters through input sources like keyboard, mouse, camera, or remote systems.

2. Permanent storage holds your system files, apps, and media. This includes hard drives, USB drives, ROM/BIOS, and network-based storage.

3. RAM is the workspace of your computer. It includes physical memory and virtual memory, which temporarily store data and programs while you’re using them.

4. Cache memory sits closer to the CPU and is split into Level 1 and Level 2. It helps speed up access to frequently used data.

5. CPU registers are the fastest and smallest memory units. They’re used directly by the processor to execute instructions almost instantly.

The higher you go in the memory pyramid, the faster and smaller the storage.

Over to you: What else will you add to improve the understanding of a computer memory’s working?

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Top Kubernetes Scaling Strategies You Must Know

1. Horizontal Pod Autoscaling or HPA
   Horizontal Pod Autoscaler automatically scales the number of Pods in a Deployment, ReplicaSet, or StatefulSet based on observed CPU utilization, memory usage, or custom metrics.

2. Vertical Pod Autoscaling or VPA
   Based on application requirements, VPA adjusts the resources allocated to individual pods, such as CPU and memory. This approach dynamically changes pod resource settings based on workload metrics.

3. Cluster Auto Scaling
   The Cluster Autoscaler automatically adjusts the number of nodes in a Kubernetes cluster. It interacts with the cloud provider to add or remove nodes based on requirements. This is important to maintain a balanced cluster.

4. Predictive Auto Scaling
   Predictive Autoscaling uses machine learning to forecast future resource requirements. It helps Kubernetes adjust resources by anticipating workload demands.

Over to you: Which other Kubernetes Scaling Strategy will you add to the list?

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Hiring Now: Tech Roles

Top ML Roles Opened in the Last 12 Hours

• Senior Machine Learning Engineer (Modeling) - Underwriting & Credit at Cash App

• Senior Principal Machine Learning Engineer at Roche

• Principal Machine Learning Engineer – AI Core Infrastructure at General Motors

• Senior Machine Learning Engineer at MD Anderson Cancer Center

• Technical Lead, Machine Learning (AI Defense at Cisco

• Sr Principal Machine Learning Engineer (GenAI/LLM) at Palo Alto Networks

• Senior Machine Learning Engineer at Adobe

• AI/Machine Learning Analyst Lead at Peraton

High Impact Roles at High Growth AI Startups this week

• Software Engineer - Data Infrastructure at Luma AI

• Staff Software Engineer, Compute Services at CoreWeave

• Senior Frontend Engineer at Typeface

• Staff/Principal Software Engineer, Web Applications at Fireworks AI

• Senior Software Engineer, iOS at Otter.ai

• Software Engineer - Security at ClickHouse

• Senior/Staff Software Engineer, Runtime at Plus

• Senior Full Stack Engineer at Invisible AI

High Salary General SWE Roles this week

• Principal Platform Software Engineer - OpenBMC Platform Architect at NVIDIA

• Director of Engineering and Site Lead at Harvey

• Managing Director, Software Engineering at West Monroe

• Tech Lead Backend Software Engineer at TikTok

• Senior Manager, AI ML Applied Field Engineering at Snowflake

• Sr. Sales Engineer at Tenstorrent

• Software Engineering PMTS at Salesforce

• Software Engineer, Model Context Protocol at Anthropic

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