System Design
Learn System Design
Introduction to System Design
How to Learn System Design?
Key Characteristics of Distributed Systems
Scalability
Availability
Latency and Performance
Concurrency and Coordination
Monitoring and Observability
Resilience and Error Handling
Fault Tolerance vs. High Availability
Flashcards Review
Chapter Assessment
Network Essentials
HTTP vs. HTTPS
TCP vs. UDP
HTTP: 1.0 vs. 1.1 vs 2.0 vs. 3.0
URL vs. URI vs. URN
What Happens When You Type a URL into the Browser
Flashcards Review
Chapter Assessment
Long-Polling vs. WebSockets vs. Server-Sent Events
Introduction to Real-Time Communication
What is Long-Polling?
What is WebSocket?
What are Server-Sent Events?
Difference Between Long-Polling, WebSockets, and Server-Sent Events
Flashcards Review
Chapter Assessment
Domain Name System (DNS)
Introduction to DNS
DNS Resolution Process
DNS Load Balancing and High Availability
Flashcards Review
Chapter Assessment
Proxies
What is a Proxy Server?
Uses of Proxies
VPN vs. Proxy Server
Flashcards Review
Chapter Assessment
Load Balancing
Introduction to Load Balancing
Load Balancing Algorithms
Uses of Load Balancing
Load Balancer Types
Stateless vs. Stateful Load Balancing
High Availability and Fault Tolerance
Scalability and Performance
Challenges of Load Balancers
Flashcards Review
Chapter Assessment
API Gateway
Introduction to API Gateway
Usage of API gateway
Advantages and disadvantages of using API gateway
Flashcards Review
Chapter Assessment
API Design
What Is an API?
What Are REST APIs?
Resources, Not Actions
HTTP Methods and Their Semantics
URL Design
Request and Response Shapes
Status Codes and Error Design
Pagination from the Consumer's View
Idempotency Keys
Versioning and Backward Compatibility
Concurrency and Conditional Requests
REST vs gRPC vs GraphQL
Flashcards Review
Chapter Assessment
Rate Limiting and Throttling
What Is Rate Limiting
Rate Limiting Algorithms
Distributed Rate Limiting
Rate Limiting in Practice
Flashcards Review
Chapter Assessment
Caching
Introduction to Caching
Why is Caching Important?
Types of Caching
Cache Replacement Policies
Cache Invalidation
Cache Read Strategies
Cache Coherence and Consistency Models
Caching Challenges
Cache Performance Metrics
Flashcards Review
Chapter Assessment
CDN
What is CDN?
Origin Server vs. Edge Server
CDN Architecture
Push CDN vs. Pull CDN
Flashcards Review
Chapter Assessment
Data Partitioning
Introduction to Data Partitioning
Partitioning Methods
Data Sharding Techniques
Benefits of Data Partitioning
Common Problems Associated with Data Partitioning
Flashcards Review
Chapter Assessment
Redundancy and Replication
What is Redundancy?
What is Replication?
Replication Methods
Data Backup vs. Disaster Recovery
Flashcards Review
Chapter Assessment
CAP & PACELC Theorems
Introduction to CAP Theorem
Components of CAP Theorem
Trade-offs in CAP Theorem
Examples of CAP Theorem in Practice
Beyond CAP Theorem
System Design Trade-offs in Interviews
Flashcards Review
Chapter Assessment
Databases (SQL vs. NoSQL)
Introduction to Databases
SQL Databases
NoSQL Databases
SQL vs. NoSQL
ACID vs BASE Properties
Real-World Examples and Case Studies
SQL Normalization and Denormalization
In-Memory Database vs. On-Disk Database
Data Replication vs. Data Mirroring
Database Federation
Flashcards Review
Chapter Assessment
Indexes
What are Indexes?
How a B-Tree Index Works
Types of Indexes
B-Tree vs. LSM Tree
Indexes in Distributed Systems
Flashcards Review
Chapter Assessment
Bloom Filters
Introduction to Bloom Filters
Benefits & Limitations of Bloom Filters
Variants and Extensions of Bloom Filters
Applications of Bloom Filters
Flashcards Review
Chapter Assessment
Quorum
Why Quorum?
What is Quorum?
Flashcards Review
Chapter Assessment
Leader and Follower
What is Leader and Follower Pattern?
Flashcards Review
Chapter Assessment
Heartbeat
What is Heartbeat?
Flashcards Review
Chapter Assessment
Checksum
What is Checksum?
Uses of Checksum
Flashcards Review
Chapter Assessment
Distributed Messaging System
Introduction to Messaging System
Introduction to Kafka
Messaging patterns
Popular Messaging Queue Systems
RabbitMQ vs. Kafka vs. ActiveMQ
Scalability and Performance
Flashcards Review
Chapter Assessment
Distributed File Systems
What is a Distributed File System?
Architecture of a Distributed File System
Key Components of a DFS
Flashcards Review
Chapter Assessment
Security
What is Security and Privacy?
What is Authentication?
What is Authorization?
Authentication vs. Authorization
OAuth vs. JWT for Authentication
What is Encryption?
What are DDoS Attacks?
Flashcards Review
Chapter Assessment
Misc Concepts
Batch Processing vs. Stream Processing
XML vs. JSON
Synchronous vs. Asynchronous Communication
Push vs. Pull Notification Systems
Microservices vs. Serverless Architecture
Message Queues vs. Service Bus
Stateful vs. Stateless Architecture
Event-Driven vs. Polling Architecture
Flashcards Review
Chapter Assessment
Quiz - System Design Fundamentals
Quiz
How to Approach a System Design Interview
System Design Interviews - A step by step guide
Functional vs. Non-functional Requirements
What are Back-of-the-Envelope Estimations?
Things to Avoid During System Design Interview
System Design Master Template
System Design Master Template
Quiz
Designing a URL Shortening Service like TinyURL
Designing a URL Shortening Service like TinyURL
Quiz - Designing URL Shortner
Designing Pastebin
Designing Pastebin
Quiz - Designing Pastebin
Designing Instagram
Designing Instagram
Quiz - Designing Instagram
Designing Dropbox
Designing Dropbox
Quiz - Designing Dropbox
Designing Facebook Messenger
Designing Facebook Messenger
Quiz - Designing Facebook Messenger
Designing Twitter
Designing Twitter
Quiz - Designing Twitter
Designing Youtube or Netflix
Designing Youtube or Netflix
Quiz - Designing Youtube
Designing Typeahead Suggestion
Designing Typeahead Suggestion
Quiz - Designing Typeahead Suggestion
Designing an API Rate Limiter
Designing an API Rate Limiter
Quiz - Designing an API Rate Limiter
Designing Twitter Search
Designing Twitter Search
Quiz - Designing Twitter Search
Designing a Web Crawler
Designing a Web Crawler
Quiz - Designing a Web Crawler
Designing Facebook’s Newsfeed
Designing Facebook’s Newsfeed
Quiz - Designing Facebook’s Newsfeed
Designing Yelp or Nearby Friends
Designing Yelp or Nearby Friends
Quiz - Designing Yelp or Nearby Friends
Designing Uber backend
Designing Uber backend
Quiz - Designing Uber backend
Designing Ticketmaster
Designing Ticketmaster
Quiz - Designing Ticketmaster
Dynamo: How to design a key value store?
Dynamo: Introduction
High-Level Architecture
Data Partitioning
Replication
Vector Clocks and Conflicting Data
The Life of Dynamo’s put() & get() Operations
Anti-entropy Through Merkle Trees
Gossip Protocol
Dynamo Characteristics and Criticism
Summary: Dynamo
Quiz: Dynamo
Mock Interview: Dynamo
Designing YouTube Likes Counter (medium)
YouTube Likes Counter
Quiz
Cassandra: How to Design a Wide-column NoSQL Database?
Cassandra: Introduction
High-level Architecture
Replication
Cassandra Consistency Levels
Gossiper
Anatomy of Cassandra's Write Operation
Anatomy of Cassandra's Read Operation
Compaction
Tombstones
Summary: Cassandra
Quiz: Cassandra
Mock Interview: Cassandra
Kafka: How to Design a Distributed Messaging System?
Messaging Systems: Introduction
Kafka: Introduction
High-level Architecture
Kafka: Deep Dive
Consumer Groups
Kafka Workflow
Role of ZooKeeper
Controller Broker
Kafka Delivery Semantics
Kafka Characteristics
Summary: Kafka
Quiz: Kafka
Mock Interview: Kafka
Chubby: How to Design a Distributed Locking Service?
Chubby: Introduction
High-level Architecture
Design Rationale
How Chubby Works
File, Directories, and Handles
Locks, Sequencers, and Lock-delays
Sessions and Events
Master Election and Chubby Events
Caching
Database
Scaling Chubby
Summary: Chubby
Quiz: Chubby
Mock Interview: Chubby
HDFS: How to Design File Storage System?
Hadoop Distributed File System: Introduction
High-level Architecture
Deep Dive
Anatomy of a Read Operation
Anatomy of a Write Operation
Data Integrity & Caching
Fault Tolerance
HDFS High Availability (HA)
HDFS Characteristics
Summary: HDFS
Quiz: HDFS
Mock Interview: HDFS
GFS: How to Design a Distributed File System Storage?
Google File System: Introduction
High-level Architecture
Single Master and Large Chunk Size
Metadata
Master Operations
Anatomy of a Read Operation
Anatomy of a Write Operation
Anatomy of an Append Operation
GFS Consistency Model and Snapshotting
Fault Tolerance, High Availability, and Data Integrity
Garbage Collection
Criticism on GFS
Summary: GFS
Quiz: GFS
Mock Interview: GFS
BigTable: How to Design a Wide Column Storage System?
BigTable: Introduction
BigTable Data Model
System APIs
Partitioning and High-level Architecture
SSTable
GFS and Chubby
Bigtable Components
Working with Tablets
The Life of BigTable's Read & Write Operations
Fault Tolerance and Compaction
BigTable Refinements
BigTable Characteristics
Summary: BigTable
Quiz: BigTable
Mock Interview: BigTable
Designing Reddit (medium)
Design Reddit
Quiz
Designing Notification Service (medium)
Designing a Notification System
Quiz
Design Google Calendar (medium)
Design Google calendar (Medium)
Quiz
Design a Recommendation System (medium)
Design a Recommendation System for Netflix
Quiz
Designing Gmail (medium)
Design Gmail
Quiz
Designing Google News (medium)
Design Google News, a Global News Aggregator System (Medium)
Quiz
Designing Unique ID Generator (medium)
Design Unique ID Generator (Easy)
Quiz
Designing Code Judging System (medium)
Design Code Judging System like LeetCode (Medium)
Quiz
Designing Payment System (hard)
Design Payment System
Quiz
Designing Flash Sale System (hard)
Design a Flash Sale for an E-commerce Site (Hard)
Quiz
Designing Reminder Alert System (hard)
Design a Reminder Alert System
Quiz
System Design Patterns
Introduction: System Design Patterns
1. Bloom Filters
2. Consistent Hashing
3. Quorum
4. Leader and Follower
5. Write-ahead Log
6. Segmented Log
7. High-Water Mark
8. Lease
9. Heartbeat
10. Gossip Protocol
11. Phi Accrual Failure Detection
12. Split Brain
13. Fencing
14. Checksum
15. Vector Clocks
16. CAP Theorem
17. PACELC Theorem
18. Hinted Handoff
19. Read Repair
20. Merkle Trees
Quiz
Batch Processing vs Stream Processing
batch processing
stream processing
event-driven architecture
A payroll system runs once a month. A fraud detector must decide before the card transaction completes. Both process data. The difference in when the answer is needed changes everything about how they are built.
Batch processing collects data over a period and processes it all at once, on a schedule. Stream processing handles each record as it arrives, and never stops.
The decision is not about which is more modern. It is about one question. How stale may the answer be? A stale answer is one built from old data. Batch answers are hours behind. Stream answers are seconds behind, and you pay for the difference.
Batch Processing
Data accumulates in a store, usually a data lake or a warehouse. A job then runs against the whole collection on a schedule: hourly, nightly, or monthly.
The input is bounded, which means it has a clear start and end. The job sees a complete set of records, and that gives it real advantages.
- It can see everything at once. It can sort the full dataset, join two very large tables, and make several passes over the same records.
- It can fail safely. If a nightly job crashes halfway, you fix the bug and run it again on the same input. The result is identical. That re-run property makes batch systems easy to trust.
- It is cheap and simple. The work runs in bursts, often on cheap spare capacity, and nothing runs between jobs.
Typical tools: Apache Spark, Hadoop MapReduce, and a scheduler such as Airflow.
Good fits: payroll, billing runs, nightly reports, warehouse loads, retraining a machine learning model, and any backfill, which means reprocessing history.
What it costs: freshness. The answer is only as fresh as the last run. If the job runs at 2 am, a question asked at 3 pm is answered with data thirteen hours old.
Stream Processing
Records are processed as they arrive, one at a time or in very small groups. The input is unbounded, which means it has no end. So the job runs forever, and the answer is always seconds behind.
The hard part is that you never hold the complete dataset. You work with an endless sequence, and you must decide how much of it to keep in memory. That decision creates three ideas that do not exist in batch.
Windows. The stream never ends, so a count needs a boundary. A window is that boundary, such as "purchases in the last five minutes". Windows come in three shapes.
- Fixed: each five-minute block, counted separately.
- Sliding: the last five minutes, recomputed every minute.
- Session: one user's activity, grouped until a gap appears.
Late events. A phone loses signal in a tunnel and uploads its events ten minutes later. Those events belong to a window you already closed and reported. You have three options, and each has a cost. Drop the events. Hold windows open longer, using a watermark, a rule that says how late a record may arrive before its window closes. Or close on time and send a correction later.
Delivery guarantees. A processing node dies in the middle of a record. Was that record handled? Most systems promise at-least-once delivery, so the record may be processed twice. Your logic must then be idempotent, meaning that doing the work twice has the same effect as once. Exactly-once processing exists in Flink and Kafka Streams, and it costs more and must be configured on purpose.
Typical tools: Apache Flink, Kafka Streams, and Spark Structured Streaming.
Good fits: fraud detection, live dashboards, alerting, and anything where a late answer is the same as no answer.
What it costs: more parts to operate, harder debugging, and a system that must stay up at all times. A failed batch job is re-run tomorrow. A failed stream job is an incident now.
The Trade-off
| Batch | Stream | |
|---|---|---|
| Input | Bounded, a finished set | Unbounded, never ends |
| Runs | On a schedule | At all times |
| Freshness | Hours behind | Seconds behind |
| Cost per record | Lower, work runs in bursts | Higher, always-on infrastructure |
| A failure is | A re-run | An incident |
| Reprocessing history | Natural, point the job at old data | Awkward, needs a replay or a second path |
| Complexity | Lower | Higher, windows and late events |
Two rows deserve a closer look. The first is cost per record. Batch is cheaper for the same volume, because streaming infrastructure runs whether data is flowing or not. If nobody needs the answer sooner, paying for streaming is paying for nothing.
The second is reprocessing. Business logic changes, and someone will ask you to recompute the last two years under the new rules. Batch does this naturally. A pure streaming system needs either a stored event log to replay, or a separate batch path added later.
Using Both
Many companies run both paths on the same data. The streaming path produces fast, approximate numbers for dashboards and alerts. A nightly batch path recomputes the same numbers exactly and replaces them. This two-path design is called a Lambda architecture, and its cost is clear: you maintain the same logic twice. The alternative, called a Kappa architecture, keeps only the stream. To reprocess, it replays a durably stored event log through a new version of the job. That works when the log, often Kafka with long retention, keeps history for as long as you need.
Choosing
Ask the one question again. How stale may this answer be before it stops being useful?
- A day is fine: batch. It is cheaper and simpler, and simpler systems break less.
- Seconds, or the answer is worthless: stream. Fraud checks, alerts, and live pricing genuinely cannot wait.
- Minutes: look at micro-batching. A micro-batch is a small batch job run every few minutes. It is often fresh enough, with far less infrastructure.
- History will be recomputed: keep a batch path, or a replayable log. Someone always asks for the recompute eventually.
💡 In the interview: do not choose streaming because it sounds more impressive. Interviewers notice when a candidate adds Kafka and Flink to a system whose users would not notice a nightly refresh. State the freshness the requirement implies, then pick. If you choose streaming, expect follow-up questions on windows and late events, because that is where the design work is. A concrete answer helps: "five-minute fixed windows, a two-minute watermark, and later events go to a correction topic."
Key takeaway: batch processes a bounded set on a schedule. It is cheap, simple, and safe to re-run, and its answers are hours old. Stream processes an unbounded flow at all times. Its answers are seconds old, and it brings real complexity: windows, late events, and delivery guarantees. Decide by asking how stale the answer may be, and say the number. Expect large systems to run both, with a fast approximate stream path and an exact batch path behind it.
Discussion
On This Page