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
System Design Trade-offs
Importance of Discussing Trade-offs
Strong vs Eventual Consistency
Latency vs Throughput
ACID vs BASE Properties in Databases
Read-Through vs Write-Through Cache
Batch Processing vs Stream Processing
Load Balancer vs. API Gateway
API Gateway vs Direct Service Exposure
Proxy vs. Reverse Proxy
API Gateway vs. Reverse Proxy
SQL vs. NoSQL
Primary-Replica vs Peer-to-Peer Replication
Data Compression vs Data Deduplication
Server-Side Caching vs Client-Side Caching
REST vs RPC
Polling vs. Long-Polling vs. WebSockets vs. Webhooks
CDN Usage vs Direct Server Serving
Serverless Architecture vs Traditional Server-based
Stateful vs Stateless Architecture
Hybrid Cloud Storage vs All-Cloud Storage
Token Bucket vs Leaky Bucket
Read Heavy vs Write Heavy System
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
2. Consistent Hashing
consistent hashing
data partitioning
virtual nodes
distributed systems
+3
Spread data across many machines, and adding or removing just one of them can force you to move almost everything. Consistent Hashing keeps that move small.
Background
Spreading data across a set of nodes is called data partitioning, and it raises two questions. First, given a piece of data, which node stores it? Second, when a node joins or leaves the cluster, which data has to move, and how do we keep that movement small?
A naive answer hashes the data key to a number, then picks a server by taking that number modulo the total number of servers. For example:
The scheme in the diagram above solves the first question: it finds a server to store or retrieve a given piece of data. It fails the second one. Add or remove a server, and we have to remap every key and move data to match the new server count. That is a lot of movement for one change.
Definition
Use Consistent Hashing to distribute data across nodes. It maps data to physical nodes so that only a small set of keys move when servers are added or removed.
Solution
Consistent Hashing stores the data a distributed system manages on a ring. Each node on the ring owns a range of data. Here is an example:
With Consistent Hashing, the ring is split into smaller, fixed ranges, and each node owns one of them. The start of a range is called a token, so each node is assigned exactly one token. Here is how we compute the range for each node:
Range start: Â Token value
Range end: Â Â Next token value - 1
The table below shows the tokens and data ranges for the four nodes in the diagram above:
<style type="text/css"> .tg {border-collapse:collapse;border-spacing:0;border-color:black;} .tg td{font-family:Arial, sans-serif;font-size:17px;padding:10px 5px;border-style:solid;border-width:1px;overflow:hidden;word-break:normal;border-color:black;color:black;background-color:#67AB9F;} .tg th{font-family:Arial, sans-serif;font-size:14px;font-weight:normal;padding:10px 5px;border-style:solid;border-width:1px;overflow:hidden;word-break:normal;border-color:black;color:#493F3a;background-color:#9DE0AD;} .tg .tg-rmb8{background-color:#C5D6C4;vertical-align:top} .tg .tg-1rmb8{background-color:#C5D6C4;vertical-align:top; font-weight:bold;} .tg .tg-1yw4l{vertical-align:top; font-weight:bold;} .tg .tg-yw4l{vertical-align:top;} </style> <table class="tg" style="border-style:solid;border-width:1px;border-color:black;width:430px" > <tr> <td class="tg-1yw4l">Server</td> <td class="tg-1yw4l">Token</td> <td class="tg-1yw4l">Range Start</td> <td class="tg-1yw4l">Range End</td> </tr> <tr> <td class="tg-1rmb8">Server 1</td> <td class="tg-rmb8">1</td> <td class="tg-rmb8">1</td> <td class="tg-rmb8">25</td> </tr> <tr> <td class="tg-1yw4l">Server 2</td> <td class="tg-yw4l">26</td> <td class="tg-yw4l">26</td> <td class="tg-yw4l">50</td> </tr> <tr> <td class="tg-1rmb8">Server 3</td> <td class="tg-rmb8">51</td> <td class="tg-rmb8">51</td> <td class="tg-rmb8">75</td> </tr> <tr> <td class="tg-1yw4l">Server 4</td> <td class="tg-yw4l">76</td> <td class="tg-yw4l">76</td> <td class="tg-yw4l">100</td> </tr> </table>Whenever the system reads or writes data, its first step is to run the key through the
. The result tells us which range the data falls in, and so which node stores it. Each node owns a fixed range, so the hash of the key is enough, on its own, to name the node.
Consistent Hashing works well when a single node joins or leaves the ring, because only the next node on the ring is affected. Remove a node, for example, and the next node inherits all of the keys the outgoing node held. What it does not do well is spread data and load evenly across nodes. Virtual nodes fix that.
Virtual nodes
Nodes join and leave distributed systems all the time. An existing node can die and need to be decommissioned. A new node can join to handle more load. Consistent Hashing handles both cases with virtual nodes, or Vnodes.
The basic version of Consistent Hashing, described above, assigns one token, one hash range, to each physical node. That is a fixed, static division: the ranges are computed once, based on however many nodes exist at the time. Adding or replacing a node then becomes expensive. We want to rebalance, spreading that node's data across every other node, and that moves a lot of data. A fixed, manual division of ranges brings a few more problems with it:
- Adding or removing nodes. Every change forces the tokens to be recomputed, which becomes real administrative overhead on a large cluster.
- Hotspots. Each node owns one large range, so when data is not spread evenly, some nodes can become .
- Node rebuilding. Each node's data is replicated, for fault tolerance, on a fixed number of other nodes. Rebuilding a failed node means pulling all of its data from just those replicas, which puts heavy load on them and can degrade the service.
Virtual nodes fix this by changing how tokens are handed out. Instead of one token per physical node, the hash range splits into many smaller ranges, and each physical node gets several of them. Each of these smaller ranges is considered a Vnode. A node no longer owns just one token; it owns many.
In practice, Vnodes are spread across the cluster at random. They are placed so that no two neighboring Vnodes are assigned to the same physical node or rack. Nodes also carry replicas of other nodes, for fault tolerance. Clusters often mix machines of different power, so some servers end up holding more Vnodes than others.
The figure below shows physical nodes A, B, C, D, and E, each holding a set of Vnodes from the ring. Every Vnode is replicated once.
Advantages of Vnodes
Vnodes give the following advantages:
- Rebalancing gets faster, because Vnodes split the hash range into many small subranges spread evenly across physical nodes. Adding or removing a node then moves less data. A new node picks up many Vnodes from existing nodes, so the cluster stays balanced. Rebuilding a failed node no longer depends on a fixed handful of replicas either. Many nodes each contribute a small part of the data instead.
- Vnodes make it easier to run a cluster of mixed machines. We can give a powerful server many subranges and a weaker one only a few.
- A node with many small ranges is far less likely to become a hotspot than a node with one big range.
Examples
Dynamo and Cassandra use Consistent Hashing to distribute their data across nodes.
Discussion
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