System Design
Learn System Design
Introduction to System Design
How to Learn System Design?
Functional vs. Non-functional Requirements
What are Back-of-the-Envelope Estimations?
Things to Avoid During System Design Interview
System Design Basics
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
API Gateway
Introduction to API Gateway
Usage of API gateway
Advantages and disadvantages of using API gateway
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
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
Domain Name System (DNS)
Introduction to DNS
DNS Resolution Process
DNS Load Balancing and High Availability
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
CDN
What is CDN?
Origin Server vs. Edge Server
CDN Architecture
Push CDN vs. Pull CDN
Data Partitioning
Introduction to Data Partitioning
Partitioning Methods
Data Sharding Techniques
Benefits of Data Partitioning
Common Problems Associated with Data Partitioning
Proxies
What is a Proxy Server?
Uses of Proxies
VPN vs. Proxy Server
Redundancy and Replication
What is Redundancy?
What is Replication?
Replication Methods
Data Backup vs. Disaster Recovery
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
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
Indexes
What are Indexes?
Types of Indexes
Bloom Filters
Introduction to Bloom Filters
Benefits & Limitations of Bloom Filters
Variants and Extensions of Bloom Filters
Applications of Bloom Filters
Long-Polling vs. WebSockets vs. Server-Sent Events
Difference Between Long-Polling, WebSockets, and Server-Sent Events
Quorum
Why Quorum?
What is Quorum?
Heartbeat
What is Heartbeat?
Checksum
What is Checksum?
Uses of Checksum
Leader and Follower
What is Leader and Follower Pattern?
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?
Distributed Messaging System
Introduction to Messaging System
Introduction to Kafka
Messaging patterns
Popular Messaging Queue Systems
RabbitMQ vs. Kafka vs. ActiveMQ
Scalability and Performance
Distributed File Systems
What is a Distributed File System?
Architecture of a Distributed File System
Key Components of a DFS
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
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
System Design Master Template
System Design Interviews - A step by step guide
System Design Master Template
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
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
Data Partitioning
data partitioning
consistent hashing
virtual nodes
dynamo
+3
What is data partitioning?
The act of distributing data across a set of nodes is called data partitioning. There are two challenges when we try to distribute data:
- How do we know on which node a particular piece of data will be stored?
- When we add or remove nodes, how do we know what data will be moved from existing nodes to the new nodes? Furthermore, how can we minimize data movement when nodes join or leave?
A naive approach will be to use a suitable hash function that maps the data key to a number. Then, find the server by applying modulo on this number and the total number of servers. For example:
The scheme described in the above diagram solves the problem of finding a server for storing/retrieving the data. But when we add or remove a server, we have to remap all the keys and move the data based on the new server count, which will be a complete mess!
Dynamo uses consistent hashing to solve these problems. The consistent hashing algorithm helps Dynamo map rows to physical nodes and also ensures that only a small set of keys move when servers are added or removed.
Consistent hashing: Dynamo's data distribution
Consistent hashing represents the data managed by a cluster as a ring. Each node in the ring is assigned a range of data. Dynamo uses the consistent hashing algorithm to determine what row is stored to what node. Here is an example of the consistent hashing ring:
With consistent hashing, the ring is divided into smaller predefined ranges. Each node is assigned one of these ranges. In Dynamo's terminology, the start of the range is called a token. This means that each node will be assigned one token. The range assigned to each node is computed as follows:
Range start: Token value
Range end: Next token value - 1
Here are the tokens and data ranges of the four nodes described in the above diagram:
Whenever Dynamo is serving a put() or a get() request, the first step it performs is to apply the
The consistent hashing scheme described above works great when a node is added or removed from the ring; as only the next node is affected in these scenarios. For example, when a node is removed, the next node becomes responsible for all of the keys stored on the outgoing node. However, this scheme can result in non-uniform data and load distribution. Dynamo solves these issues with the help of Virtual nodes.
Virtual nodes
Adding and removing nodes in any distributed system is quite common. Existing nodes can die and may need to be decommissioned. Similarly, new nodes may be added to an existing cluster to meet growing demands. Dynamo efficiently handles these scenarios through the use of virtual nodes (or Vnodes).
As we saw above, the basic Consistent Hashing algorithm assigns a single token (or a consecutive hash range) to each physical node. This was a static division of ranges that requires calculating tokens based on a given number of nodes. This scheme made adding or replacing a node an expensive operation, as, in this case, we would like to rebalance and distribute the data to all other nodes, resulting in moving a lot of data. Here are a few potential issues associated with a manual and fixed division of the ranges:
- Adding or removing nodes: Adding or removing nodes will result in recomputing the tokens causing a significant administrative overhead for a large cluster.
- Hotspots: Since each node is assigned one large range, if the data is not evenly distributed, some nodes can become .
- Node rebuilding: Since each node's data is replicated on a fixed number of nodes (discussed later), when we need to rebuild a node, only its replica nodes can provide the data. This puts a lot of pressure on the replica nodes and can lead to service degradation.
To handle these issues, Dynamo introduced a new scheme for distributing the tokens to physical nodes. Instead of assigning a single token to a node, the hash range is divided into multiple smaller ranges, and each physical node is assigned multiple of these smaller ranges. Each of these subranges is called a Vnode. With Vnodes, instead of a node being responsible for just one token, it is responsible for many tokens (or subranges).
Practically, Vnodes are randomly distributed across the cluster and are generally non-contiguous so that no two neighboring Vnodes are assigned to the same physical node. Furthermore, nodes do carry replicas of other nodes for fault-tolerance. Also, since there can be heterogeneous machines in the clusters, some servers might hold more Vnodes than others. The figure below shows how physical nodes A, B, C, D, & E are using Vnodes of the Consistent Hash ring. Each physical node is assigned a set of Vnodes and each Vnode is replicated once.
Advantages of Vnodes
Vnodes give the following advantages:
- Vnodes help spread the load more evenly across the physical nodes on the cluster by dividing the hash ranges into smaller subranges. This speeds up the rebalancing process after adding or removing nodes. When a new node is added, it receives many Vnodes from the existing nodes to maintain a balanced cluster. Similarly, when a node needs to be rebuilt, instead of getting data from a fixed number of replicas, many nodes participate in the rebuild process.
- Vnodes make it easier to maintain a cluster containing heterogeneous machines. This means, with Vnodes, we can assign a high number of ranges to a powerful server and a lower number of ranges to a less powerful server.
- Since Vnodes help assign smaller ranges to each physical node, the probability of hotspots is much less than the basic Consistent Hashing scheme which uses one big range per node.
Discussion
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