System Design Compare
Date: January 09, 2024 Tags: System Design
System Design Compare 1
| Criteria | Read-Heavy System | Write-Heavy System | Celebrated Call System |
|---|---|---|---|
| Data Access Pattern | Emphasizes frequent | Focuses on frequent | Highlights a specific |
| read operations | write operations | high-impact call | |
| Example Technologies | Content Delivery | Logging and Analytics | Payment Processing |
| Networks (CDNs) | Systems | Systems | |
| Optimizations | Caching, Indexing, | Sharding, Replication, | Caching, Load Balancing, |
| Load Balancing | Load Balancing | Rate Limiting | |
| Scalability Concerns | Horizontal Scaling | Vertical Scaling | Vertical Scaling |
| (Adding more servers) | (Upgrading hardware) | (Optimizing for the | |
| celebrated call) | |||
| Data Consistency | Eventual Consistency | Strong Consistency | May vary based on |
| specific requirements | |||
| Concurrency Handling | Optimistic Concurrency | Pessimistic | May require careful |
| Control, Locking | Locking, Transactions | handling based on | |
| the nature of the call | |||
| Examples of Systems | Social Media Feeds, | Transactional | Real-time Chat, |
| News Aggregators | Databases | Video Streaming | |
System Design Compare 2
| Criteria | Search-Intensive System | Messaging System | Geographically Distributed System | Event-Driven System |
|---|---|---|---|---|
| Data Access Pattern | Emphasizes search | Focuses on messaging and | Spreads data and workload across | Reacts to events and |
| operations | communication | multiple locations | triggers processing | |
| Example Technologies | Search Engines (e.g., | Message Brokers (e.g., | Content Delivery Networks (CDNs), | Event Sourcing, |
| Elasticsearch) | RabbitMQ, Kafka | Global Load Balancers | Apache Kafka, AWS Kinesis | |
| Optimizations | Indexing, Caching | Message Queue | Data Replication, Anycast Routing | Asynchronous Processing |
| Load Balancing | Load Balancing | Content Mirroring | ||
| Scalability Concerns | Horizontal Scaling | Horizontal Scaling | Horizontal Scaling | Horizontal Scaling |
| (Adding more servers) | (Adding more servers) | (Adding more servers) | (Adding more servers) | |
| Data Consistency | Eventually Consistent | Eventual Consistency | Consistency Models based on | Eventual Consistency |
| Specific Use Cases | ||||
| Concurrency Handling | Optimistic Concurrency | Concurrent Processing, | Multi-Region Locking, Conflict | Event Sourcing, |
| Control, Caching | Message Queues | Resolution Strategies | Distributed Transactions | |
| Examples of Systems | Search Engines (e.g., | Real-time Chat, | Multi-Datacenter Systems, | IoT Platforms, |
| Google Search) | Notifications Systems | Cloud-based Applications | Streaming Analytics | |
| Systems |
System Design Compare 3
| Criteria | Peer-to-Peer System | Data Warehousing System | Real-time Collaborative System | Content Distribution System | Recommendation System |
|---|---|---|---|---|---|
| Data Access Pattern | Decentralized data | Analytical queries | Simultaneous editing and updates | Efficient content delivery | User-specific suggestions |
| sharing, minimal | and reporting | from multiple users | and replication | based on preferences | |
| reliance on central | |||||
| servers | |||||
| Example Technologies | BitTorrent, Blockchain | Amazon Redshift, Google | Google Docs, Office 365 | Content Delivery Networks | Netflix, Spotify, Amazon |
| BigQuery, Snowflake | Recommendations | ||||
| Optimizations | Distributed hash | Query optimization, | Conflict resolution algorithms, | Caching, Load Balancing | Collaborative filtering, |
| table, Merkle trees | Data partitioning | Operational transforms | Peer-to-peer protocols | Machine learning models | |
| Scalability Concerns | Horizontal Scaling, | Horizontal Scaling | Scalability for concurrent | Content replication, | Scalability based on user |
| Fault Tolerance | edits | Load Balancing | base and data volume | ||
| Data Consistency | Eventual Consistency, | Consistency models | Operational transformations, | Consistent Hashing | Personalized experiences |
| Merkle-Dag Consistency | based on use case | Conflict resolution | based on the latest data | ||
| Concurrency Handling | Distributed Locking, | Parallel processing, | Operational transforms, CRDTs | Multi-level caching, | Collaborative filtering, |
| Consensus algorithms | Batch processing | (Conflict-Free Replicated Data) | Concurrent request handling | Real-time updates | |
| Examples of Systems | BitTorrent, Bitcoin | Amazon Redshift, Google | Google Docs, Figma, Slack | Content Delivery Networks | Netflix Recommendations, |
| BigQuery | Akamai, Cloudflare | Amazon Personalize | |||
System Design Compare 4
| Criteria | Internet of Things (IoT) System | Video Streaming System | Online Gaming System | Healthcare Information System | Fault-Tolerant System |
|---|---|---|---|---|---|
| Data Access Pattern | Continuous data streams from | On-demand video content, | Real-time player interactions | Patient records, medical data | Redundancy, Error Handling |
| IoT devices, Sensor data | live streaming events | and synchronization | monitoring, diagnostics | and Recovery | |
| Example Technologies | MQTT, CoAP, Azure IoT Hub | YouTube, Netflix, Hulu | Multiplayer platforms like | Electronic Health Record (EHR) | Replication, Load Balancing |
| Xbox Live, PlayStation | systems, Health Information | ||||
| Network | Exchanges (HIE), Health IoT | ||||
| Optimizations | Data compression, Edge | Adaptive Bitrate Streaming, | Latency optimization, | Data encryption, Access Control | Distributed consensus |
| computing, Stream processing | Content Delivery Networks, | Load balancing, | Mechanisms, Authentication | ||
| Caching, Load Balancing | Sharding | and Authorization | |||
| Scalability Concerns | Horizontal Scaling, | Horizontal Scaling | Dynamic scaling based on | Scalability for patient data, | Load Balancing, |
| Scalability for device | (Adding more servers for | concurrent players | High availability, Scalability | Replication | |
| communication and data storage | increased demand) | for medical data | |||
| Data Consistency | Eventual Consistency | Consistency based on | Consistency models for | Strong Consistency, | Consensus Algorithms |
| streaming protocols | real-time gameplay | Healthcare standards | |||
| Concurrency Handling | Optimistic Concurrency Control, | Concurrent streaming | Distributed game state | Versioning, Transactional | Rollback Strategies |
| Conflict Resolution for IoT | with chunked encoding | synchronization | Processing | ||
| Examples of Systems | Smart Home Automation, | Netflix, Twitch, YouTube | Fortnite, PUBG, World of | Electronic Health Record (EHR), | Google Cloud Spanner, |
| Industrial IoT applications, | Live, Hulu Live | Warcraft | Health Information Exchanges | Amazon DynamoDB | |
| Wearables, Environmental Sensors |
System Design Compare 5
| Criteria | Chat Application System | Social Networking System | Distributed File System | Data Analytics System | Location-Based Services System |
|---|---|---|---|---|---|
| Data Access Pattern | Real-time messaging and | User posts, comments, | File reads and writes | Large-scale data | User location tracking, |
| notification delivery | friend requests | and storage | processing, querying | geofencing, mapping | |
| Example Technologies | WhatsApp, Slack, | Facebook, Twitter, | Hadoop Distributed File | Apache Spark, HBase, | Google Maps API, |
| Telegram | Instagram, LinkedIn | System (HDFS), Google | Amazon S3, Elasticsearch | Foursquare | |
| File System (GFS) | |||||
| Optimizations | Message indexing, | Content caching, | Distributed data | Query optimization, | Caching, Load Balancing, |
| Compression, Push | Friend recommendation, | replication, | Parallel processing, | Geographical load distribution | |
| notifications | Sharding, Load Balancing | Fault tolerance | Data partitioning | ||
| Scalability Concerns | Horizontal Scaling, | Horizontal Scaling | Horizontal Scaling | Horizontal Scaling | Horizontal Scaling |
| Load Balancing | (Adding more servers) | (Adding more servers) | (Adding more servers) | (Adding more servers) | |
| Data Consistency | Eventual Consistency, | Consistency models | Consistency based on | Eventual Consistency | Consistency based on |
| Operational transforms | based on social network | replication and | and Distributed | distributed transactions | |
| use cases | consensus | Processing | |||
| Concurrency Handling | Optimistic Concurrency | Concurrent processing, | Distributed locking, | Distributed | Concurrency control, |
| Control, Conflict | Locking, Transactions | Versioning, Conflict | Transactions, Isolation | Distributed Transactions | |
| Resolution | Resolution | Resolution | |||
| Examples of Systems | WhatsApp, Slack, | Facebook, Twitter, | Hadoop Distributed File | Apache Spark, | Google Maps, Foursquare |
| Telegram | Instagram, LinkedIn | System, Google File | HBase | ||
| System |