Distributed Computing Models

 This Distributed Systems Cheatsheet Took Me a Year to Build  I spent all of 2024 learning, failing, and finally understanding these concepts. If you're starting with distributed systems in 2025, let this be your guide:   1/ Core Concepts   + Scalability:      - Vertical Scaling: Adding resources to existing machines (e.g., CPU, RAM).      - Horizontal Scaling: Adding more machines (nodes).   + Reliability: Ensure the system continues functioning correctly despite failures.   + Availability: Maintain uptime by ensuring the system is operational even during failures.   + Consistency Models: Decide the trade-off between data consistency and latency (eventual, strong, or causal).   + CAP Theorem: Systems can only achieve two of the three: Consistency, Availability, and Partition Tolerance.  ---  2/ Communication   + Remote Procedure Calls (RPC): Call a function on a remote server as if it’s local.   + Message Queues: Asynchronous communication (e.g., RabbitMQ, Kafka).   + REST vs gRPC:      - REST: HTTP-based, suitable for CRUD APIs.      - gRPC: High-performance, protocol buffer-based, ideal for low-latency communication.   + Webhooks: Get notified when specific events occur (e.g., payment success).  ---  3/ Coordination & Consensus   + Consensus Algorithms: Achieve agreement across nodes (Paxos, Raft).   + Distributed Locks: Ensure only one process accesses a critical section (e.g., Zookeeper).   + Leader Election: Decide which node should act as the leader in distributed systems.   + Gossip Protocol: Decentralized communication for node state updates.  ---  4/ Architectures   + Client-Server: Traditional request-response systems.   + Peer-to-Peer (P2P): All nodes are equal, like torrents.   + Event-Driven: Trigger actions in response to events (e.g., Kafka).   + Microservices: Small, independent services that work together.   + Lambda Architecture: Hybrid approach for real-time and batch data processing.  ---  5/ Key Technologies   + Container Orchestration: Kubernetes, Docker Swarm.   + Service Discovery: Tools like Consul to locate services dynamically.   + API Gateways: Central point for routing API requests (e.g., Kong, NGINX).   + Distributed Tracing: Tools like Jaeger to monitor requests across services.   + Infrastructure as Code: Automate server setups using Terraform or Pulumi.  ---  6/ Data Management   + Distributed Databases: MongoDB, Cassandra, CockroachDB (scalable, high availability).   + Caching: Use Redis or Memcached to speed up frequently accessed data.   + Replication Strategies: Copy data for fault tolerance (e.g., sharding, partitioning).   + Consistency Models: Understand BASE (eventual consistency) vs. ACID (strict consistency).  ---  7/ Common Pitfalls   + The Network Isn’t Reliable: Always design for potential network failures.   + Latency is Never Zero: Acknowledge real-world delays.   + Topology Changes Happen: Nodes can go offline or change—build for this flexibility.  

Understanding distributed systems fundamentals can prevent a big portion of production disasters. Here's what you need to take into account 👇 If you are building AI systems and experiencing significant user or customer growth, the reliability and value of your system will go as far as the foundation it's built upon. It's never to late to go deep on this. 𝗦𝘁𝗮𝗿𝘁 𝗛𝗲𝗿𝗲 - 𝗧𝗵𝗲 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗧𝗿𝗶𝗻𝗶𝘁𝘆 🕐 "Time, Clocks, and the Ordering of Events in a Distributed System" 🌐 "Dynamo: Amazon's Highly Available Key-value Store" ⚖️ "Brewer's Conjecture and the Feasibility of Consistent, Available, Partition-Tolerant Web Services"  𝗧𝗵𝗲 𝗛𝘂𝗺𝗯𝗹𝗶𝗻𝗴 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗖𝗵𝗲𝗰𝗸𝘀 🚫 "Impossibility of Distributed Consensus with One Faulty Process" ⚔️ "The Byzantine Generals Problem" 📝 "A Note on Distributed Computing"  𝗖𝗼𝗻𝘀𝗲𝗻𝘀𝘂𝘀 & 𝗖𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 🗳️ "In Search of an Understandable Consensus Algorithm" 🛡️ "Practical Byzantine Fault Tolerance"  📸 "Distributed Snapshots: Determining Global States of Distributed Systems" 🔍 "Unreliable Failure Detectors for Reliable Distributed Systems" 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 & 𝗥𝗲𝗽𝗮𝗶𝗿 𝗠𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺𝘀 🦠 "Epidemic Algorithms for Replicated Database Maintenance" 🏊 "SWIM: Scalable Weakly-consistent Infection-style Process Group Membership Protocol" ⏳ "Eventually Consistent"  📊 "The Load, Capacity, and Availability of Quorum Systems" 𝗗𝗮𝘁𝗮 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝘀 & 𝗩𝗲𝗿𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 🌲 "A Digital Signature Based on a Conventional Encryption Function" 🔧 "Efficient Algorithms for Sorting and Synchronization" 🕒 "Virtual Time and Global States of Distributed Systems" 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 & 𝗕𝗿𝗼𝗮𝗱𝗰𝗮𝘀𝘁 📢 "Reliable Broadcast in Distributed Networks" 💬 "Atomic Broadcast: From Simple Message Diffusion to Byzantine Agreement" 🔄 "Flexible Update Propagation for Weakly Consistent Replication" 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 🌍 "Spanner: Google's Globally-Distributed Database" 🥨 "Consistent Hashing and Random Trees"  ⚖️ "CAP Twelve Years Later: How the 'Rules' Have Changed" Advanced Concepts & Tradeoffs 🏖️ "Harvest, Yield, and Scalable Tolerant Systems" 🏗️ "Building on Quicksand" 🚫 "Life beyond Distributed Transactions" 🏛️ "The Part-Time Parliament" Let me know if there are useful sources that are not in this list.

Here's another seminal concept in distributed systems: Virtual Synchrony. Virtual Synchrony (VS) is a foundational model in distributed systems that gives a group of participants the illusion of synchrony, even in asynchronous environments. Rather than enforcing strict physical coordination, VS ensures that all non-faulty members of a group see the same events (messages and membership changes) in a consistent order. Like Paxos or Viewstamped Replication (VR), Virtual Synchrony introduces the idea of a group of processes that must act in coordination. But the goals differ. Whereas Paxos and VR focus on ensuring strong consistency and state replication across view (or leader) changes, Virtual Synchrony focuses on providing communication and coordination tools for processes within a group — even if group membership changes dynamically. Maybe a good way to think about it: Virtual Synchrony is like a choreographer in a dance company, ensuring everyone follows the same choreography in the same sequence. If a dancer leaves and later rejoins, they simply pick up where the new routine is — even if they missed a few moves. In the cloud, VR/Paxos have seen strong adoption because they solve the consistency problem of pillar systems like databases (e.g., Spanner) and configuration stores (e.g., Etcd). Virtual Synchrony has many practical use cases in systems like robots coordination and deterministic real time systems. (For example VS is used by the French air traffic control system and the US Navy AEGIS warship.) What I really like with VS is how it builds system behavior from a small set of composable broadcast primitives: -> Atomic Broadcast (ABCAST): Enforces total order — all processes receive messages in the exact same sequence. -> Causal Broadcast (CBCAST): Preserves causality — messages are only ordered if causally related. -> Group Broadcast (GBCAST): Handles group membership changes atomically and consistently — ensuring all members install the same view at the same logical time. These are really fundamental distributed systems primitives.

I read 100+ system design guides, postmortems, and interview rubrics this month. Here’s what actually moves you from “𝐈 𝐤𝐢𝐧𝐝 𝐨𝐟 𝐠𝐞𝐭 𝐢𝐭” to “𝐈 𝐜𝐚𝐧 𝐝𝐞𝐬𝐢𝐠𝐧 𝐢𝐭.” 𝐖𝐡𝐚𝐭 𝐭𝐡𝐞 𝐛𝐞𝐬𝐭 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 𝐦𝐚𝐬𝐭𝐞𝐫 𝐟𝐢𝐫𝐬𝐭: 🔹Mental models → CAP, latency budgets, throughput math, fallacies of distributed systems. 🔹Data flows → read/write paths, fan-out, fan-in, cold vs warm paths, backpressure. 🔹State → cache tiers, TTL + invalidation, idempotency, exactly-once is a trap. 🔹Queues → protect downstreams, smooth bursts, enable async retries and dead letters. 🔹Storage trade-offs → SQL for consistency & joins; NoSQL for scale & partitions; hybrid for reality. 🔹Resilience → timeouts, hedged requests, circuit breakers, bulkheads. 🔹Observability → RED + USE, tracing budgets, SLOs with error budgets. 𝐘𝐨𝐮𝐫 𝟑𝟎-𝐝𝐚𝐲 𝐩𝐚𝐭𝐡 (𝐬𝐚𝐯𝐞 𝐭𝐡𝐢𝐬): 🔹Week 1: Foundations — CAP, consistency models, latency/throughput math, partitioning + replication. 🔹Week 2: Building blocks — LB → API → cache → queue → storage; design a URL shortener & feed. 🔹Week 3: Hard parts — hot keys, cache stampede, thundering herd, out-of-order events, exactly-once myth. 🔹Week 4: Production thinking — SLOs, backfills, migrations, incident drills, cost guardrails. 𝐅𝐫𝐞𝐞 𝐫𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐭𝐨 𝐚𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐞: 🔹Starter reads & walkthroughs: https://lnkd.in/g7zs2psR 🔹Build along: pick any public spec (e.g., URL shortener) and implement stubs with metrics & SLOs. 🔹Practice: explain your design on a single whiteboard snapshot—no hand-waving, every box earns its keep. Comment the one system-design topic that still trips you up (e.g., cache invalidation, feed ranking, idempotency). I’ll pick a few and post full breakdowns with diagrams. #SystemDesign #SoftwareEngineering #DistributedSystems #SRE #TechInterviews

There are mainly three communication patterns that form the backbone of modern distributed systems. Understanding these patterns is crucial for any software architect or engineer working on scalable applications. Let's dive into these patterns and explore how they shape our systems: 1. Synchronous Communication • Direct, real-time interaction between services • Client initiates request through API Gateway • Services communicate sequentially (A → B → C) • Uses HTTP Sync at each step • Pros: Simple, immediate responses • Cons: Can create bottlenecks, potential for cascading failures Ideal for: Operations requiring immediate, consistent responses 2. Asynchronous One-to-one • Utilizes message queues for communication • Client sends request to API Gateway • Services listen to and receive from queues • Allows for decoupled, non-blocking operations • Pros: Better load handling, fault tolerance • Cons: More complex, eventual consistency Ideal for: High-load scenarios, long-running processes 3. Pub/Sub (Publish/Subscribe) • Employs a central topic for message distribution • Client interacts with API Gateway • Multiple services can subscribe to a single topic • Enables one-to-many communication • Pros: Highly scalable, great for event-driven architectures • Cons: Can be complex to manage, potential message ordering issues Ideal for: Event broadcasting, loosely coupled systems Key Considerations When Choosing a Pattern: • Scalability requirements • Response time needs • System coupling preferences • Fault tolerance and reliability • Complexity of implementation and maintenance The art of system design often involves skillfully combining these patterns to create robust, efficient, and scalable distributed systems. Each pattern has its strengths, and the best architects know how to leverage them for optimal performance. Which pattern do you find most useful ? How do you decide which to use in different scenarios?

📖 The A-to-Z of Distributed Training Parallelism • http://parallelism.aman.ai - Distributed training parallelism is crucial for efficiently training large-scale deep learning models that require extensive computational resources. This approach leverages multiple GPUs or machines to perform computations in parallel, significantly reducing training time and enabling the handling of larger datasets and models. - There are four main strategies for parallelism in distributed training: model, data, pipeline, and tensor parallelism. Each has its own mechanisms, advantages, and challenges, and understanding them is essential for optimizing training performance in different scenarios. 🔹 Types of Parallelism (Data, Model, Pipeline, Tensor) 🔹 Choosing the Right Strategy: Data v/s Model v/s Pipeline v/s Tensor Parallelism 🔹 Data Parallelism - DataParallel (DP) (How DataParallel Works, Key Steps, Code) - Distributed Data Parallel (DDP) (Key Features of DDP, Steps to Use DDP, Code) 🔹 Model Parallelism - Layer-wise Parallelism - Tensor-wise Parallelism - Operator-wise Parallelism - Comparative Analysis: Types of Model Parallelism 🔹 Hybrid (Model and Data) Parallelism - Fully Sharded Data Parallel (FSDP) (Key Features of FSDP, Technical Details, Code + Explanation) - Benefits of FSDP 🔹 Tensor Parallelism (Concept, Mechanism, Types of Tensor Parallelism, Pros and Cons, Use Cases, Implementation in PyTorch, Conclusion) 🔹 Pipeline Parallelism (Concept, Mechanism, Types of Pipeline Parallelism, Pros and Cons, Use Cases, Implementation in PyTorch, Conclusion) 🔹 DeepSpeed (Key Features of DeepSpeed, Technical Details, Code + Explanation, Benefits of DeepSpeed) 🔹 DeepSpeed ZeRO (Key Features of DeepSpeed ZeRO, Technical Details, Benefits, Code + Explanation, Comparison of ZeRO Stages) #artificialintelligence #genai #deeplearning #neuralnetworks

𝗧𝗟;𝗗𝗥: Multi-agent AI mirrors distributed systems, facing ACR tradeoffs (Agency, Control, Reliability). Like the CAP theorem, you can't optimize all three. Anthropic's research shows the winning enterprise patterns. 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗔𝗜 = 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 Everyone thinks agentic AI is magic (and hence the hype), but agentic AI is actually just distributed system architecture—orchestrating specialized agents across compute resources, managing state, handling failures, and coordinating workflows. The parallel isn't machine learning; 𝗶𝘁'𝘀 𝗺𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴. Teams already handling distributed systems are perfectly positioned for multi-agent AI. 𝗔𝗖𝗥: 𝗧𝗵𝗲 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗖𝗔𝗣? Intercom's research (https://bit.ly/4jTNA8O) reveals a fundamental tradeoff mirroring distributed systems' CAP theorem (https://bit.ly/4lbZCeV):  • 𝗔𝗴𝗲𝗻𝗰𝘆 (autonomy): Independent decision-making  • 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 (predictability): Constraining agent behavior  • 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 (consistency): Consistent results across executions High-agency agents achieve only 20-30% reliability on complex tasks. But constrained, step-based agents hit 60%+ reliability—the enterprise sweet spot. 𝗔𝗻𝘁𝗵𝗿𝗼𝗽𝗶𝗰'𝘀 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝗳𝗼𝗿 𝗠𝘂𝗹𝘁𝗶 𝗔𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 Their Research feature (https://bit.ly/408qpjZ) demonstrates distributed systems principles:  • 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿-𝗪𝗼𝗿𝗸𝗲𝗿 𝗣𝗮𝘁𝘁𝗲𝗿𝗻: Lead agent coordinates, subagents execute—like API gateways + microservices  • 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Multiple agents with separate context windows—horizontal scaling of cognitive work  • 𝗦𝘁𝗮𝘁𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Memory persistence + checkpointing for failure recovery  • 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗘𝗰𝗼𝗻𝗼𝗺𝗶𝗰𝘀: 15x token usage requires careful allocation—distributed compute cost management 𝗢𝗽𝘁𝗶𝗺𝗮𝗹 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 Multi-agent systems excel at:  • Breadth-first research (90.2% improvement)  • Information synthesis across context windows  • Tool-heavy integrations with specialized agents 𝗕𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲: Enterprise AI success comes from 𝗮𝗽𝗽𝗹𝘆𝗶𝗻𝗴 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝘁𝗼 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝗰𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻. Microservices teams are already equipped to win. Get started with OSS AWS Strands Multi-agent (https://bit.ly/4e9hrsO) or Managed Amazon Web Services (AWS) Bedrock Agents (https://go.aws/43Z97qH). 

I've conducted DE system-design interviews for 10 years. I'll teach you the key concepts to know in 10 minutes: 1. Partitioning > Process/store data based on column values. - Partitioning parallelizes work (process & reads). - Storage: Partition datasets to enable distributed systems to read in parallel. - Processing: Partitioned data allows all machines in a cluster to process independently. Columns to partition by depend on processing needs or read patterns. 2. Data storage patterns > Storing data properly ensures efficient consumers. - Partition: see ^. - Clustering: Keeps similar values in specified columns together. Ideal for high-cardinality or continuous values. - Encoding: Metadata in table/columnar file formats helps engines read only necessary data. 3. Data modeling > Table design (grain & schema) determines warehouse success. - Dimension: Rows represent entities in your business (e.g., customers). - Fact: Rows represent events (e.g., orders). Kimball’s dimensional model is the most widely used approach. 4. Data architecture Understand system interactions: - Queue/logging systems handle constant data streams. - Distributed storage is cheap for raw/processed data (use partitioning if needed). - Data processing systems (e.g., Spark) read, process & write to distributed stores. - Data access layer (e.g., Looker on Snowflake) allows end-user access. 5. Data flow > Most batch systems clean & transform data in layers: - Raw: Input data stored as is. - Bronze: Apply proper column names & types. - Silver: Model data (e.g., Kimball). Create fact/dimension tables. - Gold: Create tables for end-users or use a semantic layer to generate queries on demand. 6. Lambda & Kappa architecture > Faster insights provide competitive advantages. - Lambda: Combines batch (slow) & stream (fast) pipelines for stable & trending data. - Kappa: Uses a single stream-processing flow (e.g., Apache Flink), simplifying maintenance. 7. Stream processing Key aspects: - State & time: Store in-memory data for wide transformations (e.g., joins, windows). - Joins: Use time as a criterion; rows from one stream can’t wait indefinitely for another. - Watermark: Defines when data is complete, useful for late-arriving events. 8. Transformation types > Reduce data movement for optimized processes. - Narrow: Operates on single rows (e.g., substring, lower). - Wide: Operates on multiple rows (e.g., joins, group by). - Data shuffle: Wide operations require data movement between nodes, slowing processing. 9. Common patterns of questions > Companies focus on industry-specific needs: - Ads: Clickstream processing, modeling & user access. - Finance: Batch reporting, data modeling & quality. - Cybersecurity: Real-time intrusion detection from logs. Check out > https://lnkd.in/eVq5bwUW ---- What else should we cover? Enjoy this? Repost and follow for actionable data content. #data #dataengineering #datajobs #dataanalytics

🚀 Distributed Data Computing Tools Comparison: Apache Spark, Ray, or Daft? 🖥️ When working with distributed computing and data processing frameworks, the right tool can make all the difference. Here's a quick comparison of Apache Spark (v3.4.x), Ray, and Daft to help you decide which fits your needs: 🔹 When to Stick with Spark (v3.4.x): - Need advanced SQL optimization via Catalyst. - Rely on fault tolerance with lineage tracking for large-scale batch jobs. - Use in-memory caching to improve performance for iterative computations. - Require streaming support for real-time data processing (Structured Streaming or DStreams). - Depend on strong integration with the Hadoop ecosystem (HDFS, YARN, Hive). - Need Graph processing with GraphX for distributed graph computation. 🔹 When to Choose Ray: - Handling flexible distributed computing for diverse workloads (e.g., machine learning, reinforcement learning, real-time tasks). - Need low-latency asynchronous tasks and fine-grained task scheduling. - Working with machine learning workflows using Ray Train or Ray Tune. - Want to leverage Modin on Ray for large Pandas-like DataFrame operations using Apache Arrow. 🔹 When to Consider Daft: - Processing both structured and unstructured data (e.g., images, logs) for modern workloads. - Prefer a cloud-native, Kubernetes-first solution for lightweight distributed data processing. - Need a lightweight framework that handles data processing but doesn’t require deep SQL optimizations like Spark. 💡 Bonus: Did you know? There’s ongoing experimentation with running Spark on Ray for hybrid workflows! This allows you to submit Spark jobs from within Ray, opening the door to combining Spark's structured processing power with Ray’s dynamic task scheduling. Definitely something to watch as the distributed computing space evolves! 🔥 Each tool shines in different use cases—so choose wisely based on your needs! 🌐💡 Have any questions about which tool fits your stack? Let’s connect and discuss further! #DataEngineering #DistributedComputing #ApacheSpark #RayFramework #Daft #Kubernetes #BigData #MachineLearning #CloudNative #RealTimeData #SQLOptimization #DataProcessing