Distributed Locking Explained Simply скачать в хорошем качестве

Distributed Locking Explained Simply

#distributedsystems #systemdesign #concurrency #distributedlocking #softwarearchitecture #databaseinternals #backendengineering What is distributed locking? At a high level, distributed locking is a way for many machines or processes to agree that only one of them can do a specific thing at a time, even though they’re not on the same computer. Now let’s map that to AI. Distributed locking in AI terms 🧠 Imagine you have multiple AI agents or workers running in parallel (very common in modern AI systems). They might be: Training models Updating shared weights Writing to the same memory, database, or vector store Calling the same expensive tool or API Modifying a shared environment (like a simulator or game world) A distributed lock is the mechanism that says: “Hey, only ONE of you can touch this shared thing right now. Everyone else, wait your turn.” Simple AI analogy 🤖 Think of a group of AI agents as students working on a shared whiteboard. Without a lock → everyone writes at once → chaos With a distributed lock → One agent holds the marker Others wait When finished, the marker is passed on The marker is the lock. Concrete AI examples 1. Model training Multiple training workers might try to: Save checkpoints Update a global model state 🔒 Lock ensures: Only one worker writes the checkpoint at a time No corrupted model files 2. Reinforcement learning (multi-agent) Agents interact with a shared environment. 🔒 Lock ensures: Environment state updates happen in a consistent order Rewards and transitions don’t get mixed up 3. LLM tool use Multiple LLM instances try to: Update the same memory store Write embeddings to the same index 🔒 Lock ensures: No duplicate or partial writes Memory stays consistent 4. AI pipelines & orchestration In systems like: Distributed inference Batch data preprocessing Hyperparameter search 🔒 Lock ensures: Only one job claims a resource (GPU, dataset shard, cache entry) How it usually works (conceptually) An AI worker requests a lock A lock service (like Redis, ZooKeeper, etc.) decides: If free → grant it If taken → make the worker wait or retry Worker does the critical task Worker releases the lock From an AI perspective, this is about coordination and consistency, not intelligence. Why AI systems need distributed locks Because AI systems are: Highly parallel Distributed across machines Sharing state, memory, or resources Without locks, you get: Race conditions Corrupted models Inconsistent memory Hard-to-debug failures (the worst kind) One-sentence summary Distributed locking in AI is how multiple AI agents or workers politely take turns when accessing shared knowledge, memory, or resources in a distributed system.