MIT 6.824 Distributed Systems: Introduction & MapReduce
1:19:35MIT 6.824 Lecture 2: RPC, Concurrency & Threads in Go
1:20:22MIT 6.824 Lecture 3: The Google File System (GFS)
1:22:22MIT 6.824 Lecture 4: Primary-Backup Replication & VMware FT
1:19:20MIT 6.824 Lecture 5: Go Concurrency Patterns, Condition Variables & Raft Prep
1:22:28MIT 6.824 Lecture 6: Consensus & Fault Tolerance — Raft (Part 1)
1:20:00MIT 6.824 Lecture 7: Raft Log Compaction, Persistence & Linearizability (Part 2)
1:17:36MIT 6.824 Lecture 8: ZooKeeper — Wait-Free Coordination & Read Scaling
1:20:31Lecture 9: More Replication, CRAQ (6.824)
1:18:34Lecture 10: Cloud Replicated DB, Aurora (6.824)
1:19:38Lecture 11: Cache Consistency: Frangipani (6.824)
1:20:03Lecture 12: Distributed Transactions (6.824)
1:17:24Lecture 13: Spanner (6.824)
1:19:07Lecture 14: Optimistic Concurrency Control (6.824)
1:22:37Lecture 15: Big Data: Spark (6.824)
1:08:13Lecture 16: Cache Consistency: Memcached at Facebook (6.824)
1:18:01Lecture 17: COPS, Causal Consistency (6.824)
1:23:29Lecture 18: Fork Consistency, Certificate Transparency (6.824)
1:13:40Lecture 19: Bitcoin (6.824)
56:54Lecture 20: Blockstack (6.824)
1:19:01Lecture 16: Cache Consistency: Memcached at Facebook (6.824)
This lecture focuses on practical cache consistency, examining how Facebook uses Memcached to achieve high performance and low latency while maintaining data coherence across its vast infrastructure.
Lecture 16: Cache Consistency: Memcached at Facebook
MIT 6.824: Distributed Systems (Spring 2020)
Lecture 15: Big Data: Spark (6.824)
Next · Lecture 17Lecture 17: COPS, Causal Consistency (6.824)