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 15: Big Data: Spark (6.824)
The session investigates Apache Spark as a prominent big data processing framework, explaining its RDD abstraction, fault tolerance mechanisms, and in-memory computation model.
Lecture 15: Big Data: Spark
MIT 6.824: Distributed Systems (Spring 2020)
Lecture 14: Optimistic Concurrency Control (6.824)
Next · Lecture 16Lecture 16: Cache Consistency: Memcached at Facebook (6.824)