1.
Introduction
2.
Lectures
❱
2.1.
Lecture 1: Introduction
2.2.
Lecture 3: GFS
2.3.
Lecture 6: Fault Tolerance: Raft (1)
2.4.
Lecture 7: Fault Tolerance: Raft (2)
2.5.
Lecture 8: Zookeeper
2.6.
Lecture 10: Cloud Replicated DB, Aurora
2.7.
Lecture 12: Distributed Transactions
2.8.
Lecture 13: Spanner
2.9.
Lecture 15: Big Data: Spark
2.10.
Lecture 16: Cache Consistency: Memcached at Facebook
3.
Extras
❱
3.1.
Extra 1: Scalability Harvard CS 75
3.2.
Extra 2: Chord Algorithm Berkeley CS 162
3.3.
Extra 3: Dynamo Amazon’s Highly Available Key-value Store
3.4.
Extra 4: CAP Theorem
3.5.
Extra 5: Serverless, Coordination-free Distributed Computing, and the CALM Theorem
3.6.
Extra 6: Stellar
3.7.
Extra 7: CockroachDB, Spanner, MongoDB
3.8.
Extra 8: Scalability! But at What COST?
3.9.
Extra 9: Ray: A Distributed Execution Framework for AI
3.10.
Extra 10: Cluster Management with Borg and Kubernetes
3.11.
Extra 11: Introduction to Apache Kafka
3.12.
Extra 12: Introduction to Apache Cassandra
3.13.
Extra 13: Anna KVS and Cloudburst
3.14.
Extra 14: Physalia Millions of Tiny Databases
3.15.
Extra 15: Facebook
3.16.
Extra 16: RocksDB
3.17.
Extra 17: Snowflake and FoundationDB
3.18.
Extra 18: Chaos Engineering
3.19.
Extra 19: Networking
3.20.
Extra 20: Materialize and Timely/Differential Dataflow
3.21.
Extra 21: DynamoDB
3.22.
Extra 22: Memcache and Redis
3.23.
Extra 23: YouTube Vietess
3.24.
Extra 24: Delta Lake and Bolt-On Consistency
4.
Miscellaneous
❱
4.1.
Miscellaneous 1: Exponential Backoff And Jitter
4.2.
Miscellaneous 2: Post Mortems
4.3.
Miscellaneous 3: Cloud Computing Course from Stanford
4.4.
Miscellaneous 4: Machine Learning Systems Design
4.5.
Miscellaneous 5: Learned Indexes
Light
Rust
Coal
Navy
Ayu
Distributed Systems Notes
Introduction
MIT 6.824: Distributed Systems 2020