Latency and Consistency Tradeoffs in Modern Distributed Databases
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Just over 10 years ago, Daniel Abadi proposed a new way of thinking about the engineering tradeoffs behind building scalable, distributed systems. According to Abadi, this new model, known as the PACELC theorem, comes closer to explaining the design of NoSQL systems than the well-known CAP theorem. Watch this webinar to hear Daniel’s reflections on PACELC ten years later, explore the impact of this evolution and learn how ScyllaDB Cloud takes a unique approach to support modern applications with extreme performance and low latency at scale.
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Latency and Consistency Tradeoffs in Modern Distributed Databases
- 1. Latency and Consistency Tradeoffs in Modern Distributed Databases Dr Daniel Abadi
- 2. Agenda ■ The PACELC Theorem ■ ScyllaDB Cloud in Context • Benchmarking PACELC • Benchmark Set Up & Results ■ About ScyllaDB Cloud ■ Questions 2
- 3. 3 Dr. Daniel Abadi Daniel Abadi is the Darnell-Kanal Professor of Computer Science at University of Maryland, College Park and was named an ACM Fellow in 2020. His primary area of research is database systems, and his publications have been cited more than 23,000 times. He is known for his contributions to distributed databases, column-store databases, deterministic databases, graph databases, and stream databases.
- 4. Every modern data store replicates data for performance and availability. DB 1 DB 2 network
- 5. What should we do if replicas can’t communicate with each other? DB 1 DB 2 network partition
- 6. CAP Theorem says tradeoff: Consistency vs. Availability DB 1 DB 2 network partition
- 7. CAP Theorem says tradeoff: Consistency vs. Availability DB 1 DB 2 network partition According to the CAP theorem every system would be both consistent and available at all times except during a network partition.
- 8. CAP theorem misses the point for global apps
- 9. Having siloed / localized version of applications no longer acceptable
- 10. No replication 🡪 bad latency and availability Write Read Write Read 10ms 400 ms
- 11. Synchronous replication hurts latency Write Read 400ms Write Read Replication / consensus 400ms 10ms 10ms We get consistency, at expense of latency (e.g. Spanner, CockroachDB)
- 12. Alternative: Give each item a primary location, consistent reads/writes directed there Write Read 10ms Write Read 10ms e.g. CockroachDB (with its geo-partitioning feature), SLOG, PNUTS Read Write Write Read 400 ms 400 ms
- 13. Asynchronous replication hurts consistency Write Read 10ms Write Read 10ms Asynchronously replicate writes We get latency, at expense of consistency
- 14. PACELC • In the case of a partition (P), does the system choose availability (A) or consistency (C)? • Else (E), does the system choose latency (L) or consistency (C)? • PA/EL • Systems that replicate asynchronously (e.g. Dynamo, Cassandra, COPS, Jessy, Walter, Scylla, …) • PC/EC • Partitioned systems and systems that replicate synchronously (e.g., Spanner, CockroachDB, Calvin, SLOG, …) • PA/EC • Systems that replicate synchronously except during a partition • PC/EL: No good reason to exist 14
- 15. CockroachDB: PC/EC Goal: Consistency + CockroachDB provides near strong-1SR consistency (Jepsen) + Support ACID transactions + Provides high availability the same way ScyllaDB does — with redundancy + Maintains consistency across replicas while serving concurrent operations + Hybrid transaction scheduler makes reads linearization cost-free + Written in Go--susceptible to garbage collection spikes Comparing Consistency and Latency 15 ScyllaDB: PA/EL Goal: Availability and low latency + Async computing model + Supports a million lambda functions (“continuations”) per second, per core + Offers an API for stronger consistency that leverages lightweight transactions (LWT).* + From an ACID perspective, does not provide isolation + Written in C++, no garbage collection *Scylla 5 will introduce Raft support, which will provide a stronger type of consistency that is sometimes referred to as “immediate consistency”.
- 17. Quantifying the Latency Trade-off 17 PC/EC PA/EL
- 19. The industry’s fastest, safest, and most scalable distributed database ScyllaDB Cloud 19 + Built in C++ for low-level control + Fully asynchronous runtime + Millions of OPS per node + p99 latency less than 1ms + AWS and Google Cloud: Wide range of instance types + Isolated, dedicated instances for performance and security + Fully managed, even in your own AWS account
- 21. United States 2445 Faber St, Suite #200 Palo Alto, CA USA 94303 Israel Maskit 4 Herzliya, Israel 4673304 www.scylladb.com @scylladb Questions and Answers