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![Test Model
• 50 actors
• 5 Ads with inventory between 1000, and 1200
• Actors randomly get [1,3] times to choose per
round
• Rounds continue until entire inventory is exhausted](https://image.slidesharecdn.com/databasesandstuff-150307115800-conversion-gate01/85/Why-Distributed-Databases-148-320.jpg)
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Why Distributed Databases?
The document discusses databases and distributed systems. It provides an overview of databases, their uses, and how they are built to handle large scale and failures. It describes concepts like transactions, consistency models, and how databases are designed for horizontal and vertical scalability. Key-value store systems like Dynamo and Riak that sacrifice consistency for availability and partition tolerance are examined. The document also covers techniques like CRDTs, vector clocks, and multi-partition transactions that aim to provide both consistency and availability in distributed systems.
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Why Distributed Databases?
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- 2. What is adatabase? A database is an organized collection of data
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- 5. Internet Traffic vs.Penetration 0 25 50 75 100 0 10000 20000 30000 40000 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 IP Traffic (PB/mo) Global Penetration (%)
- 6. Number of InternetUsers in 2012
- 7. Average Distance toEvery Human
- 8. Extrapolating We have notyet Peak “Web” and we wont see it for some time
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- 13. Scale Out withCorrectness
- 14. What is aTransaction? A Unit of Work
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- 18. ACID = Atomicity Atransaction executes or it does not
- 19. ACID = Consistency Correctness;Require the database to follow set of invariants
- 20. ACID = Isolation Preventinter-actor visibility during concurrent operations
- 21. ACID = Durability Onceyou write, it will survive
- 22. Lifecycle of aTransaction
- 23. Vertically Scalability Moore’s Lawcan take us places
- 24. Biggest AWS Database •vCPUs: 32 • Memory: 244 • Storage: 3TB • IOPs: 30,000 IOPs • Networking: 10 Gigabit • Resiliency: Multi-AZ • SLA: 99.95% • Backend: Postgresql
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- 29. Do we havea natural sharding key?
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- 31. Two-phase commit? Three-phase commit? Paxos? EnhancedThree-phase commit? Wat? Egalitarian Paxos?
- 32. Do we reallywant to run NxM databases?
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- 39. Scaling SSI databases isa hard problem
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- 43. No latency winfor mutable data
- 44. Must sacrifice recency forlatency win
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- 48. “Average partition durationranged from 6 minutes for software-related failures to more than 8.2 hours for hardware-related failures (median 2.7 and 32 minutes; 95th percentile of 19.9 minutes and 3.7 days, respectively).” -The Network is Reliable WANs Fail
- 49. Is there anotherway?
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- 52. Incremental Scalability Must beable to add nodes for greater reliability, or throughput
- 53. High Availability Must beable to seamlessly handle failures, and always respond to operations
- 54. Efficiency Meet stringent latencyrequirements
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- 56. “Experience at Amazonhas shown that data stores that provide ACID guarantees tend to have poor availability.” Dynamo: Amazon’s Highly Available Key-value Store
- 57. The Ring A clustercomposed of set of virtual nodes (vnodes)
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- 81. Fault Tolerance • ReadRepair • Active Anti-Entropy • Hinted Handoff
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- 84. “A shared-data systemcan have at most two of the three following properties: Consistency, Availability, and tolerance to network Partitions.” -Dr. Eric Brewer
- 85. On Consistency • ACIDConsistency: Any transaction, or operation will bring the database from one valid state to another • CAP Consistency: All nodes see the same data at the same time (synchrony)
- 86. On Partition Tolerance •The network will be allowed to lose arbitrarily many messages sent from one node to another. • Databases systems, in order to be useful must have communication over the network • Clients count
- 87. There is nosuch thing as a 100% reliable network: Can’t choose CA http://codahale.com/you-cant-sacrifice-partition-tolerance
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- 91. “This is aspecific form of weak consistency; the storage system guarantees that if no new updates are made to the object, eventually all accesses will return the last updated value.” Definition of “Eventual Consistency” from “Eventually Consistency Revisited” - Werner Vogels
- 93. Tunable CAP Controls •R (Read Acks) tunable: Default Quorum • W (Write Acks) tunable: Default Quorum • PR (Primary Read Acks) tunable: Default 0 • PW (Primary Write Acks) tunable: Default 0 • N (replicas) tunable: Default 3
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- 95. How do youeven use this?
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- 97. Vector Clocks • Extensionof Lamport Clocks • Used to detect cause and effect in distributed systems • Can determine concurrency of events, and causality violations
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- 99. • CRDTs: • ConvergentReplicated Data Types • Commutative Replication Data Types • Enables data structures to be always writeable on both sides of a partition, and replay after healing a partition • Enable distributed computation across monotonic functions • Two Types: • CvRDTs • CmRDTs CRDTs
- 100. CvRDTs • State /value based CRDTs • Minimal state • Don’t require active garbage collection
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- 102. CmRDTs • Op /method based CRDTs • Size grows monotonically • Uses version vectors to determine order of operations
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- 104. CRDTs in theWild • Sets • Observe-remove set • Grow-only sets • Counters • Grow-only counters • PN-Counters • Flags • Maps
- 105. Data structures thatare CRDTs • Probabilistic, convergent data structures • Hyper log log • Bloom filter • Co-recursive folding functions • Maximum-counter • Running Average • Operational Transform
- 106. CRDTs • Incredibly powerfulprimitive • Not only useful for in-database manipulation but client-database interaction • You can compose them, and build your own • Garbage collection is tricky
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- 114. Have your availability andconsistency too RAMP
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- 117. Write Anywhere Beat thespeed of the light
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- 127. Build for WANlocality
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- 129. Invariant Operation AP/ CP Specify unique ID Any CP Generate unique ID Any AP > INCREMENT AP > DECREMENT CP < INCREMENT CP < DECREMENT AP Secondary Index Any AP Materialized View Any AP AUTO_INCREMENT INSERT CP Linearizability CAS CP Operations Requiring Weak Consistency vs. Strong Consistency
- 130. BASE not ACID •BasicallyAvailable: There will be a response per request (failure, or success) •Soft State: Any two reads against the system may yield different data (when measured against time) •Eventually Consistent: The system will eventually become consistent when all failures have healed, and time goes to infinity
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- 132. AWS Deployment • 6x i2.4xlarge • 732GB of RAM • 19TB of storage • 960,000 IOPs • 96 vCPUs • 3 x Replication • 10 Gigabit networking • 99.9999999997% availability
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- 134. Real World UseCase
- 135. Ad Network • Selltargeted ads with minimum latency • Two datasets: • Ads • Users
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- 139. Choose Random Ad Basedon Weight of Outstanding Impressions
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- 141. Batch System Generated targetedads in offline process
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- 146. Choose Ad Based uponweighted random
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- 148. Test Model • 50actors • 5 Ads with inventory between 1000, and 1200 • Actors randomly get [1,3] times to choose per round • Rounds continue until entire inventory is exhausted
- 149. Test Model OutstandingImpressions -300 0 300 600 900 1200 Round Number 14 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 Ad 1 Ad 2 Ad 3 Ad 4 Ad 5
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- 151. Garbage Collection Utilizes secondaryindexes in batch process to delete exhausted ads from user records
- 152. Ad Serving • Requiresbatch generation of targets • Requires external GC • Allows for multidatacenter operation
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