5. Agenda
● Consistency Models
● Transactions
● Why to distribute?
● Decentralized Architecture
● Design Techniques & Tradeoffs
● Few Real World Examples
● Conclusions
6. Consistency Model
• Restricts possible values that a read operation on
an item can return
– Some are very restrictive, others are less
– The less restrictive ones are easier to implement
• The most natural semantic for storage system is
"read should return the last written value”
– In case of concurrent accesses and multiple replicas, it's
not easy to identify what "last write" means
7. Strict Consistency
● Assumes the existence of absolute global time
● It is impossible to implement on a large distributed
system
● No two operations (in different clients) allowed at the
same time
● Example: Sequence (a) satisfies strict consistency, but
sequence (b) does not
8. Sequential Consistency
● The result of any execution is the same as if
● the read and write operations by all processes on the data
store were executed in some sequential order
● the operations of each individual process appear in this
sequence in the order specified by its program
● All processes see the same interleaving of operations
● Many interleavings are valid
● Different runs of a program might act differently
● Example: Sequence (a) satisfies sequential consistency,
but sequence (b) does not
9. Consistency vs Availability
• In large shareddata distributed systems, network
partitions are a given
• Consistency or Availability
• Both options require the client developer to be aware
of what the system is offering
10. Eventual Consistency
• An eventual consistent storage system guarantees that
if no new updates are made to the object, eventually
all accesses will return the last updated value
• If no failures occur, the maximum size of the
inconsistency window can be determined based on factors
such as:
– load on the system
– communication delays
– number of replicas
• The most popular system that implements eventual
consistency is DNS
11. Quorumbased Technique
• To enforce consistent operation in a distributed
system.
• Consider the following parameters:
– N = Total number of replicas
– W = Replicas to wait for acknowledgement during writes
– R = Replicas to access during reads
• If W+R > N
– the read set and the write set always overlap and one can
guarantee strong consistency
• If W+R <= N
– the read and write set might not overlap and consistency
cannot be guaranteed
12. Agenda
● Consistency Models
● Transactions
● Why to distribute?
● Decentralized Architecture
● Design Techniques & Tradeoffs
● Few Real World Examples
● Conclusions
13. Transactions
● Extended form of consistency across multiple operations
● Example: Transfer money from A to B
● Subtract from A
● Add to B
● What if something happens in between?
● Another transaction on A or B
● Machine Crashes
● ...
17. Why NOT to distribute?
● Within a Datacenter
● High bandwidth: 1100Gbps interconnects
● Low latency: < 1ms within a rack, < 5ms across
● Little to no cost
● Between Datacenters
● Low bandwidth: 10Mbps1Gbps
● High latency: expect 100s of ms
● High Cost for fiber
18. Agenda
● Consistency Models
● Transactions
● Why to distribute?
● Decentralized Architecture
● Design Techniques & Tradeoffs
● Few Real World Examples
● Conclusions
19. Decentralized Architecture
● Operating from multiple datacenters simultaneously
● Hard problem
● Maintaining consistency? Harder
● Transactions? Hardest
20. Option 1: Don't
● Most common
● Make sure datacenter never goes down
● Bad at catastrophic failure
● Large scale data loss
● Not great for serving
● No geolocation
21. Option 2: Primary with hot
failover(s)
● Better, but not ideal
● Mediocre at catastrophic failure
● Window of lost data
● Failover data may be inconsistent
● Geolocated for reads, not for writes
22. Option 3: Truly Distributed
● Simultaneous writes in different DCs, maintaining
consistency
● Twoway: Hard
● Nway: Harder
● Handles catastrophic failure, geolocality
● But high latency
23. Agenda
● Consistency Models
● Transactions
● Why to distribute?
● Decentralized Architecture
● Design Techniques & Tradeoffs
● Few Real World Examples
● Conclusions
24. Tradeoffs
Backups M/S MM 2PC Paxos
Consistency
Transactions
Latency
Throughput
Data Loss
Failover
25. Backups
● Make a copy
● Weak consistency
● Usually no transactions
26. Tradeoffs – Backups
Backups M/S MM 2PC Paxos
Consistency Weak
Transactions No
Latency Low
Throughput High
Data Loss High
Failover Down
27. Master/slave replication
● Usually asynchronous
● Good for throughput, latency
● Weak/eventual consistency
● Support transactions
28. Tradeoffs – Master/Slave
Backups M/S MM 2PC Paxos
Consistency Weak Eventual
Transactions No Full
Latency Low Low
Throughput High High
Data Loss High Some
Failover Down Read Only
29. Multimaster replication
● Asynchronous, eventual consistency
● Concurrent writes
● Need serialization protocol
● e.g. monotonically increasing timestamps
● Either with master election or distributed consensus protocol
● No strong consistency
● No global transactions
30. Tradeoffs Multimaster
Backups M/S MM 2PC Paxos
Consistency Weak Eventual Eventual
Transactions No Full Local
Latency Low Low Low
Throughput High High High
Data Loss High Some Some
Failover Down Read Only Read/write
32. Tradeoffs 2PC
Backups M/S MM 2PC Paxos
Consistency Weak Eventual Eventual Strong
Transactions No Full Local Full
Latency Low Low Low High
Throughput High High High Low
Data Loss High Some Some None
Failover Down Read Only Read/write Read/write
33. Paxos
● Decentralized, distributed consensus protocol
● Protocol similar to 2PC/3PC
● Lighter, but still high latency
● Three class of agents: proposers, acceptors, learners
● 1. a) prepare b) promise 2. a) accept b) accepted
● Survives minority failure
34. Tradeoffs
Backups M/S MM 2PC Paxos
Consistency Weak Eventual Eventual Strong Strong
Transactions No Full Local Full Full
Latency Low Low Low High High
Throughput High High High Low Medium
Data Loss High Some Some None None
Failover Down Read Only Read/write Read/write Read/write
35. Agenda
● Consistency Models
● Transactions
● Why to distribute?
● Decentralized Architecture
● Design Techniques & Tradeoffs
● Few Real World Examples
● Conclusions
36. Examples
● Megastore
● Google's Scalable, Highly Available Datastore
● Strong Consistency, Paxos
● Optimized for reads
● Dynamo
● Amazon’s Highly Available Keyvalue Store
● Eventual Consistency, Consistent Hashing, Vector Clocks
● Optimized for writes
● PNUTS
● Yahoo's Massively Parallel & Distributed Database System
● Timeline Consistency
● Optimized for reads
37. Conclusions
● No silver bullet
● There are no simple solutions
● Design systems based on application needs
41. Vector Clocks
• Used to capture causality between different
versions of the same object.
• A vector clock is a list of (node, counter) pairs.
• Every version of every object is associated with
one vector clock.
• If the counters on the first object’s clock are
lessthanorequal to all of the nodes in the
second clock, then the first is an ancestor of the
second and can be forgotten.
43. Partitioning Algorithm
• Consistent hashing:
– The output range of a hash
function is treated as a
fixed circular space or
“ring”.
• Virtual Nodes
– Each node can be responsible
for more than one virtual
node.
– When a new node is added, it
is assigned multiple
positions.
– Various Advantages
