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Convergent Replicated
Data Types in Riak 2.0
Gordon Guthrie
Senior Software Engineer - Basho
OLAP
OLTP
Financial
s
99.9999%
99.999%
99.99%
99.9%
99%
Availability
Pressure
Consistency
Pressure
Year End
Quarter End
Month End
Week End
End Of Day
End Of Hour
End Of Minute
Popping a Cap in the Enterprise’s Ass
Customers
Company
‘Data
Platform’
Riak Riak
Available
Replication
Consistent At
A Point In TimeCRDT’s – The Magic
Customers
Company
What is Riak?
What is it for?
• Riak is a highly-available, low-latency KV store
• it is an open-source implementation of the
Dynamo paper from Amazon
• Dynamo underpins the Amazon shopping cart
Why does it matter?
• availability matters
• latency matters
• Amazon found every 100ms of latency cost them
1% in sales
• Google found an extra 0.5 seconds in search
page generation time dropped traffic by 20%
Riak Availability
The Ring
…it’s a hash function…
If I have:
• a ring of 16 slots
• a hashed keyspace of 28
Then the slots are:
• Slot 1: 0 – 15
• Slot 2: 16 – 31
• Slot 3: 32 – 47
• etc, etc
{key: ‘Alice’, value: ‘hello’}
hash(‘Alice’, 28) -> 17
Store it in Slot 2
Mapped To Machines
Physical
Machine 1
Physical
Machine 2
Physical
Machine 3
Physical
Machine 4
Physical
Machine 5
Machines Go Down
Physical
Machine 1
Physical
Machine 2
Physical
Machine 3
Physical
Machine 4
Physical
Machine 5
And Come Back
Physical
Machine 1
Physical
Machine 2
Physical
Machine 3
Physical
Machine 4
Physical
Machine 5
but that’s not enough
That’s how the data survives a box dying
What happens if the network partitions
CAP Theorem
The Cap Theorem
Pick 2
Consistency Availability
Partition
Tolerance
Riak
• In a distributed computer system you trade:
 Consistency
 Availability
 Partition tolerance
• Pick two
 CA (Relational)
 CP (Mongo, Hbase, Redis)
 AP (Riak, Cassandra, Couchbase, Dynamo DB)
C A
P
CAP Theorem Relaxed consistency
X
• Think of C, A and P as dials on a
dashboard
• In a good implementation (like Riak)
they can be tuned to achieve the
most appropriate balance of
consistency, availability and
partition tolerance for specific
workloads
0 1
2
3
4
A
5
67
8
9
10
11
0 1
2
3
4
P
5
67
8
9
10
11
0
1
2
3
4
C
5
67
8
9
10
11
Tuning the CAP
Partitions Happen
• because Riak is a highly-available data store it will
continue to store your data
• but your data will not be consistent
AP
Physical
Machine 1
Physical
Machine 2
Physical
Machine 3
Physical
Machine 4
Physical
Machine 5
Partition
Val 1
Val 2
And Come Back
Physical
Machine 1
Physical
Machine 2
Physical
Machine 3
Physical
Machine 4
Physical
Machine 5
Now you have siblings
Physical
Machine 1
Physical
Machine 2
Physical
Machine 3
Physical
Machine 4
Physical
Machine 5
{Val 1, Val 2}
Siblings
the developer’s problem that Riak 2.0 is trying to fix
Sibling Resolution
• Developers hate siblings
• Developers hate resolving siblings
• Developers play Timestamp Roulette and go for
Last Write Wins
• but with intercontinental lag, NTP failure, clock
skew and latency sometimes Timestamps kill your
data
Google’s View
• “Designing applications to cope with concurrency
anomalies in their data is very error-prone, time-
consuming, and ultimately not worth the performance
gains.”
• “We have a lot of experience with eventual
consistency systems at Google.”
• “We find developers spend a significant fraction of
their time building extremely complex and error-prone
mechanisms to cope with eventual consistency”
The Partition Cycle
Consistent
Available
Available
Partitioned
Not Consistent
Available
(Eventually)
Consistent
Available
partitioned unpartitioned
Developer
Hell
Developer
Heaven
CRDTs
CRDTs
Consistent Replicated Data Types
Understanding CRDTs
• Not going to go into a lot of detail
• Based on the idea of Lamport Vector Clocks
• A Lamport Vector clock allows an ‘actor’ in a distributed data
system to say 2 things:
• this is when I changed this data
• and when I changed it I knew all the things every other actor
had done upto these times
• Its a vector clock because it has a separate clock for every actor
Where Are The Actors?
• Actors can be clients-side or server-side or both
• Riak is implementing an architecture that allows
users to use server-side CRDTs
• to avoid the risk of ‘actor’ explosion
Client Side Actors
• Alice’s Laptop
• Alice’s phone
• Alice’s iPad
• Alice’s daughter’s iPad (her battery is flat)
• Bob’s new laptop
• Bob’s old laptop
• etc, etc
Convergent Replicated Data Types in Riak 2.0
How They Work
• Lets take a simple example with two actions:
• add an item to a shopping cart
• empty the shopping cart
• these actions don’t commute:
• add item then clear gives an empty cart
• empty cart then add item gives a cart with one item
in it
How They Work
Consistent
Available
Available
Partitioned
Not Consistent
Available
(Eventually)
Consistent
Available
partitioned unpartitioned CRDTs
ClearAdd
Item
[{a, 🕝}] [{b, 🕟}] {[{a, 🕝}], [{b, 🕟}]}
The ‘actor’ clearing the
shopping cart didn’t know
there was anything in the
cart
Therefore there should
be something in the
cart after merging the
two updates
How They Work
Consistent
Available
Available
Partitioned
Not Consistent
Available
(Eventually)
Consistent
Available
partitioned unpartitioned CRDTs
ClearAdd
Item
[{a, 🕝}] [{a, 🕝},{b, 🕟}] {[{a, 🕝}], [{a, 🕝},{b, 🕟}]}
The ‘actor’ clearing the
shopping cart knew there
was something in the cart
Therefore there should
be nothing in the cart
after merging the two
updates
Scary Maths Stuff
• Join Semi-Lattices that have a Bottom and a
Least Upper Bound
• that have defined properties involving Associativity,
Commutativity, Idempotency
• with a Merge Function
References
Your essential CRDT reading list
http://christophermeiklejohn.com/crdt/2014/07/22/readings-in-crdts.html
CRDTs in Riak 2.0
Native Eventually Consistent
Data Types
• Flags
• Registers
• Counters
• Sets
• Maps
• can contain Flags, Registers, Counter and Maps
• the basis of composable data types
Flags
• Can have one of two values:
• enabled
• disabled
• Operations:
• enable
• disable
• Examples - use like Booleans
• has a tweet been sent?
• has the customer signed up to a pricing plan?
Registers
• Named Binaries that have contents which have a
value
• Operations:
• store new value
• Examples
• store “My New Post” in the field “Blog Post Title”
Counters
• Contain a number which can be incremented or decremented
• Operations
• increment
• decrement
• Examples
• the number of “likes” in Facebook
• the number of Twitter followers
Sets
• Collections of unique binaries
• Operations
• add an element
• remove an element
• add a list of elements
• remove a list of elements
• Examples
• shopping cart
Maps
• Maps are like hash maps
• Operations
• add a named Flag, Register, Counter, Set or Map
• remove a named Flag, Register, Counter, Set or Map
• pass through an op for an element in the Map
• Example
• the Map is used to compose the data model
Questions?
Fin

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Convergent Replicated Data Types in Riak 2.0

  • 1. Convergent Replicated Data Types in Riak 2.0 Gordon Guthrie Senior Software Engineer - Basho
  • 2. OLAP OLTP Financial s 99.9999% 99.999% 99.99% 99.9% 99% Availability Pressure Consistency Pressure Year End Quarter End Month End Week End End Of Day End Of Hour End Of Minute Popping a Cap in the Enterprise’s Ass Customers Company
  • 3. ‘Data Platform’ Riak Riak Available Replication Consistent At A Point In TimeCRDT’s – The Magic Customers Company
  • 5. What is it for? • Riak is a highly-available, low-latency KV store • it is an open-source implementation of the Dynamo paper from Amazon • Dynamo underpins the Amazon shopping cart
  • 6. Why does it matter? • availability matters • latency matters • Amazon found every 100ms of latency cost them 1% in sales • Google found an extra 0.5 seconds in search page generation time dropped traffic by 20%
  • 9. …it’s a hash function… If I have: • a ring of 16 slots • a hashed keyspace of 28 Then the slots are: • Slot 1: 0 – 15 • Slot 2: 16 – 31 • Slot 3: 32 – 47 • etc, etc {key: ‘Alice’, value: ‘hello’} hash(‘Alice’, 28) -> 17 Store it in Slot 2
  • 10. Mapped To Machines Physical Machine 1 Physical Machine 2 Physical Machine 3 Physical Machine 4 Physical Machine 5
  • 11. Machines Go Down Physical Machine 1 Physical Machine 2 Physical Machine 3 Physical Machine 4 Physical Machine 5
  • 12. And Come Back Physical Machine 1 Physical Machine 2 Physical Machine 3 Physical Machine 4 Physical Machine 5
  • 13. but that’s not enough That’s how the data survives a box dying
  • 14. What happens if the network partitions
  • 16. The Cap Theorem Pick 2 Consistency Availability Partition Tolerance Riak
  • 17. • In a distributed computer system you trade:  Consistency  Availability  Partition tolerance • Pick two  CA (Relational)  CP (Mongo, Hbase, Redis)  AP (Riak, Cassandra, Couchbase, Dynamo DB) C A P CAP Theorem Relaxed consistency X
  • 18. • Think of C, A and P as dials on a dashboard • In a good implementation (like Riak) they can be tuned to achieve the most appropriate balance of consistency, availability and partition tolerance for specific workloads 0 1 2 3 4 A 5 67 8 9 10 11 0 1 2 3 4 P 5 67 8 9 10 11 0 1 2 3 4 C 5 67 8 9 10 11 Tuning the CAP
  • 19. Partitions Happen • because Riak is a highly-available data store it will continue to store your data • but your data will not be consistent
  • 20. AP Physical Machine 1 Physical Machine 2 Physical Machine 3 Physical Machine 4 Physical Machine 5 Partition Val 1 Val 2
  • 21. And Come Back Physical Machine 1 Physical Machine 2 Physical Machine 3 Physical Machine 4 Physical Machine 5
  • 22. Now you have siblings Physical Machine 1 Physical Machine 2 Physical Machine 3 Physical Machine 4 Physical Machine 5 {Val 1, Val 2}
  • 23. Siblings the developer’s problem that Riak 2.0 is trying to fix
  • 24. Sibling Resolution • Developers hate siblings • Developers hate resolving siblings • Developers play Timestamp Roulette and go for Last Write Wins • but with intercontinental lag, NTP failure, clock skew and latency sometimes Timestamps kill your data
  • 25. Google’s View • “Designing applications to cope with concurrency anomalies in their data is very error-prone, time- consuming, and ultimately not worth the performance gains.” • “We have a lot of experience with eventual consistency systems at Google.” • “We find developers spend a significant fraction of their time building extremely complex and error-prone mechanisms to cope with eventual consistency”
  • 26. The Partition Cycle Consistent Available Available Partitioned Not Consistent Available (Eventually) Consistent Available partitioned unpartitioned Developer Hell Developer Heaven CRDTs
  • 28. Understanding CRDTs • Not going to go into a lot of detail • Based on the idea of Lamport Vector Clocks • A Lamport Vector clock allows an ‘actor’ in a distributed data system to say 2 things: • this is when I changed this data • and when I changed it I knew all the things every other actor had done upto these times • Its a vector clock because it has a separate clock for every actor
  • 29. Where Are The Actors? • Actors can be clients-side or server-side or both • Riak is implementing an architecture that allows users to use server-side CRDTs • to avoid the risk of ‘actor’ explosion
  • 30. Client Side Actors • Alice’s Laptop • Alice’s phone • Alice’s iPad • Alice’s daughter’s iPad (her battery is flat) • Bob’s new laptop • Bob’s old laptop • etc, etc
  • 32. How They Work • Lets take a simple example with two actions: • add an item to a shopping cart • empty the shopping cart • these actions don’t commute: • add item then clear gives an empty cart • empty cart then add item gives a cart with one item in it
  • 33. How They Work Consistent Available Available Partitioned Not Consistent Available (Eventually) Consistent Available partitioned unpartitioned CRDTs ClearAdd Item [{a, 🕝}] [{b, 🕟}] {[{a, 🕝}], [{b, 🕟}]} The ‘actor’ clearing the shopping cart didn’t know there was anything in the cart Therefore there should be something in the cart after merging the two updates
  • 34. How They Work Consistent Available Available Partitioned Not Consistent Available (Eventually) Consistent Available partitioned unpartitioned CRDTs ClearAdd Item [{a, 🕝}] [{a, 🕝},{b, 🕟}] {[{a, 🕝}], [{a, 🕝},{b, 🕟}]} The ‘actor’ clearing the shopping cart knew there was something in the cart Therefore there should be nothing in the cart after merging the two updates
  • 35. Scary Maths Stuff • Join Semi-Lattices that have a Bottom and a Least Upper Bound • that have defined properties involving Associativity, Commutativity, Idempotency • with a Merge Function
  • 36. References Your essential CRDT reading list http://christophermeiklejohn.com/crdt/2014/07/22/readings-in-crdts.html
  • 38. Native Eventually Consistent Data Types • Flags • Registers • Counters • Sets • Maps • can contain Flags, Registers, Counter and Maps • the basis of composable data types
  • 39. Flags • Can have one of two values: • enabled • disabled • Operations: • enable • disable • Examples - use like Booleans • has a tweet been sent? • has the customer signed up to a pricing plan?
  • 40. Registers • Named Binaries that have contents which have a value • Operations: • store new value • Examples • store “My New Post” in the field “Blog Post Title”
  • 41. Counters • Contain a number which can be incremented or decremented • Operations • increment • decrement • Examples • the number of “likes” in Facebook • the number of Twitter followers
  • 42. Sets • Collections of unique binaries • Operations • add an element • remove an element • add a list of elements • remove a list of elements • Examples • shopping cart
  • 43. Maps • Maps are like hash maps • Operations • add a named Flag, Register, Counter, Set or Map • remove a named Flag, Register, Counter, Set or Map • pass through an op for an element in the Map • Example • the Map is used to compose the data model