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Roger	
  Bodamer
roger@10gen.com
    @rogerb
Thoughts on Transaction
          and
  Consistency Models
RDBMS
(Oracle,	
  MySQL)
RDBMS
                          (Oracle,	
  MySQL)




New Gen.
 OLAP
(vertica,	
  aster,	
  
 greenplum)
RDBMS
                          (Oracle,	
  MySQL)




New Gen.                                       Non-relational
 OLAP                                           Operational
(vertica,	
  aster,	
                             Stores
 greenplum)                                       (“NoSQL”)
The database world is changing
Document Datastores, Key Value, Graph databases
The database world is changing
         Transactional model
The database world is changing
            Full Acid
The database world is changing
• memcached
scalability	
  &	
  performance


                                      • key/value


                                                                            •   RDBMS




                                             depth	
  of	
  functionality
CAP
It is impossible in the asynchronous network model to
implement a read/write data object that guarantees the
following properties:
• Availability
• Atomic consistency in all fair executions (including those
in which messages are lost).
CAP
  It is impossible in the asynchronous network model to
  implement a read/write data object that guarantees the
  following properties:
  • Availability
  • Atomic consistency in all fair executions (including those
  in which messages are lost).

Or: If the network is broken, your database won’t work

But we get to define “won’t work”
Consistency Models - CAP

• Choices are Available-P (AP) or Consistent-P (CP)
• Write Availability, not Read Availability, is the Main Question

• It’s not all about CAP
   Scale, Reduced latency:
       •Multi data center
       •Speed
       •Even load distribution
Examples of Eventually
          Consistent Systems

• Eventual Consistency:
   •“The storage system guarantees that if no new
   updates are made to the object, eventually all
   accesses will return the last updated value”

•Examples:
   • DNS
   • Async replication (RDBMS, MongoDB)
   • Memcached (TTL cache)
Eventual Consistency
Eventual Consistency




Read(x)	
  :	
  1,	
  2,	
  2,	
  4,	
  4,	
  4,	
  4	
  …
Could we get this?




Read(x)	
  :	
  1,	
  2,	
  1,	
  4,	
  2,	
  4,	
  4,	
  4	
  …
Monotonic read consistency
Prevent	
  seeing	
  writes	
  out	
  of	
  order

                                 • Appserver and slave on same
                                 box
                                 • Appserver only reads from Slave

                                 • Eventually consistent
                                 • Guaranteed to see reads in
                                 order
Monotonic read consistency
Prevent	
  seeing	
  writes	
  out	
  of	
  order

                                 • Appserver and slave on same
                                 box
                                 • Appserver only reads from Slave

                                 • Eventually consistent
                                 • Guaranteed to see reads in
                                 order

                                 • Failover ?
RYOW Consistency
                                                             • Read Your Own
                                                 Primary     Writes
Read	
  /	
  	
  Has	
  Written
                                   Replication               • Eventually consistent
                                                             for readers
                                                 Secondary   • Immediately
                                                             consistent for writers
   Read
Amazon Dynamo

•R: # of servers to read from
•W: # servers to get response from
•N: Replication factor
   • R+W>N has nice properties
Example
Example	
  1

R	
  +	
  W	
  <=	
  N

R	
  =	
  1
W	
  =	
  1
N	
  =	
  5

Possibly	
  Stale	
  data
Higher	
  availability
Example
Example	
  1                   Example	
  2

R	
  +	
  W	
  <=	
  N         R	
  +	
  W	
  >	
  N

R	
  =	
  1                    R	
  =	
  2	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  R	
  =	
  1
W	
  =	
  1                    W	
  =	
  1	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  W	
  =	
  2
N	
  =	
  5                    N	
  =	
  2	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  N	
  =	
  2

Possibly	
  Stale	
  data      Consistent	
  data
Higher	
  availability
R+W>N
    If R+W > N, we can’t
       have both fast local
       reads and writes at
       the same time if all
       the data centers are
       equal peers
Consistency levels
• Strong: W + R > N
• Weak / Eventual : W + R <= N
• Optimized Read: R=1, W=N
• Optimized Write: W=1, R=N
Multi Datacenter Strategies
• DR
   • Failover
• Single Region, Multi Datacenter
   • Useful for Strong or Eventual Consistency
• Local Reads, Remote Writes
   • All writes to master on WAN, EC on Slaves
• Intelligent Homing
   • Master copy close to user location
Network Partitions
Network Write Possibilities
•Deny all writes
   • Still Read fully consistent data
   • Give up write Availability
Network Write Possibilities
•Deny all writes
   • Still Read fully consistent data
   • Give up write Availability

•Allow writes on one side
   • Failover
   • Possible to allow reads on other side
Network Write Possibilities
•Deny all writes
   • Still Read fully consistent data
   • Give up write Availability

•Allow writes on one side
   • Failover
   • Possible to allow reads on other side

•Allow writes on both sides
   • Available
   • Give up consistency
Multiple Writer Strategies
• Last one wins
   • Use Vector clocks to decide latest

• Insert
     Inserts often really means
       if ( !exists(x)) then set(x)
    exists is hard to implement in eventually
    consistent systems
Multiple Writer Strategies
• Delete
    op1: set( { _id : 'joe', age : 40 } }
    op2: delete( { _id : 'joe' } )
    op3: set( { _id : 'joe', age : 33 } )
• Consider switching 2 and 3
• Tombstone: Remember delete, and apply last-operation-wins

• Update
 update users set age=40 where _id=’joe’
However:
 op1: update users set age=40 where _id='joe'
 op2: update users set state='ca' where _id='joe'
Multiple Writer Strategies
• Programatic Merge
   • Store operations, instead of state
   • Replay operations
   • Did you get the last operation ?
   • Not immediate

•Communtative operations
   • Conflict free?
   • Fold-able
   • Example: add, increment, decrement
Trivial Network Partitions
MongoDB Options
MongoDB Transaction Support
• Single Master / Sharded

• MongoDB Supports Atomic Operations on Single Documents
   • But not Rollback
• $ operators
   • $set, $unset, $inc, $push, $pushall, $pull, $pullall
• Update if current
   • find followed by update
• Find and modify
MongoDB


                               Primary
Read	
  /	
  Write
MongoDB


                                       Primary
Read	
  /	
  Write
                         Replication


                                       Secondary


                         Replication


                                       Secondary	
  for	
  Backup
MongoDB


                                       Primary
Read	
  /	
  Write
                         Replication


                                       Secondary


     Read                Replication


                                       Secondary	
  for	
  Backup
MongoDB
                         Replicaset

                                       Primary
Read	
  /	
  Write
                         Replication


                                       Secondary


     Read                Replication


                                       Secondary	
  for	
  Backup
Write Scalability: Sharding
read      key	
  range	
      key	
  range	
      key	
  range	
  
            0	
  ..	
  30      31	
  ..	
  60      61	
  ..	
  100

         ReplicaSet	
  1     ReplicaSet	
  2     ReplicaSet	
  3



          Primary              Primary            Primary


         Secondary           Secondary           Secondary


         Secondary           Secondary           Secondary



                                                                     write
Sometimes we need global state /
      more consistency
•Unique key constraints
   •User registration
•ACL changes
Could it be the case that…


uptime( CP + average developer )
 >=
uptime( AP + average developer )

where uptime:= system is up and non-buggy?
Thank You :-)
  @rogerb
download at mongodb.org

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  appearances,	
  and	
  meetups
                                         http://www.10gen.com/events




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Thoughts on Transaction and Consistency Models

  • 2. Thoughts on Transaction and Consistency Models
  • 4. RDBMS (Oracle,  MySQL) New Gen. OLAP (vertica,  aster,   greenplum)
  • 5. RDBMS (Oracle,  MySQL) New Gen. Non-relational OLAP Operational (vertica,  aster,   Stores greenplum) (“NoSQL”)
  • 6. The database world is changing Document Datastores, Key Value, Graph databases
  • 7. The database world is changing Transactional model
  • 8. The database world is changing Full Acid
  • 9. The database world is changing
  • 10. • memcached scalability  &  performance • key/value • RDBMS depth  of  functionality
  • 11. CAP It is impossible in the asynchronous network model to implement a read/write data object that guarantees the following properties: • Availability • Atomic consistency in all fair executions (including those in which messages are lost).
  • 12. CAP It is impossible in the asynchronous network model to implement a read/write data object that guarantees the following properties: • Availability • Atomic consistency in all fair executions (including those in which messages are lost). Or: If the network is broken, your database won’t work But we get to define “won’t work”
  • 13. Consistency Models - CAP • Choices are Available-P (AP) or Consistent-P (CP) • Write Availability, not Read Availability, is the Main Question • It’s not all about CAP Scale, Reduced latency: •Multi data center •Speed •Even load distribution
  • 14. Examples of Eventually Consistent Systems • Eventual Consistency: •“The storage system guarantees that if no new updates are made to the object, eventually all accesses will return the last updated value” •Examples: • DNS • Async replication (RDBMS, MongoDB) • Memcached (TTL cache)
  • 16. Eventual Consistency Read(x)  :  1,  2,  2,  4,  4,  4,  4  …
  • 17. Could we get this? Read(x)  :  1,  2,  1,  4,  2,  4,  4,  4  …
  • 18. Monotonic read consistency Prevent  seeing  writes  out  of  order • Appserver and slave on same box • Appserver only reads from Slave • Eventually consistent • Guaranteed to see reads in order
  • 19. Monotonic read consistency Prevent  seeing  writes  out  of  order • Appserver and slave on same box • Appserver only reads from Slave • Eventually consistent • Guaranteed to see reads in order • Failover ?
  • 20. RYOW Consistency • Read Your Own Primary Writes Read  /    Has  Written Replication • Eventually consistent for readers Secondary • Immediately consistent for writers Read
  • 21. Amazon Dynamo •R: # of servers to read from •W: # servers to get response from •N: Replication factor • R+W>N has nice properties
  • 22. Example Example  1 R  +  W  <=  N R  =  1 W  =  1 N  =  5 Possibly  Stale  data Higher  availability
  • 23. Example Example  1 Example  2 R  +  W  <=  N R  +  W  >  N R  =  1 R  =  2                                          R  =  1 W  =  1 W  =  1                                        W  =  2 N  =  5 N  =  2                                        N  =  2 Possibly  Stale  data Consistent  data Higher  availability
  • 24. R+W>N If R+W > N, we can’t have both fast local reads and writes at the same time if all the data centers are equal peers
  • 25. Consistency levels • Strong: W + R > N • Weak / Eventual : W + R <= N • Optimized Read: R=1, W=N • Optimized Write: W=1, R=N
  • 26. Multi Datacenter Strategies • DR • Failover • Single Region, Multi Datacenter • Useful for Strong or Eventual Consistency • Local Reads, Remote Writes • All writes to master on WAN, EC on Slaves • Intelligent Homing • Master copy close to user location
  • 28. Network Write Possibilities •Deny all writes • Still Read fully consistent data • Give up write Availability
  • 29. Network Write Possibilities •Deny all writes • Still Read fully consistent data • Give up write Availability •Allow writes on one side • Failover • Possible to allow reads on other side
  • 30. Network Write Possibilities •Deny all writes • Still Read fully consistent data • Give up write Availability •Allow writes on one side • Failover • Possible to allow reads on other side •Allow writes on both sides • Available • Give up consistency
  • 31. Multiple Writer Strategies • Last one wins • Use Vector clocks to decide latest • Insert Inserts often really means if ( !exists(x)) then set(x) exists is hard to implement in eventually consistent systems
  • 32. Multiple Writer Strategies • Delete op1: set( { _id : 'joe', age : 40 } } op2: delete( { _id : 'joe' } ) op3: set( { _id : 'joe', age : 33 } ) • Consider switching 2 and 3 • Tombstone: Remember delete, and apply last-operation-wins • Update update users set age=40 where _id=’joe’ However: op1: update users set age=40 where _id='joe' op2: update users set state='ca' where _id='joe'
  • 33. Multiple Writer Strategies • Programatic Merge • Store operations, instead of state • Replay operations • Did you get the last operation ? • Not immediate •Communtative operations • Conflict free? • Fold-able • Example: add, increment, decrement
  • 36. MongoDB Transaction Support • Single Master / Sharded • MongoDB Supports Atomic Operations on Single Documents • But not Rollback • $ operators • $set, $unset, $inc, $push, $pushall, $pull, $pullall • Update if current • find followed by update • Find and modify
  • 37. MongoDB Primary Read  /  Write
  • 38. MongoDB Primary Read  /  Write Replication Secondary Replication Secondary  for  Backup
  • 39. MongoDB Primary Read  /  Write Replication Secondary Read Replication Secondary  for  Backup
  • 40. MongoDB Replicaset Primary Read  /  Write Replication Secondary Read Replication Secondary  for  Backup
  • 41. Write Scalability: Sharding read key  range   key  range   key  range   0  ..  30 31  ..  60 61  ..  100 ReplicaSet  1 ReplicaSet  2 ReplicaSet  3 Primary Primary Primary Secondary Secondary Secondary Secondary Secondary Secondary write
  • 42.
  • 43. Sometimes we need global state / more consistency •Unique key constraints •User registration •ACL changes
  • 44. Could it be the case that… uptime( CP + average developer ) >= uptime( AP + average developer ) where uptime:= system is up and non-buggy?
  • 45. Thank You :-) @rogerb
  • 46. download at mongodb.org conferences,  appearances,  and  meetups http://www.10gen.com/events Facebook                    |                  Twitter                  |                  LinkedIn http://bit.ly/mongoU   @mongodb http://linkd.in/joinmongo