Different database consistency models explained by The Dude. Choosing a database is hard, most developers take the word of bloggers and huge proclamations from inventors. This slide helps in understanding various database consistency models so that you can break down your own requirements into a consistency model, and then choose the appropriate database,

1. The Dude’s
guide to
database
consistency

2. Has this ever happened to you before?

3. Well It has happened to me

4. Database Consistency

5. Consistency and Histories
Invariants are your fixed set of requirements that system should not violate.
For example: client should never see an older copy of data.
When a system holds true for all the assumed invariants the system is said to be
consistent.
A consistency model is set of all allowed histories.

6. Example
What is the output?
How will you validate the system is correct?

7. A consistency
model is set of
all allowed
histories.

8. How does history look like?

9. Consistency Models
Source https://jepsen.io/consistency
They are used to solve some
problems.
They are often organized in
terms of “anomalies” that they
avoid.

10. What are these problems?

11. Consistency Model What problems it
solves? And what it
won’t solve.

12. Read Uncommitted
Source https://jepsen.io/consistency
Antidote to dirty writes

13. In a transaction when a value is changed meanwhile other transaction overwrites
it and commits it.
What happens if T1 wants to rollback? Should x = 0 or x = 2?
What are dirty writes?

14. Read Uncommitted Solves dirty writes
Still allows dirty
reads

15. Read Committed
Source https://jepsen.io/consistency
Antidote to dirty writes and
dirty reads

16. In a transaction when an uncommitted value is observed.
What are dirty reads?

17. Read Committed Solves dirty writes
and dirty reads
It is the most widely used lowest level
consistency model.

18. Chaos Read
Uncommitted
Read
Committed

19. Cursor Stability
Source https://jepsen.io/consistency
Antidote to dirty writes, dirty
reads and lost updates

20. There is no Dirty Write since T2 commits before T1 writes x. There is no Dirty
Read since there is no read after a write. Nevertheless, at the end of this
sequence T2‘s update will be lost, even if T2 commits.
What are lost updates?

21. It introduces the concept of a cursor, which refers to a particular object being
accessed by a transaction.
Transactions may have multiple cursors.
When a transaction reads an object using a cursor, that object cannot be modified
by any other transaction until the cursor is released, or the transaction commits.
How lost updates are mitigated by Cursor Stability?

22. Cursor stability Solves dirty writes,
dirty reads and lost
updates

23. Monotonic Atomic
View
Source https://jepsen.io/consistency
Provides the A in the ACID.
How much should I observe
from other transactions?

24. Once some effects of a txn A are observed by txn B, then all effects of A are
visible to B. Also if another later version is observed then all the versions from that
transaction will be visible.
Only committed values are observed as monotonic atomic views implies read
committed.
Isolation effect of “Atomicity” in ACID — how much of a committed transaction
should be visible to others — all or nothing.
What is monotonic atomic views?

25. Repeatable Read
Source https://jepsen.io/consistency
Antidote to dirty writes, dirty
reads, lost updates, non-
repeatable reads, read skew
and write skew

26. When a transaction reads something and later the same read returns different
value.
What are non-repeatable reads?

27. You started transaction from your spring boot application
Default level was set to READ COMMITTED.
You read a value and took some decision. This triggered an update.
In the validation phase you did another read.
Values mismatched.
Someone changed your value and the decision you took was wrong.
Remedy, Use higher consistency level or use SELECT FOR UPDATE
Real life use case

28. Snapshot Isolation
Source https://jepsen.io/consistency
Antidote to dirty writes, dirty
reads, lost updates, non-
repeatable reads, read skew
and write skew

29. In a snapshot isolated system, each transaction appears to operate on an
independent, consistent snapshot of the database. Its changes are visible only to
that transaction until commit time, when all changes become visible atomically. If
transaction T1 has modified an object x, and another transaction T2 committed a
write to x after T1’s snapshot began, and before T1’s commit, then T1 must abort.
What is snapshot isolation?

30. Serializable
Source https://jepsen.io/consistency
Antidote to dirty writes, dirty
reads, lost updates, non-
repeatable reads, read skew,
write skew and phantom
reads

31. A Phantom occurs when a transaction does a predicate-based read (e.g.
SELECT… WHERE P) and another transaction writes a data item matched by that
predicate while the first transaction is still in flight.
What are phantom reads?

32. If each transaction is correct by itself, then a schedule that comprises any serial
execution of these transactions is correct: "Serial" means that transactions do not
overlap in time and cannot interfere with each other, i.e, complete isolation
between each other exists. Any order of the transactions is legitimate, if no
dependencies among them exist. As a result, a schedule that comprises any
execution (not necessarily serial) that is equivalent (in its outcome) to any serial
execution of these transactions, is correct.
What is serializable isolation level?

33. As serializability does not involve any transaction interleaving, any transaction
doing predicate queries won’t get affected by other transactions.
Other option is index crabbing.
How phantom reads are handled?

34. Linearizable
Source https://jepsen.io/consistency
strongest single-object
consistency model

35. Implies that every operation appears to take place atomically, in some order,
consistent with the real-time ordering of those operations: e.g., if operation A
completes before operation B begins, then B should logically take effect after A.
Points to note:
1. One object
2. Atomic, indivisible steps, all or nothing
3. Real time constraint if something happens before another then it should
reflect accordingly
4. Usually for single object using one key or set of keys
What is linearizability?

36. 1. Multi object.
2. Multi operations.
3. Arbitrary Order.
4. No overlap in time.
5. No real time constraint.
6. Desired by database
community
1. Single object.
2. Single operation.
3. Real time Order.
4. No overlap in time.
5. Real time constraint.
6. Desired by distributed
systems community

37. Strict Serializable
Source https://jepsen.io/consistency
Implies Serializability and
Linearizability

38. Baseball as an example for
Replicated database consistency
Source https://jepsen.io/consistency
Source https://www.microsoft.com/en-
us/research/wp-
content/uploads/2011/10/ConsistencyAndBaseball
Report.pdf

39. The game starts with the score of 0-0. The visitors bat first and remain at bat until
they make three outs. Then the home team bats until it makes three outs.
Baseball

40. 7th Innings

41. Different role players in baseball

42. Different consistency levels for different role
players in baseball

43. We saw different consistency models ...

44. But, what is the point dude?

45. What is the point dude?
Identify various actors of the system.
Based on the actors identify the problems that different actors of the
system can live with and the problems that are bad for business.
Then, please use the suitable database.
Convert the needed invariants of your system and the anomalies the
system can live with into a consistency model.
Thank You Dudes

46. If after so many slides. If you still feel like saying ….

47. You: I will just use MongoDB. It is a web scale database.

48. Dude:

49. Dude: Thank You