The document discusses replication in distributed systems, highlighting its benefits for performance, availability, and fault tolerance, yet also addressing challenges such as handling updates and failures. It explores various replication models and their trade-offs, including primary copy and update anywhere, as well as eager versus lazy replication, emphasizing the implications for consistency and availability. Additionally, it touches on the dangers of replication and potential solutions, suggesting that there is no one-size-fits-all approach and inviting potential job candidates.

1. REPLICATION
IN THE WILD
Ensar Basri Kahveci

2. Hello! Ensar Basri Kahveci
Distributed Systems Engineer @ Hazelcast
Ph.D. Candidate @ Bilkent CS
twitter: metanet | github: metanet | linkedin: basrikahveci

3. - IMDG: storage + computation + messaging
- Open source, distributed, highly available, elastic,
scalable
- Distributed Java collections, JCache, HD store
- Embedded or client - server deployment
- Clients: Java, Scala, C++, C#, Python, Node.js, ...
- Integration modules

4. - https://blog.hazelcast.com/announcing-hazel
cast-imdg-3-8
- Rolling upgrades
- User code deployment
- Hot restart improvements
- WAN replication improvements

5. REPLICATION
- Putting a data set into
multiple nodes.
- Each replica has a full copy.
- A few reasons for replication:
- Performance
- Availability and fault tolerance

6. REPLICATION + PARTITIONING
- Mostly used with
partitioning.
- Two partitions: P1, P2
- Two replicas for each
partition.

7. NOTHING FOR FREE!
- Very easy to do when the data is immutable.
- Problems start when we have multiple copies
of the data and we want to update them.
- Two main difficulties:
- Handling updates,
- Handling failures.

8. CAP PrIncIple
- Proposed by Eric Brewer in 2000 [13],
- Proved by Gilbert and Lynch in 2002 [14].
- A shared-data system cannot achieve perfect
consistency and availability in the presence of
partitions CP vs AP.
- Widespread acceptance, and yet a lot of criticism
[15 - 21].

9. consIstency AND avaIlabIlIty
- Degrees of consistency:
- Data centric, client centric
- Degrees of availability:
- High availability, sticky
availability, non-availability
- Replication is directly
related to C and A. [25]

10. The dangers of replIcatIon and a solutIon
- Gray et al. [1] classify replication models by 2
parameters:
- Where to make updates: primary copy or update
anywhere
- When to make updates: eagerly or lazily

11. WHERE: PRIMARY COPY
- There is a single replica
managing the updates.
- Concurrency control is easy.
- No conflicts and no conflict-handling logic.
- Updates are executed on the primary and
secondaries with the same order.
- When primary fails, a new primary is elected.
- Ensuring a single and good primary is hard.

12. WHERE: UPDATE ANYWHERE
- Each replica can initiate a
transaction to make an update.
- Complex concurrency control.
- Deadlocks or conflicts are
possible.
- In practice, there is also
multi-leader.

13. WHEN: EAGER REPLICATION
- Synchronously updates all
replicas as part of one atomic
transaction.
- Provides strong consistency.
- Degree of availability can
degrade on node failures.
- Consensus algorithms.

14. WHEN: LAZY REPLICATION
- Updates each replica with a
separate transaction.
- Updates can execute quite fast.
- Degree of availability is high.
- Eventual consistency.
- Data copies can diverge.
- Data loss or conflicts can occur.

15. WHERE?
WHEN?
PRIMARY COPY UPDATE ANYWHERE
EAGER
strong consistency
simple concurrency
slow
inflexible
strong consistency
complex concurrency
slow
expensive
deadlocks
LAZY
fast
sticky availability
eventual consistency
simple concurrency
inconsistency
fast
flexible
high availability
eventual consistency
inconsistency
conflicts

16. WHERE?
WHEN?
PRIMARY COPY UPDATE ANYWHERE
EAGER
Multi Paxos [5]
etcd and Consul (RAFT) [6]
Zookeeper (Zab) [7]
Kafka
VoltDB [24]
Paxos [5]
Hazelcast Cluster State Change [12]
MySQL 5.7 Group Replication [23]
LAZY
Hazelcast
MongoDB
ElasticSearch
Redis
Dynamo [4]
Cassandra
Riak
Hazelcast Active-Active WAN
Replication [22]

17. PRIMARY COPY + EAGER REPLICATION
- When the primary fails, secondaries are
guaranteed to be up to date.
- Raft, Kafka etc.
- Majority approach can be used.
- In Kafka, in-sync-replica set [11] is maintained.
- Secondaries can be used for reads.

18. UPDATE ANYWHERE + EAGER REPLICATION
- Each replica can initiate a new transaction.
- Concurrent transactions can compete with
each other.
- Possibility of deadlocks.
- In the basic Paxos algorithm, there is no
designated leader.

19. PRIMARy COPY + LAZY REPLICATION
- The primary copy can execute updates fast.
- Secondaries can fall behind the primary. It is
called replication lag.
- It can lead to data loss during leader failover, but
still no conflicts.
- Implies sticky availability.
- Secondaries can be used for reads.

20. UPDATE ANYWHERE + LAZY REPLICATION
- Dynamo-style [4] highly available databases.
- Quorums.
- Concurrent updates are first-class citizens.
- Possibility of conflicts
- Avoiding, discarding, detecting & resolving conflicts
- Eventual convergence
- Write repair, read repair and anti-entropy

21. QUORUMS
- W + R > N
- W = 3, R = 1, N = 3
- W = 2, R = 2, N = 3
- If W or R is not met
- Sloppy quorums and
hinted handoff

22. ConflIct-free replIcated data types (CRDTS)
- Special data types that achieve strong
eventual consistency and monotonicity [2]
- No conflicts
- Merge function has 3 properties:
- Commutative: A+B=B+A
- Associative: A+(B+C)=(A+B)+C
- Idempotent: f(f(x))=f(x)
- Riak Data Types [3]

23. DISCARDING CONFLICTS: LAST WRITE WINS
- When 2 updates are concurrent, define an
arbitrary order among them.
- i.e., pretend that one of them is more recent.
- Attach a timestamp to each write.
- Cassandra uses physical timestamps [8], [9].

24. DETECTING CONFLICTS: VECTOR CLOCKS
- In Dynamo paper [4], each update is done
against a particular version of a data entry.
- Multiple versions of a data entry can exist together.
- Vector clocks [10] are used to track causality.
- The system can determine the authoritative version:
syntactic reconciliation
- The system cannot reconcile multiple versions:
semantic reconciliation

25. VECTOR CLOCKS

26. ResolvIng conflIcts and EVENTUAL CONVERGENCE
- Write repair
- Read repair
- Anti-entropy
- Merkle trees

27. Recap
- We apply replication to make distributed
systems performant, available and fault
tolerant.
- It suffers from core problems of distributed systems.
- Various replication protocols are built based
on when and where to make updates.
- No silver bullet. It is a world of trade-offs.

28. - We are hiring!
- Senior Java Developer
http://stackoverflow.com/jobs/129435/senior-java-developer-hazelcast
- Software Quality and Performance Wiz
http://stackoverflow.com/jobs/126077/software-quality-and-performance-wiz
ard-hazelcast
- Solution Architech
http://stackoverflow.com/jobs/131938/solutions-architect-hazelcast

29. REFerences
[1] Gray, Jim, et al. "The dangers of replication and a solution." ACM SIGMOD Record 25.2 (1996): 173-182.
[2] Shapiro, Marc, et al. "Conflict-free replicated data types." Symposium on Self-Stabilizing Systems. Springer, Berlin, Heidelberg, 2011.
[3] http://docs.basho.com/riak/kv/2.2.0/learn/concepts/crdts/
[4] DeCandia, Giuseppe, et al. "Dynamo: amazon's highly available key-value store." ACM SIGOPS operating systems review 41.6 (2007): 205-220.
[5] Lamport, Leslie. "Paxos made simple." ACM Sigact News 32.4 (2001): 18-25.
[6] Ongaro, Diego, and John K. Ousterhout. "In Search of an Understandable Consensus Algorithm." USENIX Annual Technical Conference. 2014.
[7] Hunt, Patrick, et al. "ZooKeeper: Wait-free Coordination for Internet-scale Systems." USENIX annual technical conference. Vol. 8. 2010.
[8] http://www.datastax.com/dev/blog/why-cassandra-doesnt-need-vector-clocks
[9] https://aphyr.com/posts/299-the-trouble-with-timestamps
[10] Raynal, Michel, and Mukesh Singhal. "Logical time: Capturing causality in distributed systems." Computer 29.2 (1996): 49-56.
[11] http://kafka.apache.org/documentation.html#replication
[12] http://docs.hazelcast.org/docs/latest/manual/html-single/index.html#managing-cluster-and-member-states
[13] E. Brewer, "Towards Robust Distributed Systems," Proc. 19th Ann. ACM Symp. Principles of Distributed Computing (PODC 00), ACM, 2000, pp. 7-10
[14] https://codahale.com/you-cant-sacrifice-partition-tolerance/
[15] http://blog.nahurst.com/visual-guide-to-nosql-systems
[16] http://www.allthingsdistributed.com/2008/12/eventually_consistent.html
[17] https://www.somethingsimilar.com/2013/01/14/notes-on-distributed-systems-for-young-bloods/
[18] https://www.infoq.com/articles/cap-twelve-years-later-how-the-rules-have-changed
[19] Gilbert, Seth, and Nancy Lynch. "Brewer's conjecture and the feasibility of consistent, available, partition-tolerant web services." Acm Sigact News 33.2 (2002): 51-59.
[20] https://martin.kleppmann.com/2015/05/11/please-stop-calling-databases-cp-or-ap.html
[21] https://henryr.github.io/cap-faq/
[22] http://docs.hazelcast.org/docs/3.7/manual/html-single/index.html#wan-replication
[23] https://dev.mysql.com/doc/refman/5.7/en/group-replication.html
[24] https://www.voltdb.com/architecture
[25] Bailis, Peter, et al. "Highly available transactions: Virtues and limitations." Proceedings of the VLDB Endowment 7.3 (2013): 181-192.

30. THANKS!Any questions?