1. Clustering and Distributed Data: The Winning Combination?
Patrick Altaie paltaie

2. About the Author
From
Moved to (2013)
Weird accent
Design lead at

3. Which makes SW19 famous for

4. Disclaimer
There isn’t one

5. Agenda (all in 45min!) Akka clustering Cluster Singleton
State in an Akka cluster
Akka Distributed Data
A demo!
Agenda
(all in 45min!)

6. Akka Clustering Logical grouping for 1-n actor systems
Helps create a truly fault-tolerant application
Decentralised
Gossip based - cartesian product!
Actor systems can be heterogenous! - not all actor systems in a cluster need to be the exact same
Manuel Bernhardt's talk:
- phi accrual detector
- joining, leaving
- CRDT

7. But wait... What if I: Have an actor that maintains global state?
Need to bind to a port/have a single entry point?
Need to coordinate actions across the cluster system?
Like a scheduler maybe?

8. Cluster Singleton! Runs on the oldest node in the cluster
You can create any actor as a cluster singleton
Akka ensures only one instance of it runs in the cluster

9. But wait…(again)
Data that’s known in node[1-n] won’t be shared with the other nodes!
And what if we have a conflict?

10. Akka Distributed Data! Fancy eventually consistent distributed store
You decide the consistency requirement
Akka supplies you a Replicator actor:
DistributedData.get(context().system()).replicator();
To solve conflict problem, values must be CRDTs
Counters, sets, maps, registers. Some supplied:
GCounter, PNCounter
GSet, ORSet
ORMap, PNCounterMap, LWWMap
Flag, LWWRegister
Know your CRDT lingo!:
“G”: Grow
“PN”: Positive and Negative
“OR”: Observed Remove Set
“LWW”: Last Writer Wins
“PN”Counter is actually two GCounters (one going up and one going down)
You can roll your own data type and implement a merge function

11. An Example Let’s write a TFTP server! A subset of FTP RFC 1350
Lockstep protocol
Five message types
Requirements:
Resilient
Distributed
Who knew TFTP (1992) was a fast data system that implemented push-pull stream processing
Problem: Requested file only shows up in the RRQ!

12. All TFTP Message Types Opcode Description Direction 1
Read Request (RRQ)
Client -> Server 2
Write Request (WRQ) 3
0-512B data block (DATA)
Server -> Client 4
Acknowledgement of a DATA block (ACK) 5
Bad things! 😱 (ERROR)
Both

13. Architecture of a single node

14. Demo time!

15. Pitfalls/downsides Akka Clustering: Split brain
Akka DD: Not blazing fast
>50 ms for WriteMajority when >10 nodes in the cluster
Want blazing fast? Akka Distributed PubSub!
Akka DD: Not for “big data”
All entries are held in memory
Limit of 100,000 top-level elements
Replication via gossip gets slower
Resolutions for split brain:
Akka split brain resolver
Using an external system as an arbiter (e.g. MongoDB odd number of nodes)

16. github.com/paltaie/akka-tftp-server
It’s on GitHub!
github.com/paltaie/akka-tftp-server

17. iconsolutions.com/ipf
Shameless plug
Live on Earth? We’re hiring!
Java devs
Architects
It’s in Wimbledon
Working in payments? Instant payments got you down?
iconsolutions.com/ipf

18. Thanks!
paltaie.com
twitter.com/paltaie
github.com/paltaie