Reactive applications are becoming a de-facto industry standard and, if employed correctly, toolkits like Lightbend Reactive Platform make the implementation easier than ever. But the design of these systems might be challenging as it requires particular mindset shift to tackle problems we might not be used to. In this talk, we’re going to discuss the most common things I’ve seen in the field that prevented applications to work as expected. I’d like to talk about typical pitfalls that might cause troubles, about trade-offs that might not be fully understood or important choices that might be overlooked including persistent actors pitfalls, tackling of network partitions, proper implementations of graceful shutdown or distributed transactions, trade-offs of micro-services or actors and more. This talk should be interesting for anyone who is thinking about, implementing, or have already deployed a reactive application. My goal is to provide is to provide a comprehensive explanation of common problems to be sure they won’t be repeated by fellow developers. The talk is a little bit more focused on Lightbend platform but the understanding of the concepts we are going to talk about should be beneficial for everyone interested in this field.

1. MANCHESTER LONDON NEW YORK

2. Petr Zapletal @petr_zapletal
@ScalaByTheBay
@cakesolutions
Top Mistakes When Writing Reactive
Applications

3. Agenda
● Motivation
● Actors vs Futures
● Serialization
● Graceful Shutdown
● Distributed Transactions
● Longtail Latencies
● Quick Tips

4. Actors vs Futures
Constraints Liberate, Liberties Constrain

5. Pick the Right Tool for The Job
Akka
ACTORS
Power
Constraints
Akka
Stream

6. Pick the Right Tool for The Job
Akka
ACTORS
Power
Constraints
Akka
TYPED

7. Pick the Right Tool for The Job
Akka
TYPED
Akka
ACTORS
Power
Constraints
Akka
Stream

8. Pick the Right Tool for The Job
Local Abstractions Distribution
Akka
TYPED
Akka
ACTORS
Power
Constraints
Akka
Stream

9. Actor Use Cases
● State management
● Location transparency
● Resilience mechanisms
● Single writer
● In-memory lock-free cache
● Sharding
Akka
ACTOR

10. Future Use Cases
● Local Concurrency
● Simplicity
● Composition
● Typesafety

11. Avoid Java Serialization
Java Serialization is the default in Akka, since
it is easy to start with it, but is very slow and
footprint heavy

12. Akka
ACTOR
Sending Data Through Network
Serialization Serialization
Akka
ACTOR

13. Persisting Data
Akka
ACTOR
Serialization

14. Java Serialization - Round Trip

15. Java Serialization - Footprint

16. Java Serialization - Footprint
Java Serialization:
----sr--model.Order----h#-----J--idL--customert--Lmodel/Customer;L--descriptiont--Ljava/lang/String;L--orderLinest--Ljava/util
/List;L--totalCostt--Ljava/math/BigDecimal;xp--------ppsr--java.util.ArrayListx-----a----I--sizexp----w-----sr--model.OrderLine--
&-1-S----I--lineNumberL--costq-~--L--descriptionq-~--L--ordert--Lmodel/Order;xp----sr--java.math.BigDecimalT--W--(O---I--s
caleL--intValt--Ljava/math/BigInteger;xr--java.lang.Number-----------xp----sr--java.math.BigInteger-----;-----I--bitCountI--bitLe
ngthI--firstNonzeroByteNumI--lowestSetBitI--signum[--magnitudet--[Bxq-~----------------------ur--[B------T----xp----xxpq-~--x
q-~--
XML:
<order id="0" totalCost="0"><orderLines lineNumber="1" cost="0"><order>0</order></orderLines></order>
JSON:
{"order":{"id":0,"totalCost":0,"orderLines":[{"lineNumber":1,"cost":0,"order":0}]}}

17. Points of Interest
● Performance
● Footprint
● Schema evolution
● Implementation effort
● Human readability
● Language bindings
● Backwards & forwards compatibility
● ...

18. JSON
● Advantages:
○ Human readability
○ Simple & well known
○ Many good libraries
for all platforms
● Disadvantages:
○ Slow
○ Large
○ Object names included
○ No schema (except e.g. json
schema)
○ Format and precision issues
● json4s, circe, µPickle, spray-json, argonaut, rapture-json, play-json, …

19. Binary formats [Schema-less]
● Metadata send together with data
● Advantages:
○ Implementation effort
○ Performance
○ Footprint *
● Disadvantages:
○ No human readability
● Kryo, Binary JSON (MessagePack, BSON, ... )

20. Binary formats [Schema]
● Schema defined by some kind of DSL
● Advantages:
○ Performance
○ Footprint
○ Schema evolution
● Disadvantages:
○ Implementation effort
○ No human readability
● Protobuf (+ projects like Flatbuffers, Cap’n Proto, etc.), Thrift, Avro

21. Summary
● Should be always changed
● Depends on particular use case
● Quick tips:
○ json4s
○ kryo
○ protobuf

22. Graceful Shutdown
We have thousands of sharded actors on
multiple nodes and we want to shut one of
them down

23. Graceful Shutdown

24. High-level Procedure

25. High-level Procedure
1. JVM gets the shutdown signal

26. High-level Procedure
1. JVM gets the shutdown signal
2. Coordinator tells all local ShardRegions to shut down gracefully

27. High-level Procedure
1. JVM gets the shutdown signal
2. Coordinator tells all local ShardRegions to shut down gracefully
3. Node leaves cluster

28. High-level Procedure
1. JVM gets the shutdown signal
2. Coordinator tells all local ShardRegions to shut down gracefully
3. Node leaves cluster
4. Coordinator gives singletons a grace period to migrate

29. High-level Procedure
1. JVM gets the shutdown signal
2. Coordinator tells all local ShardRegions to shut down gracefully
3. Node leaves cluster
4. Coordinator gives singletons a grace period to migrate
5. Actor System & JVM Termination

30. Integration with Sharded Actors
● Handling of added messages
○ Passivate() message for graceful stop
○ Context.stop() for immediate stop
● Priority mailbox
○ Priority message handling
○ Message retrying support

31. Summary
● We don’t want to lose data (usually)
● Shutdown coordinator on every node
● Integration with sharded actors

32. Distributed Transactions
Any situation where a single event results in
the mutation of two separate sources of data
which cannot be committed atomically

33. What’s Wrong With Them
● Simple happy paths
● 7 Fallacies of Distributed Programming
○ The network is reliable.
○ Latency is zero.
○ Bandwidth is infinite.
○ The network is secure.
○ Topology doesn't change.
○ There is one administrator.
○ Transport cost is zero.
○ The network is homogeneous.

34. Two-phase commit (2PC)
Stage 1 - Prepare Stage 2 - Commit
Prepare
Prepared
Prepare
Prepared
Com
m
it
Com
m
itted
Commit
Committed
Resource
Manager
Resource
Manager
Transaction
Manager
Resource
Manager
Resource
Manager
Transaction
Manager

35. Saga Pattern
T1 T2 T3 T4
C1 C2 C3 C4

36. The Big Trade-Off
● Distributed transactions can be usually avoided
○ Hard, expensive, fragile and do not scale
● Every business event needs to result in a single synchronous commit
● Other data sources should be updated asynchronously
● Introducing eventual consistency

37. Longtail Latencies
Consider a system where each service
typically responds in 10ms but with a 99th
percentile latency of one second

38. Longtail Latencies
Latency Normal vs. Longtail
Legend:
Normal
Longtail
50
40
30
20
10
0
25 50 75 90 99 99.9
Latency(ms)
Percentile

39. Longtails really matter
● Latency accumulation
● Not just noise
● Don’t have to be power users
● Real problem

40. Tolerating Longtail Latencies

41. Tolerating Longtail Latencies
● Hedging your bet

42. Tolerating Longtail Latencies
● Hedging your bet
● Tied requests

43. Tolerating Longtail Latencies
● Hedging your bet
● Tied requests
● Selectively increase replication factors

44. Tolerating Longtail Latencies
● Hedging your bet
● Tied requests
● Selectively increase replication factors
● Put slow machines on probation

45. Tolerating Longtail Latencies
● Hedging your bet
● Tied requests
● Selectively increase replication factors
● Put slow machines on probation
● Consider ‘good enough’ responses

46. Tolerating Longtail Latencies
● Hedging your bet
● Tied requests
● Selectively increase replication factors
● Put slow machines on probation
● Consider ‘good enough’ responses
● Hardware update

47. Quick Tips

48. Quick Tips
● Monitoring

49. Quick Tips
● Monitoring
● Network partitions & Split Brain Resolver

50. Quick Tips
● Monitoring
● Network partitions & Split Brain Resolver
● Blocking

51. Quick Tips
● Monitoring
● Network partitions & Split Brain Resolver
● Blocking
● Too many actor systems

52. Quick Tips
● Monitoring
● Network partitions & Split Brain Resolver
● Blocking
● Too many actor systems
● Error Handling

53. Questions
MANCHESTER LONDON NEW YORK

54. MANCHESTER LONDON NEW YORK
@petr_zapletal @cakesolutions
347 708 1518
petrz@cakesolutions.net
We are hiring
http://www.cakesolutions.net/careers

55. References
● http://www.slideshare.net/ktoso/zen-of-akka
● http://eishay.github.io/jvm-serializers/prototype-results-page/
● http://java-persistence-performance.blogspot.com/2013/08/optimizing-java-serialization-java-vs.html
● https://github.com/romix/akka-kryo-serialization
● http://gotocon.com/dl/goto-chicago-2015/slides/CaitieMcCaffrey_ApplyingTheSagaPattern.pdf
● http://www.grahamlea.com/2016/08/distributed-transactions-microservices-icebergs/
● http://www.cs.duke.edu/courses/cps296.4/fall13/838-CloudPapers/dean_longtail.pdf
● https://engineering.linkedin.com/performance/who-moved-my-99th-percentile-latency
● http://doc.akka.io/docs/akka/rp-15v09p01/scala/split-brain-resolver.html

56. Backup Slides
MANCHESTER LONDON NEW YORK

57. Adding Shutdown Hook

58. Adding Shutdown Hook

59. Adding Shutdown Hook

60. Tell Local Regions to Shutdown

61. Tell Local Regions to Shutdown

62. Tell Local Regions to Shutdown

63. Tell Local Regions to Shutdown

64. Node Leaves the Cluster

65. Node Leaves the Cluster

66. Node Leaves the Cluster

67. Wait for Singletons to Migrate

68. Wait for Singletons to Migrate

69. Wait for Singletons to Migrate

70. Wait for Singletons to Migrate

71. Actor System & JVM Termination

72. Actor System & JVM Termination

73. Actor System & JVM Termination

74. Actor System & JVM Termination