In this presentation, we will cover: - How to performance test and optimize a Pulsar cluster. We will present how we load tested Pulsar with locust and, following this, how we tuned our configurations for our use cases. - Event sourcing pattern with Apache Pulsar. Avro schema usage, compatibility choices and schema evolution on pulsar topics that worked for us. - Bonus: How we source Apache Flink from apache pulsar and run our workflows. By attending this webinar, you can expect to come away with: - How to performance test a Pulsar cluster for your use case. - How to leverage the highly configurable broker and Bookkeeper to suit your needs. - Event sourcing patterns on top of Apache Pulsar. - Avro schema usage, compatibility choices, and evolution. - Familiarise with pulsar connector for Flink and possible use cases.

2. ● Senior Developer at
Nutanix responsible for all
things pulsar
● Love spending time with
data (stores, steam,
analytics etc)
● Ex-MySQL - started out
with 3 great years building
MySQL Replication
● Contributions to pulsar &
MySQL
Who am I ?
https://www.linkedin.com/in/shivjijha/
https://twitter.com/ShivjiJha

3. ● Helping customers
manage cost and security
for hybrid cloud.
● Crunch (& stream) data to
find insights about cost
and security
● Needed pub/sub to store
events and replay when
required
What do we do ?
https://www.nutanix.com/products/beam

4. Platforms We Use

5. Platforms We Use

6. How do we
Choose
a platform ??

7. Avoid bias
towards
familiar
technology

8. The First Steps

9. Summarising the github comment
1. Kafka alternative - incubating apache project PULSAR
2. Open sourced by Yahoo
3. Hundreds of billions of messages per day in pulsar at Yahoo
4. Solving annoying problems in kafka like:
a. Topic management
b. Disruptive rebalances
5. Same raw power (throughput, latencies etc)
6. Stateless brokers
7. Apache bookkeeper for storage
8. Stream + queue

10. Wow, that is
a lot of
Promise!!

11. First principles - Requirements?
1. Coordination
2. Persistence
3. Scale compute and storage independently
4. Reliability
5. High Availability
6. Fault tolerance
7. Client ecosystem

12. Requirement # 1
✓ Coordination

13. Requirement # 1
✓ Coordination

14. Requirement # 1
1. Coordination

15. Requirement # 2
✓ Persistence

16. Requirement # 2
✓ Persistence

17. Requirement # 2
✓ Persistence

18. Requirement # 2
✓ Persistence

19. Requirement # 3
✓ Scale compute and storage independently

20. Requirement # 3
✓ Scale compute and storage independently

21. Requirement # 3
✓ Scale compute and storage independently
Brokers => serve msg

22. Requirement # 3
✓ Scale compute and storage independently
Bookies => store
Brokers => serve msg

23. Requirement # 3
✓ Scale compute and storage independently
Bookies => store
Brokers => serve msg

24. Requirement # 4
✓ High Availability

25. Requirement # 4
✓ High Availability

26. Requirement # 4
✓ High Availability
Replicated brokers
Replicated bookies

27. Requirement # 4
✓ High Availability
Replicated brokers
Replicated bookies

28. Requirement # 5
✓ Fault tolerance
✓ Replicated compute (brokers)
✓ Replicated store (bookkeeper / bookies)

29. Requirement # 5
✓ Fault tolerance
✓ Replicated compute (brokers)
✓ Replicated store (bookkeeper / bookies)
✓ Tunable fault tolerance (bookkeeper)
✓ Ensemble size
✓ Write quorum size
✓ Ack quorum size
--ensemble 2 --writeQuorum 2 --ackQuorum 2

30. Requirement # 5
✓ Fault tolerance
✓ Replicated compute (brokers)
✓ Replicated store (bookkeeper / bookies)
✓ Tunable fault tolerance (bookkeeper)
✓ Ensemble size
✓ Write quorum size
✓ Ack quorum size
When scaling bookie cluster,
finetune quorum sizes
--ensemble 2 --writeQuorum 2 --ackQuorum 2

31. Requirement # 5
✓ Fault tolerance
✓ Replicated compute (brokers)
✓ Replicated store (bookkeeper / bookies)
✓ Tunable fault tolerance (bookkeeper)
✓ Ensemble size
✓ Write quorum size
✓ Ack quorum size
--ensemble 2 --writeQuorum 2 --ackQuorum 2
When scaling bookie cluster,
finetune quorum sizes

32. Requirement # 6
✓ Client ecosystem
✓ Work in progress

33. Requirement # 6
✓ Client ecosystem
✓ Work in progress
✓ Compensating factors:
✓ Clients are easier to change, just a library afterall!
✓ Very active community (slack)
✓ Quick turnaround (and quick fixes) for critical issues

34. Requirement # 6
✓ Client ecosystem
✓ Work in progress
✓ Compensating factors:
✓ Clients are easier to change, just a library afterall!
✓ Very active community (slack)
✓ Quick turnaround (and quick fixes) for critical issues
✓ Bonus features
✓ Load balancer auto balances topics among brokers
✓ Tiered storage
✓ Unified platform (Stream + Queue)
✓ Tiered topic structure

35. Requirement # 6
✓ Client ecosystem
✓ Work in progress
✓ Compensating factors:
✓ Clients are easier to change, just a library afterall!
✓ Very active community (slack)
✓ Quick turnaround (and quick fixes) for critical issues
✓ Bonus features
✓ Load balancer auto balances topics among brokers
✓ Tiered storage
✓ Unified platform (Stream + Queue)
✓ Tiered topic structure

36. Tuning Configurations
✓ Configurations could be optimized for backward compatibility
✓ Not necessarily for performance
✓ Not necessarily for latest features
✓ Perf Test for your use cases and tune!

37. Performance Testing
Pulsar with

39. Test Sync
Message
Test Async
Message

40. Tuning Configurations
✓ Durability vs throughput (bookkeeper.conf)
# Maximum latency to impose on a journal write to achieve grouping
journalMaxGroupWaitMSec=2

41. Tuning Configurations
✓ Disable auto recovery in bookkeeper when out for maintenance!
bookkeeper shell autorecovery -disable
STOP / MAINTENANCE / START
bookkeeper shell autorecovery -enable

42. Tuning Configurations
✓ Auto recovery vs throughput (broker.conf)
✓ If you have a small number of bookies, and a bookie goes down, auto recovery
may overwhelm the remaining bookies
✓ Number of entries that a replication will re-replicate in parallel
maxPendingReadRequestsPerThread=2500
rereplicationEntryBatchSize=100

43. Contribute to stay in sync
1. Development is fast, in fact very fast
a. Don’t maintain forks, easier to contribute
https://github.com/apache/pulsar/graphs/contributors

44. Contribute to stay in sync
1. Development is fast, in fact very fast
a. Don’t maintain forks, easier to contribute
2. We do the same!
https://github.com/apache/pulsar/graphs/contributors

45. Pulsar Use cases In Beam
&

46. Event Sourcing
1. Persisting your application's state by storing the history that
determines the current state of your application.
State of application at
any point in time
State of application at
this instant of time
https://docs.microsoft.com/en-us/previous-versions/msp-n-p/jj591559(v=pandp.10)

47. ● History of events
● Past Tense verbs
● Immutable
● Ordered
● Restore for state at any
point in time
● Use: CQRS, Audit trail etc
Event Sourcing
https://docs.microsoft.com/en-us/azure/architecture/patterns/event-sourcing

48. Representing Events (Schema)
1. Pulsar supports bytes, string, avro, ptobuff, json etc
2. Schemaless?
a. Any code that manipulates the data needs to make some
assumptions about its structure
b. All producers and consumers know the hidden implicit schema
3. Opinion : Use schema as far as possible.
a. Pulsar supports schema registry out of the box

49. Representing Events (Schema)
1. Of course, Schemalessness offers a pragmatic alternative at times.
https://martinfowler.com/articles/schemaless/#non-uniform-types

50. Representing Events (Schema)
1. Of course, Schemalessness offers a pragmatic alternative at times.
https://martinfowler.com/articles/schemaless/#non-uniform-types
Add custom
fields for UI etc

51. Representing Events (Schema)
1. Of course, Schemalessness offers a pragmatic alternative at times.
https://martinfowler.com/articles/schemaless/#non-uniform-types
Add custom
fields for UI etc
Different attributes
depending on kind
of event

52. Representing Events (Schema)
1. Of course, Schemalessness offers a pragmatic alternative at times.
https://martinfowler.com/articles/schemaless/#non-uniform-types
Add custom
fields for UI etc
Different attributes
depending on kind
of event
Obviously, easy for
schemaless,
still needs care!

53. What to put on ONE topic?
1. Two choices:
a. Topic == collection of events of same type
b. Topic == events that need relative ordering guarantee.
https://martin.kleppmann.com/2018/01/18/event-types-in-kafka-topic.html

54. What to put on ONE topic?
1. Two choices:
a. Topic == collection of events of same type
b. Topic == events that need relative ordering guarantee.
2. Winner: choice (b)
https://martin.kleppmann.com/2018/01/18/event-types-in-kafka-topic.html

55. Avro / Proto (Struct) Schema
1. Language agnostic schema. Being stuck with one language sucks!
2. JSON seems first pick if you use REST, but
a. slow and
b. too verbose.
c. Complete Schema shipped with every message
3. Avro and proto are good.
4. We like Avro for its wide adoption.
a. And use pulsar’s built in schema registry
5. Consider keeping schema flat and fat (denormalize)!
https://martin.kleppmann.com/2012/12/05/schema-evolution-in-avro-protocol-buffers-thrift.html

56. Schema Evolution
1. Choose a schema-auto-update strategy that suits use case.
a. We keep it forward compatible (add fields, delete optional fields)
b. Data produced with new schema can be read by consumers using last schema
c. Update producer, then consumers when they have time / need.
2. Each avro message contains an avro schema id & version.
3. Decode with the exact writer schema.

57. Summarizing Lessons
✓ Avoid bias to “known” when choosing a platform.
✓ Tune re-replication (ensemble, write quorum, ack quorum) when
scaling out bookies horizontally.
✓ Use schema, as far as possible!
✓ Tune configuration for size, resource, throughput, durability etc.
May be optimized for backward compatibility.
✓ Disable auto-recovery of bookie before taking down.
✓ Balance recovery with incoming user traffic.
✓ Put events that require ordering on same topic.

58. Q & A time