Machine Intelligence Guild_ Build ML Enhanced Event Streaming Applications with Java and Python Microservices https://github.com/tspannhw/SpeakerProfile https://github.com/tspannhw/pulsar-ml-recipes https://github.com/tspannhw/FLiPStackWeekly https://github.com/tspannhw/pulsar-thermal-pinot https://medium.com/@tspann/transit-watch-real-time-feeds-d98ff62b3bbb https://github.com/tspannhw/pulsar-pychat-function https://github.com/tspannhw/pulsar-transit-function https://streamnative.io/blog/engineering/2021-11-17-building-edge-applications-with-apache-pulsar/ http://tinyurl.com/bdha5p4r https://dzone.com/trendreports/enterprise-ai-1 Introduction What is Apache Pulsar? Why Apache Pulsar? Remember Hadoop and Kafka? Functions Apache NiFi Apache Flink Demos Q&A Topic: Build ML Enhanced Event Streaming Applications with Java and Python Microservices

1. BUILD ML ENHANCED
EVENT STREAMING
APPLICATIONS WITH
MICROSERVICES
Tim Spann | Developer Advocate

2. ● Introduction
● What is Apache Pulsar?
● Why Apache Pulsar?
● Remember Hadoop and
Kafka?
● Functions
● Apache NiFi
● Apache Flink
● Demos
● Q&A

3. Tim Spann
Developer Advocate
Tim Spann
Developer Advocate at StreamNative
● FLiP(N) Stack = Flink, Pulsar and NiFi Stack
● Streaming Systems & Data Architecture Expert
● Experience:
○ 15+ years of experience with streaming technologies including Pulsar, Flink, Spark, NiFi, Big
Data, Cloud, MXNet, IoT, Python and more.
○ Today, he helps to grow the Pulsar community sharing rich technical knowledge and experience
at both global conferences and through individual conversations.

4. FLiP Stack Weekly
This week in Apache Flink, Apache Pulsar, Apache
NiFi, Apache Spark and open source friends.
https://bit.ly/32dAJft

6. Apache Pulsar has a vibrant community
560+
Contributors
10,000+
Commits
7,000+
Slack Members
1,000+
Organizations
Using Pulsar

7. It is often assumed that Pulsar and Kafka have equal capabilities. In reality,
Pulsar offers a superset of Kafka.
● Pulsar is streaming and queuing together
● Pulsar is cloud-native with stateless brokers
● Natively includes geo-replication, multi-tenancy, and end-to-end
security out of the box
● Pulsar provides automated rebalancing
● Pulsar offers 100X lower latency w/ 2.5 greater throughput than Kafka
Advantages of Apache Pulsar

8. CREATED
Originally
developed inside
Yahoo! as Cloud
Messaging
Service
GROWTH
10x Contributors
10MM+ Downloads
Ecosystem Expands
Kafka on Pulsar
AMQ on Pulsar
Functions
. . .
2012 2016 2018 TODAY
APACHE TLP
Pulsar
becomes
Apache top
level project.
OPEN SOURCE
Pulsar
committed
to open source.
Apache Pulsar Timeline

9. Evolution of Pulsar Growth

10. Pulsar Has a Built-in Super Set of OSS
Features
Durability
Scalability Geo-Replication
Multi-Tenancy
Unified Messaging
Model
Reduced Vendor Dependency
Functions
Open-Source Features

11. Apache Pulsar is built to support legacy applications, handle the
needs of modern apps, and supports NextGen applications
Support legacy workloads.
Compatible with popular
messaging and streaming tools.
Legacy
Built for today's real-time
event driven applications.
Modern
Scalable, adaptive architecture
ready for the future of real-time
streaming.
NextGen

12. Apache Pulsar features
Cloud native with decoupled
storage and compute layers.
Built-in compatibility with your
existing code and messaging
infrastructure.
Geographic redundancy and high
availability included.
Centralized cluster management
and oversight.
Elastic horizontal and vertical
scalability.
Seamless and instant partitioning
rebalancing with no downtime.
Flexible subscription model
supports a wide array of use cases.
Compatible with the tools you use
to store, analyze, and process data.

13. Component Description
Value /
Data payload
The data carried by the message. All Pulsar messages contain raw bytes,
although message data can also conform to data schemas.
Key Messages are optionally tagged with keys, used in partitioning and also is useful
for things like topic compaction.
Properties An optional key/value map of user-defined properties.
Producer name The name of the producer who produces the message. If you do not specify a
producer name, the default name is used.
Sequence ID Each Pulsar message belongs to an ordered sequence on its topic. The
sequence ID of the message is its order in that sequence.
Messages - the Basic Unit of Apache Pulsar

14. ● “Bookies”
● Stores messages and cursors
● Messages are grouped in
segments/ledgers
● A group of bookies form an
“ensemble” to store a ledger
● “Brokers”
● Handles message routing and
connections
● Stateless, but with caches
● Automatic load-balancing
● Topics are composed of
multiple segments
●
● Stores metadata for both
Pulsar and BookKeeper
● Service discovery
Store
Messages
Metadata &
Service Discovery
Metadata &
Service Discovery
Pulsar Cluster
Metadata
Storage
Pulsar Cluster

15. Different subscription modes
have different semantics:
Exclusive/Failover -
guaranteed order, single active
consumer
Shared - multiple active
consumers, no order
Key_Shared - multiple active
consumers, order for given key
Producer 1
Producer 2
Pulsar Topic
Subscription D
Consumer D-1
Consumer D-2
Key-Shared
<
K
1,
V
10
>
<
K
1,
V
11
>
<
K
1,
V
12
>
<
K
2
,V
2
0
>
<
K
2
,V
2
1>
<
K
2
,V
2
2
>
Subscription C
Consumer C-1
Consumer C-2
Shared
<
K
1,
V
10
>
<
K
2
,V
2
1>
<
K
1,
V
12
>
<
K
2
,V
2
0
>
<
K
1,
V
11
>
<
K
2
,V
2
2
>
Subscription A Consumer A
Exclusive
Subscription B
Consumer B-1
Consumer B-2
In case of failure in
Consumer B-1
Failover
Apache Pulsar Subscription Modes

16. Streaming
Consumer
Consumer
Consumer
Subscription
Shared
Failover
Consumer
Consumer
Subscription
In case of failure in
Consumer B-0
Consumer
Consumer
Subscription
Exclusive
X
Consumer
Consumer
Key-Shared
Subscription
Pulsar
Topic/Partition
Messaging

19. Messaging Use Cases Streaming Use Cases
Service x commands service y to make some
change.
Example: order service removing item from
inventory service
Moving large amounts of data to another service
(real-time ETL).
Example: logs to elasticsearch
Distributing messages that represent work
among n workers.
Example: order processing not in main “thread”
Periodic jobs moving large amounts of data and
aggregating to more traditional stores.
Example: logs to s3
Sending “scheduled” messages.
Example: notification service for marketing emails
or push notifications
Computing a near real-time aggregate of a message
stream, split among n workers, with order being
important.
Example: real-time analytics over page views
Messaging vs Streaming

20. Messaging Use Case Streaming Use Case
Retention The amount of data retained is
relatively small - typically only a day
or two of data at most.
Large amounts of data are retained,
with higher ingest volumes and
longer retention periods.
Throughput Messaging systems are not designed
to manage big “catch-up” reads.
Streaming systems are designed to
scale and can handle use cases
such as catch-up reads.
Differences in Consumption

21. byte[] msgIdBytes = // Some byte
array
MessageId id =
MessageId.fromByteArray(msgIdBytes);
Reader<byte[]> reader =
pulsarClient.newReader()
.topic(topic)
.startMessageId(id)
.create();
Create a reader that will read from
some message between earliest and
latest.
Reader
Apache Pulsar Reader Interface

22. ● New Consumer type added in Pulsar 2.10 that provides a
continuously updated key-value map view of compacted topic data.
● An abstraction of a changelog stream from a primary-keyed table,
where each record in the changelog stream is an update on the
primary-keyed table with the record key as the primary key.
● READ ONLY DATA STRUCTURE!
Apache Pulsar TableView

23. Schema Registry
schema-1 (value=Avro/Protobuf/JSON) schema-2 (value=Avro/Protobuf/JSON) schema-3
(value=Avro/Protobuf/JSON)
Schema
Data
ID
Local Cache
for Schemas
+
Schema
Data
ID +
Local Cache
for Schemas
Send schema-1
(value=Avro/Protobuf/JSON) data
serialized per schema ID
Send (register)
schema (if not in
local cache)
Read schema-1
(value=Avro/Protobuf/JSON) data
deserialized per schema ID
Get schema by ID (if
not in local cache)
Producers Consumers
Schema Registry

24. ● Utilizing JSON Data with a JSON Schema
● Consistency, Contracts, Clean Data
● This enables easy SQL:
○ Pulsar SQL (Presto SQL)
○ Flink SQL
○ Spark Structured Streaming
Use Schemas

25. • Functions - Lightweight Stream
Processing (Java, Python, Go)
• Connectors - Sources & Sinks
(Cassandra, Kafka, …)
• Protocol Handlers - AoP (AMQP), KoP
(Kafka), MoP (MQTT)
• Processing Engines - Flink, Spark,
Presto/Trino via Pulsar SQL
• Data Offloaders - Tiered Storage - (S3)
Sources, Sinks and Processing

26. Kafka on Pulsar (KoP)

27. MQTT on Pulsar (MoP)

28. AMQP on Pulsar (AoP)

29. The FLiPN Kitten crosses the stream,
4 ways with Pulsar
MoP AoP KoP WebSockets

30. Use Apache Pulsar For Ingest

31. Use Apache Pulsar To Stream to Lakehouses

32. ● Lightweight computation similar
to AWS Lambda.
● Specifically designed to use
Apache Pulsar as a message
bus.
● Function runtime can be
located within Pulsar Broker.
● Java Functions
A serverless event
streaming framework
Pulsar Functions

33. ● Consume messages from one or
more Pulsar topics.
● Apply user-supplied processing
logic to each message.
● Publish the results of the
computation to another topic.
● Support multiple programming
languages (Java, Python, Go)
● Can leverage 3rd-party libraries
to support the execution of ML
models on the edge.
Pulsar Functions

34. ● Route
● Enrich
● Convert
● Lookups
● Run
Machine Learning
● Logging
● Auditing
● Parse
● Split
● Convert
Pulsar Functions

35. ML Java Coding (Deep Java Library)

36. Java Pulsar Functions

37. from pulsar import Function
import json
class Chat(Function):
def __init__(self):
pass
def process(self, input, context):
logger = context.get_logger()
logger.info("Message Content: {0}".format(input))
msg_id = context.get_message_id()
row = { }
row['id'] = str(msg_id)
json_string = json.dumps(row)
return json_string
Python Pulsar Functions

38. from pulsar import Function
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
import json
class Chat(Function):
def __init__(self):
pass
def process(self, input, context):
fields = json.loads(input)
sid = SentimentIntensityAnalyzer()
ss = sid.polarity_scores(fields["comment"])
row = { }
row['id'] = str(msg_id)
if ss['compound'] < 0.00:
row['sentiment'] = 'Negative'
else:
row['sentiment'] = 'Positive'
row['comment'] = str(fields["comment"])
json_string = json.dumps(row)
return json_string
Entire Function
Pulsar Python NLP Function

39. Why Pulsar Functions for Microservices?
Desired Characteristic Pulsar Functions…
Highly maintainable and testable Are small pieces of code written in popular
languages such as Java, Python, or Go. They can be
easily maintained in source control repositories and
tested with existing frameworks automatically.
Loosely coupled with other
services
Are not directly linked to one another and
communicate via messages.
Independently deployable Are designed to be deployed independently
Can be developed by a small team Are often developed by a single developer.
Inter-service Communication Support all message patterns using Pulsar as the
underlying message bus.
Deployment & Composition Can run as individual threads, processes, or K8s
pods. The Function Mesh allows you to deploy
multiple Pulsar Functions as a single unit.

40. Function Mesh
Pulsar Functions, along with Pulsar
IO/Connectors, provide a powerful API for
ingesting, transforming, and outputting data.
Function Mesh, another StreamNative
project, makes it easier for developers to
create entire applications built from sources,
functions, and sinks all through a declarative
API.

41. Edge AI

42. ● Apache Pulsar’s two-tier architecture separates the compute and storage layers, and interact
with one another over a TCP/IP connection. This allows us to run the computing layer (Broker)
on either Edge servers or IoT Gateway devices.
● Pulsar’s serverless computing framework, know as Pulsar Functions, can run inside the
Broker as threads. Effectively “stretching” the data processing layer.
Edge Computing with Pulsar

43. ● Pulsar’s Serverless computing framework can run inside the Pulsar Broker as a
thread pool. This framework can be used as the execution environment for ML
models.
● The Apache Pulsar Broker supports the MQTT protocol and therefore can directly
receive incoming data from the sensor hubs and store it in a topic.
Benefits of Running Pulsar Broker on the Edge

44. ● You can leverage 3rd
party libraries
within Pulsar Functions
● DeepLearning4J
● JPMML
● DJL.AI
● Keras
● Pulsar Functions are able to
support:
● A variety of ML model types.
● Models developed with
different languages and toolkits
Pulsar Function – Third Party Library Support

45. Building Real-Time Apps Requires a Team

46. https://www.influxdata.com/integration/mqtt-monitoring/
https://www.influxdata.com/integration/mqtt-monitoring/
• Guaranteed delivery
• Data buffering
- Backpressure
- Pressure release
• Prioritized queuing
• Flow specific QoS
- Latency vs. throughput
- Loss tolerance
• Data provenance
• Supports push and pull
models
• Hundreds of processors
• Visual command and
control
• Over a 300 components
• Flow templates
• Pluggable/multi-role
security
• Designed for extension
• Clustering
• Version Control
Apache NiFi Basics

47. Apache NiFi - Apache Pulsar Connector

48. https://github.com/streamnative/pulsar-nifi-bundle
Apache NiFi - Apache Pulsar Connector

49. Apache NiFi - Apache Pulsar Connector

50. Apache NiFi - Apache Pulsar Connector

51. ● Unified computing engine
● Batch processing is a special case of stream processing
● Stateful processing
● Massive Scalability
● Flink SQL for queries, inserts against Pulsar Topics
● Streaming Analytics
● Continuous SQL
● Continuous ETL
● Complex Event Processing
● Standard SQL Powered by Apache Calcite
Apache Flink

52. Apache Flink Job Dashboard

54. NLP Streaming Architecture

55. IoT Streaming Architecture

56. Streaming FLiPN Java App

57. Learn More

60. Apache Pulsar Training
● Instructor-led courses
○ Pulsar Fundamentals
○ Pulsar Developers
○ Pulsar Operations
● On-demand learning with labs
● 300+ engineers, admins and
architects trained!
Now Available
On-Demand
Pulsar Training
Academy.StreamNative.io
StreamNative Academy

61. ● https://github.com/tspannhw/pulsar-pychat-function
● https://streamnative.io/apache-nifi-connector/
● https://nightlies.apache.org/flink/flink-docs-master/docs/conne
ctors/datastream/pulsar/
● https://streamnative.io/en/blog/release/2021-04-20-flink-sql-o
n-streamnative-cloud
● https://github.com/streamnative/flink-example
● https://pulsar.apache.org/docs/en/adaptors-spark/
Apache Pulsar Links

62. ● https://github.com/tspannhw/FLiP-Pi-BreakoutGarden
● https://github.com/tspannhw/FLiP-Pi-Thermal
● https://github.com/tspannhw/FLiP-Pi-Weather
● https://github.com/tspannhw/FLiP-RP400
● https://github.com/tspannhw/FLiP-Py-Pi-GasThermal
● https://github.com/tspannhw/FLiP-PY-FakeDataPulsar
● https://github.com/tspannhw/FLiP-Py-Pi-EnviroPlus
● https://github.com/tspannhw/PythonPulsarExamples
● https://github.com/tspannhw/pulsar-pychat-function
● https://github.com/tspannhw/FLiP-PulsarDevPython101
Apache Pulsar Examples

63. Deploying AI With an
Event-Driven
Platform
https://dzone.com/trendreports/enterprise-ai-1

65. Apache Pulsar in Action
http://tinyurl.com/bdha5p4r
Please enjoy David’s complete book which is the ultimate guide to Pulsar.

66. https://streamnative.io/blog/engineering/2021-11-17-building-edge-applications-with-apache-pulsar/

67. Scan the QR code
to learn more about
Apache Pulsar and
StreamNative.

68. Scan the QR code
to build your own
apps today.

69. Tim Spann
Developer Advocate
@PaaSDev
https://www.linkedin.com/in/timothyspann
https://github.com/tspannhw
Let’s Keep in Touch