1. Streaming Data Platform
for Cloud-Native
Event-driven Applications
Tim Spann
Developer Advocate

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Agenda
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● Welcome
● Introduction to Apache Pulsar
○ Basics of Pulsar
○ Use Cases
● Let’s Build an App!
○ Demo
● Resources
● Q&A

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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.

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

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Agenda
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• Introduction
• Demo
• What is Apache Pulsar?
• Apache Flink
• Pulsar to NiFi, Pulsar to SSB
• Q&A

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Founded by the original creators of
Apache Pulsar.
StreamNative employs more than 50% of
the active core committers to Apache
Pulsar.
StreamNative has more experience
designing, deploying, and running
large-scale Apache Pulsar instances
than any team in the world.

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Building Real-Time
Requires a Team

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https://github.com/tspannhw/pulsar-csp-ce
https://github.com/tspannhw/FLiPN-FLaNK-KafkaConnectToMoP
https://github.com/tspannhw/create-nifi-pulsar-flink-apps
Demos

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

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Guaranteed
Message
Delivery
14
Unified
Messaging
Platform
Resiliency
The basics
Infinite
Scalability

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Pulsar Cluster
● “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
Metadata
Storage

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Tenants
(Compliance)
Tenants
(Data Services)
Namespace
(Microservices)
Topic-1
(Cust Auth)
Topic-1
(Location Resolution)
Topic-2
(Demographics)
Topic-1
(Budgeted Spend)
Topic-1
(Acct History)
Topic-1
(Risk Detection)
Namespace
(ETL)
Namespace
(Campaigns)
Namespace
(ETL)
Tenants
(Marketing)
Namespace
(Risk Assessment)
Pulsar Cluster
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Tenant - Namespaces - Topics

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Messages - the basic unit of Pulsar
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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.

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Producer-Consumer
Publisher sends data and
doesn't know about the
subscribers or their status.
Producer Consumer
Topic
All interactions go through
Pulsar and it handles all
communication.
Subscriber receives data
from publisher and never
directly interacts with it.

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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

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Pulsarʼs Publish-Subscribe model
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● Producers send messages.
● Topics are an ordered, named channels that producers use to transmit messages to
subscribed consumers.
● Messages belong to a topic and contain an arbitrary payload.
● Brokers handle connections and routes messages between producers / consumers.
● Subscriptions are named configuration rules that determine how messages are
delivered to consumers.
● Consumers receive messages.
Broker
Subscription
Consumer 1
Consumer 2
Consumer 3
Topic
Producer 1
Producer 2

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Pulsar Subscription Modes
21
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
21
>
<
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

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Integrated Schema Registry
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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

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The FliPN kitten crosses the stream
4 ways with Apache Pulsar
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Kafka on Pulsar (KoP)
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MQTT on Pulsar (MoP)
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AMQP on Pulsar (AoP)
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Kafka to Pulsar
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A serverless event streaming
framework
Pulsar Functions
● 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

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Pulsar Functions
● 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.

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Data Offloaders
(Tiered Storage)
Client Libraries
StreamNative Pulsar ecosystem
hub.streamnative.io
Connectors
(Sources & Sinks)
Protocol Handlers
Pulsar Functions
(Lightweight Stream
Processing)
Processing Engines
… and more!
… and more!

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

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Apache NiFi - Apache Pulsar Connector
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Apache NiFi - Apache Pulsar Connector
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https://github.com/david-streamlio/pulsar-nifi-bundle/releases/tag/v1.14.0

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Flink-Pulsar Sink Connector

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Cloudera SQL Stream Builder -> Pulsar Connector
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Cloudera SQL Stream Builder -> Pulsar Connector
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Apache Pulsar
in Action
Please enjoy David’s complete
book which is the ultimate
guide to Pulsar.

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● https://medium.com/@tspann/using-apache-
pulsar-with-cloudera-sql-builder-apache-flin
k-b518aa9eadff
● https://medium.com/@tspann/apache-pulsar
-processor-updated-for-the-new-apache-nif
i-1-18-0-af781d722e63

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@PassDev
https://www.linkedin.com/in/timothyspann
https://github.com/tspannhw
https://streamnative.io/pulsar-python/
40
Tim Spann
Developer Advocate
at StreamNative

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Additional Slides

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Concentrate data for multiple clusters into a single one where main processing happens.
Data Aggregation

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Processing happens identical in both clusters.
Active - Active

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Only active cluster processes data - Passive cluster is for failover.
Active - Passive