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Timothy Spann
Developer Advocate
Serverless Event Streaming
Applications as Functions on K8
16 May 2022 I Valencia, Spain
Apache Pulsar is a Cloud-Native Messaging
and Event-Streaming Platform.
Key Pulsar Concepts: Messaging vs
Streaming
Message Queueing - Queueing
systems are ideal for work queues
that do not require tasks to be
performed in a particular order.
Streaming - Streaming works
best in situations where the
order of messages is important.
Pulsar Cluster
CLUSTER
Global ZooKeeper
Configuration Store
ZK3
ZK2
ZK1
Local
ZooKeeper
Quorum
Bookie 0 Bookie 1 Bookie 2
BookKeeper
Bookie
Ensemble
Pulsar Broker 0 Pulsar Broker 1 Pulsar Broker 2
● 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
Pulsar Functions
import java.util.function.Function;
public class MyFunction implements Function<String, String> {
public String apply(String input) {
return doBusinessLogic(input);
}
}
Entire Function
Why Pulsar
Functions?
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
https://github.com/tspannhw/pulsar-pychat-function
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.
K8 Deploy
Function Execution
Apache Pulsar Kafka K8
https://docs.streamnative.io/platform/v1.3.0/quickstart
Webinar Series:
Building Microservices
with Pulsar
Watch now Read more
Learn how Pulsar Functions, can be
leveraged to build a message bus for
event-driven microservices
Apache Pulsar Resources
● https://pulsar.apache.org/docs/en/kubernetes-helm/
● https://pulsar.apache.org/charts
● https://streamnative.io/streamnativecloud/
● https://medium.com/@_oleksii_/how-to-deploy-apache-puls
ar-cluster-in-kubernetes-808ecdfd87
Academy.StreamNative.io
Visit My Booth #S91
Stop By
Scan the QR Code to learn more about
StreamNative and our exclusive booth offerings.
StreamNative Solution
Application Messaging Data Pipelines Real-time Contextual Analytics
Tiered Storage
APP Layer
Computing
Layer
Storage
Layer
StreamNative
Platform
IaaS Layer
Micro
Service
Notification Dashboard Risk Control Auditing
Payment ETL
Timothy Spann
Developer Advocate
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, Kafka, Big Data, Cloud, MXNet, IoT
and more.
Today, he helps to grow the Pulsar community sharing rich
technical knowledge and experience at both global
conferences and through individual conversations.
FLiP Stack Weekly
This week in Apache Flink, Apache Pulsar, Apache
NiFi, Apache Spark, Elasticsearch and open source
friends.
https://bit.ly/32dAJft
Accessing historical as well as real-time data
Pub/sub model enables event streams to be sent from multiple
producers, and consumed by multiple consumers
To process large amounts of data in a highly scalable way
When is Messaging and Streaming used?
CONFIDENTIAL. DO NOT SHARE.
Apache Pulsar adoption is
being driven by
organizations seeking
cloud-native architectures
and new uses cases.
Microservices
Apache Pulsar - Built for Containers / Modern Cloud
Cloud Native
Hybrid & Multi-Cloud Containers
● “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 Store
(ZK, RocksDB, etcd, …)
Pulsar Cluster
Building
Microservices
Asynchronous
Communication
Building Real Time
Applications
Highly Resilient
Tiered storage
Pulsar Benefits
Apache Pulsar
Apache BookKeeper
Offloader & Tiered Storage
Offloading
Broker 0
Producer Consumer
Broker 1 Broker 2
Bookie 0 Bookie 1 Bookie 2 Bookie 3 Bookie 4
S S S S
T
1
T
2
T
3
T
4
T
0
S

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Serverless Event Streaming Applications as Functionson K8

  • 1. Timothy Spann Developer Advocate Serverless Event Streaming Applications as Functions on K8 16 May 2022 I Valencia, Spain
  • 2. Apache Pulsar is a Cloud-Native Messaging and Event-Streaming Platform.
  • 3. Key Pulsar Concepts: Messaging vs Streaming Message Queueing - Queueing systems are ideal for work queues that do not require tasks to be performed in a particular order. Streaming - Streaming works best in situations where the order of messages is important.
  • 4. Pulsar Cluster CLUSTER Global ZooKeeper Configuration Store ZK3 ZK2 ZK1 Local ZooKeeper Quorum Bookie 0 Bookie 1 Bookie 2 BookKeeper Bookie Ensemble Pulsar Broker 0 Pulsar Broker 1 Pulsar Broker 2
  • 5. ● 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 Pulsar Functions
  • 6. import java.util.function.Function; public class MyFunction implements Function<String, String> { public String apply(String input) { return doBusinessLogic(input); } } Entire Function Why Pulsar Functions?
  • 7. 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 https://github.com/tspannhw/pulsar-pychat-function
  • 8. 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.
  • 11. Apache Pulsar Kafka K8 https://docs.streamnative.io/platform/v1.3.0/quickstart
  • 12. Webinar Series: Building Microservices with Pulsar Watch now Read more Learn how Pulsar Functions, can be leveraged to build a message bus for event-driven microservices
  • 13. Apache Pulsar Resources ● https://pulsar.apache.org/docs/en/kubernetes-helm/ ● https://pulsar.apache.org/charts ● https://streamnative.io/streamnativecloud/ ● https://medium.com/@_oleksii_/how-to-deploy-apache-puls ar-cluster-in-kubernetes-808ecdfd87 Academy.StreamNative.io
  • 14. Visit My Booth #S91 Stop By Scan the QR Code to learn more about StreamNative and our exclusive booth offerings.
  • 15. StreamNative Solution Application Messaging Data Pipelines Real-time Contextual Analytics Tiered Storage APP Layer Computing Layer Storage Layer StreamNative Platform IaaS Layer Micro Service Notification Dashboard Risk Control Auditing Payment ETL
  • 16. Timothy Spann Developer Advocate 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, Kafka, Big Data, Cloud, MXNet, IoT and more. Today, he helps to grow the Pulsar community sharing rich technical knowledge and experience at both global conferences and through individual conversations.
  • 17. FLiP Stack Weekly This week in Apache Flink, Apache Pulsar, Apache NiFi, Apache Spark, Elasticsearch and open source friends. https://bit.ly/32dAJft
  • 18. Accessing historical as well as real-time data Pub/sub model enables event streams to be sent from multiple producers, and consumed by multiple consumers To process large amounts of data in a highly scalable way When is Messaging and Streaming used?
  • 19. CONFIDENTIAL. DO NOT SHARE. Apache Pulsar adoption is being driven by organizations seeking cloud-native architectures and new uses cases. Microservices Apache Pulsar - Built for Containers / Modern Cloud Cloud Native Hybrid & Multi-Cloud Containers
  • 20. ● “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 Store (ZK, RocksDB, etcd, …) Pulsar Cluster
  • 22. Apache Pulsar Apache BookKeeper Offloader & Tiered Storage Offloading Broker 0 Producer Consumer Broker 1 Broker 2 Bookie 0 Bookie 1 Bookie 2 Bookie 3 Bookie 4 S S S S T 1 T 2 T 3 T 4 T 0 S