SlideShare ist ein Scribd-Unternehmen logo
1 von 45
Downloaden Sie, um offline zu lesen
Pulsar Summit
San Francisco
Hotel Nikko
August 18 2022
Timothy Spann
Developer Advocate, StreamNative
Apache Pulsar
Development 101
with Python
Tim Spann
Developer Advocate
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.
https://streamnative.io/pulsar-python/
Example Sensor Device
FLiP Stack Weekly
This week in Apache Flink, Apache Pulsar, Apache
NiFi, Apache Spark and open source friends.
https://bit.ly/32dAJft
Python Application for ADS-B Data
Diagram
Python App REST CALL
LOGGING
ANALYTICS
SEND TO
PULSAR
https://github.com/tspannhw/FLiP-Py-ADS-B
Apache Pulsar Training
● Instructor-led courses
○ Pulsar Fundamentals
○ Pulsar Developers
○ Pulsar Operations
● On-demand learning with labs
● 300+ engineers, admins and architects trained!
StreamNative Academy
Now Available
FREE On-Demand
Pulsar Training
Academy.StreamNative.io
What is Apache Pulsar?
Unified
Messaging
Platform
Guaranteed
Message Delivery Resiliency Infinite
Scalability
● “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 Store
(ZK, RocksDB, etcd, …)
Pulsar’s Publish-Subscribe model
Broker
Subscription
Consumer 1
Consumer 2
Consumer 3
Topic
Producer 1
Producer 2
● Producers send messages.
● Topics are an ordered, named channel 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.
Subscription Modes
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
Messages - the Basic Unit of Pulsar
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.
Connectivity
• Functions - Lightweight Stream
Processing (Java, Python, Go)
• Connectors - Sources & Sinks
(Cassandra, Kafka, …)
• Protocol Handlers - AoP (AMQP), KoP
(Kafka), MoP (MQTT), RoP (RocketMQ)
• Processing Engines - Flink, Spark,
Presto/Trino via Pulsar SQL
• Data Offloaders - Tiered Storage - (S3)
hub.streamnative.io
● 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
#!/usr/bin/env python
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
Entire Function
Pulsar
Functions
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.
Function Execution
MQTT
On Pulsar
(MoP)
Kafka On
Pulsar (KoP)
Spark + Pulsar
https://pulsar.apache.org/docs/en/adaptors-spark/
val dfPulsar = spark.readStream.format("
pulsar")
.option("
service.url", "pulsar://pulsar1:6650")
.option("
admin.url", "http://pulsar1:8080
")
.option("
topic", "persistent://public/default/airquality").load()
val pQuery = dfPulsar.selectExpr("*")
.writeStream.format("
console")
.option("truncate", false).start()
____ __
/ __/__ ___ _____/ /__
_ / _ / _ `/ __/ '_/
/___/ .__/_,_/_/ /_/_ version 3.2.0
/_/
Using Scala version 2.12.15
(OpenJDK 64-Bit Server VM, Java 11.0.11)
● 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?
SQL
select aqi, parameterName, dateObserved, hourObserved, latitude,
longitude, localTimeZone, stateCode, reportingArea from
airquality
select max(aqi) as MaxAQI, parameterName, reportingArea from
airquality group by parameterName, reportingArea
select max(aqi) as MaxAQI, min(aqi) as MinAQI, avg(aqi) as
AvgAQI, count(aqi) as RowCount, parameterName, reportingArea
from airquality group by parameterName, reportingArea
● Buffer
● Batch
● Route
● Filter
● Aggregate
● Enrich
● Replicate
● Dedupe
● Decouple
● Distribute
Schema Registry
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
Streaming FLiP-Py Apps
StreamNative Hub
StreamNative Cloud
Unified Batch and Stream COMPUTING
Batch
(Batch + Stream)
Unified Batch and Stream STORAGE
Offload
(Queuing + Streaming)
Tiered Storage
Pulsar
---
KoP
---
MoP
---
Websocket
Pulsar
Sink
Streaming
Edge Gateway
Protocols
CDC
Apps
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.
● Python Functions
A serverless event streaming
framework
● 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
Python 3 Coding
Code Along With Tim
<<DEMO>>
Run a Local Standalone Bare Metal
wget
https://archive.apache.org/dist/pulsar/pulsar-2.9.1/apache-pulsar-2.9.1-bi
n.tar.gz
tar xvfz apache-pulsar-2.9.1-bin.tar.gz
cd apache-pulsar-2.9.1
bin/pulsar standalone
(For Pulsar SQL Support)
bin/pulsar sql-worker start
https://pulsar.apache.org/docs/en/standalone/
<or> Run in StreamNative Cloud
Scan the QR code to earn
$200 in cloud credit
Building Tenant, Namespace, Topics
bin/pulsar-admin tenants create conference
bin/pulsar-admin namespaces create conference/pythonweb
bin/pulsar-admin tenants list
bin/pulsar-admin namespaces list conference
bin/pulsar-admin topics create persistent://conference/pythonweb/first
bin/pulsar-admin topics list conference/pythonweb
Install Python 3 Pulsar Client
pip3 install pulsar-client=='2.9.1[all]'
# Depending on Platform May Need to Build C++ Client
For Python on Pulsar on Pi https://github.com/tspannhw/PulsarOnRaspberryPi
https://pulsar.apache.org/docs/en/client-libraries-python/
Building a Python 3 Producer
import pulsar
client = pulsar.Client('pulsar://localhost:6650')
producer = client.create_producer('persistent://conference/pythonweb/first')
producer.send(('Simple Text Message').encode('utf-8'))
client.close()
Building a Python 3 Cloud Producer Oath
python3 prod.py -su pulsar+ssl://name1.name2.snio.cloud:6651 -t
persistent://public/default/pyth --auth-params
'{"issuer_url":"https://auth.streamnative.cloud", "private_key":"my.json",
"audience":"urn:sn:pulsar:name:myclustr"}'
from pulsar import Client, AuthenticationOauth2
parse = argparse.ArgumentParser(prog=prod.py')
parse.add_argument('-su', '--service-url', dest='service_url', type=str,
required=True)
args = parse.parse_args()
client = pulsar.Client(args.service_url,
authentication=AuthenticationOauth2(args.auth_params))
https://github.com/streamnative/examples/blob/master/cloud/python/OAuth2Producer.py
https://github.com/tspannhw/FLiP-Pi-BreakoutGarden
Example Avro Schema Usage
import pulsar
from pulsar.schema import *
from pulsar.schema import AvroSchema
class thermal(Record):
uuid = String()
client = pulsar.Client('pulsar://pulsar1:6650')
thermalschema = AvroSchema(thermal)
producer =
client.create_producer(topic='persistent://public/default/pi-thermal-avro',
schema=thermalschema,properties={"producer-name": "thrm" })
thermalRec = thermal()
thermalRec.uuid = "unique-name"
producer.send(thermalRec,partition_key=uniqueid)
https://github.com/tspannhw/FLiP-Pi-Thermal
Example Json Schema Usage
import pulsar
from pulsar.schema import *
from pulsar.schema import JsonSchema
class weather(Record):
uuid = String()
client = pulsar.Client('pulsar://pulsar1:6650')
wschema = JsonSchema(thermal)
producer =
client.create_producer(topic='persistent://public/default/weathe
r,schema=wschema,properties={"producer-name": "wthr" })
weatherRec = weather()
weatherRec.uuid = "unique-name"
producer.send(weatherRec,partition_key=uniqueid)
https://github.com/tspannhw/FLiP-Pi-Weather
Building a Python3 Consumer
import pulsar
client = pulsar.Client('pulsar://localhost:6650')
consumer =
client.subscribe('persistent://conference/pythonweb/first',subscription_na
me='my-sub')
while True:
msg = consumer.receive()
print("Received message: '%s'" % msg.data())
consumer.acknowledge(msg)
client.close()
MQTT from Python
pip3 install paho-mqtt
import paho.mqtt.client as mqtt
client = mqtt.Client("rpi4-iot")
row = { }
row['gasKO'] = str(readings)
json_string = json.dumps(row)
json_string = json_string.strip()
client.connect("pulsar-server.com", 1883, 180)
client.publish("persistent://public/default/mqtt-2",
payload=json_string,qos=0,retain=True)
https://www.slideshare.net/bunkertor/data-minutes-2-apache-pulsar-with-mqtt-for-edge-computing-lightning-2022
Web Sockets from Python
pip3 install websocket-client
import websocket, base64, json
topic = 'ws://server:8080/ws/v2/producer/persistent/public/default/webtopic1'
ws = websocket.create_connection(topic)
message = "Hello Python Web Conference"
message_bytes = message.encode('ascii')
base64_bytes = base64.b64encode(message_bytes)
base64_message = base64_bytes.decode('ascii')
ws.send(json.dumps({'payload' : base64_message,'properties': {'device' :
'jetson2gb','protocol' : 'websockets'},'context' : 5}))
response = json.loads(ws.recv())
https://pulsar.apache.org/docs/en/client-libraries-websocket/
https://github.com/tspannhw/FLiP-IoT/blob/main/wspulsar.py
https://github.com/tspannhw/FLiP-IoT/blob/main/wsreader.py
Kafka from Python
pip3 install kafka-python
from kafka import KafkaProducer
from kafka.errors import KafkaError
row = { }
row['gasKO'] = str(readings)
json_string = json.dumps(row)
json_string = json_string.strip()
producer = KafkaProducer(bootstrap_servers='pulsar1:9092',retries=3)
producer.send('topic-kafka-1', json.dumps(row).encode('utf-8'))
producer.flush()
https://github.com/streamnative/kop
https://docs.streamnative.io/platform/v1.0.0/concepts/kop-concepts
Pulsar IO Functions in Python
https://github.com/tspannhw/pulsar-pychat-function
Pulsar IO Functions in Python
bin/pulsar-admin functions create --auto-ack true --py py/src/sentiment.py
--classname "sentiment.Chat" --inputs "persistent://public/default/chat"
--log-topic "persistent://public/default/logs" --name Chat --output
"persistent://public/default/chatresult"
https://github.com/tspannhw/pulsar-pychat-function
Pulsar IO Functions in Python
from pulsar import Function
import json
class Chat(Function):
def __init__(self):
pass
def process(self, input, context):
logger = context.get_logger()
msg_id = context.get_message_id()
fields = json.loads(input)
https://github.com/tspannhw/pulsar-pychat-function
Python For Pulsar on Pi
● 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
Let’s Keep in Touch
Tim Spann
Developer Advocate
@PassDev
https://www.linkedin.com/in/timothyspann
https://github.com/tspannhw
https://streamnative.io/pulsar-python/
Pulsar Summit
San Francisco
Hotel Nikko
August 18 2022
Tim Spann
Thank you!
tim@streamnative.io
@PaaSDev
github.com/tspannhw

Weitere ähnliche Inhalte

Was ist angesagt?

Simplify CDC Pipeline with Spark Streaming SQL and Delta Lake
Simplify CDC Pipeline with Spark Streaming SQL and Delta LakeSimplify CDC Pipeline with Spark Streaming SQL and Delta Lake
Simplify CDC Pipeline with Spark Streaming SQL and Delta LakeDatabricks
 
Running Kafka On Kubernetes With Strimzi For Real-Time Streaming Applications
Running Kafka On Kubernetes With Strimzi For Real-Time Streaming ApplicationsRunning Kafka On Kubernetes With Strimzi For Real-Time Streaming Applications
Running Kafka On Kubernetes With Strimzi For Real-Time Streaming ApplicationsLightbend
 
Deep Dive into Building Streaming Applications with Apache Pulsar
Deep Dive into Building Streaming Applications with Apache Pulsar Deep Dive into Building Streaming Applications with Apache Pulsar
Deep Dive into Building Streaming Applications with Apache Pulsar Timothy Spann
 
Exactly-once Stream Processing with Kafka Streams
Exactly-once Stream Processing with Kafka StreamsExactly-once Stream Processing with Kafka Streams
Exactly-once Stream Processing with Kafka StreamsGuozhang Wang
 
Fundamentals of Apache Kafka
Fundamentals of Apache KafkaFundamentals of Apache Kafka
Fundamentals of Apache KafkaChhavi Parasher
 
Troubleshooting Kerberos in Hadoop: Taming the Beast
Troubleshooting Kerberos in Hadoop: Taming the BeastTroubleshooting Kerberos in Hadoop: Taming the Beast
Troubleshooting Kerberos in Hadoop: Taming the BeastDataWorks Summit
 
Dynamically Scaling Data Streams across Multiple Kafka Clusters with Zero Fli...
Dynamically Scaling Data Streams across Multiple Kafka Clusters with Zero Fli...Dynamically Scaling Data Streams across Multiple Kafka Clusters with Zero Fli...
Dynamically Scaling Data Streams across Multiple Kafka Clusters with Zero Fli...Flink Forward
 
Practical learnings from running thousands of Flink jobs
Practical learnings from running thousands of Flink jobsPractical learnings from running thousands of Flink jobs
Practical learnings from running thousands of Flink jobsFlink Forward
 
InfluxDB IOx Tech Talks: Replication, Durability and Subscriptions in InfluxD...
InfluxDB IOx Tech Talks: Replication, Durability and Subscriptions in InfluxD...InfluxDB IOx Tech Talks: Replication, Durability and Subscriptions in InfluxD...
InfluxDB IOx Tech Talks: Replication, Durability and Subscriptions in InfluxD...InfluxData
 
Real-time Analytics with Trino and Apache Pinot
Real-time Analytics with Trino and Apache PinotReal-time Analytics with Trino and Apache Pinot
Real-time Analytics with Trino and Apache PinotXiang Fu
 
Bringing Kafka Without Zookeeper Into Production with Colin McCabe | Kafka Su...
Bringing Kafka Without Zookeeper Into Production with Colin McCabe | Kafka Su...Bringing Kafka Without Zookeeper Into Production with Colin McCabe | Kafka Su...
Bringing Kafka Without Zookeeper Into Production with Colin McCabe | Kafka Su...HostedbyConfluent
 
Kafka Tutorial - Introduction to Apache Kafka (Part 1)
Kafka Tutorial - Introduction to Apache Kafka (Part 1)Kafka Tutorial - Introduction to Apache Kafka (Part 1)
Kafka Tutorial - Introduction to Apache Kafka (Part 1)Jean-Paul Azar
 
Native Support of Prometheus Monitoring in Apache Spark 3.0
Native Support of Prometheus Monitoring in Apache Spark 3.0Native Support of Prometheus Monitoring in Apache Spark 3.0
Native Support of Prometheus Monitoring in Apache Spark 3.0Databricks
 
Disaster Recovery Options Running Apache Kafka in Kubernetes with Rema Subra...
 Disaster Recovery Options Running Apache Kafka in Kubernetes with Rema Subra... Disaster Recovery Options Running Apache Kafka in Kubernetes with Rema Subra...
Disaster Recovery Options Running Apache Kafka in Kubernetes with Rema Subra...HostedbyConfluent
 
Getting Started with Confluent Schema Registry
Getting Started with Confluent Schema RegistryGetting Started with Confluent Schema Registry
Getting Started with Confluent Schema Registryconfluent
 
Storage Capacity Management on Multi-tenant Kafka Cluster with Nurettin Omeroglu
Storage Capacity Management on Multi-tenant Kafka Cluster with Nurettin OmerogluStorage Capacity Management on Multi-tenant Kafka Cluster with Nurettin Omeroglu
Storage Capacity Management on Multi-tenant Kafka Cluster with Nurettin OmerogluHostedbyConfluent
 
Building an Asynchronous Application Framework with Python and Pulsar - Pulsa...
Building an Asynchronous Application Framework with Python and Pulsar - Pulsa...Building an Asynchronous Application Framework with Python and Pulsar - Pulsa...
Building an Asynchronous Application Framework with Python and Pulsar - Pulsa...StreamNative
 

Was ist angesagt? (20)

Simplify CDC Pipeline with Spark Streaming SQL and Delta Lake
Simplify CDC Pipeline with Spark Streaming SQL and Delta LakeSimplify CDC Pipeline with Spark Streaming SQL and Delta Lake
Simplify CDC Pipeline with Spark Streaming SQL and Delta Lake
 
Running Kafka On Kubernetes With Strimzi For Real-Time Streaming Applications
Running Kafka On Kubernetes With Strimzi For Real-Time Streaming ApplicationsRunning Kafka On Kubernetes With Strimzi For Real-Time Streaming Applications
Running Kafka On Kubernetes With Strimzi For Real-Time Streaming Applications
 
Apache Kafka Security
Apache Kafka Security Apache Kafka Security
Apache Kafka Security
 
Deep Dive into Building Streaming Applications with Apache Pulsar
Deep Dive into Building Streaming Applications with Apache Pulsar Deep Dive into Building Streaming Applications with Apache Pulsar
Deep Dive into Building Streaming Applications with Apache Pulsar
 
Exactly-once Stream Processing with Kafka Streams
Exactly-once Stream Processing with Kafka StreamsExactly-once Stream Processing with Kafka Streams
Exactly-once Stream Processing with Kafka Streams
 
Fundamentals of Apache Kafka
Fundamentals of Apache KafkaFundamentals of Apache Kafka
Fundamentals of Apache Kafka
 
Troubleshooting Kerberos in Hadoop: Taming the Beast
Troubleshooting Kerberos in Hadoop: Taming the BeastTroubleshooting Kerberos in Hadoop: Taming the Beast
Troubleshooting Kerberos in Hadoop: Taming the Beast
 
Apache Kafka at LinkedIn
Apache Kafka at LinkedInApache Kafka at LinkedIn
Apache Kafka at LinkedIn
 
Dynamically Scaling Data Streams across Multiple Kafka Clusters with Zero Fli...
Dynamically Scaling Data Streams across Multiple Kafka Clusters with Zero Fli...Dynamically Scaling Data Streams across Multiple Kafka Clusters with Zero Fli...
Dynamically Scaling Data Streams across Multiple Kafka Clusters with Zero Fli...
 
Practical learnings from running thousands of Flink jobs
Practical learnings from running thousands of Flink jobsPractical learnings from running thousands of Flink jobs
Practical learnings from running thousands of Flink jobs
 
Envoy and Kafka
Envoy and KafkaEnvoy and Kafka
Envoy and Kafka
 
InfluxDB IOx Tech Talks: Replication, Durability and Subscriptions in InfluxD...
InfluxDB IOx Tech Talks: Replication, Durability and Subscriptions in InfluxD...InfluxDB IOx Tech Talks: Replication, Durability and Subscriptions in InfluxD...
InfluxDB IOx Tech Talks: Replication, Durability and Subscriptions in InfluxD...
 
Real-time Analytics with Trino and Apache Pinot
Real-time Analytics with Trino and Apache PinotReal-time Analytics with Trino and Apache Pinot
Real-time Analytics with Trino and Apache Pinot
 
Bringing Kafka Without Zookeeper Into Production with Colin McCabe | Kafka Su...
Bringing Kafka Without Zookeeper Into Production with Colin McCabe | Kafka Su...Bringing Kafka Without Zookeeper Into Production with Colin McCabe | Kafka Su...
Bringing Kafka Without Zookeeper Into Production with Colin McCabe | Kafka Su...
 
Kafka Tutorial - Introduction to Apache Kafka (Part 1)
Kafka Tutorial - Introduction to Apache Kafka (Part 1)Kafka Tutorial - Introduction to Apache Kafka (Part 1)
Kafka Tutorial - Introduction to Apache Kafka (Part 1)
 
Native Support of Prometheus Monitoring in Apache Spark 3.0
Native Support of Prometheus Monitoring in Apache Spark 3.0Native Support of Prometheus Monitoring in Apache Spark 3.0
Native Support of Prometheus Monitoring in Apache Spark 3.0
 
Disaster Recovery Options Running Apache Kafka in Kubernetes with Rema Subra...
 Disaster Recovery Options Running Apache Kafka in Kubernetes with Rema Subra... Disaster Recovery Options Running Apache Kafka in Kubernetes with Rema Subra...
Disaster Recovery Options Running Apache Kafka in Kubernetes with Rema Subra...
 
Getting Started with Confluent Schema Registry
Getting Started with Confluent Schema RegistryGetting Started with Confluent Schema Registry
Getting Started with Confluent Schema Registry
 
Storage Capacity Management on Multi-tenant Kafka Cluster with Nurettin Omeroglu
Storage Capacity Management on Multi-tenant Kafka Cluster with Nurettin OmerogluStorage Capacity Management on Multi-tenant Kafka Cluster with Nurettin Omeroglu
Storage Capacity Management on Multi-tenant Kafka Cluster with Nurettin Omeroglu
 
Building an Asynchronous Application Framework with Python and Pulsar - Pulsa...
Building an Asynchronous Application Framework with Python and Pulsar - Pulsa...Building an Asynchronous Application Framework with Python and Pulsar - Pulsa...
Building an Asynchronous Application Framework with Python and Pulsar - Pulsa...
 

Ähnlich wie Apache Pulsar Development 101 with Python

Machine Intelligence Guild_ Build ML Enhanced Event Streaming Applications wi...
Machine Intelligence Guild_ Build ML Enhanced Event Streaming Applications wi...Machine Intelligence Guild_ Build ML Enhanced Event Streaming Applications wi...
Machine Intelligence Guild_ Build ML Enhanced Event Streaming Applications wi...Timothy Spann
 
Fast Streaming into Clickhouse with Apache Pulsar
Fast Streaming into Clickhouse with Apache PulsarFast Streaming into Clickhouse with Apache Pulsar
Fast Streaming into Clickhouse with Apache PulsarTimothy Spann
 
[March sn meetup] apache pulsar + apache nifi for cloud data lake
[March sn meetup] apache pulsar + apache nifi for cloud data lake[March sn meetup] apache pulsar + apache nifi for cloud data lake
[March sn meetup] apache pulsar + apache nifi for cloud data lakeTimothy Spann
 
Big mountain data and dev conference apache pulsar with mqtt for edge compu...
Big mountain data and dev conference   apache pulsar with mqtt for edge compu...Big mountain data and dev conference   apache pulsar with mqtt for edge compu...
Big mountain data and dev conference apache pulsar with mqtt for edge compu...Timothy Spann
 
Python web conference 2022 apache pulsar development 101 with python (f li-...
Python web conference 2022   apache pulsar development 101 with python (f li-...Python web conference 2022   apache pulsar development 101 with python (f li-...
Python web conference 2022 apache pulsar development 101 with python (f li-...Timothy Spann
 
Python Web Conference 2022 - Apache Pulsar Development 101 with Python (FLiP-Py)
Python Web Conference 2022 - Apache Pulsar Development 101 with Python (FLiP-Py)Python Web Conference 2022 - Apache Pulsar Development 101 with Python (FLiP-Py)
Python Web Conference 2022 - Apache Pulsar Development 101 with Python (FLiP-Py)Timothy Spann
 
Pulsar summit asia 2021 apache pulsar with mqtt for edge computing
Pulsar summit asia 2021   apache pulsar with mqtt for edge computingPulsar summit asia 2021   apache pulsar with mqtt for edge computing
Pulsar summit asia 2021 apache pulsar with mqtt for edge computingTimothy Spann
 
bigdata 2022_ FLiP Into Pulsar Apps
bigdata 2022_ FLiP Into Pulsar Appsbigdata 2022_ FLiP Into Pulsar Apps
bigdata 2022_ FLiP Into Pulsar AppsTimothy Spann
 
The Dream Stream Team for Pulsar and Spring
The Dream Stream Team for Pulsar and SpringThe Dream Stream Team for Pulsar and Spring
The Dream Stream Team for Pulsar and SpringTimothy Spann
 
JConf.dev 2022 - Apache Pulsar Development 101 with Java
JConf.dev 2022 - Apache Pulsar Development 101 with JavaJConf.dev 2022 - Apache Pulsar Development 101 with Java
JConf.dev 2022 - Apache Pulsar Development 101 with JavaTimothy Spann
 
Timothy Spann: Apache Pulsar for ML
Timothy Spann: Apache Pulsar for MLTimothy Spann: Apache Pulsar for ML
Timothy Spann: Apache Pulsar for MLEdunomica
 
ApacheCon2022_Deep Dive into Building Streaming Applications with Apache Pulsar
ApacheCon2022_Deep Dive into Building Streaming Applications with Apache PulsarApacheCon2022_Deep Dive into Building Streaming Applications with Apache Pulsar
ApacheCon2022_Deep Dive into Building Streaming Applications with Apache PulsarTimothy Spann
 
Why Spring Belongs In Your Data Stream (From Edge to Multi-Cloud)
Why Spring Belongs In Your Data Stream (From Edge to Multi-Cloud)Why Spring Belongs In Your Data Stream (From Edge to Multi-Cloud)
Why Spring Belongs In Your Data Stream (From Edge to Multi-Cloud)Timothy Spann
 
Apache Pulsar with MQTT for Edge Computing - Pulsar Summit Asia 2021
Apache Pulsar with MQTT for Edge Computing - Pulsar Summit Asia 2021Apache Pulsar with MQTT for Edge Computing - Pulsar Summit Asia 2021
Apache Pulsar with MQTT for Edge Computing - Pulsar Summit Asia 2021StreamNative
 
DBCC 2021 - FLiP Stack for Cloud Data Lakes
DBCC 2021 - FLiP Stack for Cloud Data LakesDBCC 2021 - FLiP Stack for Cloud Data Lakes
DBCC 2021 - FLiP Stack for Cloud Data LakesTimothy Spann
 
CODEONTHEBEACH_Streaming Applications with Apache Pulsar
CODEONTHEBEACH_Streaming Applications with Apache PulsarCODEONTHEBEACH_Streaming Applications with Apache Pulsar
CODEONTHEBEACH_Streaming Applications with Apache PulsarTimothy Spann
 
Princeton Dec 2022 Meetup_ NiFi + Flink + Pulsar
Princeton Dec 2022 Meetup_ NiFi + Flink + PulsarPrinceton Dec 2022 Meetup_ NiFi + Flink + Pulsar
Princeton Dec 2022 Meetup_ NiFi + Flink + PulsarTimothy Spann
 
Serverless Event Streaming Applications as Functionson K8
Serverless Event Streaming Applications as Functionson K8Serverless Event Streaming Applications as Functionson K8
Serverless Event Streaming Applications as Functionson K8Timothy Spann
 
Unified Messaging and Data Streaming 101
Unified Messaging and Data Streaming 101Unified Messaging and Data Streaming 101
Unified Messaging and Data Streaming 101Timothy Spann
 
Building Modern Data Streaming Apps with Python
Building Modern Data Streaming Apps with PythonBuilding Modern Data Streaming Apps with Python
Building Modern Data Streaming Apps with PythonTimothy Spann
 

Ähnlich wie Apache Pulsar Development 101 with Python (20)

Machine Intelligence Guild_ Build ML Enhanced Event Streaming Applications wi...
Machine Intelligence Guild_ Build ML Enhanced Event Streaming Applications wi...Machine Intelligence Guild_ Build ML Enhanced Event Streaming Applications wi...
Machine Intelligence Guild_ Build ML Enhanced Event Streaming Applications wi...
 
Fast Streaming into Clickhouse with Apache Pulsar
Fast Streaming into Clickhouse with Apache PulsarFast Streaming into Clickhouse with Apache Pulsar
Fast Streaming into Clickhouse with Apache Pulsar
 
[March sn meetup] apache pulsar + apache nifi for cloud data lake
[March sn meetup] apache pulsar + apache nifi for cloud data lake[March sn meetup] apache pulsar + apache nifi for cloud data lake
[March sn meetup] apache pulsar + apache nifi for cloud data lake
 
Big mountain data and dev conference apache pulsar with mqtt for edge compu...
Big mountain data and dev conference   apache pulsar with mqtt for edge compu...Big mountain data and dev conference   apache pulsar with mqtt for edge compu...
Big mountain data and dev conference apache pulsar with mqtt for edge compu...
 
Python web conference 2022 apache pulsar development 101 with python (f li-...
Python web conference 2022   apache pulsar development 101 with python (f li-...Python web conference 2022   apache pulsar development 101 with python (f li-...
Python web conference 2022 apache pulsar development 101 with python (f li-...
 
Python Web Conference 2022 - Apache Pulsar Development 101 with Python (FLiP-Py)
Python Web Conference 2022 - Apache Pulsar Development 101 with Python (FLiP-Py)Python Web Conference 2022 - Apache Pulsar Development 101 with Python (FLiP-Py)
Python Web Conference 2022 - Apache Pulsar Development 101 with Python (FLiP-Py)
 
Pulsar summit asia 2021 apache pulsar with mqtt for edge computing
Pulsar summit asia 2021   apache pulsar with mqtt for edge computingPulsar summit asia 2021   apache pulsar with mqtt for edge computing
Pulsar summit asia 2021 apache pulsar with mqtt for edge computing
 
bigdata 2022_ FLiP Into Pulsar Apps
bigdata 2022_ FLiP Into Pulsar Appsbigdata 2022_ FLiP Into Pulsar Apps
bigdata 2022_ FLiP Into Pulsar Apps
 
The Dream Stream Team for Pulsar and Spring
The Dream Stream Team for Pulsar and SpringThe Dream Stream Team for Pulsar and Spring
The Dream Stream Team for Pulsar and Spring
 
JConf.dev 2022 - Apache Pulsar Development 101 with Java
JConf.dev 2022 - Apache Pulsar Development 101 with JavaJConf.dev 2022 - Apache Pulsar Development 101 with Java
JConf.dev 2022 - Apache Pulsar Development 101 with Java
 
Timothy Spann: Apache Pulsar for ML
Timothy Spann: Apache Pulsar for MLTimothy Spann: Apache Pulsar for ML
Timothy Spann: Apache Pulsar for ML
 
ApacheCon2022_Deep Dive into Building Streaming Applications with Apache Pulsar
ApacheCon2022_Deep Dive into Building Streaming Applications with Apache PulsarApacheCon2022_Deep Dive into Building Streaming Applications with Apache Pulsar
ApacheCon2022_Deep Dive into Building Streaming Applications with Apache Pulsar
 
Why Spring Belongs In Your Data Stream (From Edge to Multi-Cloud)
Why Spring Belongs In Your Data Stream (From Edge to Multi-Cloud)Why Spring Belongs In Your Data Stream (From Edge to Multi-Cloud)
Why Spring Belongs In Your Data Stream (From Edge to Multi-Cloud)
 
Apache Pulsar with MQTT for Edge Computing - Pulsar Summit Asia 2021
Apache Pulsar with MQTT for Edge Computing - Pulsar Summit Asia 2021Apache Pulsar with MQTT for Edge Computing - Pulsar Summit Asia 2021
Apache Pulsar with MQTT for Edge Computing - Pulsar Summit Asia 2021
 
DBCC 2021 - FLiP Stack for Cloud Data Lakes
DBCC 2021 - FLiP Stack for Cloud Data LakesDBCC 2021 - FLiP Stack for Cloud Data Lakes
DBCC 2021 - FLiP Stack for Cloud Data Lakes
 
CODEONTHEBEACH_Streaming Applications with Apache Pulsar
CODEONTHEBEACH_Streaming Applications with Apache PulsarCODEONTHEBEACH_Streaming Applications with Apache Pulsar
CODEONTHEBEACH_Streaming Applications with Apache Pulsar
 
Princeton Dec 2022 Meetup_ NiFi + Flink + Pulsar
Princeton Dec 2022 Meetup_ NiFi + Flink + PulsarPrinceton Dec 2022 Meetup_ NiFi + Flink + Pulsar
Princeton Dec 2022 Meetup_ NiFi + Flink + Pulsar
 
Serverless Event Streaming Applications as Functionson K8
Serverless Event Streaming Applications as Functionson K8Serverless Event Streaming Applications as Functionson K8
Serverless Event Streaming Applications as Functionson K8
 
Unified Messaging and Data Streaming 101
Unified Messaging and Data Streaming 101Unified Messaging and Data Streaming 101
Unified Messaging and Data Streaming 101
 
Building Modern Data Streaming Apps with Python
Building Modern Data Streaming Apps with PythonBuilding Modern Data Streaming Apps with Python
Building Modern Data Streaming Apps with Python
 

Mehr von Timothy Spann

Generative AI on Enterprise Cloud with NiFi and Milvus
Generative AI on Enterprise Cloud with NiFi and MilvusGenerative AI on Enterprise Cloud with NiFi and Milvus
Generative AI on Enterprise Cloud with NiFi and MilvusTimothy Spann
 
April 2024 - NLIT Cloudera Real-Time LLM Streaming 2024
April 2024 - NLIT Cloudera Real-Time LLM Streaming 2024April 2024 - NLIT Cloudera Real-Time LLM Streaming 2024
April 2024 - NLIT Cloudera Real-Time LLM Streaming 2024Timothy Spann
 
Real-Time AI Streaming - AI Max Princeton
Real-Time AI  Streaming - AI Max PrincetonReal-Time AI  Streaming - AI Max Princeton
Real-Time AI Streaming - AI Max PrincetonTimothy Spann
 
Conf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
Conf42-LLM_Adding Generative AI to Real-Time Streaming PipelinesConf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
Conf42-LLM_Adding Generative AI to Real-Time Streaming PipelinesTimothy Spann
 
2024 XTREMEJ_ Building Real-time Pipelines with FLaNK_ A Case Study with Tra...
2024 XTREMEJ_  Building Real-time Pipelines with FLaNK_ A Case Study with Tra...2024 XTREMEJ_  Building Real-time Pipelines with FLaNK_ A Case Study with Tra...
2024 XTREMEJ_ Building Real-time Pipelines with FLaNK_ A Case Study with Tra...Timothy Spann
 
28March2024-Codeless-Generative-AI-Pipelines
28March2024-Codeless-Generative-AI-Pipelines28March2024-Codeless-Generative-AI-Pipelines
28March2024-Codeless-Generative-AI-PipelinesTimothy Spann
 
TCFPro24 Building Real-Time Generative AI Pipelines
TCFPro24 Building Real-Time Generative AI PipelinesTCFPro24 Building Real-Time Generative AI Pipelines
TCFPro24 Building Real-Time Generative AI PipelinesTimothy Spann
 
2024 Build Generative AI for Non-Profits
2024 Build Generative AI for Non-Profits2024 Build Generative AI for Non-Profits
2024 Build Generative AI for Non-ProfitsTimothy Spann
 
2024 February 28 - NYC - Meetup Unlocking Financial Data with Real-Time Pipel...
2024 February 28 - NYC - Meetup Unlocking Financial Data with Real-Time Pipel...2024 February 28 - NYC - Meetup Unlocking Financial Data with Real-Time Pipel...
2024 February 28 - NYC - Meetup Unlocking Financial Data with Real-Time Pipel...Timothy Spann
 
Conf42-Python-Building Apache NiFi 2.0 Python Processors
Conf42-Python-Building Apache NiFi 2.0 Python ProcessorsConf42-Python-Building Apache NiFi 2.0 Python Processors
Conf42-Python-Building Apache NiFi 2.0 Python ProcessorsTimothy Spann
 
Conf42Python -Using Apache NiFi, Apache Kafka, RisingWave, and Apache Iceberg...
Conf42Python -Using Apache NiFi, Apache Kafka, RisingWave, and Apache Iceberg...Conf42Python -Using Apache NiFi, Apache Kafka, RisingWave, and Apache Iceberg...
Conf42Python -Using Apache NiFi, Apache Kafka, RisingWave, and Apache Iceberg...Timothy Spann
 
2024 Feb AI Meetup NYC GenAI_LLMs_ML_Data Codeless Generative AI Pipelines
2024 Feb AI Meetup NYC GenAI_LLMs_ML_Data Codeless Generative AI Pipelines2024 Feb AI Meetup NYC GenAI_LLMs_ML_Data Codeless Generative AI Pipelines
2024 Feb AI Meetup NYC GenAI_LLMs_ML_Data Codeless Generative AI PipelinesTimothy Spann
 
DBA Fundamentals Group: Continuous SQL with Kafka and Flink
DBA Fundamentals Group: Continuous SQL with Kafka and FlinkDBA Fundamentals Group: Continuous SQL with Kafka and Flink
DBA Fundamentals Group: Continuous SQL with Kafka and FlinkTimothy Spann
 
NY Open Source Data Meetup Feb 8 2024 Building Real-time Pipelines with FLaNK...
NY Open Source Data Meetup Feb 8 2024 Building Real-time Pipelines with FLaNK...NY Open Source Data Meetup Feb 8 2024 Building Real-time Pipelines with FLaNK...
NY Open Source Data Meetup Feb 8 2024 Building Real-time Pipelines with FLaNK...Timothy Spann
 
OSACon 2023_ Unlocking Financial Data with Real-Time Pipelines
OSACon 2023_ Unlocking Financial Data with Real-Time PipelinesOSACon 2023_ Unlocking Financial Data with Real-Time Pipelines
OSACon 2023_ Unlocking Financial Data with Real-Time PipelinesTimothy Spann
 
Building Real-Time Travel Alerts
Building Real-Time Travel AlertsBuilding Real-Time Travel Alerts
Building Real-Time Travel AlertsTimothy Spann
 
JConWorld_ Continuous SQL with Kafka and Flink
JConWorld_ Continuous SQL with Kafka and FlinkJConWorld_ Continuous SQL with Kafka and Flink
JConWorld_ Continuous SQL with Kafka and FlinkTimothy Spann
 
[EN]DSS23_tspann_Integrating LLM with Streaming Data Pipelines
[EN]DSS23_tspann_Integrating LLM with Streaming Data Pipelines[EN]DSS23_tspann_Integrating LLM with Streaming Data Pipelines
[EN]DSS23_tspann_Integrating LLM with Streaming Data PipelinesTimothy Spann
 
Evolve 2023 NYC - Integrating AI Into Realtime Data Pipelines Demo
Evolve 2023 NYC - Integrating AI Into Realtime Data Pipelines DemoEvolve 2023 NYC - Integrating AI Into Realtime Data Pipelines Demo
Evolve 2023 NYC - Integrating AI Into Realtime Data Pipelines DemoTimothy Spann
 
AIDevWorldApacheNiFi101
AIDevWorldApacheNiFi101AIDevWorldApacheNiFi101
AIDevWorldApacheNiFi101Timothy Spann
 

Mehr von Timothy Spann (20)

Generative AI on Enterprise Cloud with NiFi and Milvus
Generative AI on Enterprise Cloud with NiFi and MilvusGenerative AI on Enterprise Cloud with NiFi and Milvus
Generative AI on Enterprise Cloud with NiFi and Milvus
 
April 2024 - NLIT Cloudera Real-Time LLM Streaming 2024
April 2024 - NLIT Cloudera Real-Time LLM Streaming 2024April 2024 - NLIT Cloudera Real-Time LLM Streaming 2024
April 2024 - NLIT Cloudera Real-Time LLM Streaming 2024
 
Real-Time AI Streaming - AI Max Princeton
Real-Time AI  Streaming - AI Max PrincetonReal-Time AI  Streaming - AI Max Princeton
Real-Time AI Streaming - AI Max Princeton
 
Conf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
Conf42-LLM_Adding Generative AI to Real-Time Streaming PipelinesConf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
Conf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
 
2024 XTREMEJ_ Building Real-time Pipelines with FLaNK_ A Case Study with Tra...
2024 XTREMEJ_  Building Real-time Pipelines with FLaNK_ A Case Study with Tra...2024 XTREMEJ_  Building Real-time Pipelines with FLaNK_ A Case Study with Tra...
2024 XTREMEJ_ Building Real-time Pipelines with FLaNK_ A Case Study with Tra...
 
28March2024-Codeless-Generative-AI-Pipelines
28March2024-Codeless-Generative-AI-Pipelines28March2024-Codeless-Generative-AI-Pipelines
28March2024-Codeless-Generative-AI-Pipelines
 
TCFPro24 Building Real-Time Generative AI Pipelines
TCFPro24 Building Real-Time Generative AI PipelinesTCFPro24 Building Real-Time Generative AI Pipelines
TCFPro24 Building Real-Time Generative AI Pipelines
 
2024 Build Generative AI for Non-Profits
2024 Build Generative AI for Non-Profits2024 Build Generative AI for Non-Profits
2024 Build Generative AI for Non-Profits
 
2024 February 28 - NYC - Meetup Unlocking Financial Data with Real-Time Pipel...
2024 February 28 - NYC - Meetup Unlocking Financial Data with Real-Time Pipel...2024 February 28 - NYC - Meetup Unlocking Financial Data with Real-Time Pipel...
2024 February 28 - NYC - Meetup Unlocking Financial Data with Real-Time Pipel...
 
Conf42-Python-Building Apache NiFi 2.0 Python Processors
Conf42-Python-Building Apache NiFi 2.0 Python ProcessorsConf42-Python-Building Apache NiFi 2.0 Python Processors
Conf42-Python-Building Apache NiFi 2.0 Python Processors
 
Conf42Python -Using Apache NiFi, Apache Kafka, RisingWave, and Apache Iceberg...
Conf42Python -Using Apache NiFi, Apache Kafka, RisingWave, and Apache Iceberg...Conf42Python -Using Apache NiFi, Apache Kafka, RisingWave, and Apache Iceberg...
Conf42Python -Using Apache NiFi, Apache Kafka, RisingWave, and Apache Iceberg...
 
2024 Feb AI Meetup NYC GenAI_LLMs_ML_Data Codeless Generative AI Pipelines
2024 Feb AI Meetup NYC GenAI_LLMs_ML_Data Codeless Generative AI Pipelines2024 Feb AI Meetup NYC GenAI_LLMs_ML_Data Codeless Generative AI Pipelines
2024 Feb AI Meetup NYC GenAI_LLMs_ML_Data Codeless Generative AI Pipelines
 
DBA Fundamentals Group: Continuous SQL with Kafka and Flink
DBA Fundamentals Group: Continuous SQL with Kafka and FlinkDBA Fundamentals Group: Continuous SQL with Kafka and Flink
DBA Fundamentals Group: Continuous SQL with Kafka and Flink
 
NY Open Source Data Meetup Feb 8 2024 Building Real-time Pipelines with FLaNK...
NY Open Source Data Meetup Feb 8 2024 Building Real-time Pipelines with FLaNK...NY Open Source Data Meetup Feb 8 2024 Building Real-time Pipelines with FLaNK...
NY Open Source Data Meetup Feb 8 2024 Building Real-time Pipelines with FLaNK...
 
OSACon 2023_ Unlocking Financial Data with Real-Time Pipelines
OSACon 2023_ Unlocking Financial Data with Real-Time PipelinesOSACon 2023_ Unlocking Financial Data with Real-Time Pipelines
OSACon 2023_ Unlocking Financial Data with Real-Time Pipelines
 
Building Real-Time Travel Alerts
Building Real-Time Travel AlertsBuilding Real-Time Travel Alerts
Building Real-Time Travel Alerts
 
JConWorld_ Continuous SQL with Kafka and Flink
JConWorld_ Continuous SQL with Kafka and FlinkJConWorld_ Continuous SQL with Kafka and Flink
JConWorld_ Continuous SQL with Kafka and Flink
 
[EN]DSS23_tspann_Integrating LLM with Streaming Data Pipelines
[EN]DSS23_tspann_Integrating LLM with Streaming Data Pipelines[EN]DSS23_tspann_Integrating LLM with Streaming Data Pipelines
[EN]DSS23_tspann_Integrating LLM with Streaming Data Pipelines
 
Evolve 2023 NYC - Integrating AI Into Realtime Data Pipelines Demo
Evolve 2023 NYC - Integrating AI Into Realtime Data Pipelines DemoEvolve 2023 NYC - Integrating AI Into Realtime Data Pipelines Demo
Evolve 2023 NYC - Integrating AI Into Realtime Data Pipelines Demo
 
AIDevWorldApacheNiFi101
AIDevWorldApacheNiFi101AIDevWorldApacheNiFi101
AIDevWorldApacheNiFi101
 

Kürzlich hochgeladen

Unlocking the Future of AI Agents with Large Language Models
Unlocking the Future of AI Agents with Large Language ModelsUnlocking the Future of AI Agents with Large Language Models
Unlocking the Future of AI Agents with Large Language Modelsaagamshah0812
 
5 Signs You Need a Fashion PLM Software.pdf
5 Signs You Need a Fashion PLM Software.pdf5 Signs You Need a Fashion PLM Software.pdf
5 Signs You Need a Fashion PLM Software.pdfWave PLM
 
The Real-World Challenges of Medical Device Cybersecurity- Mitigating Vulnera...
The Real-World Challenges of Medical Device Cybersecurity- Mitigating Vulnera...The Real-World Challenges of Medical Device Cybersecurity- Mitigating Vulnera...
The Real-World Challenges of Medical Device Cybersecurity- Mitigating Vulnera...ICS
 
CALL ON ➥8923113531 🔝Call Girls Badshah Nagar Lucknow best Female service
CALL ON ➥8923113531 🔝Call Girls Badshah Nagar Lucknow best Female serviceCALL ON ➥8923113531 🔝Call Girls Badshah Nagar Lucknow best Female service
CALL ON ➥8923113531 🔝Call Girls Badshah Nagar Lucknow best Female serviceanilsa9823
 
How To Troubleshoot Collaboration Apps for the Modern Connected Worker
How To Troubleshoot Collaboration Apps for the Modern Connected WorkerHow To Troubleshoot Collaboration Apps for the Modern Connected Worker
How To Troubleshoot Collaboration Apps for the Modern Connected WorkerThousandEyes
 
call girls in Vaishali (Ghaziabad) 🔝 >༒8448380779 🔝 genuine Escort Service 🔝✔️✔️
call girls in Vaishali (Ghaziabad) 🔝 >༒8448380779 🔝 genuine Escort Service 🔝✔️✔️call girls in Vaishali (Ghaziabad) 🔝 >༒8448380779 🔝 genuine Escort Service 🔝✔️✔️
call girls in Vaishali (Ghaziabad) 🔝 >༒8448380779 🔝 genuine Escort Service 🔝✔️✔️Delhi Call girls
 
Steps To Getting Up And Running Quickly With MyTimeClock Employee Scheduling ...
Steps To Getting Up And Running Quickly With MyTimeClock Employee Scheduling ...Steps To Getting Up And Running Quickly With MyTimeClock Employee Scheduling ...
Steps To Getting Up And Running Quickly With MyTimeClock Employee Scheduling ...MyIntelliSource, Inc.
 
Shapes for Sharing between Graph Data Spaces - and Epistemic Querying of RDF-...
Shapes for Sharing between Graph Data Spaces - and Epistemic Querying of RDF-...Shapes for Sharing between Graph Data Spaces - and Epistemic Querying of RDF-...
Shapes for Sharing between Graph Data Spaces - and Epistemic Querying of RDF-...Steffen Staab
 
Unveiling the Tech Salsa of LAMs with Janus in Real-Time Applications
Unveiling the Tech Salsa of LAMs with Janus in Real-Time ApplicationsUnveiling the Tech Salsa of LAMs with Janus in Real-Time Applications
Unveiling the Tech Salsa of LAMs with Janus in Real-Time ApplicationsAlberto González Trastoy
 
Software Quality Assurance Interview Questions
Software Quality Assurance Interview QuestionsSoftware Quality Assurance Interview Questions
Software Quality Assurance Interview QuestionsArshad QA
 
Learn the Fundamentals of XCUITest Framework_ A Beginner's Guide.pdf
Learn the Fundamentals of XCUITest Framework_ A Beginner's Guide.pdfLearn the Fundamentals of XCUITest Framework_ A Beginner's Guide.pdf
Learn the Fundamentals of XCUITest Framework_ A Beginner's Guide.pdfkalichargn70th171
 
Optimizing AI for immediate response in Smart CCTV
Optimizing AI for immediate response in Smart CCTVOptimizing AI for immediate response in Smart CCTV
Optimizing AI for immediate response in Smart CCTVshikhaohhpro
 
Reassessing the Bedrock of Clinical Function Models: An Examination of Large ...
Reassessing the Bedrock of Clinical Function Models: An Examination of Large ...Reassessing the Bedrock of Clinical Function Models: An Examination of Large ...
Reassessing the Bedrock of Clinical Function Models: An Examination of Large ...harshavardhanraghave
 
Tech Tuesday-Harness the Power of Effective Resource Planning with OnePlan’s ...
Tech Tuesday-Harness the Power of Effective Resource Planning with OnePlan’s ...Tech Tuesday-Harness the Power of Effective Resource Planning with OnePlan’s ...
Tech Tuesday-Harness the Power of Effective Resource Planning with OnePlan’s ...OnePlan Solutions
 
The Ultimate Test Automation Guide_ Best Practices and Tips.pdf
The Ultimate Test Automation Guide_ Best Practices and Tips.pdfThe Ultimate Test Automation Guide_ Best Practices and Tips.pdf
The Ultimate Test Automation Guide_ Best Practices and Tips.pdfkalichargn70th171
 
SyndBuddy AI 2k Review 2024: Revolutionizing Content Syndication with AI
SyndBuddy AI 2k Review 2024: Revolutionizing Content Syndication with AISyndBuddy AI 2k Review 2024: Revolutionizing Content Syndication with AI
SyndBuddy AI 2k Review 2024: Revolutionizing Content Syndication with AIABDERRAOUF MEHENNI
 
Short Story: Unveiling the Reasoning Abilities of Large Language Models by Ke...
Short Story: Unveiling the Reasoning Abilities of Large Language Models by Ke...Short Story: Unveiling the Reasoning Abilities of Large Language Models by Ke...
Short Story: Unveiling the Reasoning Abilities of Large Language Models by Ke...kellynguyen01
 
W01_panagenda_Navigating-the-Future-with-The-Hitchhikers-Guide-to-Notes-and-D...
W01_panagenda_Navigating-the-Future-with-The-Hitchhikers-Guide-to-Notes-and-D...W01_panagenda_Navigating-the-Future-with-The-Hitchhikers-Guide-to-Notes-and-D...
W01_panagenda_Navigating-the-Future-with-The-Hitchhikers-Guide-to-Notes-and-D...panagenda
 
+971565801893>>SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHAB...
+971565801893>>SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHAB...+971565801893>>SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHAB...
+971565801893>>SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHAB...Health
 

Kürzlich hochgeladen (20)

Unlocking the Future of AI Agents with Large Language Models
Unlocking the Future of AI Agents with Large Language ModelsUnlocking the Future of AI Agents with Large Language Models
Unlocking the Future of AI Agents with Large Language Models
 
5 Signs You Need a Fashion PLM Software.pdf
5 Signs You Need a Fashion PLM Software.pdf5 Signs You Need a Fashion PLM Software.pdf
5 Signs You Need a Fashion PLM Software.pdf
 
The Real-World Challenges of Medical Device Cybersecurity- Mitigating Vulnera...
The Real-World Challenges of Medical Device Cybersecurity- Mitigating Vulnera...The Real-World Challenges of Medical Device Cybersecurity- Mitigating Vulnera...
The Real-World Challenges of Medical Device Cybersecurity- Mitigating Vulnera...
 
CALL ON ➥8923113531 🔝Call Girls Badshah Nagar Lucknow best Female service
CALL ON ➥8923113531 🔝Call Girls Badshah Nagar Lucknow best Female serviceCALL ON ➥8923113531 🔝Call Girls Badshah Nagar Lucknow best Female service
CALL ON ➥8923113531 🔝Call Girls Badshah Nagar Lucknow best Female service
 
How To Troubleshoot Collaboration Apps for the Modern Connected Worker
How To Troubleshoot Collaboration Apps for the Modern Connected WorkerHow To Troubleshoot Collaboration Apps for the Modern Connected Worker
How To Troubleshoot Collaboration Apps for the Modern Connected Worker
 
call girls in Vaishali (Ghaziabad) 🔝 >༒8448380779 🔝 genuine Escort Service 🔝✔️✔️
call girls in Vaishali (Ghaziabad) 🔝 >༒8448380779 🔝 genuine Escort Service 🔝✔️✔️call girls in Vaishali (Ghaziabad) 🔝 >༒8448380779 🔝 genuine Escort Service 🔝✔️✔️
call girls in Vaishali (Ghaziabad) 🔝 >༒8448380779 🔝 genuine Escort Service 🔝✔️✔️
 
Microsoft AI Transformation Partner Playbook.pdf
Microsoft AI Transformation Partner Playbook.pdfMicrosoft AI Transformation Partner Playbook.pdf
Microsoft AI Transformation Partner Playbook.pdf
 
Steps To Getting Up And Running Quickly With MyTimeClock Employee Scheduling ...
Steps To Getting Up And Running Quickly With MyTimeClock Employee Scheduling ...Steps To Getting Up And Running Quickly With MyTimeClock Employee Scheduling ...
Steps To Getting Up And Running Quickly With MyTimeClock Employee Scheduling ...
 
Shapes for Sharing between Graph Data Spaces - and Epistemic Querying of RDF-...
Shapes for Sharing between Graph Data Spaces - and Epistemic Querying of RDF-...Shapes for Sharing between Graph Data Spaces - and Epistemic Querying of RDF-...
Shapes for Sharing between Graph Data Spaces - and Epistemic Querying of RDF-...
 
Unveiling the Tech Salsa of LAMs with Janus in Real-Time Applications
Unveiling the Tech Salsa of LAMs with Janus in Real-Time ApplicationsUnveiling the Tech Salsa of LAMs with Janus in Real-Time Applications
Unveiling the Tech Salsa of LAMs with Janus in Real-Time Applications
 
Software Quality Assurance Interview Questions
Software Quality Assurance Interview QuestionsSoftware Quality Assurance Interview Questions
Software Quality Assurance Interview Questions
 
Learn the Fundamentals of XCUITest Framework_ A Beginner's Guide.pdf
Learn the Fundamentals of XCUITest Framework_ A Beginner's Guide.pdfLearn the Fundamentals of XCUITest Framework_ A Beginner's Guide.pdf
Learn the Fundamentals of XCUITest Framework_ A Beginner's Guide.pdf
 
Optimizing AI for immediate response in Smart CCTV
Optimizing AI for immediate response in Smart CCTVOptimizing AI for immediate response in Smart CCTV
Optimizing AI for immediate response in Smart CCTV
 
Reassessing the Bedrock of Clinical Function Models: An Examination of Large ...
Reassessing the Bedrock of Clinical Function Models: An Examination of Large ...Reassessing the Bedrock of Clinical Function Models: An Examination of Large ...
Reassessing the Bedrock of Clinical Function Models: An Examination of Large ...
 
Tech Tuesday-Harness the Power of Effective Resource Planning with OnePlan’s ...
Tech Tuesday-Harness the Power of Effective Resource Planning with OnePlan’s ...Tech Tuesday-Harness the Power of Effective Resource Planning with OnePlan’s ...
Tech Tuesday-Harness the Power of Effective Resource Planning with OnePlan’s ...
 
The Ultimate Test Automation Guide_ Best Practices and Tips.pdf
The Ultimate Test Automation Guide_ Best Practices and Tips.pdfThe Ultimate Test Automation Guide_ Best Practices and Tips.pdf
The Ultimate Test Automation Guide_ Best Practices and Tips.pdf
 
SyndBuddy AI 2k Review 2024: Revolutionizing Content Syndication with AI
SyndBuddy AI 2k Review 2024: Revolutionizing Content Syndication with AISyndBuddy AI 2k Review 2024: Revolutionizing Content Syndication with AI
SyndBuddy AI 2k Review 2024: Revolutionizing Content Syndication with AI
 
Short Story: Unveiling the Reasoning Abilities of Large Language Models by Ke...
Short Story: Unveiling the Reasoning Abilities of Large Language Models by Ke...Short Story: Unveiling the Reasoning Abilities of Large Language Models by Ke...
Short Story: Unveiling the Reasoning Abilities of Large Language Models by Ke...
 
W01_panagenda_Navigating-the-Future-with-The-Hitchhikers-Guide-to-Notes-and-D...
W01_panagenda_Navigating-the-Future-with-The-Hitchhikers-Guide-to-Notes-and-D...W01_panagenda_Navigating-the-Future-with-The-Hitchhikers-Guide-to-Notes-and-D...
W01_panagenda_Navigating-the-Future-with-The-Hitchhikers-Guide-to-Notes-and-D...
 
+971565801893>>SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHAB...
+971565801893>>SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHAB...+971565801893>>SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHAB...
+971565801893>>SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHAB...
 

Apache Pulsar Development 101 with Python

  • 1. Pulsar Summit San Francisco Hotel Nikko August 18 2022 Timothy Spann Developer Advocate, StreamNative Apache Pulsar Development 101 with Python
  • 2. Tim Spann Developer Advocate 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. https://streamnative.io/pulsar-python/
  • 4. FLiP Stack Weekly This week in Apache Flink, Apache Pulsar, Apache NiFi, Apache Spark and open source friends. https://bit.ly/32dAJft
  • 5. Python Application for ADS-B Data Diagram Python App REST CALL LOGGING ANALYTICS SEND TO PULSAR https://github.com/tspannhw/FLiP-Py-ADS-B
  • 6. Apache Pulsar Training ● Instructor-led courses ○ Pulsar Fundamentals ○ Pulsar Developers ○ Pulsar Operations ● On-demand learning with labs ● 300+ engineers, admins and architects trained! StreamNative Academy Now Available FREE On-Demand Pulsar Training Academy.StreamNative.io
  • 7. What is Apache Pulsar? Unified Messaging Platform Guaranteed Message Delivery Resiliency Infinite Scalability
  • 8. ● “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 Store (ZK, RocksDB, etcd, …)
  • 9. Pulsar’s Publish-Subscribe model Broker Subscription Consumer 1 Consumer 2 Consumer 3 Topic Producer 1 Producer 2 ● Producers send messages. ● Topics are an ordered, named channel 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.
  • 10. Subscription Modes 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
  • 11. Messages - the Basic Unit of Pulsar 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.
  • 12. Connectivity • Functions - Lightweight Stream Processing (Java, Python, Go) • Connectors - Sources & Sinks (Cassandra, Kafka, …) • Protocol Handlers - AoP (AMQP), KoP (Kafka), MoP (MQTT), RoP (RocketMQ) • Processing Engines - Flink, Spark, Presto/Trino via Pulsar SQL • Data Offloaders - Tiered Storage - (S3) hub.streamnative.io
  • 13. ● 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
  • 14. #!/usr/bin/env python 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 Entire Function Pulsar Functions
  • 15. 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.
  • 19. Spark + Pulsar https://pulsar.apache.org/docs/en/adaptors-spark/ val dfPulsar = spark.readStream.format(" pulsar") .option(" service.url", "pulsar://pulsar1:6650") .option(" admin.url", "http://pulsar1:8080 ") .option(" topic", "persistent://public/default/airquality").load() val pQuery = dfPulsar.selectExpr("*") .writeStream.format(" console") .option("truncate", false).start() ____ __ / __/__ ___ _____/ /__ _ / _ / _ `/ __/ '_/ /___/ .__/_,_/_/ /_/_ version 3.2.0 /_/ Using Scala version 2.12.15 (OpenJDK 64-Bit Server VM, Java 11.0.11)
  • 20. ● 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?
  • 21. SQL select aqi, parameterName, dateObserved, hourObserved, latitude, longitude, localTimeZone, stateCode, reportingArea from airquality select max(aqi) as MaxAQI, parameterName, reportingArea from airquality group by parameterName, reportingArea select max(aqi) as MaxAQI, min(aqi) as MinAQI, avg(aqi) as AvgAQI, count(aqi) as RowCount, parameterName, reportingArea from airquality group by parameterName, reportingArea
  • 22. ● Buffer ● Batch ● Route ● Filter ● Aggregate ● Enrich ● Replicate ● Dedupe ● Decouple ● Distribute
  • 23. Schema Registry 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
  • 24. Streaming FLiP-Py Apps StreamNative Hub StreamNative Cloud Unified Batch and Stream COMPUTING Batch (Batch + Stream) Unified Batch and Stream STORAGE Offload (Queuing + Streaming) Tiered Storage Pulsar --- KoP --- MoP --- Websocket Pulsar Sink Streaming Edge Gateway Protocols CDC Apps
  • 25. 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. ● Python Functions A serverless event streaming framework
  • 26. ● 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
  • 27. Python 3 Coding Code Along With Tim <<DEMO>>
  • 28. Run a Local Standalone Bare Metal wget https://archive.apache.org/dist/pulsar/pulsar-2.9.1/apache-pulsar-2.9.1-bi n.tar.gz tar xvfz apache-pulsar-2.9.1-bin.tar.gz cd apache-pulsar-2.9.1 bin/pulsar standalone (For Pulsar SQL Support) bin/pulsar sql-worker start https://pulsar.apache.org/docs/en/standalone/
  • 29. <or> Run in StreamNative Cloud Scan the QR code to earn $200 in cloud credit
  • 30. Building Tenant, Namespace, Topics bin/pulsar-admin tenants create conference bin/pulsar-admin namespaces create conference/pythonweb bin/pulsar-admin tenants list bin/pulsar-admin namespaces list conference bin/pulsar-admin topics create persistent://conference/pythonweb/first bin/pulsar-admin topics list conference/pythonweb
  • 31. Install Python 3 Pulsar Client pip3 install pulsar-client=='2.9.1[all]' # Depending on Platform May Need to Build C++ Client For Python on Pulsar on Pi https://github.com/tspannhw/PulsarOnRaspberryPi https://pulsar.apache.org/docs/en/client-libraries-python/
  • 32. Building a Python 3 Producer import pulsar client = pulsar.Client('pulsar://localhost:6650') producer = client.create_producer('persistent://conference/pythonweb/first') producer.send(('Simple Text Message').encode('utf-8')) client.close()
  • 33. Building a Python 3 Cloud Producer Oath python3 prod.py -su pulsar+ssl://name1.name2.snio.cloud:6651 -t persistent://public/default/pyth --auth-params '{"issuer_url":"https://auth.streamnative.cloud", "private_key":"my.json", "audience":"urn:sn:pulsar:name:myclustr"}' from pulsar import Client, AuthenticationOauth2 parse = argparse.ArgumentParser(prog=prod.py') parse.add_argument('-su', '--service-url', dest='service_url', type=str, required=True) args = parse.parse_args() client = pulsar.Client(args.service_url, authentication=AuthenticationOauth2(args.auth_params)) https://github.com/streamnative/examples/blob/master/cloud/python/OAuth2Producer.py https://github.com/tspannhw/FLiP-Pi-BreakoutGarden
  • 34. Example Avro Schema Usage import pulsar from pulsar.schema import * from pulsar.schema import AvroSchema class thermal(Record): uuid = String() client = pulsar.Client('pulsar://pulsar1:6650') thermalschema = AvroSchema(thermal) producer = client.create_producer(topic='persistent://public/default/pi-thermal-avro', schema=thermalschema,properties={"producer-name": "thrm" }) thermalRec = thermal() thermalRec.uuid = "unique-name" producer.send(thermalRec,partition_key=uniqueid) https://github.com/tspannhw/FLiP-Pi-Thermal
  • 35. Example Json Schema Usage import pulsar from pulsar.schema import * from pulsar.schema import JsonSchema class weather(Record): uuid = String() client = pulsar.Client('pulsar://pulsar1:6650') wschema = JsonSchema(thermal) producer = client.create_producer(topic='persistent://public/default/weathe r,schema=wschema,properties={"producer-name": "wthr" }) weatherRec = weather() weatherRec.uuid = "unique-name" producer.send(weatherRec,partition_key=uniqueid) https://github.com/tspannhw/FLiP-Pi-Weather
  • 36. Building a Python3 Consumer import pulsar client = pulsar.Client('pulsar://localhost:6650') consumer = client.subscribe('persistent://conference/pythonweb/first',subscription_na me='my-sub') while True: msg = consumer.receive() print("Received message: '%s'" % msg.data()) consumer.acknowledge(msg) client.close()
  • 37. MQTT from Python pip3 install paho-mqtt import paho.mqtt.client as mqtt client = mqtt.Client("rpi4-iot") row = { } row['gasKO'] = str(readings) json_string = json.dumps(row) json_string = json_string.strip() client.connect("pulsar-server.com", 1883, 180) client.publish("persistent://public/default/mqtt-2", payload=json_string,qos=0,retain=True) https://www.slideshare.net/bunkertor/data-minutes-2-apache-pulsar-with-mqtt-for-edge-computing-lightning-2022
  • 38. Web Sockets from Python pip3 install websocket-client import websocket, base64, json topic = 'ws://server:8080/ws/v2/producer/persistent/public/default/webtopic1' ws = websocket.create_connection(topic) message = "Hello Python Web Conference" message_bytes = message.encode('ascii') base64_bytes = base64.b64encode(message_bytes) base64_message = base64_bytes.decode('ascii') ws.send(json.dumps({'payload' : base64_message,'properties': {'device' : 'jetson2gb','protocol' : 'websockets'},'context' : 5})) response = json.loads(ws.recv()) https://pulsar.apache.org/docs/en/client-libraries-websocket/ https://github.com/tspannhw/FLiP-IoT/blob/main/wspulsar.py https://github.com/tspannhw/FLiP-IoT/blob/main/wsreader.py
  • 39. Kafka from Python pip3 install kafka-python from kafka import KafkaProducer from kafka.errors import KafkaError row = { } row['gasKO'] = str(readings) json_string = json.dumps(row) json_string = json_string.strip() producer = KafkaProducer(bootstrap_servers='pulsar1:9092',retries=3) producer.send('topic-kafka-1', json.dumps(row).encode('utf-8')) producer.flush() https://github.com/streamnative/kop https://docs.streamnative.io/platform/v1.0.0/concepts/kop-concepts
  • 40. Pulsar IO Functions in Python https://github.com/tspannhw/pulsar-pychat-function
  • 41. Pulsar IO Functions in Python bin/pulsar-admin functions create --auto-ack true --py py/src/sentiment.py --classname "sentiment.Chat" --inputs "persistent://public/default/chat" --log-topic "persistent://public/default/logs" --name Chat --output "persistent://public/default/chatresult" https://github.com/tspannhw/pulsar-pychat-function
  • 42. Pulsar IO Functions in Python from pulsar import Function import json class Chat(Function): def __init__(self): pass def process(self, input, context): logger = context.get_logger() msg_id = context.get_message_id() fields = json.loads(input) https://github.com/tspannhw/pulsar-pychat-function
  • 43. Python For Pulsar on Pi ● 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
  • 44. Let’s Keep in Touch Tim Spann Developer Advocate @PassDev https://www.linkedin.com/in/timothyspann https://github.com/tspannhw https://streamnative.io/pulsar-python/
  • 45. Pulsar Summit San Francisco Hotel Nikko August 18 2022 Tim Spann Thank you! tim@streamnative.io @PaaSDev github.com/tspannhw