Kafka Connect & Kafka Streams/KSQL - the ecosystem around Kafka

BASEL BERN BRUGG DÜSSELDORF FRANKFURT A.M. FREIBURG I.BR. GENF
HAMBURG KOPENHAGEN LAUSANNE MÜNCHEN STUTTGART WIEN ZÜRICH
Kafka Connect & Kafka Streams/KSQL
The Ecosystem around Kafka
Guido Schmutz – 4.4.2018
@gschmutz guidoschmutz.wordpress.com

Guido Schmutz
Working at Trivadis for more than 21 years
Oracle ACE Director for Fusion Middleware and SOA
Consultant, Trainer Software Architect for Java, Oracle, SOA and
Big Data / Fast Data
Head of Trivadis Architecture Board
Technology Manager @ Trivadis
More than 30 years of software development experience
Contact: guido.schmutz@trivadis.com
Blog: http://guidoschmutz.wordpress.com
Slideshare: http://www.slideshare.net/gschmutz
Twitter: gschmutz
Kafka Connect & Streams - the Ecosystem around Kafka

Our company.
Trivadis is a market leader in IT consulting, system integration, solution engineering
and the provision of IT services focusing on and
technologies
in Switzerland, Germany, Austria and Denmark. We offer our services in the following
strategic business fields:
Trivadis Services takes over the interacting operation of your IT systems.
O P E R A T I O N

COPENHAGEN
MUNICH
LAUSANNE
BERN
ZURICH
BRUGG
GENEVA
HAMBURG
DÜSSELDORF
FRANKFURT
STUTTGART
FREIBURG
BASEL
VIENNA
With over 600 specialists and IT experts in your region.
14 Trivadis branches and more than
600 employees
200 Service Level Agreements
Over 4,000 training participants
Research and development budget:
CHF 5.0 million
Financially self-supporting and
sustainably profitable
Experience from more than 1,900
projects per year at over 800
customers

Agenda
1. What is Apache Kafka?
2. Kafka Connect
3. Kafka Streams
4. KSQL
5. Kafka and "Big Data" / "Fast Data" Ecosystem
6. Kafka in Software Architecture

What is Apache Kafka?

Apache Kafka History
2012 2013 2014 2015 2016 2017
Cluster mirroring
data compression
Intra-cluster
replication
0.7
0.8
0.9
Data Processing
(Streams API)
0.10
Data Integration
(Connect API)
0.11
2018
Exactly Once
Semantics
Performance
Improvements
KSQL Developer
Preview
1.0 JBOD Support
Support Java 9

Apache Kafka – A Streaming Platform
Kafka Connect & Kafka Streams/KSQL
High-Level Architecture
Distributed Log at the Core
Scale-Out Architecture
Logs do not (necessarily) forget

Strong Ordering Guarantees
most business systems need strong
ordering guarantees
messages that require relative
ordering need to be sent to the same
partition
supply same key for
all messages that
require a relative order
To maintain global ordering use a
single partition topic
Producer 1
Consumer 1
Broker 1
Broker 2
Broker 3
Consumer 2
Consumer 3
Key-1
Key-2
Key-3
Key-4
Key-5
Key-6
Key-3
Key-1

Durable and Highly Available Messaging
Producer 1
Broker 1
Broker 2
Broker 3
Producer 1
Broker 1
Broker 2
Broker 3
Consumer 1 Consumer 1
Consumer 2Consumer 2
Microservices with Kafka Ecosystem14

Durable and Highly Available Messaging (II)
Producer 1
Broker 1
Broker 2
Broker 3
Producer 1
Broker 1
Broker 2
Broker 3
Consumer 1 Consumer 1
Consumer
2
Consumer 2

Hold Data for Long-Term – Data Retention
Producer 1
Broker 1
Broker 2
Broker 3
1. Never
2. Time based (TTL)
log.retention.{ms | minutes | hours}
3. Size based
log.retention.bytes
4. Log compaction based
(entries with same key are removed):
kafka-topics.sh --zookeeper zk:2181
--create --topic customers
--replication-factor 1
--partitions 1
--config cleanup.policy=compact

Keep Topics in Compacted Form
0 1 2 3 4 5 6 7 8 9 10 11
K1 K2 K1 K1 K3 K2 K4 K5 K5 K2 K6 K2
V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11
Offset
Key
Value
3 4 6 8 9 10
K1 K3 K4 K5 K2 K6
V4 V5 V7 V9 V10 V11
Offset
Key
Value
Compaction
V1 V2
V3
V4 V5
V6
V7
V8
V9V10 V11
K1 K3 K4 K5K2 K6

How to get a Kafka environment
On Premises
• Bare Metal Installation
• Docker
• Mesos / Kubernetes
• Hadoop Distributions
Cloud
• Oracle Event Hub Cloud Service
• Azure HDInsight Kafka
• Confluent Cloud
• …

Demo (I)
Truck-2
truck
position
Truck-1
Truck-3
console
consumer
Testdata-Generator by Hortonworks
1522846456703,101,31,1927624662,Normal,37.31,-
94.31,-4802309397906690837

Demo (I) – Create Kafka Topic
$ kafka-topics --zookeeper zookeeper:2181 --create
--topic truck_position --partitions 8 --replication-factor 1
$ kafka-topics --zookeeper zookeeper:2181 –list
__consumer_offsets
_confluent-metrics
_schemas
docker-connect-configs
docker-connect-offsets
docker-connect-status
truck_position

Demo (I) – Run Producer and Kafka-Console-Consumer

Demo (I) – Java Producer to "truck_position"
Constructing a Kafka Producer
private Properties kafkaProps = new Properties();
kafkaProps.put("bootstrap.servers","broker-1:9092);
kafkaProps.put("key.serializer", "...StringSerializer");
kafkaProps.put("value.serializer", "...StringSerializer");
producer = new KafkaProducer<String, String>(kafkaProps);
ProducerRecord<String, String> record =
new ProducerRecord<>("truck_position", driverId, eventData);
try {
metadata = producer.send(record).get();
} catch (Exception e) {}

Demo (II) – devices send to MQTT instead of Kafka
Truck-2
truck/nn/
position
Truck-1
Truck-3
1522846456703,101,31,1927624662,Normal,37.31,-
94.31,-4802309397906690837

Demo (II) – devices send to MQTT instead of Kafka

Demo (II) - devices send to MQTT instead of Kafka –
how to get the data into Kafka?
Truck-2
truck/nn/
position
Truck-1
Truck-3
truck
position raw
?
1522846456703,101,31,1927624662,Normal,37.31,-
94.31,-4802309397906690837

Apache Kafka – wait there is more!
Source
Connector
trucking_
driver
Kafka Broker
Sink
Connector
Stream
Processing

Kafka Connect

Kafka Connect - Overview
Source
Connector
Sink
Connector

Kafka Connect – Single Message Transforms (SMT)
Simple Transformations for a single message
Defined as part of Kafka Connect
• some useful transforms provided out-of-the-box
• Easily implement your own
Optionally deploy 1+ transforms with each
connector
• Modify messages produced by source
connector
• Modify messages sent to sink connectors
Makes it much easier to mix and match connectors
Some of currently available
transforms:
• InsertField
• ReplaceField
• MaskField
• ValueToKey
• ExtractField
• TimestampRouter
• RegexRouter
• SetSchemaMetaData
• Flatten
• TimestampConverter

Kafka Connect – Many Connectors
60+ since first release (0.9+)
20+ from Confluent and Partners
Source: http://www.confluent.io/product/connectors
Confluent supported Connectors
Certified Connectors Community Connectors

Demo (III)
Truck-2
truck/nn/
position
Truck-1
Truck-3
mqtt to
kafka
truck_
position
console
consumer
1522846456703,101,31,1927624662,Normal,37.31,-
94.31,-4802309397906690837

Demo (III) – Create MQTT Connect through REST API
#!/bin/bash
curl -X "POST" "http://192.168.69.138:8083/connectors"
-H "Content-Type: application/json"
-d $'{
"name": "mqtt-source",
"config": {
"connector.class":
"com.datamountaineer.streamreactor.connect.mqtt.source.MqttSourceConnector",
"connect.mqtt.connection.timeout": "1000",
"tasks.max": "1",
"connect.mqtt.kcql":
"INSERT INTO truck_position SELECT * FROM truck/+/position",
"name": "MqttSourceConnector",
"connect.mqtt.service.quality": "0",
"connect.mqtt.client.id": "tm-mqtt-connect-01",
"connect.mqtt.converter.throw.on.error": "true",
"connect.mqtt.hosts": "tcp://mosquitto:1883"
}
}'

Demo (III) – Call REST API and Kafka Console
Consumer

Demo (III)
Truck-2
truck/nn/
position
Truck-1
Truck-3
mqtt to
kafka
truck_
position
console
consumer
what about some
analytics ?
1522846456703,101,31,1927624662,Normal,37.31,-
94.31,-4802309397906690837

Kafka Streams

Kafka Streams - Overview
• Designed as a simple and lightweight library in Apache
Kafka
• no external dependencies on systems other than Apache
Kafka
• Part of open source Apache Kafka, introduced in 0.10+
• Leverages Kafka as its internal messaging layer
• Supports fault-tolerant local state
• Event-at-a-time processing (not microbatch) with millisecond
latency
• Windowing with out-of-order data using a Google DataFlow-like
model

Kafka Stream DSL and Processor Topology
KStream<Integer, String> stream1 =
builder.stream("in-1");
KStream<Integer, String> stream2=
builder.stream("in-2");
KStream<Integer, String> joined =
stream1.leftJoin(stream2, …);
KTable<> aggregated =
joined.groupBy(…).count("store");
aggregated.to("out-1");
1 2
lj
a
t
State

Kafka Streams Cluster
Processor Topology
Kafka Cluster
input-1
input-2
store (changelog)
output
1 2
lj
a
t
State

Kafka Cluster
Processor Topology
input-1
Partition 0
Partition 1
Partition 2
Partition 3
input-2
Partition 0
Partition 1
Partition 2
Partition 3
Kafka Streams 1
Kafka Streams 2

Kafka Cluster
Processor Topology
input-1
Partition 0
Partition 1
Partition 2
Partition 3
input-2
Partition 0
Partition 1
Partition 2
Partition 3
Kafka Streams 1 Kafka Streams 2
Kafka Streams 3 Kafka Streams 4

Stream-Table Duality
We can view a table as a stream
We can view a stream as a table
A stream can be considered a
changelog of a table, where each
data record in the stream captures
a state change of the table
A table can be considered a
snapshot of the latest value for
each key in a stream
Source: Confluent

Stream vs. Table
Event Stream State Stream (Change Log Stream)
2017-10-02T20:18:46 11,Normal,41.87,-87.67
2017-10-02T20:18:55 11,Normal,40.38,-89.17
2017-10-02T20:18:59 21,Normal,42.23,-91.78
2017-10-02T20:19:01 21,Normal,41.71,-91.32
2017-10-02T20:19:02 11,Normal,38.65,-90.2
2017-10-02T20:19:23 21,Normal41.71,-91.32
11 2017-10-02T20:18:46,11,Normal,41.87,-87.67
11 2017-10-02T20:18:55,11,Normal,40.38,-89.17
21 2017-10-02T20:18:59, 21,Normal,42.23,-91.78
21 2017-10-02T20:19:01,21,Normal,41.71,-91.32
11 2017-10-02T20:19:02,11,Normal,38.65,-90.2
21 2017-10-02T20:19:23,21,Normal41.71,-91.32
KStream KTable

Kafka Streams: Key Features
• Native, 100%-compatible Kafka integration
• Secure stream processing using Kafka's security features
• Elastic and highly scalable
• Fault-tolerant
• Stateful and stateless computations
• Interactive queries
• Time model
• Windowing
• Supports late-arriving and out-of-order data
• Millisecond processing latency, no micro-batching
• At-least-once and exactly-once processing guarantees

Demo (IV)
Truck-2
truck/nn/
position
Truck-1
Truck-3
mqtt to
kafka
truck_
position_s
detect_danger
ous_driving
dangerous_
driving
console
consumer
1522846456703,101,31,1927624662,Normal,37.31,-
94.31,-4802309397906690837

Demo (IV) - Create Stream
final KStreamBuilder builder = new KStreamBuilder();
KStream<String, String> source =
builder.stream(stringSerde, stringSerde, "truck_position");
KStream<String, TruckPosition> positions =
source.map((key,value) ->
new KeyValue<>(key, TruckPosition.create(key,value)));
KStream<String, TruckPosition> filtered =
positions.filter(TruckPosition::filterNonNORMAL);
filtered.map((key,value) -> new KeyValue<>(key,value.toCSV()))
.to("dangerous_driving");

KSQL

KSQL: a Streaming SQL Engine for Apache Kafka
• Enables stream processing with zero coding required
• The simples way to process streams of data in real-time
• Powered by Kafka and Kafka Streams: scalable, distributed, mature
• All you need is Kafka – no complex deployments
• available as Developer preview!
• STREAM and TABLE as first-class citizens
• STREAM = data in motion
• TABLE = collected state of a stream
• join STREAM and TABLE

KSQL Deployment Models
Standalone Mode Cluster Mode
Source: Confluent

Demo (IV) - Start Kafka KSQL
$ docker-compose exec ksql-cli ksql-cli local --bootstrap-server broker-1:9092
======================================
= _ __ _____ ____ _ =
= | |/ // ____|/ __ | | =
= | ' /| (___ | | | | | =
= | < ___ | | | | | =
= | . ____) | |__| | |____ =
= |_|______/ __________| =
= =
= Streaming SQL Engine for Kafka =
Copyright 2017 Confluent Inc.
CLI v0.1, Server v0.1 located at http://localhost:9098
Having trouble? Type 'help' (case-insensitive) for a rundown of how things work!
ksql>

ksql> CREATE STREAM truck_position_s
(ts VARCHAR,
truckId VARCHAR,
driverId BIGINT,
routeId BIGINT,
eventType VARCHAR,
latitude DOUBLE,
longitude DOUBLE,
correlationId VARCHAR)
WITH (kafka_topic='truck_position',
value_format='DELIMITED');
Message
----------------
Stream created

ksql> SELECT * FROM truck_position_s;
1522847870317 | "truck/13/position0 | 1522847870310 | 44 | 13 | 1390372503 |
Normal | 41.71 | -91.32 | -2458274393837068406
1522847870376 | "truck/14/position0 | 1522847870370 | 35 | 14 | 1961634315 |
Normal | 37.66 | -94.3 | -2458274393837068406
1522847870418 | "truck/21/position0 | 1522847870410 | 58 | 21 | 137128276 |
Normal | 36.17 | -95.99 | -2458274393837068406
1522847870397 | "truck/29/position0 | 1522847870390 | 18 | 29 | 1090292248 |
Normal | 41.67 | -91.24 | -2458274393837068406
ksql> SELECT * FROM truck_position_s WHERE eventType != 'Normal';
1522847914246 | "truck/11/position0 | 1522847914240 | 54 | 11 | 1198242881 |
Lane Departure | 40.86 | -89.91 | -2458274393837068406
1522847915125 | "truck/10/position0 | 1522847915120 | 93 | 10 | 1384345811 |
Overspeed | 40.38 | -89.17 | -2458274393837068406
1522847919216 | "truck/12/position0 | 1522847919210 | 75 | 12 | 24929475 |
Overspeed | 42.23 | -91.78 | -2458274393837068406

ksql> CREATE STREAM dangerous_driving_s
WITH (kafka_topic= dangerous_driving_s',
value_format='JSON')
AS SELECT * FROM truck_position_s
WHERE eventtype != 'Normal';
Message
----------------------------
Stream created and running
ksql> select * from dangerous_driving_s;
1522848286143 | "truck/15/position0 | 1522848286125 | 98 | 15 | 987179512 |
Overspeed | 34.78 | -92.31 | -2458274393837068406
1522848295729 | "truck/11/position0 | 1522848295720 | 54 | 11 | 1198242881 |
Unsafe following distance | 38.43 | -90.35 | -2458274393837068406
1522848313018 | "truck/11/position0 | 1522848313000 | 54 | 11 | 1198242881 |
Overspeed | 41.87 | -87.67 | -2458274393837068406

Demo (V)
Truck-2
truck/nn/
position
Truck-1
Truck-3
mqtt-
source
truck_
position
detect_danger
ous_driving
dangerous_
driving
Truck
Driver
jdbc-
source
trucking_
driver
join_dangerou
s_driving_driv
er
dangerous_dri
ving_driver
27, Walter, Ward, Y, 24-JUL-85, 2017-10-02 15:19:00
console
consumer
{"id":27,"firstName":"Walter",
"lastName":"Ward","available
":"Y","birthdate":"24-JUL-
85","last_update":150692305
2012}
1522846456703,101,31,1927624662,Normal,37.31,-
94.31,-4802309397906690837

Demo (V) – Create JDBC Connect through REST API
#!/bin/bash
curl -X "POST" "http://192.168.69.138:8083/connectors"
-H "Content-Type: application/json"
-d $'{
"name": "jdbc-driver-source",
"config": {
"connector.class": "JdbcSourceConnector",
"connection.url":"jdbc:postgresql://db/sample?user=sample&password=sample",
"mode": "timestamp",
"timestamp.column.name":"last_update",
"table.whitelist":"driver",
"validate.non.null":"false",
"topic.prefix":"trucking_",
"key.converter":"org.apache.kafka.connect.json.JsonConverter",
"key.converter.schemas.enable": "false",
"value.converter":"org.apache.kafka.connect.json.JsonConverter",
"value.converter.schemas.enable": "false",
"name": "jdbc-driver-source",
"transforms":"createKey,extractInt",
"transforms.createKey.type":"org.apache.kafka.connect.transforms.ValueToKey",
"transforms.createKey.fields":"id",
"transforms.extractInt.type":"org.apache.kafka.connect.transforms.ExtractField$Key",
"transforms.extractInt.field":"id"
}
}'

Demo (V) – Create JDBC Connect through REST API

Demo (V) - Create Table with Driver State
ksql> CREATE TABLE driver_t
(id BIGINT,
first_name VARCHAR,
last_name VARCHAR,
available VARCHAR)
WITH (kafka_topic='trucking_driver',
value_format='JSON',
key='id');
Message
----------------
Table created

Demo (V) - Create Table with Driver State
ksql> CREATE STREAM dangerous_driving_and_driver_s
WITH (kafka_topic='dangerous_driving_and_driver_s',
value_format='JSON')
AS SELECT driverId, first_name, last_name, truckId, routeId, eventtype
FROM truck_position_s
LEFT JOIN driver_t
ON dangerous_driving_and_driver_s.driverId = driver_t.id;
Message
----------------------------
Stream created and running
ksql> select * from dangerous_driving_and_driver_s;
1511173352906 | 21 | 21 | Lila | Page | 58 | 1594289134 | Unsafe tail distance
1511173353669 | 12 | 12 | Laurence | Lindsey | 93 | 1384345811 | Lane Departure
1511173435385 | 11 | 11 | Micky | Isaacson | 22 | 1198242881 | Unsafe tail
distance

Kafka and "Big Data" / "Fast Data"
Ecosystem

Kafka and the Big Data / Fast Data ecosystem
Kafka integrates with many popular products / frameworks
• Apache Spark Streaming
• Apache Flink
• Apache Storm
• Apache Apex
• Apache NiFi
• StreamSets
• Oracle Stream Analytics
• Oracle Service Bus
• Oracle GoldenGate
• Oracle Event Hub Cloud Service
• Debezium CDC
• …
Additional Info: https://cwiki.apache.org/confluence/display/KAFKA/Ecosystem

Kafka in Software Architecture

Hadoop Clusterd
Hadoop Cluster
Big Data Cluster
Traditional Big Data Architecture
BI Tools
Enterprise Data
Warehouse
Billing &
Ordering
CRM /
Profile
Marketing
Campaigns
File Import / SQL Import
SQL
Search / Explore
Online & Mobile
Apps
Search
SQL
Export
Service
NoSQL
Parallel Batch
Processing
Distributed
Filesystem
• Machine Learning
• Graph Algorithms
• Natural Language Processing

Event
Hub
Event
Hub
Hadoop Clusterd
Hadoop Cluster
Big Data Cluster
Event Hub – handle event stream data
BI Tools
Enterprise Data
Warehouse
Location
Social
Click
stream
Sensor
Data
Billing &
Ordering
CRM /
Profile
Marketing
Campaigns
Event
Hub
Call
Center
Weather
Data
Mobile
Apps
SQL
Search / Explore
Online & Mobile
Apps
Search
SQL
Export
ServiceData Flow
Data
Flow
NoSQL
Parallel Batch
Processing
Distributed
Filesystem
• Machine Learning
• Graph Algorithms
• Natural Language Processing
C
hange
D
ata
C
apture

Hadoop Clusterd
Hadoop Cluster
Big Data Cluster
Event Hub – taking Velocity into account
Location
Social
Click
stream
Sensor
Data
Billing &
Ordering
CRM /
Profile
Marketing
Campaigns
Call
Center
Mobile
Apps
Batch Analytics
Streaming Analytics
Results
Parallel Batch
Processing
Distributed
Filesystem
Stream Analytics
NoSQL
Reference /
Models
SQL
Search
SQL
Export
Service
Dashboard
BI Tools
Enterprise Data
Warehouse
Search / Explore
Online & Mobile
Apps
Weather
Data
Event
Hub
Event
Hub
Event
Hub
Data
Flow
Data
Flow
Data
Flow
Change
Data
Capture

Container
Hadoop Clusterd
Hadoop Cluster
Big Data Cluster
Event Hub – Asynchronous Microservice Architecture
Location
Social
Click
stream
Sensor
Data
Billing &
Ordering
CRM /
Profile
Marketing
Campaigns
Call
Center
Mobile
Apps
Parallel
Batch
ProcessingDistributed
Filesystem
Microservice
NoSQLRDBMS
SQL
Search
SQL
Export
Service
BI Tools
Enterprise Data
Warehouse
Search / Explore
Online & Mobile
Apps
Weather
Data
{ }
API
Event
Hub
Event
Hub
Event
Hub
Data
Flow
Data
Flow
Data
Flow
Change
Data
Capture

Technology on its own won't help you.
You need to know how to use it properly.

Kafka Connect & Kafka Streams/KSQL - the ecosystem around Kafka

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