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Kafka … de haut en bas !
University
Florent Ramière @framiere
Jean-Louis Boudart @jlboudart
Nicolas Romanetti @nromanetti
1
2
Massive volumes of
new data generated
every day
Mobile Cloud Microservices Internet of
Things
Machine
Learning
Distributed across
apps, devices,
datacenters, clouds
Structured,
unstructured
polymorphic
What
3
Problem ?
4
Silos explained by Data Gravity concept
As data accumulates (builds mass) there is a greater
likelihood that additional services and applications will
be attracted to this data.
This is the same effect gravity has on objects around a
planet. As the mass or density increases, so does the
strength of gravitational pull.
5
With
6
How
7
Store & ETL Process
Publish &
Subscribe
In short
8
From a simple idea
9
From a simple idea
10
with great properties !
• Scalability
• Retention
• Durability
• Replication
• Security
• Resiliency
• Throughput
• Ordering
• Exactly Once Semantic
• Transaction
• Idempotency
• Immutability
• …
11
11
Producer
12
Anatomy of a Message
13
14
Producing to Kafka - No Key
Time
Messages will be produced in
a round robin fashion
15
Producing to Kafka - With Key
Time
A
B
C
D
hash(key) %
numPartitions = N
16
Partition Leadership and Replication
Broker 1
Topic1
partition1
Broker 2 Broker 3 Broker 4
Topic1
partition1
Topic1
partition1
Leader Follower
Topic1
partition2
Topic1
partition2
Topic1
partition2
Topic1
partition3
Topic1
partition4
Topic1
partition3
Topic1
partition3
Topic1
partition4
Topic1
partition4
17
Partition Leadership and Replication - node failure
Broker 1
Topic1
partition1
Broker 2 Broker 3 Broker 4
Topic1
partition1
Topic1
partition1
Leader Follower
Topic1
partition2
Topic1
partition2
Topic1
partition2
Topic1
partition3
Topic1
partition4
Topic1
partition3
Topic1
partition3
Topic1
partition4
Topic1
partition4
18
Producer Guarantees
P
Broker 1 Broker 2 Broker 3
Topic1
partition1
Leader Follower
Topic1
partition1
Topic1
partition1
Producer Properties
acks=0
19
Producer Guarantees
P
Broker 1 Broker 2 Broker 3
Topic1
partition1
Leader Follower
Topic1
partition1
Topic1
partition1
ack
Producer Properties
acks=1
20
Producer Guarantees
P
Broker 1 Broker 2 Broker 3
Topic1
partition1
Leader Follower
Topic1
partition1
Topic1
partition1
Producer Properties
acks=all
min.insync.replica=2
First copy returns ack
ack
21
21
Consumer
22
Consuming From Kafka - Single Consumer
C
23
Consuming From Kafka - Grouped Consumers
CC
C1
CC
C2
24
Consuming From Kafka - Grouped Consumers
C C
C C
25
Consuming From Kafka - Grouped Consumers
0 1
2 3
26
Consuming From Kafka - Grouped Consumers
0 1
2 3
27
Consuming From Kafka - Grouped Consumers
0, 3 1
2 3
28
Compacted Topics – Keep only the most recent value for a key
29
29
Destroy all the magic!
30
Open protocol
https://kafka.apache.org/protocol
31
31
Broker Lifecycle
32
Anatomy of a Producer Request on a Broker
33
Anatomy of a Fetch Request on a Broker
34
34
Not so fast !
35
Set up secure Kafka
& build your first app
Understand streaming
Monitor & manage a
mission-critical solution
Set up secure Kafka &
build your first app
Understand streaming
Infrastructure & apps
across LOBs
Monitor & manage a
mission-critical solution
Set up secure Kafka &
build your first app
Understand streaming
Self-service on shared
Kafka
Infrastructure &
applications across
LOBs
Monitor & manage a
mission-critical solution
Set up secure Kafka &
build your first app
Understand streamingUnderstand streaming
Pre-streamingValue
Stream Everything
05Break Silos
04
03
Go To Production
02
Learn Kafka
01
Investment & Time
Solve A Critical
Need
Maturity model
36
Set up secure Kafka
& build your first app
Understand streaming
Monitor & manage a
mission-critical solution
Set up secure Kafka &
build your first app
Understand streaming
Infrastructure & apps
across LOBs
Monitor & manage a
mission-critical solution
Set up secure Kafka &
build your first app
Understand streaming
Self-service on shared
Kafka
Infrastructure &
applications across
LOBs
Monitor & manage a
mission-critical solution
Set up secure Kafka &
build your first app
Understand streamingUnderstand streaming
Pre-streamingValue
Stream Everything
05Break Silos
04
03
Go To Production
02
Learn Kafka
01
Solve A Critical
Need
Maturity model
37
Set up secure Kafka
& build your first app
Understand streaming
Monitor & manage a
mission-critical solution
Set up secure Kafka &
build your first app
Understand streaming
Infrastructure & apps
across LOBs
Monitor & manage a
mission-critical solution
Set up secure Kafka &
build your first app
Understand streaming
Self-service on shared
Kafka
Infrastructure &
applications across
LOBs
Monitor & manage a
mission-critical solution
Set up secure Kafka &
build your first app
Understand streamingUnderstand streaming
Pre-streamingValue
Stream Everything
05Break Silos
04
03
Go To Production
02
Learn Kafka
01
Solve A Critical
Need
Maturity model
38
Business Value!
39
39
This is a full platform
40
… spawned a full platform
Apache Kafka®
Core | Connect API | Streams API
Stream Processing & Compatibility
KSQL | Schema Registry
Operations
Replicator | Auto Data Balancer | Connectors | MQTT Proxy | Operator
Database
Changes
Log Events IoT Data Web Events other events
Hadoop
Database
Data
Warehouse
CRM
other
DATA INTEGRATION
Transformations
Custom Apps
Analytics
Monitoring
other
REAL-TIME
APPLICATIONS
OPEN SOURCE FEATURES COMMERCIAL FEATURES
Datacenter Public Cloud Confluent Cloud
CONFLUENT PLATFORM
Administration & Monitoring
Control Center | Security
Connectivity
Clients | Connectors | REST Proxy
CONFLUENT FULLY-MANAGEDCUSTOMER SELF-MANAGED
41
41
ETL
42
I
43
43
Start small
44
45
46
47
48
49
50
50
More “Real life”
databases
51
52
T1
53
T1,T2,T3
54
T1,T2,T3
… T214 ?
55
T1,T2,T3
… T214
T1-T70
T71,T139
T140,T214
56
T1,T2,T3
… T214
T1-T70
T140,T214
T71,T139
57
T1,T2,T3
… T214
T1-T70
T140,T214
T71,T104
T105,139
58
T1,T2,T3
… T214
T1-T70
T71,T139
T140,T214
59
T1,T2,T3
… T223
T1-T70
T71,T139
T140,T214
?
60
T1,T2,T3
… T223
T1-T70
T71,T139
T140,T214
?
61
Apache Kafka Connect API: Import and Export Data In & Out of Kafka
JDBC
Mongo
MySQL
Elastic
Cassandra
HDFS
Kafka Connect API
Kafka Pipeline
Connector
Connector
Connector
Connector
Connector
Connector
Sources Sinks
Fault tolerant
Manage hundreds of
data sources and sinks
Preserves data schema
Integrated within
Confluent Control Center
62
Connectors: Connect Kafka Easily with Data Sources and Sinks
Databases Datastore/File Store
Analytics Applications / Other
63
Kafka Connect API, Part of the Apache Kafka™ Project
Connect any source to any target system
Integrated
• 100% compatible with Kafka v0.9 and
higher
• Integrated with Confluent’s Schema
Registry
• Easy to manage with Confluent Control
Center
Flexible
• 40+ open source connectors available
• Easy to develop additional connectors
• Flexible support for data types and
formats
Compatible
• Maintains critical metadata
• Preserves schema information
• Supports schema evolution
Reliable
• Automated failover
• Exactly-once guarantees
• Balances workload between nodes
64
Confluent Hub - The Kafka App Store
65
65
Connectivity
66
Clients: Communicate with Kafka in a Broad Variety of Languages
Apache Kafka
Confluent Platform Community Supported
Proxy http/REST
stdin/stdout
Confluent Platform Clients developed and fully supported by Confluent
67
REST Proxy: Talking to Non-native Kafka Apps and Outside the Firewall
REST Proxy
Non-Java Applications
Native Kafka Java
Applications
Schema Registry
REST / HTTP
Simplifies administrative
actions
Simplifies message creation
and consumption
Provides a RESTful
interface to a Kafka cluster
68
68
Processing
69
Stream Processing by Analogy
Kafka Cluster
Connect API Stream Processing Connect API
$ cat < in.txt | grep "ksql" | tr a-z A-Z > out.txt
70
• subscribe()
• poll()
• send()
• flush()
Consumer,
Producer
Flexibility Simplicity
Trade offs
71
Low Level API
Consumer
Producer
72
• subscribe()
• poll()
• send()
• flush()
Consumer,
Producer
• mapValues()
• filter()
• punctuate()
Kafka Streams
Flexibility Simplicity
Trade offs
73
High level API
App
Streams
API
Not running
inside brokers!
Consumer
Group
Protocol
Power!
App
Streams
API
App
Streams
API
App
Streams
API
Same app, many instances
76
Before
DashboardProcessing Cluster
Your Job
Shared Database
77
After
Dashboard
APP
Streams
API
78
Things Kafka Streams Does
Runs
everywhere
Clustering
done for you
Exactly-once
processing
Event-time
processing
Integrated
database
Joins, windowing,
aggregation
S/M/L/XL/XXL/XXXL
sizes
79
• subscribe()
• poll()
• send()
• flush()
Consumer,
Producer
• mapValues()
• filter()
• punctuate()
Kafka Streams
Flexibility Simplicity
Trade offs
80
80
Kafka Streams
Time time time
81
Time! Time! Time! Time! Time! Time! Time! Time!
82
Windowing in Kafka Streams
83
Tumbling time windows
83
84
Hopping time windows
84
85
Session windows
85
86
Event Time Processing
Event-time
”The point in time when an event or data record occurred, i.e. was originally created
"by the source". Achieving event-time semantics typically requires embedding
timestamps in the data records at the time a data record is being produced.”
Processing-time
”The point in time when the event or data record happens to be processed by the
stream processing application, i.e. when the record is being consumed. The
processing-time may be milliseconds, hours, or days etc. later than the original event-
time.”
Ingestion-time
“The point in time when an event or data record is stored in a topic partition by a
Kafka broker.”
87
87
Kafka Streams
Exactly once semantic
88
Delivery Guarantee
At most once
“Messages may be lost but are never redelivered.”
At least once
“Messages are never lost but may be redelivered.“
Exactly once
“Each message is delivered once and only once.“
89
Exactly Once principle
90
Failure Scenario : Duplicate Writes
91
Failure Scenario : Duplicate Processing
92
Producer Guarantees - without exactly once guarantees
P
Broker 1 Broker 2 Broker 3
Topic1
partition1
Leader Follower
Topic1
partition1
Topic1
partition1
Producer Properties
acks=all
min.insync.replica=2
{key: 1234 data: abcd} - offset 3345
Failed ack
Successful write
93
Producer Guarantees - without exactly once guarantees
P
Broker 1 Broker 2 Broker 3
Topic1
partition1
Leader Follower
Topic1
partition1
Topic1
partition1
Producer Properties
acks=all
min.insync.replica=2
{key: 1234, data: abcd} - offset 3345
{key: 1234, data: abcd} - offset 3346
retry
ack
dupe!
94
Producer Guarantees - with exactly once guarantees
P
Broker 1 Broker 2 Broker 3
Topic1
partition1
Leader Follower
Topic1
partition1
Topic1
partition1
Producer Properties
enable.idempotence=true
max.inflight.requests.per.connection=1
acks = “all”
retries > 0 (preferably MAX_INT)
(pid, seq) [payload]
(100, 1) {key: 1234, data: abcd} - offset 3345
(100, 1) {key: 1234, data: abcd} - rejected, ack re-sent
(100, 2) {key: 5678, data: efgh} - offset 3346
retry
ack
no dupe!
95
Exactly once
Idempotent Producer
Transactions
Isolation Level
• Read committed
• Read uncommitted
95
96
Transactions !
97
Exactly once made simple with Kafka Streams
98
98
Kafka Streams
Interactive Queries
99
Interactive Queries
App
Streams API
kTable = aStream
.groupByKey()
.reduce(reducer,materialize)
From our App, how to query the state store?
State
Store
Kafka
Cluster
100
Interactive Queries
App
Streams API
store = kafkaStreams
.store(name, types)
value = store.get(key)
From our App, how to query the state store?
- Get the store « by name & types»
- Then the value « by key »
READ ONLY (Streams DSL)
Kafka
Cluster
101
Interactive Queries
App
Streams API
store = kafkaStreams
.store(name, types)
value = store.get(key)
You can serve that value to your client
Front
End key Kafka
Cluster
102
Interactive Queries
App
Streams
API
store = kafkaStreams
.store(name, types)
value = store.get(key)
We add App nodes to make it scale
Which App to call to get the value ?
Front
End App
Streams
API
App
Streams
API
?
?
?
key
Kafka
Cluster
103
Interactive Queries
App
Streams
API
store = kafkaStreams
.store(name, types)
value = store.get(key)
We add App nodes to make it scale
Which App to call to get the value ?
è Any node
è We shift the problem to the App
Front
End App
Streams
API
App
Streams
API
key Kafka
Cluster
104
Interactive Queries
App
Streams
API
metadata = kafkaStreams
.metadataForKey(name,key)
host = metadata.host()
port = metadata.port()
How does the App locate the value?
- Thanks to the metadata exchanged
with the coordinator
- Some simple configuration is
required
Front
End App
Streams
API
App
Streams
API
key Kafka
Cluster
Metadata
105
Interactive Queries
App
Streams
API
metadata = kafkaStreams
.metadataForKey(name,key)
host = metadata.host()
port = metadata.port()
Once the data is located, the App
forwards the call to the target node
Front
End App
Streams
API
App
Streams
API
key Kafka
Cluster
Metadata
106
Interactive Queries
App
Streams
API
metadata = kafkaStreams
.metadataForKey(name,key)
host = metadata.host()
port = metadata.port()
Beware!
The state store can be queried only in
« RUNNING » state
è Not during a rebalance
è May impact your SLAs if you expose the
data to your customers
Front
End App
Streams
API
App
Streams
API
key
App
Streams
API
Kafka
Cluster
107
Interactive Queries
App
Streams
API
metadata = kafkaStreams
.metadataForKey(name,key)
host = metadata.host()
port = metadata.port()
Solution ?
Second App cluster, but:
- More resources...
- 1 more hop
Front
End
key Kafka
Cluster
App
Streams
API
App
Streams
API
App
Streams
API
App
Streams
API
App (b)
Streams
API
App (a)
Streams
API
108
• subscribe()
• poll()
• send()
• flush()
Consumer,
Producer
• mapValues()
• filter()
• punctuate()
Kafka Streams
• Select…from…
• Join…where…
• Group by..
KSQL
Flexibility Simplicity
Trade offs
109
KSQL for Data Exploration
SELECT status, bytes
FROM clickstream
WHERE user_agent =
'Mozilla/5.0 (compatible; MSIE 6.0)';
110
KSQL for Streaming ETL
Fact 1 Fact 2 Fact 3 Fact 4 Fact 5 Fact 6 id 1 id 2 id 3Business
111
KSQL for Streaming ETL
Fact 1 Fact 2 Fact 3 Fact 4 Fact 5 Fact 6 id 1 id 2 id 3
Fact X Fact Y Fact Z
112
KSQL for Streaming ETL
Fact 1 Fact 2 Fact 3 Fact 4 Fact 5 Fact 6 id 1 id 2 id 3
Fact X Fact Y Fact Z
Fact A Fact B Fact C Fact D Fact E
Fact K Fact L Fact M Fact N Id X
113
KSQL for Streaming ETL
CREATE STREAM vip_actions AS
SELECT userid, page, action FROM clickstream c
LEFT JOIN users u ON c.userid = u.user_id
WHERE u.level = 'Platinum';
114
Nested Types
SELECT eventid, address.city
FROM users
WHERE address.state = 'CA';
115
User Defined Functions (UDF)
SELECT eventid, anomaly(sensorinput)
FROM sensor
@Udf(description = "apply analytic model to sensor input")
public String anomaly(String sensorinput){ return your_logic; }
116
KSQL for Anomaly Detection
CREATE TABLE possible_fraud AS
SELECT card_number, count(*)
FROM authorization_attempts
WINDOW TUMBLING (SIZE 5 SECONDS)
GROUP BY card_number
HAVING count(*) > 3;
117
118
Plenty of KSQL Recipies
https://www.confluent.io/stream-processing-cookbook/
119
Plenty of KSQL Recipies
https://www.confluent.io/stream-processing-cookbook/
120
Plenty of KSQL Recipies
https://www.confluent.io/stream-processing-cookbook/
121
KSQL: Enable Stream Processing using SQL-like Semantics
Example Use Cases
• Streaming ETL
• Anomaly detection
• Event monitoring
Leverage Kafka Streams API
without any coding required
KSQL server
Engine
(runs queries)
REST API
CLIClients
Confluent
Control Center
GUI
Kafka Cluster
Use any programming language
Connect via CLI or Control Center
user interface
122
KSQL is really Kafka Stream ? ... yes!
123
• subscribe()
• poll()
• send()
• flush()
Consumer,
Producer
• mapValues()
• filter()
• punctuate()
Kafka Streams
• Select…from…
• Join…where…
• Group by..
KSQL
Flexibility Simplicity
Trade offs
124
Lowering the Bar to Enter the World of Streaming
Kafka User Population
CodingSophistication
Core Java developers
Core developers who don’t use Java/Scala
Data engineers, architects, DevOps/SRE
BI analysts
streams
125
125
Schema
126
The Challenge of Data Compatibility at Scale : implicit à explicit !
App 1
App 2
App 3
Many sources without a policy
causes mayhem in a centralized
data pipeline
Ensuring downstream systems can
use the data is key to an
operational stream pipeline
Example: Date formats
Even within a single application,
different formats can be
presented
Incompatibly formatted message
127
Schema Registry: Make Data Backwards Compatible and Future-Proof
● Define the expected fields for each Kafka topic
● Automatically handle schema changes (e.g. new
fields)
● Prevent backwards incompatible changes
● Support multi-data center environments
Elastic
Cassandra
HDFS
Example Consumers
Serializer
App 1
Serializer
App 2
!
Kafka Topic!
Schema
Registry
128
128
Deployment
129
Which one do you prefer ?
• Zip
• Yum/apt
• Ansible
• Docker
• DC/OS
• Helm-charts
• Confluent Operator
• ... Cloud!
130
130
Tools
131
Plenty !
https://cwiki.apache.org/confluence/display/KAFKA/System+Tools
https://github.com/dharmeshkakadia/awesome-kafka
https://www.google.com/ J
132
132
Monitoring
133
System Health
Are all brokers and topics available?
How much data is being processed?
What can be tuned to improve
performance?
End-to-End SLA Monitoring
Does Kafka process all events <15 seconds?
Is the 8am report missing data?
Are there duplicate events?
134
Monitoring
https://github.com/framiere/monitoring-demo
135
Confluent Control Center– Cluster Health & Administration
Cluster health dashboard
• Monitor the health of your
Kafka clusters
and get alerts if any problems
occur
• Measure system load,
performance,
and operations
• View aggregate statistics or
drill down
by broker or topic
Cluster administration
• Monitor topic configurations
136
View consumer-partition lag across
topics for a consumer group
Alert on max consumer group lag
across all topics
Consumer Lag Monitoring
136
137
137
Resources
138
Confluent resources
139
Optimizing Your Apache Kafka® Deployment
https://www.confluent.io/white-paper/optimizing-your-apache-kafka-deployment/
140
Resources - Confluent Enterprise Reference Architecture
https://www.confluent.io/whitepaper/confluent-enterprise-reference-architecture/
141
141
Community
142
Resources – Community Slack and Mailing List
https://slackpass.io/confluentcommunity
https://groups.google.com/forum/#!forum/confluent-platform
143
Confluent Blog
144
Confluent Platform Demo : cp-demo
https://github.com/confluentinc/cp-demo
With security inside!
145
Examples Examples Examples !
https://github.com/confluentinc/examples
146
A Kafka Story
https://github.com/framiere/a-kafka-story
147
Kafka Boom Boom
https://github.com/Dabz/kafka-boom-boom
148
148
Take Away
149
Kafka Provides a
Central Nervous
System for the
Modern Digital
Enterprise
Enabling companies to respond
accurately and in real time to
business events
150
150
Jeudi: Neil Avery
KAFKA - THE ASYNCHRONOUS MICROSERVICES RUNTIME FOR STATE, SCALE
AND PERFORMANCE
Vendredi 14:30 - 15:15 - Florent & Loulou
APACHE KAFKA : PATTERNS / ANTI-PATTERNS
Vendredi: 15:30 – 17:30 - Florent, Nicolas & Loulou
APACHE KAFKA - LES MAINS DEDANS

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