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Apache Kafka® – Pattern and Anti-Pattern
How to fail at Apache Kafka brilliantly!
This is me
- Engineer at Confluent
- Ex-MongoDB, ex-Oracle
- Cute smooth curly hair
Apache Kafka – (Pattern and) Anti-Pattern
Apache Kafka – (Pattern and) Anti-Pattern
Distributed Logs
Scalable, Fault Tolerant, Concurrent, Strongly Ordered, Retentive
Kafka: a Streaming Platform
The Log ConnectorsConnectors
Producer Consumer
Streaming Engine
Producer Consumer
Partitions
Kafka Cluster
Apache Kafka – (Pattern and) Anti-Pattern
… which is true for
any data systems
And today we will discuss
about the most common one
Problem #1
Not thinking about Durability
* How to fail *
Apache Kafka – (Pattern and) Anti-Pattern
Durability is achieved
throught Replication
When will the cluster acknowlege ?
L
Producer
As soon as it is on the page cache of
the leader...
L
Producer
Acks
Acks=1 (default value) : good for latency
Acks=all : good for durability
Replication before acknolowdging
L
Producer
Wait for Data
replication
Acks=all 
Acks = All
This means the leader will wait for the
full set of in-sync replicas
to acknowledge the record.
But only to the In-Sync Replicas...
L
Producer
Acks=all 
… which could be only one server
L
Producer
Acks=all 
Apache Kafka – (Pattern and) Anti-Pattern
min.insync.replicas
minimum number of replicas that must
Acknowledge (default 1)
Data Durability while Producing
Tune it with the parameters 
acks and min.isr
The default values are optimized
For availability & latency 
If durability is more important, tune it !
For consumer, it’s a lot
easier
Consumer can read only replicated data
If you are using transaction
isolation.level=read_commited
(default read_uncommited)
Think about data durability and decide
of the best trade-off for you
* How to succeed *
Problem #2
Focusing only on
the happy path
* How to fail *
Apache Kafka – (Pattern and) Anti-Pattern
Some parameters to know
Builtin Retries
Retries : will cause the client to resend any record
whose send fails with a potentially transient error.
Default value : MAX_INT (before AK 2.1 : 0...)
What’s happening in case of issue with
retry ?
Producer No longer leader
L
The leader moved to a
different broker
Use builtin retries !
* How to succeed *
But you are exposed to a
different kind of issue
Message duplication
L
Producer
Timeout
Builtin Idempotency
enable.idempotence: When set to 'true',
the producer will ensure that exactly
one copy of each message is written.
Default value: false
With Retries and Idempotency
L
Producer
Timeout
UUID
UUID
With Retries and Idempotency
L
Producer
Timeout
UUID
UUID
UUID
UUIDUUID
Use builtin idempotency!
* How to succeed *
Problem #4
No Idempotent consumer
* How to fail *
Apache Kafka – (Pattern and) Anti-Pattern
At least once (default)
At most once
Exactly Once ?
Embrace at least once !
* How to succeed *
Rely on Kafka Streams with
Exactly Once !
* How to succeed *
Apache Kafka – (Pattern and) Anti-Pattern
Problem #4
No exception handling
* How to fail *
Apache Kafka – (Pattern and) Anti-Pattern
Producer Consumer
What to do in case of an error ?
Plenty of potential issues
1) A message can not be processed
2) A message doesn’t have the expected
schema
Multiple strategy
1) Retry
while (this.getRunning()) {
try {
var consumerRecords = consumer.poll(1000) ;
} catch (Exception e) {
Logger.error(e);
continue ;
}
for (var record : consumerRecords) {
try {
/* Processing messages */
} catch (Exception e) { … }
}
}
2) Write to a dead letter queue and
continue
while (this.getRunning()) {
var consumerRecords = consumer.poll(1000) ;
for (var record : consumerRecords) {
try {
/* Processing messages */
} catch (Exception e) {
producer.send(« dead-my-topic », new ProducerRecord(…)) ;
Logger.error(e);
}
}
}
3) Ignore & continue
kafkaProducer.send(record, (
(metadata, exception)→ {
if (exception != null) {
/* Something bad happened */
/* But those are ephemaral data anyway */
Logger.error(exception) ;
}
})
);
There is no silver bullet
solution
Handle the exceptions !
* How to succeed *
https://eng.uber.com/reliable-reprocessing/
Problem #5
No data governance
* How to fail *
Apache Kafka – (Pattern and) Anti-Pattern
Changes in producers might
impact consumers
Sharing dataset!
As soon as producer and consumer are
Part of different teams.
Consider using a Schema Registry !
* How to succeed *
Consumer
Producer
Application 1
Consider using quotas
as soon as there is multi-tenancy
Producer
Application 2
Application 3
Consumer
Application 4
Problem #6
Installing prod Sunday night
* How to fail *
A typical devops team
Misconfiguration !
If you use the default configuration…
You will have surprises!
* How to fail *
The best things to do is to .…
Read the goddam manual !
Running Apache Kafka in Production
Running Apache Zookeeper in Production
* How to succeed *
Not using security
* How to fail *
Ordering?
* How to fail *
Too many partitions
* How to fail *
Too few partitions
* How to fail *
Calling external service in
Kafka Streams
* How to fail *
No Monitoring
* How to fail *
Problems
Data durability
Error Handling & transient issues
Data governance
Misconfiguration
Monitoring
DO
Understand Apache Kafka
Configure your clients & cluster properly
Follow engineering best-practices
https://www.confluent.io/white-paper/optimizing-your-apache-kafka-deployment/
And if you can not do all
of this
Consider using a Cloud Service
And if you can
We are hiring !
References
• Running Apache Kafka in production : https://docs.confluent.io/current/kafka/deployment.html
• Running Apache Zookeeper in production : https://docs.confluent.io/current/zookeeper/deployment.html
• Confluent Reference Architecutre :
https://www.confluent.io/resources/apache-kafka-confluent-enterprise-reference-architecture/
• The goddam documentation : https://docs.confluent.io/current/getting-started.html
• Kafka Summit : https://kafka-summit.org/
I hope it has been useful :=)
Questions ?

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