Learnings from the Field. Lessons from Working with Dozens of Small & Large Deployments (Mitchell Henderson, Confluent) Kafka Summit 2020

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- Upgrades should be done often to get bug fixes and improvements, following the upgrade guide carefully. Start with a healthy cluster and upgrade components outward from Zookeeper to Kafka brokers to clients. Don't rush the process or have any unresolved partition reassignments. - Collect JMX metrics to monitor the cluster as outages can be prolonged without visibility. The Kafka defaults are suitable for single node deployments but replication factor, threads, and broker configuration should be tuned for larger clusters. - Quotas like replication throttling and bandwidth/request limits per client or topic should be used to protect the cluster and clients. Log files should separate each component and be retained for a few days. Consider multiple clusters by SLA

Learnings from the Field
Lessons from working dozens of
small & large deployments
Mitch Henderson
2020 - Kafka Summit

Who am I?
Mitch Henderson
Customer Success Technical Architect
At Confluent for ~3 years.
1000s of support cases
Exposed to 1000s of deployments

4
Why live with
known bugs?
Apache Kafka 2.5.0
● 92 Bug fixes
● 65 improvements.
● 7 new features

6
How to upgrade?
● Read the upgrade guide 3 times.
○ Do you understand the
API/Protocol versions? This
is important.
● Start with a healthy cluster!
○ No URP! Seriously, NONE!
● Work outward. Zookeeper ->
Kafka brokers ->
connect/Streams/SR -> clients
● One node(JMV instance) at a
time!
● Upgrade binaries.
● Wait for URP to return to none!

7
What not to do? ● Replace old brokers with new
brokers, unless you have to.
● Upgrade multiple components at
the same time
● Multiple changes at once.
● Start with unhealthy cluster.
● Rush the process
● Do not move on to next step with
any URP!!!!!

Want to prolong an outage?
Don’t have metric collection.

12
Common Questions ● What tool to use?
● How often to poll the JMX
interface?
● Will this cause performance issues?
● How long do I need to keep these
metrics?

Apache Kafka defaults are
suitable for single node
deployments.
Which to change?

14
Replication factor=3
num.partitions
num.network.threads=
8
num.io.threads
Number of disks or 8
broker.rack
auto.topics.create.enabled=false
replica.selector.class:
org.apache.kafka.common.replica.RackAwareReplicaSelector
num.replica.fetchers=16
Brokers:
JVM Heap

16
Clients:
acks=ALL
Application dependant:
batch.size
linger.ms
delivery.timeout.ms
client.rack
Do you really need EOS?
Topics:
Retention.ms & retention.bytes
min.insync.replicas

Logging - Can’t know where
you’re going without knowing
where you’ve been

18
Each component should go-to its own log files.
org.apache.log4j.RollingFileAppender is your friend use it!
Without it you will fill up your logging disk and bad things
will happen!
You should plan to keep at least a few days of logs.
Do not be afraid to turn on debug level logging. There is a
JMX bean for this! No more need to restart brokers.

Quotas! Protect the cluster
and the clients!

20
Mandatory Quotas!
Replication quota!
This prevents a broker that’s recovering overwhelming the leaders!
This will also prevent a rebalance from stealing all the cluster
resources!
It will save your butt at 3am!
bin/kafka-configs … --alter
--add-config
'leader.replication.throttled.rate=10000'
--entity-type broker

21
Two types of client quotas
Bandwidth
Bytes in/out
Request based
Everything in
Kafka is a request

22
Bandwidth quotas
● Easy to reason about
● Easy to implement.
● Easy to monitor
○ per-client metric to indicate throttle times
● Great way to capacity plan your cluster!

23
Request quotas
● Added in KIP-124
● Motivation was to limit clients from overwhelming the
network threads and request threads
● defined as a percent of utilization of:
((num.io.threads + num.network.threads) * 100%)
● More difficult to reason about but very useful in environments where
clients are concerned about latency.

24
Storage Quotas
also called retention
retention.ms & retention.bytes
If you’re not setting these BOTH on every
single topic you’re asking for trouble.

25
Suggestions:
SET A QUOTA FOR ALL CLIENTS!
Set a retention on all topics!

27
Answer: Many clusters!
Bucket by SLA or Criticality.
Easier maintenance.
Easier tuning.
Better monitoring.
Safer!
Why not? More sprawl
It’s a balance.

28
Single tenant
Any SLA
Multi-tenant but groups by
application group or LOB
Medium SLA
Multi-tenant
high SLA
SLA

Thank you!
@mr_mitchellh
mitch@confluent.io
cnfl.io/meetups cnfl.io/slackcnfl.io/blog

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1. Learnings from the Field Lessons from working dozens of small & large deployments Mitch Henderson 2020 - Kafka Summit

2. Who am I? Mitch Henderson Customer Success Technical Architect At Confluent for ~3 years. 1000s of support cases Exposed to 1000s of deployments

3. Upgrades! Do them often!

4. 4 Why live with known bugs? Apache Kafka 2.5.0 ● 92 Bug fixes ● 65 improvements. ● 7 new features

5. 5

6. 6 How to upgrade? ● Read the upgrade guide 3 times. ○ Do you understand the API/Protocol versions? This is important. ● Start with a healthy cluster! ○ No URP! Seriously, NONE! ● Work outward. Zookeeper -> Kafka brokers -> connect/Streams/SR -> clients ● One node(JMV instance) at a time! ● Upgrade binaries. ● Wait for URP to return to none!

7. 7 What not to do? ● Replace old brokers with new brokers, unless you have to. ● Upgrade multiple components at the same time ● Multiple changes at once. ● Start with unhealthy cluster. ● Rush the process ● Do not move on to next step with any URP!!!!!

8. Want to prolong an outage? Don’t have metric collection.

9. 9 JMXReleased September 2004

10. 10

11. 11

12. 12 Common Questions ● What tool to use? ● How often to poll the JMX interface? ● Will this cause performance issues? ● How long do I need to keep these metrics?

13. Apache Kafka defaults are suitable for single node deployments. Which to change?

14. 14 Replication factor=3 num.partitions num.network.threads= 8 num.io.threads Number of disks or 8 broker.rack auto.topics.create.enabled=false replica.selector.class: org.apache.kafka.common.replica.RackAwareReplicaSelector num.replica.fetchers=16 Brokers: JVM Heap

15. 15

16. 16 Clients: acks=ALL Application dependant: batch.size linger.ms delivery.timeout.ms client.rack Do you really need EOS? Topics: Retention.ms & retention.bytes min.insync.replicas

17. Logging - Can’t know where you’re going without knowing where you’ve been

18. 18 Each component should go-to its own log files. org.apache.log4j.RollingFileAppender is your friend use it! Without it you will fill up your logging disk and bad things will happen! You should plan to keep at least a few days of logs. Do not be afraid to turn on debug level logging. There is a JMX bean for this! No more need to restart brokers.

19. Quotas! Protect the cluster and the clients!

20. 20 Mandatory Quotas! Replication quota! This prevents a broker that’s recovering overwhelming the leaders! This will also prevent a rebalance from stealing all the cluster resources! It will save your butt at 3am! bin/kafka-configs … --alter --add-config 'leader.replication.throttled.rate=10000' --entity-type broker

21. 21 Two types of client quotas Bandwidth Bytes in/out Request based Everything in Kafka is a request

22. 22 Bandwidth quotas ● Easy to reason about ● Easy to implement. ● Easy to monitor ○ per-client metric to indicate throttle times ● Great way to capacity plan your cluster!

23. 23 Request quotas ● Added in KIP-124 ● Motivation was to limit clients from overwhelming the network threads and request threads ● defined as a percent of utilization of: ((num.io.threads + num.network.threads) * 100%) ● More difficult to reason about but very useful in environments where clients are concerned about latency.

24. 24 Storage Quotas also called retention retention.ms & retention.bytes If you’re not setting these BOTH on every single topic you’re asking for trouble.

25. 25 Suggestions: SET A QUOTA FOR ALL CLIENTS! Set a retention on all topics!

26. Single Cluster or Many Clusters?

27. 27 Answer: Many clusters! Bucket by SLA or Criticality. Easier maintenance. Easier tuning. Better monitoring. Safer! Why not? More sprawl It’s a balance.

28. 28 Single tenant Any SLA Multi-tenant but groups by application group or LOB Medium SLA Multi-tenant high SLA SLA

29. Thank you! @mr_mitchellh mitch@confluent.io cnfl.io/meetups cnfl.io/slackcnfl.io/blog

Learnings from the Field. Lessons from Working with Dozens of Small & Large Deployments (Mitchell Henderson, Confluent) Kafka Summit 2020

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Learnings from the Field. Lessons from Working with Dozens of Small & Large Deployments (Mitchell Henderson, Confluent) Kafka Summit 2020