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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
Upgrades! Do them often!
4
Why live with
known bugs?
Apache Kafka 2.5.0
● 92 Bug fixes
● 65 improvements.
● 7 new features
5
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.
9
JMXReleased September 2004
10
11
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
15
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!
Single Cluster or Many Clusters?
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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