This document discusses scaling challenges with large Kafka clusters and proposes a solution of using multiple, smaller Kafka clusters organized hierarchically. The key points are: 1) Large monolithic Kafka clusters have issues like slow operations and increased latency; 2) The solution is to create many smaller "immutable" Kafka clusters and connect them with a routing service; 3) This allows scaling producers and consumers across clusters rather than just brokers.

1. @allenxwang
MultiCluster, MultiTenant and
Hierarchical Kafka Messaging Service
Allen Wang

2. Growing Pains for A Kafka Cluster
● A dozen brokers, handful topics, tens of partitions
○ Wonderful!
● Tens of brokers, tens of topics, hundreds of
partitions
○ Life is good!

3. ● A hundred brokers, a hundred topics, thousands of
partitions
○ … OK
● Hundreds of brokers, hundreds of topics, one
hundred thousand partitions
○ ???

4. Why Huge Kafka Cluster Does Not Work
● Significant time increase on operations
○ Rolling restart/binary update
■ Three minutes per broker, 500 brokers = 1 whole day
○ Rolling AMI (image) update with data copying
■ One hour per broker, 500 brokers = 20 days

5. ● Increased latency due to number of partitions
○ https://www.confluent.io/blog/how-to-choose-the-number
-of-topicspartitions-in-a-kafka-cluster/
● Vulnerability to ZK/Controller failures

6. Scaling and Data Balancing Challenge
● The problem with partition reassignment
○ Time consuming
○ Replication traffic taking bandwidth
○ Complexity of bin packing for data balancing

7. The Consumer Fan-out Problem

8. BytesOut = (numberOfConsumers + replicationFactor - 1) ✕ BytesIn
● A single cluster may easily fit for bytes in, but not
necessarily for bytes out

9. Solve Consumer Fan-out with Hierarchies

10. Inevitability of MultiCluster

11. The Idea
● Create many small and mostly “immutable”
clusters
● Organize them in a topology with routing service
connecting the clusters

12. Multi-Cluster Kafka Service At Netflix
Router
(w/ simple ETL)
Fronting
Kafka
Event
Producer
Consumer
Kafka
Management
HTTP
PROXY
Consumers

13. MultiCluster Producers
● Support producing to multiple clusters at the same
time
● High level producer API implemented by multiple
embedded Kafka producers
public interface KsProducer<V> {
// ...
<T extends V> CompletableFuture<SendResult> send(T obj)
}

14. ● Dynamic topic to cluster mapping
{
"t1, t2" : {
"where" : [{
"sink" : "fronting-kafka-1"
}]
},
"t3" : {
"where" : [{
"sink" : "fronting-kafka-2"
}]
},
"__default__" : {
"where" : [ {
"sink" : "fronting-kafka-2"
}]
}
}

15. @Stream("foo") // send to topic “foo”
public class Foo {
// ...
}
@Stream("bar") // send to topic “bar”
public class Bar {
// ...
}
KsProducer<Object> producer = // …
producer.send(new Foo()); // Send to Kafka cluster which has “foo” topic
producer.send(new Bar()); // Send to Kafka cluster which has “bar” topic

16. Fronting Kafka
● For data collection and buffering
● Optimized for producers
○ Only consumers are routers

17. Scaling of Fronting Kafka
● Creating / destroying Kafka clusters
○ E.g., create new topic on new clusters and update topic to
cluster mapping
● No partition reassignment

18. Data Balancing
● Assign the same number of partitions of any topic
to every brokers
○ E.g., for clusters of 12 brokers, create topics with partitions
of 12, 24, 36
○ Guaranteed even distribution of data (aside from
occasional leader imbalance)
● Balance data among clusters by moving topics
○ Must dynamically update topic to cluster mapping

19. Topic Move
RouterFronting
Kafka
Event
Producer
Consumer
Kafka
Create topic “foo”
Consumer
“foo”
“foo”

20. Consumer Kafka
● Optimized for consumers
○ Only producers are the routers
● Scaling
○ Add brokers and partitions for small cluster
○ Create new cluster, update routing and move consumers

21. Future Plan
● Cross-cluster topic
○ load sharing beyond single cluster
○ Auto-scale
○ Consumer/producer support needed

22. Multi-Cluster Consumer (Ongoing work)
● Same Kafka consumer interface
● Consume from multiple clusters with dynamic
topic to cluster mapping
○ Keep subscription state
○ Receive mapping updates
○ Create and delegate to underlying Kafka consumer for each
associated cluster on the fly

23. Multi-Cluster Consumer Topic to Cluster Mapping and
Code Example
{
"foo": [
{"vip": "consumerKafka1"},
{"vip": "consumerKafka2"}
],
“bar”: [
{“vip”: “consumerKafka3”}
]
}
// Create a multi-cluster consumer
Consumer<String, String> multiClusterConsumer = ...
// subscribe as usual and keep subscription state
consumer.subscribe(new ArrayList<String>(“foo”));
while (...) {
// fetch from both clusters for topic “foo” and
// return the aggregated records
ConsumerRecords<String, String> records =
consumer.poll(2000);
process(records);
}

24. Topic move for Multi-cluster Consumers
Multi-cluster Consumer
Producer
“foo”: “cluster1” “foo”: [“cluster1”]
“foo”: “cluster2”
“foo”: [“cluster1”, “cluster2]
“foo”: [“cluster2”]
cluster1
cluster2

25. Our Vision
Producers
“foo”
“foo”
“bar”
“bar”
“bar”
Multi-cluster
Consumer
Advanced Consumer
Router
Basic Consumer
Fronting Kafka w/
Cross-cluster Topics
Consumer Kafka

26. What About Keyed Messages
● Few topics requiring keyed messages
● Concerns for keyed messages
○ Inflexible/skewed load balancing
○ Difficult to scale
● Handling of keyed messages
○ Currently only produced by routers to consumer Kafka
○ Loose ordering guarantee
○ Strict key-consumer affinity guarantee

27. Think Differently on Scaling Kafka
The “broker” way The “cluster” way
Scale up Add brokers Add clusters
Data balance Move partitions to
different brokers
Move/expand topics to
different clusters
Producer Produce to different
brokers at the same time
Produce to different clusters at
the same time
Consumer Consume from different
brokers at the same time
Consume from different
clusters at the same time

28. Thank You
https://medium.com/netflix-techblog
https://jobs.netflix.com/