Multi cluster, multitenant and hierarchical kafka messaging service slideshare
8 likes3,395 views
This document discusses Netflix's approach to scaling Apache Kafka through a hierarchical, multi-cluster architecture. It addresses the challenges of scaling a single Kafka cluster, such as increased latency and vulnerability to failures. Netflix uses multiple "fronting" Kafka clusters for data collection and buffering optimized for producers. Consumer clusters scale by adding brokers or moving partitions to new clusters. Producers and consumers can operate across multiple clusters simultaneously using routing services. This multi-cluster approach allows independent scaling of producers and consumers while balancing data distribution.
![● Dynamic topic to cluster mapping
○ Enabled by NetflixOSS/Archaius
"t1, t2" : {
"where" : [{
"sink" : "fronting-kafka-1"
}]
},
"t3" : {
"where" : [{
"sink" : "fronting-kafka-2"
}]
},
"__default__" : {
"where" : [ {
"sink" : "fronting-kafka-2"
}]
}](https://image.slidesharecdn.com/multiclustermultitenantandhierarchicalkafkamessagingservice-slideshare-170830000149/85/Multi-cluster-multitenant-and-hierarchical-kafka-messaging-service-slideshare-14-320.jpg)
![Multi-Cluster Consumer Topic to Cluster Mapping and
Code Example
{
"foo": [
{"vip": "cluster1"},
{"vip": "cluster2"}
],
“bar”: [
{“vip”: “cluster2”}
]
}
// 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 =
multiClusterConsumer.poll(2000);
process(records);
}](https://image.slidesharecdn.com/multiclustermultitenantandhierarchicalkafkamessagingservice-slideshare-170830000149/85/Multi-cluster-multitenant-and-hierarchical-kafka-messaging-service-slideshare-23-320.jpg)
![Topic move for Multi-cluster Consumers
Multi-cluster Consumer
Producer
“foo”: “cluster1” “foo”: [“cluster1”]
“foo”: “cluster2”
“foo”: [“cluster1”, “cluster2”]
“foo”: [“cluster2”]
cluster1
cluster2](https://image.slidesharecdn.com/multiclustermultitenantandhierarchicalkafkamessagingservice-slideshare-170830000149/85/Multi-cluster-multitenant-and-hierarchical-kafka-messaging-service-slideshare-24-320.jpg)
Multi cluster, multitenant and hierarchical kafka messaging service slideshare
- 1. @allenxwang Multi-cluster, Multi-tenant and Hierarchical Kafka Messaging Service Allen Wang
- 2. Growing Pains for A Kafka Cluster ● A few 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 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
- 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. Multi-cluster 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 ○ Enabled by NetflixOSS/Archaius "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 ● Scaling ○ Add brokers and partitions for small cluster for non-keyed topics ○ Create same topics on a new cluster 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": "cluster1"}, {"vip": "cluster2"} ], “bar”: [ {“vip”: “cluster2”} ] } // 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 = multiClusterConsumer.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 Fronting Kafka w/ Cross-cluster Topics Consumer Kafka Multi-cluster Consumer
- 26. What About Keyed Messages ● Few topics requiring keyed messages in Netflix ● A word of caution for keyed messages ○ Inflexible/skewed load balancing ○ Difficult to scale ● Handling of keyed messages ○ Currently only produced by routers to consumer Kafka ○ Hard to guarantee message ordering in multi-cluster setting ○ Key-consumer affinity is guaranteed
- 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