Kafka Summit SF 2017 - MultiCluster, MultiTenant and Hierarchical Kafka Messaging Service
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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.
![● Dynamic topic to cluster mapping
{
"t1, t2" : {
"where" : [{
"sink" : "fronting-kafka-1"
}]
},
"t3" : {
"where" : [{
"sink" : "fronting-kafka-2"
}]
},
"__default__" : {
"where" : [ {
"sink" : "fronting-kafka-2"
}]
}
}](https://image.slidesharecdn.com/allenwangnetflix8-170907173136/85/Kafka-Summit-SF-2017-MultiCluster-MultiTenant-and-Hierarchical-Kafka-Messaging-Service-14-320.jpg)
![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);
}](https://image.slidesharecdn.com/allenwangnetflix8-170907173136/85/Kafka-Summit-SF-2017-MultiCluster-MultiTenant-and-Hierarchical-Kafka-Messaging-Service-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/allenwangnetflix8-170907173136/85/Kafka-Summit-SF-2017-MultiCluster-MultiTenant-and-Hierarchical-Kafka-Messaging-Service-24-320.jpg)
Kafka Summit SF 2017 - MultiCluster, MultiTenant and Hierarchical Kafka Messaging Service
- 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
- 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