Kafka

https://www.slideshare.net/mumrah/kafka-talk-tri-hug

Key Choices

  • pub/sub messaging pattern
  • messages are persistent (stored in disk)
  • consumer keep their own state (stored in zookeeper)

Technology Summary

Concept Notes
Brokers Receive messages from producers (sequential write, push) and deliver messages to consumers (sequential read, pull)
Messages are flushed to append-only log files
Topics Logical collection of partitions mapped across many brokers
Partition Physical append-only log files, a broker contains some of the partitions for a topic
Replication Partitions are replicated, one broker is the leader and all writes/reads must go through it (replication is for fault tolerance only), replication can be tuned to write to N replicas
Producer Responsible for load balancing messages among brokers, they can discover all brokers from a single one
High level api: Producer#send(String topic, K key, V value)
Determines the partition based on the key (default hash mod) e.g. send("A", "foo", message) in the example below: "foo" mod 2
No total ordering across partitions
Guaranteed ordering inside the partition. Useful if the key is a PK, if so all the messages related with that key will be ordered.
Consumer Request a range of messages from a broker, responsible for their own state i.e. its own iterator
High level api: Map<String, List<KafkaStream>> Consumer.connector(Collections.singletonMap("topic", nPartitions))
Blocking/non blocking behavior
Consumer Group Multiple consumers can be part of a consumer group coordinated with zookeeper, in a group each partition will be consumed by exactly one consumer
Consequence: broadcast/pubsub (If all the consumer instances have different consumer groups) and load balance/queue (If all the consumer instances have the same consumer group)

Useful numbers

Applications

  • Notification: A updates a record and sends a “record updated” message, B consumes the message and asks A for the updated record to sync its copy
  • Stream Processing: Data is produced and written into kafka, consumer groups process these messages and write them back to kafka

References