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Exercise 12: Temperature Average using Kafka Streams.

Before

Re-run exercise 5.

or

  • create a TemperatureKey and Temperature classes
  • create a temperature topic
bin/kafka-topics --bootstrap-server localhost:9092 --create \
                      --partitions 2 \
                      --replication-factor 2 \
                      --topic temperature

TODOs

  • define a SerDe, i.e., Serializer and Deserializers

A KafkaStream application typically does both the tasks, so you must implement them both of them and a wrapper class

public class TemperatureSerde implements Serde<Temperature>...

You can re-use you previously defined serializers and deserializers from exercise 5

Calculating the average temperature per room.

formula

This suggests we need to keep a local count and then a total sum finally divide it.

Clearly a stateful computation

Can we do this in one-pass?

  • Let's introduce an helper structure Tuple (Long,Long)
  • We can group across the stream per key

topology

Topologies:
   Sub-topology: 0
    Source: KSTREAM-SOURCE-0000000000 (topics: [temperature])
      --> KSTREAM-MAPVALUES-0000000001
    Processor: KSTREAM-MAPVALUES-0000000001 (stores: [])
      --> KSTREAM-AGGREGATE-0000000003
      <-- KSTREAM-SOURCE-0000000000
    Processor: KSTREAM-AGGREGATE-0000000003 (stores: [KSTREAM-AGGREGATE-STATE-STORE-0000000002])
      --> KTABLE-MAPVALUES-0000000004
      <-- KSTREAM-MAPVALUES-0000000001
    Processor: KTABLE-MAPVALUES-0000000004 (stores: [])
      --> KTABLE-TOSTREAM-0000000005
      <-- KSTREAM-AGGREGATE-0000000003
    Processor: KTABLE-TOSTREAM-0000000005 (stores: [])
      --> KSTREAM-PRINTER-0000000006
      <-- KTABLE-MAPVALUES-0000000004
    Processor: KSTREAM-PRINTER-0000000006 (stores: [])
      --> none
      <-- KTABLE-TOSTREAM-0000000005