Kafka as a Platform: the Ecosystem from the Ground Up

Kafka as a Platform: the Ecosystem from the Ground Up Robin Moffatt | #GOTOpia | @rmoff

EVENTS @rmoff | #GOTOpia | @confluentinc

• • EVENTS d e n e p p a h g n i h t e Som d e n e p p a h t a Wh

Human generated events A Sale A Stock movement @rmoff | #GOTOpia | @confluentinc

Machine generated events IoT Networking Applications @rmoff | #GOTOpia | @confluentinc

EVENTS are EVERYWHERE @rmoff | #GOTOpia | @confluentinc

EVENTS y r e v ^ are POWERFUL @rmoff | #GOTOpia | @confluentinc

K V

LOG @rmoff | #GOTOpia | @confluentinc

K V

Immutable Event Log Old New Events are added at the end of the log @rmoff | #GOTOpia | @confluentinc

TOPICS @rmoff | #GOTOpia | @confluentinc

Topics Clicks Orders Customers Topics are similar in concept to tables in a database @rmoff | #GOTOpia | @confluentinc

PARTITIONS @rmoff | #GOTOpia | @confluentinc

Partitions Clicks p0 P1 P2 Messages are guaranteed to be strictly ordered within a partition @rmoff | #GOTOpia | @confluentinc

PUB / SUB @rmoff | #GOTOpia | @confluentinc

Producing data Old New Messages are added at the end of the log @rmoff | #GOTOpia | @confluentinc

partition 0 … partition 1 producer … partition 2 … Partitioned Topic

package main import ( “gopkg.in/confluentinc/confluent-kafka-go.v1/kafka” ) func main() { topic := “test_topic” p, _ := kafka.NewProducer(&kafka.ConfigMap{ “bootstrap.servers”: “localhost:9092”}) defer p.Close() p.Produce(&kafka.Message{ TopicPartition: kafka.TopicPartition{Topic: &topic, Partition: 0}, Value: []byte(“Hello world”)}, nil) }

Producing to Kafka - No Key Time Partition 1 Partition 2 Partition 3 Messages will be produced in a round robin fashion Partition 4 @rmoff | #GOTOpia | @confluentinc

Producing to Kafka - With Key Time Partition 1 A Partition 2 B hash(key) % numPartitions = N Partition 3 C Partition 4 D @rmoff | #GOTOpia | @confluentinc

Producers partition 0 … partition 1 producer … partition 2 … Partitioned Topic • A client application • Puts messages into topics • Handles partitioning, network protocol • Java, Go, .NET, C/C++, Python • Also every other language Plus REST proxy if not

PUB / SUB @rmoff | #GOTOpia | @confluentinc

Consuming data - access is only sequential Read to offset & scan Old New @rmoff | #GOTOpia | @confluentinc

Consumers have a position of their own Old New Sally is here @rmoff | Scan #GOTOpia | @confluentinc

Consumers have a position of their own Old New Fred is here Sally is here Scan @rmoff | Scan #GOTOpia | @confluentinc

Consumers have a position of their own Rick is here Scan Old New Fred is here Sally is here Scan @rmoff | Scan #GOTOpia | @confluentinc

c, _ := kafka.NewConsumer(&cm) defer c.Close() c.Subscribe(topic, nil) for { select { case ev := <-c.Events(): switch ev.(type) { case *kafka.Message: km := ev.(*kafka.Message) fmt.Printf(“✅ Message ‘%v’ received from topic ‘%v’\n”, string(km.Value), string(*km.TopicPartition.Topic)) } } }

Consuming From Kafka - Single Consumer Partition 1 Partition 2 C Partition 3 Partition 4 @rmoff | #GOTOpia | @confluentinc

Consuming From Kafka - Multiple Consumers C1 Partition 1 Partition 2 Partition 3 C2 Partition 4 @rmoff | #GOTOpia | @confluentinc

Consuming From Kafka - Grouped Consumers CC1 1 CC1 1 Partition 1 Partition 2 Partition 3 C2 Partition 4 @rmoff | #GOTOpia | @confluentinc

Consuming From Kafka - Grouped Consumers Partition 1 Partition 2 Partition 3 C1 C2 C3 C4 Partition 4 @rmoff | #GOTOpia | @confluentinc

Consuming From Kafka - Grouped Consumers Partition 1 Partition 2 Partition 3 C1 C2 C3 3 #GOTOpia | Partition 4 @rmoff | @confluentinc

Consuming From Kafka - Grouped Consumers Partition 1 C1 Partition 2 Partition 3 C2 C3 Partition 4 @rmoff | #GOTOpia | @confluentinc

Consumers partition 0 … partition 1 … consumer A consumer A consumer A partition 2 … Partitioned Topic consumer B • A client application • Reads messages from topics • Horizontally, elastically scalable (if stateless) • Java, Go, .NET, C/C++, Python, everything else Plus REST proxy if not

BROKERS and REPLICATION @rmoff | #GOTOpia | @confluentinc

Leader Partition Leadership and Replication Follower Partition 1 Partition 2 Partition 3 Partition 4 Broker 1 Broker 2 Broker 3 @rmoff | #GOTOpia | @confluentinc

Leader Partition Leadership and Replication Follower Partition 1 Partition 1 Partition 1 Partition 2 Partition 2 Partition 2 Partition 3 Partition 3 Partition 3 Partition 4 Partition 4 Partition 4 Broker 1 Broker 2 Broker 3 @rmoff | #GOTOpia | @confluentinc

So far, this is Pretty good @rmoff | #GOTOpia | @confluentinc

So far, this is Pretty good but I’ve not finished yet… @rmoff | #GOTOpia | @confluentinc

Streaming Pipelines Amazon S3 RDBMS HDFS @rmoff | #GOTOpia | @confluentinc

Evolve processing from old systems to new Existing New App <x> App RDBMS @rmoff | #GOTOpia | @confluentinc

Streaming Integration with Kafka Connect syslog Sources Kafka Connect @rmoff | Kafka Brokers #GOTOpia | @confluentinc

Streaming Integration with Kafka Connect Amazon Sinks Google Kafka Connect @rmoff | Kafka Brokers #GOTOpia | @confluentinc

Streaming Integration with Kafka Connect Amazon syslog Google Kafka Connect @rmoff | Kafka Brokers #GOTOpia | @confluentinc

Look Ma, No Code! { “connector.class”: “io.confluent.connect.jdbc.JdbcSourceConnector”, “connection.url”: “jdbc:mysql://asgard:3306/demo”, “table.whitelist”: “sales,orders,customers” } @rmoff | #GOTOpia | @confluentinc

Extensible Connector Transform(s) @rmoff Converter | #GOTOpia | @confluentinc

hub.confluent.io @rmoff | #GOTOpia | @confluentinc

K V

K V ? s i h t s ’ t a h w … t i a W

Lack of schemas – Coupling teams and services 2001 2001 Citrus Heights-Sunrise Blvd Citrus_Hghts 60670001 3400293 34 SAC Sacramento SV Sacramento Valley SAC Sacramento County APCD SMA8 Sacramento Metropolitan Area CA 6920 Sacramento 28 6920 13588 7400 Sunrise Blvd 95610 38 41 56 38.6988889 121 16 15.98999977 -121.271111 10 4284781 650345 52 @rmoff | #GOTOpia | @confluentinc

Serialisation & Schemas JSON Avro Protobuf Schema JSON CSV @rmoff | #GOTOpia | @confluentinc

Serialisation & Schemas JSON Avro Protobuf Schema JSON CSV 👍 👍 👍 😬 https://rmoff.dev/qcon-schemas @rmoff | #GOTOpia | @confluentinc

Schemas Schema Registry Topic producer … consumer

partition 0 consumer A … consumer A partition 1 … consumer A partition 2 … consumer B Partitioned Topic @rmoff | #GOTOpia | @confluentinc

consumer A consumer A consumer A @rmoff | #GOTOpia | @confluentinc

} “reading_ts”: “2020-02-14T12:19:27Z”, “sensor_id”: “aa-101”, “production_line”: “w01”, “widget_type”: “acme94”, “temp_celcius”: 23, “widget_weight_g”: 100 Photo by Franck V. on Unsplash { @rmoff | #GOTOpia | @confluentinc

Streams of events Time @rmoff | #GOTOpia | @confluentinc

Stream Processing Stream: widgets Stream: widgets_red @rmoff | #GOTOpia | @confluentinc

Stream Processing with Kafka Streams Stream: widgets final StreamsBuilder builder = new StreamsBuilder() .stream(“widgets”, Consumed.with(stringSerde, widgetsSerde)) .filter( (key, widget) -> widget.getColour().equals(“RED”) ) .to(“widgets_red”, Produced.with(stringSerde, widgetsSerde)); Stream: widgets_red @rmoff | #GOTOpia | @confluentinc

Streams Application Streams Application Streams Application @rmoff | #GOTOpia | @confluentinc

Stream Processing with ksqlDB Stream: widgets ksqlDB CREATE STREAM widgets_red AS SELECT * FROM widgets WHERE colour=’RED’; Stream: widgets_red @rmoff | #GOTOpia | @confluentinc

} “reading_ts”: “2020-02-14T12:19:27Z”, “sensor_id”: “aa-101”, “production_line”: “w01”, “widget_type”: “acme94”, “temp_celcius”: 23, “widget_weight_g”: 100 Photo by Franck V. on Unsplash { @rmoff | #GOTOpia | @confluentinc

SELECT * FROM WIDGETS WHERE WEIGHT_G > 120 { SELECT COUNT(*) FROM WIDGETS GROUP BY PRODUCTION_LINE } “reading_ts”: “2020-02-14T12:19:27Z”, “sensor_id”: “aa-101”, “production_line”: “w01”, “widget_type”: “acme94”, “temp_celcius”: 23, “widget_weight_g”: 100 Photo by Franck V. on Unsplash SELECT AVG(TEMP_CELCIUS) AS TEMP FROM WIDGETS GROUP BY SENSOR_ID HAVING TEMP>20 CREATE SINK CONNECTOR dw WITH ( Object store, ‘connector.class’ = ‘S3Connector’, data warehouse, ‘topics’ = ‘widgets’ RDBMS …); @rmoff | #GOTOpia | @confluentinc

Photo by Raoul Droog on Unsplas DEMO @rmoff | #GOTOpia | @confluentinc

Summary @rmoff | #GOTOpia | @confluentinc

@rmoff | #GOTOpia | @confluentinc

K V @rmoff | #GOTOpia | @confluentinc

The Log @rmoff | #GOTOpia | @confluentinc

Producer Consumer The Log @rmoff | #GOTOpia | @confluentinc

Producer Consumer Connectors The Log @rmoff | #GOTOpia | @confluentinc

Producer Consumer Connectors The Log Streaming Engine @rmoff | #GOTOpia | @confluentinc

Apache Kafka Producer Consumer Connectors The Log Streaming Engine @rmoff | #GOTOpia | @confluentinc

Confluent Platform ksqlDB Producer Consumer Connectors The Log Schema Registry Streaming Engine @rmoff | #GOTOpia | @confluentinc

Free Books! https://rmoff.dev/gotopia @rmoff | #GOTOpia | @confluentinc

60 DE VA DV $200 USD off your bill each calendar month for the first three months when you sign up https://rmoff.dev/ccloud Free money! (additional $60 towards your bill 😄 ) Fully Managed Kafka as a Service * T&C: https://www.confluent.io/confluent-cloud-promo-disclaimer

Learn Kafka. Start building with Apache Kafka at Confluent Developer. developer.confluent.io

#EOF @rmoff rmoff.dev/talks youtube.com/rmoff