Kafka as a Platform: the Ecosystem from the Ground Up Robin Moffatt | #GOTOpia | @rmoff
A presentation at GOTOpia in November 2020 in by Robin Moffatt
Kafka as a Platform: the Ecosystem from the Ground Up Robin Moffatt | #GOTOpia | @rmoff
EVENTS @rmoff | #GOTOpia | @confluentinc
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
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LOG @rmoff | #GOTOpia | @confluentinc
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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
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
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
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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
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
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Learn Kafka. Start building with Apache Kafka at Confluent Developer. developer.confluent.io
#EOF @rmoff rmoff.dev/talks youtube.com/rmoff
