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Apache Kafka
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Eko Kurniawan Khannedy
August 30, 2017
Technology
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Apache Kafka
JVM Meetup #5 - Apache Kafka at Blibli.com
Eko Kurniawan Khannedy
August 30, 2017
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Transcript
APACHE KAFKA EKO KURNIAWAN KHANNEDY
APACHE KAFKA EKO KURNIAWAN KHANNEDY ▸ Principal Software Development Engineer
at Blibli.com ▸ Part of RnD Team at Blibli.com ▸
[email protected]
APACHE KAFKA AGENDA ▸ Kafka Intro ▸ Kafka Internals ▸
Installing Kafka ▸ Kafka Producer ▸ Kafka Consumer ▸ Kafka in blibli.com ▸ Demo ▸ Conclusion
KAFKA INTRO APACHE KAFKA
APACHE KAFKA BEFORE PUBLISH / SUBSCRIBE MESSAGING MEMBER ORDER RISK
PAYMENT … ERP FINANCE …
APACHE KAFKA PUBLISH / SUBSCRIBE MESSAGING MEMBER ORDER RISK PAYMENT
… ERP FINANCE … MESSAGING SYSTEM / MESSAGE BROKER
None
APACHE KAFKA WHAT IS KAFKA ▸ Apache Kafka is a
publish/subscribe messaging system, or more recently a “distributing streaming platform” ▸ Opensource project under Apache Software Foundation.
APACHE KAFKA KAFKA HISTORY ▸ Kafka was born to solve
the data pipeline problem in LinkedIn. ▸ The development team at LinkedIn was led by Jay Kreps, now CEO of Confluent. ▸ Kafka was released as an Open Source project on Github in late 2010, and join Apache Software Foundation in 2011.
KAFKA INTERNALS APACHE KAFKA
APACHE KAFKA BROKER TOPIC A PARTITION 0 TOPIC A PARTITION
1 KAFKA BROKER
APACHE KAFKA CLUSTER TOPIC A PARTITION 0 TOPIC A PARTITION
1 (LEADER) KAFKA BROKER 1 TOPIC A PARTITION 0 TOPIC A PARTITION 1 (LEADER) KAFKA BROKER 2
APACHE KAFKA TOPICS ▸ Messages in Kafka are categorized into
Topics. ▸ The closest analogy for topic is a database table, or a folder in filesystem.
APACHE KAFKA PARTITIONS
APACHE KAFKA REPLICATION FACTOR TOPIC A PARTITION 0 TOPIC A
PARTITION 1 KAFKA BROKER 1 TOPIC A PARTITION 0 KAFKA BROKER 2 TOPIC A PARTITION 1 KAFKA BROKER 3 TOPIC A PARTITION 0 TOPIC A PARTITION 1 KAFKA BROKER 4
APACHE KAFKA CONSUMER GROUP
APACHE KAFKA CONSUMER GROUP (2)
APACHE KAFKA RETENTION POLICY ▸ A key feature of Apache
Kafka is that of retention, or the durable storage of messages for some period of time. ▸ We can set retention policy per topics by time or by size.
APACHE KAFKA MIRROR MAKER
INSTALLING KAFKA APACHE KAFKA
APACHE KAFKA JAVA ▸ Kafka using Java 8.
APACHE KAFKA ZOOKEEPER KAFKA BROKER PRODUCER CONSUMER ZOOKEEPER Metadata
APACHE KAFKA KAFKA BROKER # Minimum Broker Configuration broker.id=0 #
must unique in cluster zookeeper.connect=localhost:2181 log.dirs=data/kafka-logs
APACHE KAFKA CREATE / UPDATE TOPIC kafka-topics.sh --create --zookeeper localhost:2181
-- replication-factor 1 --partitions 1 --topic topic_name kafka-topics.sh --zookeeper localhost:2181 --alter --topic topic_name --partitions 2 --replication-factor 2
KAFKA PRODUCER APACHE KAFKA
APACHE KAFKA PRODUCER RECORD PRODUCER RECORD TOPIC PARTITION KEY VALUE
APACHE KAFKA SERIALIZER PRODUCER RECORD TOPIC PARTITION KEY VALUE SERIALIZER
APACHE KAFKA PARTITIONER PRODUCER RECORD TOPIC PARTITION KEY VALUE SERIALIZER
PARTITIONER Send to Broker
APACHE KAFKA KAFKA PRODUCER Properties props = new Properties(); props.put("bootstrap.servers",
"broker1:9092,broker2:9092"); props.put("key.serializer", “org.apache.kafka.common.serialization.StringSerializer"); props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer"); producer = new KafkaProducer<String, String>(kafkaProps);
APACHE KAFKA SEND MESSAGE record = new ProducerRecord<>(topicName, key, value);
producer.send(record);
KAFKA CONSUMER APACHE KAFKA
APACHE KAFKA CONSUMER GROUP
APACHE KAFKA PARTITION REBALANCE
APACHE KAFKA PARTITION REBALANCE
APACHE KAFKA PARTITION REBALANCE
APACHE KAFKA CONSUMER RECORD & DESERIALIZER CONSUMER RECORD TOPIC PARTITION
KEY VALUE DESERIALIZER From Broker
APACHE KAFKA KAFKA CONSUMER Properties props = new Properties(); props.put("bootstrap.servers",
"broker1:9092,broker2:9092"); props.put("group.id", "GroupName"); props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer"); props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer"); consumer = new KafkaConsumer<String, String>(props);
APACHE KAFKA GET MESSAGES consumer.subscribe(Collections.singletonList("topicName")); Long timeout = 1000L; ConsumerRecords<String,
String> records = consumer.poll(timeout);
KAFKA IN BLIBLI APACHE KAFKA
APACHE KAFKA API GATEWAY EVENT API GATEWAY MEMBER API GATEWAY
COMMON API GATEWAY … KAFKA ANALYTICS … …
APACHE KAFKA CURRENT PRODUCT (CODENAME X) X MEMBER X CART
X AUTH X WISHLIST API GATEWAY X YYYY X XXX X ORDER X PRODUCT
APACHE KAFKA NEW PRODUCT (CODENAME VERONICA) VERONICA MEMBER VERONICA CORE
VERONICA MERCHANT KAFKA VERONICA NOTIFICATION API GATEWAY
DEMO
CONCLUSION APACHE KAFKA
APACHE KAFKA WHY KAFKA? ▸ Multiple Consumer ▸ Flexible Scalability
▸ Flexible Durability ▸ High Performance ▸ Multi-Datacenter
WE ARE HIRING!
[email protected]
APACHE KAFKA
APACHE KAFKA REFERENCES ▸ http://kafka.apache.org/ ▸ https://engineering.linkedin.com/kafka/benchmarking-apache-kafka-2-million- writes-second-three-cheap-machines ▸ https://engineering.linkedin.com/kafka/running-kafka-scale