Kafka basics
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Intro to Apache Kafka
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Kafka basics
- 2. What’s Kafka • It’s an open-source message broker written in Scala Java… • Which aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds. • Whose design is heavily influenced by transaction logs.
- 3. Kafka it’s also… • A distributed, partitioned, replicated commit log service. • A streaming process platform. • Both queue and publish/subscribe paradigms
- 4. Use Cases
- 5. Kafka concepts • Maintains feeds of messages in categories called topics. • Processes that publish messages to Kafka are called producers. • Processes that subscribe to topics and process the feed of published messages are called consumers. • Run as a cluster comprised of one or more servers each of which is called a broker.
- 6. Data Retention • Kafka retains all published messages for a configurable period of time. • Retaining lots of data is not a problem.
- 7. Producers and Consumers Producers send messages over the network to the Kafka cluster which in turn serves them up to consumers like this:
- 8. The Topic A topic is a category or feed name to which messages are published. For each topic, the Kafka cluster maintains a partitioned log that looks like this:
- 9. The Partition • Each partition is an ordered, immutable sequence of messages that is continually appended to. • The messages in the partitions are each assigned a sequential number called the offset. • The offset uniquely identifies each message within the partition.
- 11. More on partitions • Partitions in the log allow it to scale beyond a size that would fit on a single server. • A topic may have many partitions. • Partitions also act as the unit of parallelism.
- 12. Partitions… again… • Partitions are distributed over the servers in the Kafka cluster. • Each partition is replicated across servers for fault tolerance.
- 13. Guess what… Yep, partitions… • Each partition has one server which acts as the “leader". • Each partition has zero or more servers which act as “followers". • If the leader fails, one of the followers will become the leader.
- 14. … • The leader handles all requests for the partition while the followers replicate the leader. • Each server/node/broker acts as a leader for some of its partitions and a follower for others.
- 15. Data Replication
- 16. Producers • Producers publish data to the topics of their choice. • The producer is responsible for choosing which message to assign to which partition within the topic.
- 17. Producers
- 18. Consumers • Kafka offers a single consumer abstraction called the consumer group. • Consumers label themselves with a consumer group name. • Each message published to a topic is delivered to one consumer within each consumer group.
- 19. Consumers Groups
- 20. Consumers Groups
- 21. Consumers Groups
- 22. Consumers Groups
- 23. Consumers Groups
- 24. Guarantees • Messages sent by a producer to a particular topic partition will be appended in the order they are sent. • A consumer instance sees messages in the order they are stored in the log. • For a topic with replication factor N, Kafka will tolerate up to N-1 server failures without losing any messages committed to the log.
- 25. Zookeeper • Kafka uses Zookeeper to store metadata about the Kafka cluster, as well as consumer client details.
- 26. AVRO • AVRO is the preferred serialization format for Kafka messages. • It’s independent of platform and/or language. • Allows schemas to be evolved. • Schemas are defined in a JSON like format.
- 27. AVRO {"namespace": "customerManagement.avro", "type": "record", "name": "Customer", "fields": [ {"name": "id", "type": "int"}, {"name": "name", "type": "string""}, {"name": "faxNumber", "type": ["null", "string"], "def ] }
- 28. Schema Registry • It’s a REST service. • Allows a AVRO schema to be registered to one or more topics. • Stores multiple versions of a schema. • Validates schemas compatibility.
- 29. Schema Registry
- 31. Monitoring http://aebb8cb14eec211e8810a0a4e9b68c10-1296651079.us-east- 1.elb.amazonaws.com:9000/
- 32. Monitoring
- 33. Kafka Streams • Is a client library for building applications and microservices, where the input and output data are stored in Kafka clusters.
- 34. Kafka Streams • A stream is the most important abstraction provided by Kafka Streams. It represents an unbounded, continuously updating data set. • A stream processing application is any program that makes use of the Kafka Streams library. • A stream processor is a node in the processor topology. • There are two special processors in the topology: • Source Processor: A source processor is a special type of stream processor that does not have any upstream processors. • Sink Processor: A sink processor is a special type of stream processor that does not have down-stream processors.
- 35. • Dataframe Schema (Reading) Spark & Kafka
- 36. • Dataframe Schema (Writing) Spark & Kafka
- 37. • Required configurations (Reading) Spark & Kafka
- 38. • Required configurations (Writing) Spark & Kafka
- 39. Spark & Kafka val ordersStreamDF = spark .readStream .format("kafka") .option("kafka.bootstrap.servers", brokers) .option("subscribe", topic) .option("startingOffsets", "earliest") .load() • Reading
- 40. • Writing Spark & Kafka val ordersDFQueryKafka = ordersWithItemsAndProductsDF .selectExpr("CAST(Timestamp as STRING) as key", "CAST(Discount as .writeStream .format(“kafka") .option("kafka.bootstrap.servers", brokers) .option("topic", topic + "-out") .option(“checkpointLocation", checkpointBucketKafka) .start()
- 42. HANDS ON
- 43. THANK YOU!