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| Before we dive in… • Goal – Using a practical example, familiarize you with a tech stack for dealing with fast/real time/streaming data • Agenda – 101s - Kafka, Kafka Streams & Redis – Sample app & implementation (using Oracle Cloud) – Q & A • Content – Slideshare – Github
| (traditional) Messaging based solution Message Broker EVENTS EVENTS EVENTS DB App Consumer Polling etc. 1. Designed for in-memory 2. Consume and delete Stream Processing @ scale ?? DIY !
| Stream processing to the rescue! • Streams – Unbounded/infinite data set – Has volume and velocity. Not just Big, but fast data • Stream Processing – Crunching/processing streams of data.. asap! – Req-response – Streaming - Batch – Time, ordering, state etc. http://www.capturearkansas.com/photos/550197
| Tech stack for a Streaming solution Partitions Partitions Lists Sorted Set Service App UI <polls> SSE Kafka - Event Store Kafka Streams - Processor Redis – State Store Dashboard Simulated Producer
| Apache Kafka: the Event Store Partitions Partitions Lists Sorted Set Service App UI <polls> SSE Kafka - Event Store Kafka Streams - Processor Redis – State Store Dashboard Simulated Producer
| So… What is Kafka ?? • At its core: a distributed commit log • Messaging system (Pub Sub + Queue) • Reactive (& sharded) key-value store • Database – read this and check out KSQL (a streaming SQL engine for Kafka) • Data pipeline – thanks to Kafka Connect • Streaming platform – stay awake to learn more on this !
| • Streams API: no need to deal with the Kafka Consumer, Producer API explicitly • Use cases – big data, fast data, microservices, monoliths etc. • Piggy backs on Kafka for scalability & fault-tolerance • One-record-at-a-time processing (no micro batching) • Separate infra isn’t mandatory – think about Spark, Storm etc. – deploy (and scale) anywhere – its just a Java app after all! • Programming styles: High (fluent DSL) and low level (Processor) APIs • Stateful processing support + Interactive queries • Windowing, aggregations, joins etc. Kafka Streams: what is it ?
| Scaling out is not the only option • Techniques – Scale OUT – more instances – Scale UP – more threads • Max parallelism – [No. of topic partitions / no. of threads per instance] e.g. 50 / 5 = 10 https://issues.apache.org/jira/browse/KAFKA-5683
| Stateful stream processing with Kafka Streams State stores • Conceptually: lightweight embedded database within your stream processing layer to store ‘intermediate’ processing state (state is local to each app instance) • Options: in-memory, persistent (RocksDB), custom store (e.g. external DB) • State stores expose their internals using Interactive Queries Interactive queries • No additional data store.. Just ask your app ! • Needs some dev work to make your app (interactively) query-able
| Stateful processing & (interactive) querying Kafka External app Custom RPC layer (e.g. REST API) machine1:8080 machine2:8080 Local state stores App Instance 1 App instance 2 application.server config + StreamsMetadata API Query and get back the ‘complete’ state using custom API
| Redis: the State Store Partitions Partitions Lists Sorted Set Service App UI <polls> SSE Kafka - Event Store Kafka Streams - Processor Redis – State Store Dashboard Simulated Producer
| • Stands for: RE(mote) DI(ctionary) S(erver) • Versatile data structure server (written in C) • Focus on in-memory with (tunable) persistence •Not just any KV store • Keys – From a simple string to binary – Max 512 MB (same for values) – Can be expired • Values – any of the following – String, List, Hash – Set, Sorted Set – Geospatial, HyperLogLog – etc. Hello
| Redis data structures • Sorted Sets – Each element has an associated score (basis for sort) – Basic Ops: ZADD, ZINCRBY, ZREM, ZSCORE, ZCARD – View: ZRANGEBYSCORE, ZREVRANGEBYSCORE – Ranking: ZRANK, ZREVRANK • Lists – To be specific: a Linked List – Operations at head (LPUSH) & tail (RPUSH) are O(1), search by index is O(N) – LRANGE, RPOP, LPOP to extract data & LTRIM to cap the size – Blocking ops: BLPOP, BRPOP https://redis.io/commands
| State store FAQs • Redis vs Kafka Streams state store – Horses for courses! • Can we combine both ? – Depending on the use case, yes! • Oh and you can also use the Cache which comes with Oracle Application Container Cloud ! – (Yet another) Blog - http://bit.ly/2yEN35q
| Dashboard app: Oracle Application Container Cloud • JAX-RS & (Jersey) Server Sent Events • CDI: Jedis (Redis) client @Producer • EJB: TimerService and @Asynchronous • Others: Jackson Note: SSE and JSON-B are available in Java EE 8
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