BigDataFest Building Modern Data Streaming Apps
- 1. Building Modern Data Streaming Apps Tim Spann Principal Developer Advocate 25-May-2023
- 4. 4 FLaNK Stack Tim Spann @PaasDev // Blog: www.datainmotion.dev Principal Developer Advocate. Princeton Future of Data Meetup. ex-Pivotal, ex-Hortonworks, ex-StreamNative, ex-PwC https://github.com/tspannhw/EverythingApacheNiFi https://medium.com/@tspann Apache NiFi x Apache Kafka x Apache Flink x Java
- 5. 5 FLaNK Stack Weekly This week in Apache NiFi, Apache Flink, Apache Pulsar, Apache Spark, Apache Iceberg, Python, Java and Open Source friends. https://bit.ly/32dAJft
- 6. © 2023 Cloudera, Inc. All rights reserved. 6 Future of Data - Princeton + Virtual @PaasDev https://www.meetup.com/futureofdata-princeton From Big Data to AI to Streaming to Containers to Cloud to Analytics to Cloud Storage to Fast Data to Machine Learning to Microservices to ...
- 8. STREAMING
- 10. 10 Streaming From … To … Data distribution as a first class citizen IOT Devices LOG DATA SOURCES ON-PREM DATA SOURCES BIG DATA CLOUD SERVICES CLOUD BUSINESS PROCESS SERVICES * CLOUD DATA* ANALYTICS /SERVICE (Cloudera DW) App Logs Laptops /Servers Mobile Apps Security Agents CLOUD WAREHOUSE UNIVERSAL DATA DISTRIBUTION (Ingest, Transform, Deliver) Ingest Processors Ingest Gateway Router, Filter & Transform Processors Destination Processors
- 11. © 2023 Cloudera, Inc. All rights reserved. 11 BUILDING REAL-TIME REQUIRES A TEAM
- 12. APACHE KAFKA
- 13. © 2023 Cloudera, Inc. All rights reserved. 13 What is Apache Kafka? Distributed: horizontally scalable Partitioned: the data is split-up and distributed across the brokers Replicated: allows for automatic failover Unique: Kafka does not track the consumption of messages (the consumers do) Fast: designed from the ground up with a focus on performance and throughput Kafka was built at Linkedin in 2011 Open sourced as an Apache project
- 14. © 2023 Cloudera, Inc. All rights reserved. 14 Yes, Franz, It’s Kafka Let’s do a metamorphosis on your data. Don’t fear changing data. You don’t need to be a brilliant writer to stream data. Franz Kafka was a German-speaking Bohemian novelist and short-story writer, widely regarded as one of the major figures of 20th-century literature. His work fuses elements of realism and the fantastic. Wikipedia
- 15. © 2023 Cloudera, Inc. All rights reserved. 15 What is Can You Do With Apache Kafka? Web site activity: track page views, searches, etc. in real time Events & log aggregation: particularly in distributed systems where messages come from multiple sources Monitoring and metrics: aggregate statistics from distributed applications and build a dashboard application Stream processing: process raw data, clean it up, and forward it on to another topic or messaging system Real-time data ingestion: fast processing of a very large volume of messages
- 16. © 2023 Cloudera, Inc. All rights reserved. 16 Kafka Terms ● Kafka is a publish/subscribe messaging system comprised of the following components: ○ Topic: a message feed ○ Producer: a process that publishes messages to a topic ○ Consumer: a process that subscribes to a topic and processes its messages ○ Broker: a server in a Kafka cluster
- 17. © 2023 Cloudera, Inc. All rights reserved. 17 • Highly reliable distributed messaging system • Decouple applications, enables many-to-many patterns • Publish-Subscribe semantics • Horizontal scalability • Efficient implementation to operate at speed with big data volumes • Organized by topic to support several use cases Source System Source System Source System Kafka Fraud Detection Security Systems Real-Time Monitoring Many-To-Many Publish-Subscribe EVENTS
- 18. APACHE FLINK
- 19. © 2023 Cloudera, Inc. All rights reserved. 19 Flink SQL https://www.datainmotion.dev/2021/04/cloudera-sql-stream-builder-ssb-updated.html ● Streaming Analytics ● Continuous SQL ● Continuous ETL ● Complex Event Processing ● Standard SQL Powered by Apache Calcite
- 20. 20 Flink SQL -- specify Kafka partition key on output SELECT foo AS _eventKey FROM sensors -- use event time timestamp from kafka -- exactly once compatible SELECT eventTimestamp FROM sensors -- nested structures access SELECT foo.’bar’ FROM table; -- must quote nested column -- timestamps SELECT * FROM payments WHERE eventTimestamp > CURRENT_TIMESTAMP-interval '10' second; -- unnest SELECT b.*, u.* FROM bgp_avro b, UNNEST(b.path) AS u(pathitem) -- aggregations and windows SELECT card, MAX(amount) as theamount, TUMBLE_END(eventTimestamp, interval '5' minute) as ts FROM payments WHERE lat IS NOT NULL AND lon IS NOT NULL GROUP BY card, TUMBLE(eventTimestamp, interval '5' minute) HAVING COUNT(*) > 4 -- >4==fraud -- try to do this ksql! SELECT us_west.user_score+ap_south.user_score FROM kafka_in_zone_us_west us_west FULL OUTER JOIN kafka_in_zone_ap_south ap_south ON us_west.user_id = ap_south.user_id; Key Takeaway: Rich SQL grammar with advanced time and aggregation tools
- 22. © 2023 Cloudera, Inc. All rights reserved. 22 Apache NiFi Enable easy ingestion, routing, management and delivery of any data anywhere (Edge, cloud, data center) to any downstream system with built in end-to-end security and provenance ACQUIRE PROCESS DELIVER • Over 300 Prebuilt Processors • Easy to build your own • Parse, Enrich & Apply Schema • Filter, Split, Merger & Route • Throttle & Backpressure • Guaranteed Delivery • Full data provenance from acquisition to delivery • Diverse, Non-Traditional Sources • Eco-system integration Advanced tooling to industrialize flow development (Flow Development Life Cycle) FTP SFTP HL7 UDP XML HTTP EMAIL HTML IMAGE SYSLO G FTP SFTP HL7 UDP XML HTTP EMAIL HTML IMAGE SYSLO G HASH MERGE EXTRACT DUPLICATE SPLIT ROUTE TEXT ROUTE CONTENT ROUTE CONTEXT CONTROL RATE DISTRIBUTE LOAD GEOENRICH SCAN REPLACE TRANSLATE CONVERT ENCRYPT TALL EVALUATE EXECUTE
- 23. © 2023 Cloudera, Inc. All rights reserved. 23 Provenance
- 24. 24 Extensibility ● Built from the ground up with extensions in mind ● Service-loader pattern for… ○ Processors ○ Controller Services ○ Reporting Tasks ○ Prioritizers ● Extensions packaged as NiFi Archives (NARs) ○ Deploy NiFi lib directory and restart ○ Same model as standard components
- 26. © 2023 Cloudera, Inc. All rights reserved. 26 Parquet Reader/ Writers ● Native Record Processors for Apache Parquet Files! ● CSV <-> Parquet ● XML <-> Parquet ● AVRO <-> Parquet ● JSON <-> Parquet ● More...
- 27. © 2023 Cloudera, Inc. All rights reserved. 27 NiFi Load Balancing • Improve NiFi cluster throughput • Defined at connection level • Configurable balancing strategies • Critical for scale up paradigm in Kubernetes
- 28. © 2023 Cloudera, Inc. All rights reserved. 28 ReadyFlow Gallery • Cloudera provided flow definitions • Cover most common data flow use cases • Optimized to work with CDP sources/destinations • Can be deployed and adjusted as needed
- 29. © 2023 Cloudera, Inc. All rights reserved. 29 Flow Catalog • Central repository for flow definitions • Import existing NiFi flows • Manage flow definitions • Initiate flow deployments
- 30. Apache NiFi with Python Custom Processors Python as a 1st class citizen
- 31. © 2023 Cloudera, Inc. All rights reserved. 31 Processing millions of events with NiFi
- 32. DEMO AND CODE
- 35. CREATE TABLE `sr1`.`default_database`.`traveladvisory` ( `title` VARCHAR(2147483647), `pubdate` VARCHAR(2147483647), `link` VARCHAR(2147483647), `guid` VARCHAR(2147483647), `advisoryId` VARCHAR(2147483647), `domain` VARCHAR(2147483647), `category` VARCHAR(2147483647), `description` VARCHAR(2147483647), `uuid` VARCHAR(2147483647), `ts` BIGINT NOT NULL ) COMMENT 'traveladvisory' WITH ( 'properties.bootstrap.servers' = 'kafka:9092', 'avro-cloudera.properties.schema.registry.url' = 'http://schema-registry:7788/api/v1', 'connector' = 'kafka', 'avro-cloudera.schema-name' = 'traveladvisory', 'format' = 'avro-cloudera', 'topic' = 'traveladvisory', 'scan.startup.mode' = 'latest-offset' )
- 42. 42 Streaming Tech Debt Tips ● Version Control All Assets ● Operationalize with K8 ● Use DevOps and APIs ● Latest Java and Python ● Stream Sizing (NiFi, Kafka, Flink) ● Unit and Integration Test ● Backup everything ● Scale in 3s
- 43. 43 Streaming Resources ● https://dzone.com/articles/real-time-stream-processing-with-hazelcast-and- streamnative ● https://flipstackweekly.com/ ● https://www.datainmotion.dev/ ● https://www.flankstack.dev/ ● https://github.com/tspannhw ● https://medium.com/@tspann ● https://medium.com/@tspann/predictions-for-streaming-in-2023-ad4d7395 d714 ● https://www.apachecon.com/acna2022/slides/04_Spann_Tim_Citizen_Stre aming_Engineer.pdf
- 44. 44 Resources
- 45. © 2023 Cloudera, Inc. All rights reserved. 45
- 46. 46 TH N Y U