Unified, Efficient, and Portable Data Processing with Apache Beam
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Apache Beam is a unified programming model designed for portable data processing pipelines, effectively handling both batch and streaming use cases. It allows users to express data-parallel algorithms with one API while separating data processing logic from runtime requirements, supporting execution across various distributed environments. The presentation covers the evolution of Apache Beam, its core concepts, and demonstrates pipeline portability and efficient execution using different runners like Apache Flink and Google Cloud Dataflow.
![● Beam’s Java SDK runs on multiple
runtime environments, including:
○ Apache Apex
○ Apache Spark
○ Apache Flink
○ Google Cloud Dataflow
○ [in development] Apache Gearpump
● Cross-language infrastructure is in
progress.
○ Beam’s Python SDK currently runs
on Google Cloud Dataflow
Beam Vision: as of April 2017
Beam Model: Fn Runners
Apache
Spark
Cloud
Dataflow
Beam Model: Pipeline Construction
Apache
Flink
Java
Java
Python
Python
Apache
Apex
Apache
Gearpump](https://image.slidesharecdn.com/april51750googlebonaci-170410125118/85/Unified-Efficient-and-Portable-Data-Processing-with-Apache-Beam-21-320.jpg)
Unified, Efficient, and Portable Data Processing with Apache Beam
- 1. Abstract Unbounded, unordered, global scale datasets are increasingly common in day-to-day business, and consumers of these datasets have detailed requirements for latency, cost, and completeness. Apache Beam defines a new data processing programming model that evolved from more than a decade of experience building Big Data infrastructure within Google, including MapReduce, FlumeJava, Millwheel, and Cloud Dataflow. Apache Beam handles both batch and streaming use cases, offering a powerful, unified model. It neatly separates properties of the data from run-time characteristics, allowing pipelines to be portable across multiple run-time environments, both open source, including Apache Apex, Apache Flink, Apache Gearpump, Apache Spark, and proprietary. Finally, Beam's model enables newer optimizations, like dynamic work rebalancing and autoscaling, resulting in an efficient execution. This talk will cover the basics of Apache Beam, touch on its evolution, and describe main concepts in its powerful programming model. We'll show how Beam unifies batch and streaming use cases, and show efficient execution in real-world scenarios. Finally, we'll demonstrate pipeline portability across Apache Apex, Apache Flink, Apache Spark and Google Cloud Dataflow in a live setting. This session is a Technical (Intermediate) talk in our IoT and Streaming track. It focuses on Apache Flink, Apache Kafka, Apache Spark, Cloud and is geared towards Architect, Data Scientist, Developer / Engineer audiences.
- 2. Unified, Efficient and Portable Data Processing with Apache Beam Davor Bonaci PMC Chair, Apache Beam Software Engineer, Google Inc.
- 3. Apache Beam: Open Source data processing APIs ● Expresses data-parallel batch and streaming algorithms using one unified API ● Cleanly separates data processing logic from runtime requirements ● Supports execution on multiple distributed processing runtime environments
- 4. The evolution of Apache Beam MapReduce Apache Beam Cloud Dataflow BigTable DremelColossus FlumeMegastore Spanner PubSub Millwheel
- 5. Agenda 1. Expressing data-parallel pipelines with the Beam model 2. The Beam vision for portability 3. Parallel and portable pipelines in practice
- 6. Apache Beam is a unified programming model designed to provide portable data processing pipelines (efficient too)
- 7. Expressing data-parallel pipelines with the Beam model A unified model for batch and streaming
- 8. Processing time vs. event time
- 9. The Beam Model: asking the right questions What results are calculated? Where in event time are results calculated? When in processing time are results materialized? How do refinements of results relate?
- 10. PCollection<KV<String, Integer>> scores = input .apply(Sum.integersPerKey()); The Beam Model: What is being computed?
- 11. The Beam Model: What is being computed?
- 12. PCollection<KV<String, Integer>> scores = input .apply(Window.into(FixedWindows.of(Duration.standardMinutes(2))) .apply(Sum.integersPerKey()); The Beam Model: Where in event time?
- 13. The Beam Model: Where in event time?
- 14. PCollection<KV<String, Integer>> scores = input .apply(Window.into(FixedWindows.of(Duration.standardMinutes(2)) .triggering(AtWatermark())) .apply(Sum.integersPerKey()); The Beam Model: When in processing time?
- 15. The Beam Model: When in processing time?
- 16. PCollection<KV<String, Integer>> scores = input .apply(Window.into(FixedWindows.of(Duration.standardMinutes(2)) .triggering(AtWatermark() .withEarlyFirings( AtPeriod(Duration.standardMinutes(1))) .withLateFirings(AtCount(1))) .accumulatingFiredPanes()) .apply(Sum.integersPerKey()); The Beam Model: How do refinements relate?
- 17. The Beam Model: How do refinements relate?
- 18. Customizing What Where When How 3 Streaming 4 Streaming + Accumulation 1 Classic Batch 2 Windowed Batch
- 19. The Beam vision for portablility Write once, run anywhere“ ”
- 20. Beam Vision: mix and match SDKs and runtimes ● The Beam Model: the abstractions at the core of Apache Beam Runner 1 Runner 3Runner 2 ● Choice of SDK: Users write their pipelines in a language that’s familiar and integrated with their other tooling ● Choice of Runners: Users choose the right runtime for their current needs -- on-prem / cloud, open source / not, fully managed / not ● Scalability for Developers: Clean APIs allow developers to contribute modules independently The Beam Model Language A Language CLanguage B The Beam Model Language A SDK Language C SDK Language B SDK
- 21. ● Beam’s Java SDK runs on multiple runtime environments, including: ○ Apache Apex ○ Apache Spark ○ Apache Flink ○ Google Cloud Dataflow ○ [in development] Apache Gearpump ● Cross-language infrastructure is in progress. ○ Beam’s Python SDK currently runs on Google Cloud Dataflow Beam Vision: as of April 2017 Beam Model: Fn Runners Apache Spark Cloud Dataflow Beam Model: Pipeline Construction Apache Flink Java Java Python Python Apache Apex Apache Gearpump
- 22. Example Beam Runners Apache Spark ● Open-source cluster-computing framework ● Large ecosystem of APIs and tools ● Runs on premise or in the cloud Apache Flink ● Open-source distributed data processing engine ● High-throughput and low-latency stream processing ● Runs on premise or in the cloud Google Cloud Dataflow ● Fully-managed service for batch and stream data processing ● Provides dynamic auto-scaling, monitoring tools, and tight integration with Google Cloud Platform
- 23. How do you build an abstraction layer? Apache Spark Cloud Dataflow Apache Flink ???????? ????????
- 24. Beam: the intersection of runner functionality?
- 25. Beam: the union of runner functionality?
- 28. Parallel and portable pipelines in practice Demo and Use Case
- 29. Demo!
- 30. Getting Started with Apache Beam Quickstarts ● Java SDK ● Python SDK Example walkthroughs ● Word Count ● Mobile Gaming Extensive documentation
- 31. Related sessions Hadoop Summit San Jose 2016 ● Apache Beam: A Unified Model for Batch and Streaming Data Processing ○ Speaker: Davor Bonaci Hadoop Summit Melbourne 2016 ● Stream/Batch processing portable across on-premise and Cloud with Apache Beam ○ Speaker: Eric Anderson DataWorks Summit San Jose 2017 ● Realizing the promise of portable data processing with Apache Beam ○ Speaker: Davor Bonaci ● Stateful processing of massive out-of-order streams with Apache Beam ○ Speaker: Kenneth Knowles
- 32. Apache Beam is a unified programming model designed to provide portable data processing pipelines (efficient too)
- 33. Learn more! Apache Beam https://beam.apache.org Join the Beam mailing lists! [email protected] [email protected] Follow @ApacheBeam on Twitter
- 34. Demo screenshots because if I make them, I won’t need to use them