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The Lyft data platform: Now and in the future
- 1. Now and The Future Lyft Data Platform Mark Grover | @mark_grover Deepak Tiwari | @_deepaktiwari_
- 2. Improve people’s lives with the world’s best transportation ● 30.7M riders in 2018 ● 1.9M drivers in 2018 ● 1B+ cumulative rides ● 300+ markets in US & Canada
- 3. Data is at the core of decisions at Lyft Automated decisions - What’s the price for the ride? - What driver to match? - What’s the ETA? Analyzing business performance - How are key business metrics trending? - How do predicted ETAs compare to actual? Human business decisions - Which opportunities to invest in? - Which path to take (via experimentation)?
- 4. Data platform users 4 Data Modelers Analysts Data Scientists General Managers Data Platform Engineers ExperimentersPMs/Execs Analytics Biz ops Building apps Experimentation
- 5. By numbers... ● Millions of BI queries per week doubling quarterly ● 5X increase in productivity of ML models in 2018 ● 20X scaling of support of maps to users through streaming platform in 2018
- 6. Product Teams, Applied ML, Forecasting ML Platform Data Platform and Infra Source: The AI Hierarchy of Needs, Monica Rogati (8/2017) Data as a platform to accelerate the business and reduce risk...
- 7. ● Think ahead in the future (e.g. streaming, machine learning, security and privacy, visualization, discovery, etc.). ● Provide a step change (vs incremental) in the capability. ● Move fast. ● Create a competitive advantage. ● Focus on impact: Develop jointly with application verticals. ● Build enterprise grade platform. ● Have a clearly defined contract with applications (e.g. SLAs). ● Give a serverless application for the product teams. Guiding principles for the data platform team... Innovative Impactful Reliable
- 8. Use case #1
- 9. Unmet need for business metric observability Business metric observability What’s the health of the business? Grafana Operational observability What’s the health of the service? ● Is the service up? ● Is it throwing errors? ● In near real-time (< 1 min)
- 10. Requirements for biz metric observability See results within 1 - 30 minutes Be the source of truthNear real-time Impact on business metrics Derive business metrics from raw data (aka ETL) Don’t widen the gap for reconciliation
- 11. 11 Project F2 architecture Data Discovery app - Amundsen Operational Data stores (e.g. Dynamo) Apache Superset CDC Online flow Offline flow
- 12. Magic of CDC - Change Data Capture Operational Data stores (e.g. Dynamo) Analytical Data stores (e.g. Hive/Presto, BQ) 1. Tail the operational Data stores 2. Persist the raw change log 3. Upsert the change log to table periodically (~30 m)
- 13. Advantages of CDC Data Engineer Productivity See results within 30 minutes Near real-time Source of truth No need to reconcile Same data as operational DBs No need to recreate ETL from events Easier primitives to build ETL on top of
- 14. ● Measuring reliability ○ How to distinguish late arriving data from missing data? ○ How do you trace a single missing revision through all moving parts? ● Lots of moving parts ○ Tailer, tied to implementation of operational DB ○ Ingest pipeline ○ Kafka, Kinesis ○ Analytic Database Challenges of the architecture
- 15. CDC + Streaming = Lots of business value
- 16. Use case #2
- 17. Data Science use cases - Driver app
- 18. Data Science use cases - Pricing
- 19. Requirements for streaming applications In Streaming, just like in Batch Quick and simple ways of cleaning data Prototype in a language of choice (Python, R, SQL) Quick and simple ways of cleaning data
- 20. 20 Services (e.g. ETA, Pricing) Models + Applications (e.g. ETA, Pricing) Flyte Streaming architecture
- 21. Investments in Streaming Dryft Fully managed data processing engine, powering real-time features and events - Needed for consistent feature generation - Batch processing for bulk creation of features for training - Stream processing for real-time creation of features for scoring - Uses Flink SQL under the hood Apache Beam Open source unified, portable and extensible model for both batch and streaming use-cases - Enables streaming use cases for teams using non JVM languages - Uses Flink under the hood
- 22. ● Things we find at scale ○ Intermittent AWS service errors ○ Can’t be naive about pub-sub consumption ● Integration ○ Things work in isolation, but … ○ Flink Kinesis Connector ■ Connector that work at scale are hard Challenges of the architecture
- 23. Sharing your batch and streaming compute will pay huge benefits
- 25. 25 Data Platform architecture Data Discovery app - Amundsen Services (e.g. ETA, Pricing) Operational Data stores (e.g. Dynamo) Models + Applications (e.g. ETA, Pricing) Apache Superset BI/Data Viz Marketplace Operations app ... Other custom apps Custom apps Flyte
- 26. Kafka is better but …. • Has limitations around fan-in Kafka vs. Kinesis Kinesis scaling limitations • We require high throughput & high fan-out • Default limit of 500 shards • Resharding is expensive and slow • Built a fan-out system to work around limitations
- 27. ● Apache Flink vs. Apache Spark vs. Apache Beam ● 2 dimensions of comparison ○ APIs (the kinds of applications you can write) ○ Operations (the kind of applications you can support) ● Apache Beam for multi-language support (Python and Go) ● Spark Streaming - operations were hard, no state evolution, cumulative latencies with multi-stage graphs. ● Know when to put all your eggs in the same basket (Spark), when not to. Streaming engines
- 28. Interactive querying: ● Redshift ○ Historical but dying ● Druid ○ Interactive use-cases ● Presto (on S3) ○ Super handy interactive query engine ○ Lacking real-time ingestion support ● BigQuery ○ Interactive query engine (like Presto) ○ Expensive, but great streaming support! ETL: ● Hive (on S3) ○ Mostly for ETL and adhoc queries that are too large to run on Presto ● Spark ○ Some ETL, potential for all ETL to be in Spark Data Storage and processing Future of Interactive querying Unified access layer e.g. DAL, Genie, DALi Views Future of ETL - Easily schedule with dependencies, a SQL query to be an ETL job - Diagnose job failures with lineage and dashboards on data skew, etc.
- 29. ● Airflow ○ Most ETL jobs ○ Python heavy DAGs ○ Really good community to support ● Flyte ○ Focussed on ML workflows ○ Built in Provenance ○ Intermediate caching, discovery of previously computed artifacts Workflow engines
- 30. Conclusion
- 31. ● We think about data as a platform and a competitive advantage. ● Our data and platform usage is growing really really fast. ● We support Data Science, Ops, Analytics, Experimentation and other use cases. ● We have seen tremendous benefit from CDC data + Streaming frameworks to deliver business metric observability. ● We have learned and gained a lot in operational excellence by sharing our batch and stream compute frameworks. ● We are investing in Data Discovery, Streaming, and Machine Learning. Conclusion
- 32. Attend Streaming at Lyft session tomorrow!
- 33. Attend Meetup at Level39 tonight!
- 34. Thank you go.lyft.com/lyftdataplatformMay 2nd, 2019 Mark Grover | @mark_grover Deepak Tiwari | @_deepaktiwari_