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A primer on building real time data-driven products

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An architectural overview of how to build stream data processing applications.

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A primer on building real time data-driven products

  1. 1. A primer on building real-time data-driven products Lars Albertsson, independent consultant Øyvind Løkling, Schibsted Media Group
  2. 2. Who’s talking? ● Swedish Institute of Computer Science (distributed system test+debug tools) ● Sun Microsystems (very large machines) ● Google (Hangouts, productivity) ● Recorded Future (NLP startup) ● Cinnober Financial Tech. (trading systems) ● Spotify (data processing & modelling) ● Schibsted Media Group (data processing & modelling) ● Mapflat - independent data engineering consultant
  3. 3. Why stream processing? ● Increasing number of data-driven features ● 90+% fed by batch processing ○ Simpler, better tooling ○ 1+ hour data reaction time ● Stream processing for ○ 100ms - 1 hour reaction ○ Decoupled, asynchronous microservices User content Professional content Ads / partners User behaviour Systems Ads System diagnostics Recommendations Data-based features Curated content Pushing Business intelligence Experiments Exploration
  4. 4. The organic past ● Many paths ● Synchronous ● Link failure -> chain failure ● Heterogeneous ● Difficult to recover from transformation bugs Service Service Service App App App DB Poll Queue Aggregate logs NFS Hourly dump Data warehouse ETL Queue NFS scp DB HTTP
  5. 5. ● Publish data in streams ● Replicated, sharded append-only log ● Pub / sub with history ○ Kafka, Google Pub/Sub, AWS Kinesis ● Tap to data lake for batch processing Unified log The unified log Ads Search Feed App App App StreamStream Stream Data lake
  6. 6. ● Decoupled producers/consumers ○ In source/deployment ○ In space ○ In time ● Publish results to log ● Recovers from link failures ● Replay on job bug fix Stream processing Job Ads Search Feed App App App StreamStream Stream Stream Stream Stream Job Job Stream Stream Stream Job Job Data lake Business intelligence Job
  7. 7. ● Applications need current state in DB. ● Should match stream. ● Which holds truth? A. Dual write. Simple & fragile. B. Change data capture C. Event sourcing / Command & Query Responsibility Segregation Databases vs streams Stream Service Stream Service Stream Service Job A B C
  8. 8. Stream processing building blocks ● Aggregate ○ Calculate time windows ○ Aggregate state (in memory / local database / shared database) ● Filter ○ Slim down stream ○ Privacy, security concerns ● Join ○ Enrich by joining with datasets, e.g. geo IP lookup, demographics ○ Join streams within time windows, e.g. click-through rate ● Transform ○ Bring data into same “shape”, schema
  9. 9. Stream processing technologies ● Spark Streaming ○ Ideal if you are already using Spark, same model ○ Bridges gap between data science / data engineers, batch and stream ● Kafka Streams ○ Library - new, positions itself as a lightweight alternative ○ Tightly coupled to Kafka ● Others ○ Storm, Heron, Flink, Samza, Google Dataflow, AWS Lambda
  10. 10. ● Update database table, e.g. for polling dashboard ● Create service index table n+1. Notify service to switch. ● Post to external web service ● Push stream to client Egress Service Stream Stream Job Job
  11. 11. Pushing streams to clients ● Reactive streams ○ Akka streams, … ● Standard for back pressure ○ Protects consumers ● Suited for streams to terminal Increasing resources Decreasing resources Job Service App Stream Stream
  12. 12. Real life challenges ● Events are delayed ● Events arrive out of order ● Events are duplicated ● Software changes ● Software has bugs ● Machines fail Tooling, patterns, components do not provide sufficient solutions. You need to plan and be aware.
  13. 13. Architectural patterns Strategies for handling imperfection - bugs, late events, volumes Stream Stream pipeline Service Batch pipeline Lambda Data lake Kappa Stream Stream job version n Stream job version n+1 Data lake Service Stream Switch Dataflow Stream Stream pipeline Service Late events Updates Delta Stream Stream pipeline Data lake Service Batch pipeline Online Nearline Offline
  14. 14. There is always a schema ● Schema on write + Stronger safety net - catch bugs earlier - Requires upfront schema design before data can be received - Synchronised deployment of whole pipeline ● Schema on read + Allows data to be captured as is + Easier to add/change fields - More work to keep data consistent
  15. 15. Schema representation ● Avro, JSON Schema ● Records should include schema ○ Bundled ○ Id + schema registry ● Evolution must be planned ○ E.g. Backward compatible changes allowed; Incompatible -> new topic. ○ Plan for replay of old messages
  16. 16. Secret tip: miscount! ● Sparse data structures ○ Trade little accuracy, 1-3% ○ Gain space, factor 100 - 10000 ● Basic approximate building blocks ○ Approximate filter (Bloom filter), unique item counters (HyperLogLog), top lists (TopK), per-item counter (Count-Min Sketch), percentiles (T-digest), nearest neighbours ○ Sparse memories ● Sufficient for machine learning ○ Collaborative filtering - recommendations ○ Clustering ○ Outlier detection ○ Similarity search
  17. 17. Thank you. Questions? Credits: Øyvind Løkling, Schibsted Media Group ● Content inspiration Confluent, LinkedIn, Google, Netflix, Apache Samza ● Images Tracey Saxby, Integration and Application Network, University of Maryland Center for Environmental Science (