Building Scalable Big Data Infrastructure Using Open Source Software Presentation 1.ppt
- 1. Building Scalable Big Data Infrastructure Using Open Source Software Sam William
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- 4. StumbleUpon
- 5. The Data Challenge 1 Data collection 2 Real time metrics 3 Batch processing / ETL 4 Data warehousing & ad-hoc analysis 5 Business intelligence & Reporting
- 6. Challenges in data collection • Different services deployed of different tech stacks • Add minimal latency to the production services • Application DBs for Analytics / batch processing – From HBase & MySQL Site Rec /Ad Server Other internal services Apache / PHP Scala / Finagle Java / Scala / PHP
- 7. Data Processing and Warehousing Raw Data E T L Warehouse (HDFS) Tables Massively Denormalized Tables Challenges/Requirements: •Scale over 100 TBs of data •End product works with easy querying tools/languages •Reliable and Scalable – powers analytics and internal reporting.
- 8. Real-time analytics and metrics • Atomic counters • Tracking product launches • Monitoring the health of the site • Latency – live metrics makes sense • A/B tests
- 9. Open Source at SU
- 10. Data Collection at SU
- 11. Activity Streams and Logs All messages are Protocol Buffers Fast and Efficient Multiple Language Bindings ( Java/ C++ / PHP ) Compact Very well documented Extensible
- 12. Apache Kafka • Distributed pub-sub system • Developed @ LinkedIn • Offers message persistence • Very high throughput – ~300K messages/sec • Horizontally scalable • Multiple subscribers for topics . – Easy to rewind
- 13. Kafka • Near real time process can be taken offline and done at the consumer level • Semantic partitioning through topics • Partitions for parallel consumption • High-level consumer API using ZK • Simple to deploy- only requires Zookeeper
- 14. Kafka At SU • 4 Broker nodes with RAID10 disks • 25 topics • Peak of 3500 msg/s • 350 bytes avg. message size • 30 days of data retention
- 15. Sutro • Scala/Finagle • Generic Kafka message producer • Deployed on all prod servers • Local http daemon • Publishes to Kafka asynchronously • Snowflake to generate unique Ids
- 16. Sutro - Kafka Site - Apache/PHP Sutro Ad Server - Scala/Finagle Sutro Rec Server - Scala/Finagle Sutro Other Services Sutro Kafka Broker Broker Broker
- 17. Application Data for Analytics & Batch Processing • HBase inter-cluster replication (from production to batch cluster) • Near real-time sync on batch cluster • Readily available in Hive for analysis HBase • MySQL replication to Batch DB Servers • Sqoop incremental data transfer to HDFS • HDFS flat files mapped to Hive tables & made available for analysis MySQL
- 18. Real-time metrics 1. HBase – Atomic Counters 2. Asynchbase - Coalesced counter inc++ 1. OpenTSDB (developed at SU) – A distributed time-series DB on HBase – Collects over 2 Billion data points a day – Plotting time series graphs – Tagged data points
- 19. Real-time counters Real time metrics from OpenTSDB
- 20. Kafka Consumer framework aka Postie • Distributed system for consuming messages • Scala/Akka -on top of Kafka’s consumer API • Generic consumer - understands protobuf • Predefined sinks HBase / HDFS (Text/Binary) / Redis • Consumers configured with configuration files • Distributed / uses ZK to co-ordinate • Extensible
- 21. Postie
- 22. Akka • Building concurrent applications made easy !! • The distributed nodes are behind Remote Actors • Load balancing through custom Routers • The predefined sink and services are accessed through local actors • Fault-tolerance through actor monitoring
- 23. Postie
- 24. Batch processing / ETL Batch processing / ETL GOAL: Create simplified data-sets from complex data Create highly denormalized data sets for faster querying Power the reporting DB with daily stats Output structured data for specific analysis e.g. Registration Flow analysis
- 25. Our favourite ETL tools: • Pig – Optional Schema – Work on byte arrays – Many simple operations can be done without UDFs – Developing UDFs is simple (understand Bags/Tuples) – Concise scripts compared to the M/R equivalents • Scalding – Functional programming in Scala over Hadoop – Built on top of Cascading – Operating over tuples is like operating over collections in Scala – No UDFs .. Your entire program is in a full-fledged general purpose language
- 26. Warehouse - Hive Uses SQL-like querying language All Analysts and Data Scientists versed in SQL Supports Hadoop Streaming (Python/R) UDFs and Serdes make it highly extensible Supports partitioning with many table properties configurable at the partition level
- 27. Hive at StumbleUpon HBaseSerde • Reads binary data from HBase • Parses composite binary values into multiple columns in Hive (mainly on key) ProtobufSerde • For creating Hive tables on top of binary protobuf files stored in HDFS • Serde uses Java reflection to parse and project columns
- 28. Data Infrastructure at SU
- 29. Data Consumption
- 30. End Users of Data Data Pipeline Warehouse Who uses this data? • Data Scientists/Analysts • Offline Rec pipeline • Ads Team … all this work allows them to focus on querying and analysis which is critical to the business.
- 31. Business Analytics / Data Scientists • Feature-rich set of data to work on • Enriched/Denormalized tables reduce JOINs, simplifies and speeds queries – shortening path to analysis. • R: our favorite tool for analysis post Hadoop/Hive.
- 32. Recommendation platform • URL score pipeline – M/R and Hive on Oozie – Filter / Classify into buckets – Score / Loop – Load ES/HBase index • Keyword generation pipeline – Parse URL data – Generate Tag mappings
- 34. Advertisement Platform • Billing Service – RT Kafka consumer – Calculates skips – Bills customers • Audience Estimation tool – Pre-crunched data into multiple dimensions – A UI tool for Advertisers to estimate target audience • Sales team tools – Built with PHP leveraging Hive or pre-crunched ETL data in HBase
- 35. More stuff on the pipeline • Storm from Twitter – Scope for lot more real time analytics. – Very high throughput and extensible – Applications in JVM language • BI tools – Our current BI tools / dashboards are minimal – Google charts powered by our reporting DB (HBase primarily).
- 36. Open Source FTW!! • Actively developed and maintained • Community support • Built with web-scale in mind • Distributed systems – Easy with Akka/ZK/Finagle • Inexpensive • Only one major catch !! – Hire and retain good engineers !!
Editor's Notes
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