Liferay & Big Data Dev Con 2014
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The document discusses big data architectures and technologies. It introduces concepts like Hadoop, HDFS, MapReduce, Spark, Storm and Kafka. It proposes a reference architecture using these technologies with data sources like databases, user tracking, logs and streaming data. The architecture includes an event broker to handle streaming data which is then processed via Spark, Storm or Hadoop and stored in data warehouses or search indexes. It also provides examples of using these technologies for analytics, machine learning and graph processing.
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Liferay & Big Data Dev Con 2014
- 1. Liferay & Big Data Getting value from your data ! Miguel Ángel Pastor Olivar [email protected]
- 2. Who am I? ! • Some random guy ! • Member of the Liferay core infrastructure team ! •Disclaimer: Not a computer scientist ! • @miguelinlas3
- 3. What are we going to talk about? ! • Big Data: what is this about? ! • Simple architecture proposal ! • Use cases ! • Questions (and hopefully answers)
- 4. Big Data?
- 5. • Data is so big that regular solutions are: ! –Extremely slow ! –Too small ! –Really expensive ! • How we use all the data we already own
- 6. ! • Volume –Transactions, data streaming from social media, … ! • Velocity –Torrents of data in real time ! • Variety –Numerical data, text, email, video, audio, …
- 8. • Recommender systems ! • Predicting the future: – Netflix does autoscaling based on past network data traffic ! • Churn models – Big telco companies build social networks to reduce the churn
- 9. • Sentiment analysis –Are talking about you in the Internet? ! • Real Time Bidding –Optimise advertising ! • Health care –Improve patients health while reducing costs –Improve quality of life of multiple sclerosis patients
- 10. Terminology
- 11. • Storage models • How to store relevant information ! • Computation models • Process and transform all the information ! • Analytics • How we can take actions based on the previous steps
- 13. Data storage
- 14. Hadoop Distributed File System (HDFS) ! • Java based file system ! • Scalable, fault-tolerant, distributed storage ! • Designed to run on commodity hardware ! • Closely related to MapReduce
- 16. NoSQL storage
- 17. • Semistructured data ! • Focused on ! • Horizontal scalability ! • Availability ! • Different trade-offs: CAP, BASE, … !
- 18. NewSQL storage
- 19. • Modern relational databases ! • Same scalable performance than NoSQL for OLTP ! • Maintain ACID guarantees ! • A few alternatives: VoltDB, Google Spanner, FoundationDB, …
- 21. Apache Hadoop
- 22. Apache Hadoop Map Reduce ! • Distributed processing ! • Large datasets ! •Clusters of computers #LRNAS2014 ! • Simple programming model ! • Verbose and hard to use API
- 23. Liferay projects is the best Open Source project best: 1 is: 1 Liferay: 1 Open: 1 project: 2 Source: 1 the: 1 (index, “…”) (index, “…”) (index, “…”) (index, “…”) (index, “…”) Sort and shuffle (best, [1]) (is, [1]) (Liferay: 1) (Open, [1]) (project, [1,1]) (Source, [1]) (the, [1])
- 24. • Batch model data crunching ! • Not so good event stream processing ! • But … ! • Many algorithms hard to implement using MapReduce ! • Cascading, Scalding, Cascalog, Impala, …
- 25. Apache Storm
- 26. • Distributed realtime computation system ! • Easy to reliably process unbounded streams of data ! • Multi language support ! • Realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, …
- 27. Spout Spout Bolt Bolt Bolt
- 28. Apache Spark
- 29. • Fast and general-purpose cluster computing • Developed by Berkeley AMP ! • High level APIs (not MapReduce) ! • Optimised engine: • supports general execution graphs ! • Higher-level tools: • Spark SQL, MLib, Spark Streaming, Graphx
- 30. Apache Mahout
- 31. ! • Scalable machine learning library #LRNAS2014 ! • Built on top of Hadoop ! • Some algorithms don’t require Hadoop at all #LRNAS2014
- 32. R language
- 33. • Focused on: • Data visualisation • Statistical computations • Analysis of data ! • Tons of built-in packages ! • Connect to Hadoop through Hadoop Streaming ! • Not a fast language
- 35. RDBMS Event Broker Hadoop User Tracking NoSQL Storage System Events Search Data Logs Monitoring Dataware House Streaming Social Graph
- 36. Datasources
- 37. RDBMS Event Broker Hadoop User Tracking NoSQL Storage System Events Search Data Logs Monitoring Dataware House Streaming Social Graph
- 38. • System events ! • User tracking (client side) • Clicks, navigation, activities, … ! • Monitoring (transactions, load page times, …) ! • Models (message boards, blogs, wiki …) ! • Custom developments …
- 39. Event broker
- 40. RDBMS Event Broker Hadoop User Tracking NoSQL Storage System Events Search Data Logs Monitoring Dataware House Streaming Social Graph
- 41. Data Source 0 1 2 3 4 5 6 7 8 Writes 9 Reads Reads System A System B
- 42. Apache Kafka ! • Publish-subscribe as distributed commit log ! • Fast ! • Scalable ! • Durable ! • Distributed by design
- 43. Broker A Broker B Producer Consumer Broker C ZooKeeper
- 45. RDBMS Event Broker Hadoop User Tracking NoSQL Storage System Events Search Data Logs Monitoring Dataware House Streaming Social Graph
- 46. Batch processing? ! Real time processing? ! Machine learning algorithms? ! Graph analysis? ! Unified programming model?
- 48. ! • Fast and general engine for large-scale data processing ! • Write your apps in Java, Scala or Python ! • Run on YARN cluster manager ! • Can read any existing Hadoop data (HDFS) ! • In memory or disk
- 49. Apache Spark Main Components Apache Spark Spark SQL Spark Streaming MLib GraphX
- 50. Spark Core
- 51. • Driver main function and executes various parallel operations on a cluster ! • Resilient Distributed Datasets (RDD) • HDFS (or any Hadoop file system) ! • Scala collection ! • Second abstraction: shared variables
- 52. Spark SQL
- 53. • Mix SQL queries with Spark programs ! • Unified Data Access ! • Hive compatibility ! • Standard JDBC or ODBC connectivity ! • Same engine for both interactive and long running queries
- 54. Spark Streaming
- 55. • Build your apps using high-level operators ! • Fault tolerance: exactly-once semantics out of the box ! • Combine streaming with batch and interactive queries ! • Can read from HDFS, Flume, Kafka, Twitter and ZeroMQ ! • Define your own custom data sources
- 56. Spark MLib
- 57. ! • Basic statistics • Summary statistics • Correlations • …. ! • Classification and regression • Linear models • Decision tress • Naive Bayes
- 58. ! • Clustering • K-Means ! • Collaborative filtering • Alternate least squares ! • Dimensionality reduction • Singular value decomposition ! • Principal component analysis
- 59. Spark GraphX
- 60. ! • Graphs API and graph-parallel computation ! • Growing scale and importance • From social networks to language modelling ! • Directed multigraph with properties attached to each vertex and edge ! • Growing collection of graph algorithms and builders
- 61. Live demo! Building a messages classifier
- 62. Takeaways
- 63. • Not about data size, but how you use it ! • You already own tons of data, you just need to take get value from it ! • There is no silver bullet: you’ve plenty of alternatives ! • JVM Big data related techs are usually a great choice ! • Try it yourself!!
- 64. References
- 65. !• Apache Kafka ! • Apache Spark ! • Apache Storm ! • Apache Hadoop ! • Big Data definition at Wikipedia ! • Liferay Kafka Bridge ! • What every software engineer should know about a log
- 66. Thank you!!
- 67. Questions (and hopefully answers)