Big Data Beyond Hadoop*: Research Directions for the Future

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Michael Wrinn …

Michael Wrinn
Research Program Director, University Research Office,
Intel Corporation
Jason Dai
Engineering Director and Principal Engineer,
Intel Corporation

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  • 1. Big Data Beyond Hadoop*:Research Directions for the FutureJason Dai, Engineering Director and PrincipalEngineer, Software and Solutions GroupMichael Wrinn, PhD, Research Program Director,University Research Office, Intel Labs ACAS002
  • 2. Agenda • Big data and the Hadoop* ecosystem • Intel university collaborations on big data research • Efficient in-memory implementations of map reduce • Efficient graph algorithms for analytics • Intel’s efforts moving research to production The PDF for this Session presentation is available from our Technical Session Catalog at the end of the day at: intel.com/go/idfsessionsBJ URL is on top of Session Agenda Pages in Pocket Guide2
  • 3. Agenda • Big data and the Hadoop* ecosystem • Intel university collaborations on bit data research • Efficient in-memory implementations of mapreduce • Efficient graph algorithms for analytics • Intel’s efforts moving research to production3
  • 4. What is Big Data? Big Data is data that is too big, too fast or too hard for existing systems and algorithms to handle • Too Big – Terabytes going on petabytes – Smart (not brute force) massive parallelism required • Too Fast – Sensor tagging everything creates a firehose – Ingest problem • Too Hard – Complex analytics are required (e.g., to find patterns, trends and relationships) – Need to combine diverse data types (No Schema, Uncurated, Inconsistent Syntax and Semantics) Data should be a resource, not a load Existing data processing tools are not a good fit4 Samuel Madden ISTC Director and Professor EECS, MIT
  • 5. Example: Web Analytics Large web enterprises: thousands of servers, millions of users, and terabytes per day of “click data” Not just simple reporting: e.g., in real time, determine what users are likely to do next, or what ad to serve them, or which user they are most similar to Existing analytics systems either: do not scale to required volumes, or do not provide required sophistication5 Samuel Madden ISTC Director and Professor EECS, MIT
  • 6. Example: Sensor Analytics Smartphone providers tolling agencies municipalities insurance companies doctors businesses Capturing massive streams of video, position, acceleration, and other data from phones and other devices This data needs to be stored, processed, and mined, e.g., to measure traffic, driving risk, or medical prognosis6 Samuel Madden ISTC Director and Professor EECS, MIT
  • 7. Hadoop* in the Big Data Ecosystem Era of Data Exchange Cost-effective Vertical Solutions eCommerce Healthcare Manufacturing Energy - Scientific FSI Traditional Business Solutions New Analytics Models Business Processing Innovation In-Memory DB – Integrated Analytics –Systems & Appliances EXALYTICS Big Data Compute EP Platform Topology EX MIC Fabric Traditional Business Solutions Connecting to New Analytics Models for Real-time Value Opportunities7
  • 8. Agenda • Big data and the Hadoop* ecosystem • Intel university collaborations on big data research • Efficient in-memory implementations of map reduce • Efficient graph algorithms for analytics • Intel’s efforts moving research to production8
  • 9. Intel Activity Landscape on Big Data Apps Corporate Data Solution Programs for Big Data and Analytics Services Trust Broker Location Based Healthcare, Telco, … (McAfee*) Service (Telmap) Data Usage Visualization End user tools Big Data HiTune* and other tools for Hadoop Market Internet of Sizing and Things / M2M Analytics Segmentation (Intel Labs & (with Bain) university Distributed Machine Business Video Analytics collaborators) Learning Intelligence (university and Hadoop* Data Management collaborators) Distributed Video and Processing Analytics Hadoop Distribution & Service Hadoop performance & Distributed Data Delivery Computing Architecture Architecture (Guavus) and Storage Platform Microservers End-to-End Data Security Compression & Federated Device Large Object Architecture Decompression IPs Storage Intel Intel Intel Intel Others Architecture Software Labs IT9
  • 10. Agenda • Big data and the Hadoop* ecosystem • Intel university collaborations on big data research • Efficient in-memory implementations of map reduce • Efficient graph algorithms for analytics • Intel’s efforts moving research to production10
  • 11. Algorithms, Machines, People (AMPLab) Adaptive/Active Machine Learning and Analytics Massive and Diverse Data CrowdSourcing/ Human Cloud Computing Computation All software released as BSD Open Source11
  • 12. Berkeley Data Analysis System • Mesos*: resource management platform • SCADS: scale-independent storage systems • PIQL, Spark: processing frameworks SharkQuery Languages * … Higher Hive* Pig / PIQL … Processing Frameworks * MPI … Spark Hadoop Mesos Resource Management SCADS Storage HDFS 3rd party AMPLab12
  • 13. Data Center Programming: Spark • In-memory cluster computing framework for applications that reuse working sets of data – Iterative algorithms: machine learning, graph processing, optimization – Interactive data mining: order of magnitude faster than disk-based tools • Key idea: RDDs “resilient distributed datasets” that can automatically be rebuilt on failure – Keep large working sets in memory – Fault tolerance mechanism based on “lineage”13
  • 14. Spark: Motivation Complex jobs, interactive queries and online processing all need one thing that Hadoop* MR lacks: • Efficient primitives for data sharing Query 1 Stage 1 Stage 2 Stage 3 Job 1 Job 2 Query 2 … Query 3 Iterative job Interactive mining Stream processing14
  • 15. Xfer and Sharing in Hadoop* HDFS HDFS HDFS HDFS read write read write Iter. 1 Iter. 2 . . . Input HDFS Query 1 Result 1 read Query 2 Result 2 Query 3 Result 3 Input . . .15
  • 16. Spark: In-Memory Data Sharing Iter. 1 Iter. 2 . . . Input Query 1 One-time processing Query 2 Query 3 Input Distributed memory . . .16
  • 17. Introducing Shark • Spark + Hive* (the SQL in NoSQL) • Utilizes Spark’s in-memory RDD caching and flexible language capabilities: result reuse, and low latency • Scalable, fault-tolerant, fast • Query Compatible with Hive17
  • 18. Benchmarks: Query 1 30GB input table SELECT * FROM grep WHERE field LIKE ‘%XYZ%’;18
  • 19. Benchmark: Query 2 5 GB input table SELECT pagerank, pageURL FROM rankings WHERE pagerank > 10; *19
  • 20. Agenda • Big data and the Hadoop* ecosystem • Intel university collaborations on big data research • Efficient in-memory implementations of map reduce • Efficient graph algorithms for analytics • Intel’s efforts moving research to production20
  • 21. Data Parallelism (MapReduce) 2 1 8 6 4 1 2 8 3 2 7 4 2 4 8 1 4 CPU 1 CPU 2 CPU 3 CPU 4 7 4 2 5 . . . . . . 1 5 9 5 3 4 9 3 4 3 8 Solve a huge number of independent subproblems21
  • 22. MapReduce for Data-Parallel ML • Excellent for large data-parallel tasks! Data-Parallel Graph-Parallel MapReduce Is there more to Feature Cross Machine Extraction Validation Computing Sufficient Learning ? Statistics22
  • 23. Machine Learning Pipeline Extract Graph Features Formation Structured Data Value Machine from Learning Data Algorithm similar face faces belief images faces labels propagation docs important doc shared LDA words topics movie words side collaborative ratings rated movie info filtering movies recommend23
  • 24. Parallelizing Machine Learning Extract Graph Features Formation Structured Data Value Machine from Learning Data Algorithm Graph Ingress Graph-Structured mostly data-parallel Computation graph-parallel24
  • 25. Addressing Graph-Parallel ML Data-Parallel Graph-Parallel Map Reduce Graph-Parallel Abstraction Map Reduce? Feature Cross Graphical Semi-Supervised Extraction Validation Models Learning Gibbs Sampling Label Propagation Computing Sufficient Belief Propagation CoEM Statistics Variational Opt. Collaborative Data-Mining Filtering PageRank Triangle Counting Tensor Factorization25
  • 26. Example: Never Ending Learner Project (CoEM) 16 Better 14 Optimal Hadoop* 12 95 Cores 7.5 hrs 10 GraphLab 16 Cores 30 min Speedup 8 GraphLab CoEM Distributed 6x fewer CPUs! 32 EC2 80 secs 6 15x Faster! GraphLab 4 machines 2 0 0.3% of Hadoop time 0 2 4 6 8 10 12 14 16 Number of CPUs26
  • 27. Example: PageRank 5.5 hrs. Hadoop* 1 hr. Twister* 8 min. GraphLab 40M Webpages, 1.4 Billion Links27
  • 28. Agenda • Big data and the Hadoop* ecosystem • Intel university collaborations on big data research • Efficient in-memory implementations of map reduce • Efficient graph algorithms for analytics • Intel’s efforts moving research to production28
  • 29. Intel’s Efforts on Hadoop* • Intel® Distribution for Apache Hadoop* – Performance, security and management – Downloadable from http://hadoop.intel.com/ • Intel’s open source initiatives for Hadoop – HiBench: comprehensive Hadoop benchmark suite  https://github.com/intel-hadoop/hibench – Project Panthera: efficient support of standard SQL features on Hadoop  https://github.com/intel-hadoop/project-panthera – Project Rhino: enhanced data protection for the Apache Hadoop ecosystem  https://github.com/intel-hadoop/project-rhino – Graph Builder: scalable graph construction using Hadoop  http://graphlab.org/intel-graphbuilder/29
  • 30. Using Spark/Shark for In-memory, Real-time Data Analysis • Use case 1: ad-hoc & interactive queries – Interactive queries (exploratory ad-hoc queries, BI charting & mining) – Similar projects: Google* Dremel, Facebook* Peregrine, Cloudera* Impala, Apache* Drill, etc. (several seconds latency) – Use Shark/Spark to achieve close to sub-second latency for interactive queries • Use case 2: in-memory, real-time analysis – Iterative data mining, online analysis (e.g., loading table into memory for online analysis, caching intermediate results for iterative machine learning) – Similar projects: Google PowerDrill – Use Shark/Spark to reliably load data in distributed memory for online analysis30
  • 31. Using Spark/Shark for In-memory, Real-time Data Analysis • Use case 3: stream processing – Streaming analysis, CEP (e.g., intrusion detection, real-time statistics, etc.) – Similar projects: Twitter* Storm, Apache* S4, Facebook* Puma – Use Spark streaming for stream processing  Better reliability  Unified framework and application for both offline, online & streaming analysis • Use case 4: graph-parallel analysis & machine learning – Use case: graph algorithms, machine learning (e.g., social network analysis, recommendation engine) – Similar projects: Google* Pregel, CMU GraphLab* – Use Bagel (Pregel on Spark) for graph parallel analysis & machine learning on Spark31
  • 32. Summary • MapReduce as implemented in Hadoop* is extremely useful, but: – In-memory implementations show serious advantages – Graph algorithms may be more suitable for problem at hand • Intel continues to work with university researchers • Intel works to implement research results into production environments32
  • 33. Call to Action • Use Intel Research results in your own big data efforts! • Work with us on the next-gen, in-memory, real-time analysis using Spark/Shark33
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