What’s New in the Berkeley Data Analytics Stack
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Presenter: Prof. Mike Franklin, AMPLab UC Berkeley
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What’s New in the Berkeley Data Analytics Stack
- 1. What’s Next for the Berkeley Data Analytics Stack UC BERKELEY Michael Franklin July 20 2015 Data Science Summit SF
- 2. The Berkeley AMPLab 80+ Students, Postdocs, Faculty and Staff from: Databases, Machine Learning, Systems, Security, and Netwo Mission Statement: Making Sense of Data at Scale by Integratin • Algorithms – Machine Learning, Statistical Methods, • Machines – Cluster and Cloud Computing • People – Crowdsourcing and Human Computation Franklin Jordan Stoica Patterson ShenkerRechtKatzJosephGoldberg Mahoney PopaGonzalez
- 3. AMPLab: A Public/Private Partnership NSF CISE Expedition Award: Part of 2012 White House Big Data Initiative Darpa XData Program DoE/Lawrence Berkeley National Lab And these Industrial Sponsors:
- 4. Velox Model Serving Tachyon Spark Streamin g Shark BlinkDB GraphX MLlib MLBa se Spark R Cancer Genomics, Energy Debugging, Smart Buildings Sample Clean In House Applications Spark Berkeley Data Analytics Stack (Apache and BSD open source) HDFS, S3, …Mesos Yarn Access and Interfaces Processing Engine Resource Virtualization Tachyon Storage
- 5. Big Data Ecosystem Evolution MapReduce Pregel Dremel GraphLab Storm Giraph Drill Tez Impala S4 … Specialized systems (iterative, interactive and streaming apps) General batch processing
- 6. AMPLab Unification Philosophy Don’t specialize MapReduce – Generalize it! Two additions to Hadoop MR can enable all the models shown earlier! 1. General Task DAGs 2. Data Sharing For Users: Fewer Systems to Use Less Data Movement Spark Streaming GraphX …SparkSQL MLbase
- 7. In-Memory Dataflow System M. Zaharia, M. Choudhury, M. Franklin, I. Stoica, S. Shenker, “Spark: Cluster Computing with Working Sets, USENIX HotCloud, 2010. • Developed in AMPLab and its predecessor the RADLab • Alternative to Hadoop MapReduce • 10-100x speedup for ML and interactive queries • Central component of the BDAS Stack • “Graduated” to Apache Foundation -> Apache Spark
- 8. Apache Spark Meetups Around the World (Jan ‘15)
- 9. Apache Spark Meetups Around the World (July ‘15) + 72% +124 + 79%+ 57%
- 10. Berkeley Data Analytics Stack Resource Virtualization Storage Processing Engine Access and Interfaces In-house Apps Mesos Spark Core Spark Streaming SparkSQL BlinkDB GraphX MLlib MLBase Hadoop Yarn SampleCle an G-OLA SparkR Cancer Genomics, Energy Debugging, Smart Buildings Velox MLPipelin es Splash Tachyon HDFS, S3, Ceph, … Succinct
- 11. Berkeley Data Analytics Stack Resource Virtualization Storage Processing Engine Access and Interfaces In-house Apps Mesos Spark Streaming SparkSQL BlinkDB GraphX MLlib MLBase Hadoop Yarn SampleCle an G-OLA SparkR Cancer Genomics, Energy Debugging, Smart Buildings Velox MLPipelin es Splash Tachyon HDFS, S3, Ceph, … Succinct Spark Core • Major rearchitecture and features (community) – DataFrames API – Tungsten: bringing Spark closer to bare metal • Memory Management and Binary Processing • Cache-aware computation • Code generation • R interface • Spark SQL and Spark Streaming enhancements • Still rapidly growing!
- 12. Resource Virtualization Storage Processing Engine Access and Interfaces In-house Apps Mesos Spark Core Spark Streaming SparkSQL BlinkDB GraphX MLlib MLBase Hadoop Yarn SampleCle an G-OLA SparkR Cancer Genomics, Energy Debugging, Smart Buildings MLPipelin es Splash Tachyon HDFS, S3, Ceph, … Succinct Velox • Velox – Model Serving and Personalization – KeystoneML integration – Improved service APIs and deployment tools – Open source alpha release BDAS: Latest Developments
- 13. 13 Data Model Where do models go? Conference Papers Sales Reports Drive Actions Training Introducing Velox: Model Serving
- 14. Driving Actions 14 Suggesting Items at Checkout Fraud Detection Cognitive Assistance Internet of Things Low-Latency Personalized Rapidly Changing
- 15. Problem: Separate Systems 15 Offline Analytics Systems Sophisticated ML on static data. Low-Latency data serving How do we serve low-latency predictions and train on live data? Online Serving Systems MongoDB
- 16. Velox Model Serving System Decompose personalized predictive models: 16 [CIDR’15]
- 17. Velox Model Serving System Decompose personalized predictive models: 17 [Crankshaw, Bailis, Gonzalez et al. CIDR’15] Split Personalization Model Feature Model OnlineBatch Feature Caching Approx. Features Online Updates Active Learning Order-of-magnitude reductions in prediction latencies.
- 18. Access and Interfaces BDAS: Latest Developments Resource Virtualization Storage Processing Engine In-house Apps Mesos Spark Core Spark Streaming SparkSQL BlinkDB GraphX MLlib MLBase Hadoop Yarn SampleCle an G-OLA SparkR Cancer Genomics, Energy Debugging, Smart Buildings Velox Splash Tachyon HDFS, S3, Ceph, … Succinct MLPipelin es • MLPipelines KeystoneML – Alpha release – End-to-end pipelines in vision, speech, and NLP – Horizontal scalability to 100’s of machines and multi-terabyte datasets
- 19. What is KeystoneML? Software framework for building scalable end-to-end machine learning pipelines. Helps us explore how to build systems for robust, scalable, end- to-end advanced analytics workloads and the patterns that emerge. Example pipelines that achieve state-of-the-art results on large scale datasets in computer vision, NLP, and speech - fast. Previewed at AMP Camp 5 and on AMPLab Blog as “ML Pipelines” Public release last month! http://keystone-ml.org/
- 20. How does it fit with BDAS? Spark MLlibGraphX ml-matrix KeystoneML Batch Model Training Velox Model Server Real Time Serving http://amplab.github.io/velox-modelserver
- 21. Example: Image Classification Images (VOC2007) .fit( ) Resize Grayscale SIFT PCA Fisher Vector MaxClassifier Linear Regression Resize Grayscale SIFT MaxClassifier PCA Map Fisher Encoder Linear Model Achieves performance of Chatfield et. al., 2011 Embarassingly parallel featurization and evaluation 15 min on a modest cluster 5K examples, 40K features, 20 classes
- 22. Current Software FeaturesData Loaders » CSV, CIFAR, ImageNet, VOC, TIMIT, 20 Newsgroups Transformers » NLP - Tokenization, n-grams, term frequency, NER*, parsing* » Images - Convolution, Grayscaling, LCS, SIFT*, FisherVector*, Pooling, Windowing, HOG, Daisy » Speech - MFCCs* » Stats - Random Features, Normalization, Scaling*, Signed Hellinger Mapping, FFT » Utility/misc - Caching, Top-K classifier, indicator label mapping, sparse/dense encoding transformers. Estimators » Learning - Block linear models, Linear Discriminant Analysis, PCA, ZCA Whitening, Naive Bayes*, GMM* • Example Pipelines • NLP - 20 Newsgroups, Wikipedia Language model • Images - MNIST, CIFAR, VOC, ImageNet • Speech - TIMIT • Evaluation Metrics • Binary Classification • Multiclass Classification • Multilabel Classification * - Links to external library: MLlib, ml-matrix, VLFeat, EncEval
- 23. Research Direction: Automatic Resource Estimation Long-complicated pipelines. » Just a composition of dataflows! How long will this thing take to run? When do I cache? » Pose as a constrained optimization problem. Enables Efficient Hyperparameter Tuning (ref. E. Sparks et al. “Automating Model Search for Large Scale Machine Learning”, SOCC, Aug 2015) Resize Grayscale SIFT PCA Fisher Vector Top 5 Classifier LCS PCA Fisher Vector Block Linear Solver Weighted Block Linear Solver
- 24. Resource Virtualization Storage Processing Engine Access and Interfaces In-house Apps Mesos Spark Core Spark Streaming SparkSQL BlinkDB GraphX MLlib MLBase Hadoop Yarn G-OLA SparkR Cancer Genomics, Energy Debugging, Smart Buildings Velox MLPipelin es Splash Tachyon HDFS, S3, Ceph, … Succinct SampleCle an • Released two Spark Packages – SampleClean: SparkSQL-integrated library for record dedup, entity resolution, and active learning – AMPCrowd: web service for crowdsourcing through Amazon Mechanical Turk or a "internal" crowd • REST API to allow for human-in-the-loop, BDAS: Latest Developments
- 25. SampleClean Framework Current research focus: Latency Reduction for human-in-the- loop • Straggler Mitigation • Pool Maintenance • Active Learning
- 26. Summary • AmpLab project • Cross-disciplinary team, Industry engagement • Open Source development and community building • BDAS philosophy: Unification • Spark + SQL + Graphs + ML + … • After graduating Mesos, Tachyon & Spark we are moving up the stack to support declarative and real-time Machine Learning and analytics.
- 27. To find out more or get involved: amplab.berkeley.edu franklin@berkeley.e du UC BERKELEY Thanks to NSF CISE Expeditions in Computing, DARPA XData, Founding Sponsors: Amazon Web Services, Google, IBM, and SAP, the Thomas and Stacy Siebel Foundation, all our industrial sponsors and partners, and all the members of the AMPLab Team.
Editor's Notes
- Connect to political bias story
- Spark batch analytics vs low-latency serving system
- Use rect linear loss