The Future of Hadoop: A deeper look at Apache Spark
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Jai Ranganathan, Senior Director of Product Management, discusses why Spark has experienced such wide adoption and provide a technical deep dive into the architecture. Additionally, he presents some use cases in production today. Finally, he shares our vision for the Hadoop ecosystem and why we believe Spark is the successor to MapReduce for Hadoop data processing.
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The Future of Hadoop: A deeper look at Apache Spark
- 1. 1 An Introduction to Spark Jai Ranganathan, Senior Director Product Management, Cloudera Denny Lee, Senior Director Data Sciences Engineering, Concur
- 2. Agenda • Cloudera’s Enterprise Data Hub • Why Spark? • Spark Use Cases • Concur Case Study • Cloudera and Spark • Future of Spark 2 ©2014 Cloudera, Inc. All rights reserved.
- 3. Cloudera’s Enterprise Data Hub 3 ©2014 Cloudera, Inc. All rights reserved. 3RD PARTY APPS STORAGE FOR ANY TYPE OF DATA UNIFIED, ELASTIC, RESILIENT URE BATCH PROCESSING MAPREDUCE, SPARK ANALYTIC SQL IMPALA SEARCH ENGINE SOLR MACHINE LEARNING SPARK, PARTNERS, MAHOUT, MLLIB STREAM PROCESSING SPARK WORKLOAD MANAGEMENT YARN FILESYSTEM HDFS ONLINE NOSQL HBASE MANAGEMENT CLOUDERA NAVIGATOR DATA MANAGEMENT CLOUDERA MANAGER SYSTEM , SECURE SENTRY
- 4. Spark: Easy and Fast Big Data Easy to Develop • Rich APIs in Java, Scala, Python • Interactive shell Fast to Run • General execution graphs • In-memory storage 2-5× less code Up to 10× faster on disk, 4 ©2014 Cloudera, Inc. All rights reserved. 100× in memory
- 5. Easy: Expressive API • map • filter • groupBy • sort • union • join • leftOuterJoin • rightOuterJoin • reduce • count • fold • reduceByKey • groupByKey • cogroup • cross • zip 5 ©2014 Cloudera, Inc. All rights reserved. • sample • take • first • partitionBy • mapWith • pipe • save ...
- 6. Example: Logistic Regression data = spark.textFile(...).map(readPoint).cache() w = numpy.random.rand(D) for i in range(iterations): gradient = data .map(lambda p: (1 / (1 + exp(-p.y * w.dot(p.x)))) * p.y * p.x) .reduce(lambda x, y: x + y) w -= gradient print “Final w: %s” % w 6 ©2014 Cloudera, Inc. All rights reserved.
- 7. Spark Introduces Concept of RDD to Take Advantage of Memory RDD = Resilient Distributed Datasets • Memory caching layer that stores data in a distributed, fault-tolerant cache • Created by parallel transformations on data in stable storage Two observations: a. Can fall back to disk when data-set does not fit in memory b. Provides fault-tolerance through concept of lineage 7 ©2014 Cloudera, Inc. All rights reserved.
- 8. Fast: Using RAM, Operator Graphs In-Memory Caching • Data Partitions read from RAM instead of disk Operator Graphs • Scheduling Optimizations • Fault Tolerance C: D: E: 8 ©2014 Cloudera, Inc. All rights reserved. join B: B: groupBy filter F: Ç √ Ω map A: map take = RDD = cached partition
- 9. Easy: Out of the Box Functionality Hadoop Integration • Standard Hadoop data formats • Runs under YARN in mixed clusters Libraries • Mllib – Machine Learning toolkit • GraphX (alpha) – Graph analytics based on PowerGraph abstractions • Spark Streaming – Near real-time analytics • Spark SQL – direct SQL interface in a Spark application Language support: • SparkR (upcoming) • Java 8 • Schema support in Spark’s APIs • SQL support in Spark Streaming (upcoming) 9 ©2014 Cloudera, Inc. All rights reserved.
- 10. Logistic Regression Performance (Data Fits in Memory) 4000 3500 3000 2500 2000 1500 1000 500 0 1 5 10 20 30 Running Time (s) Number of Iterations 10 ©2014 Cloudera, Inc. All rights reserved. 110 s / iteration Hadoop Spark first iteration 80 s further iterations 1 s
- 11. Spark Streaming What is it? • Run continuous processing of data using Spark’s core API. Extends Spark concepts to fault tolerant, transformable streams • Adds “rolling window” operations. E.g. compute rolling averages or counts for data over last five minutes. Why do you care? • Same programming paradigm for streaming and batch – reuse knowledge and code in both contexts • High level API with automatic DAG generation – simplicity of development • Excellent throughput – can scale easily to really large volumes of data ingest • Combine elements like MLLib & Oryx into a streaming application Example use cases: • “On-the-fly” ETL as data is ingested into Hadoop/HDFS • Detecting anomalous behavior and triggering alerts. • Continuous reporting of summary metrics for incoming data. 11 ©2014 Cloudera, Inc. All rights reserved.
- 12. Streaming Architectures with Spark Data sources Integration layer Ingest HDFS Spark Stream processing • Flume • Kafka 12 ©2014 Cloudera, Inc. All rights reserved. Data prep Aggregation / Scoring HBase Spark long-term analytics / model building Real-time result serving
- 13. Cloudera Customer Use Cases – Core Spark Sector Use case Replaces Financial • Multiple use cases to calculate VaR for portfolio risk analysis – Services Monte Carlo simulations as well as Var-Covar methods • ETL pipeline speed-up • Analyzing stock data for 20 years 13 ©2014 Cloudera, Inc. All rights reserved. • Home grown applications Genomics • Two use cases to identify disease causing genes in full human genome • MySQL engine Data services • Trend analysis using statistical methods on large data sets • Document classification (LDA) • Fraud analytics • Netezza replacement • Net new Healthcare • Calculating Jaccard scores on health care data sets • Net new
- 14. Cloudera Customer Use Cases – Streaming Sector Use case Replaces Financial Services • On-line fraud detection • Net new Many • Continuous ETL Retail • On-line recommender systems • Inventory management 14 ©2014 Cloudera, Inc. All rights reserved. • Custom apps
- 15. 15 Spark at Concur
- 16. 16 About Concur What do we do? • Leading provider of spend management solutions and (Travel, Invoice, TripIt, etc.) services in the world • Global customer base of 20,000 clients and 25 million users • Processing more than $50 Billion in Travel & Expense (T&E) spend each year
- 17. 17 About the Speaker Who Am I? • Long time SQL Server BI guy (24TB Yahoo! Cube) • Project Isotope (Hadoop on Windows and Azure) • At Concur, helping with Big Data and Data Sciences
- 18. 18 A long time ago… • We started using Hadoop because • It was free • i.e. Didn’t want to pay for a big data warehouse • Could slowly extract from hundreds of relational data sources, consolidate it, and query it • We were not thinking about advanced analytics • We were thinking …. “cheaper reporting” • We have some hardware lying around … let’s cobble it together and now we have reports
- 19. 19 Themes Consolidate Visualize Insight Recommend
- 20. 20 BTS Travel Weather Invoice Web Analytics Expense
- 21. Can quickly switch to map mode and determine where most itineraries are from in 2013 21
- 22. 22 Or even quickly map out the airport locations on a map to see that Sun Moon Lake Airport is in the center of Taiwan
- 23. 23 Starbucks Store #3313 601 108th Ave NE Bellevue, WA (425) 646-9602 ------------------------------- Chk 713452 05/14/2014 11:04 AM 1961558 Drawer: 1 Reg: 1 ------------------------------- Bacon Art Brkfst 3.45 Warmed T1 Latte 2.70 Triple 1.50 Soy 0.60 Gr Vanilla Mac 4.15 Reload Card 50.00 AMEX $50.00 XXXXXXXXXXXXXXXXXX1004 SBUX Card $13.56 SUBTOTAL $62.40 New Caffe Espresso Frappuccino(R) Blended beverage Our Signature Frappuccino(R) roast coffee and fresh milk, blended with ice. Topped with our new espresso whipped cream and new Italian roast drizzle Expense Categorization One of my receipts that I had OCRed One of the issues we’re trying to solve is to auto-categorize this, so how can we do this? Below is a simplistic solution using WordCount Note, a real solution should involve machine learning algorithms
- 24. 24 Spark assembly has been built with Hive, including Datanucleus jars on classpath Welcome to ____ __ / __/__ ___ _____/ /__ _ / _ / _ `/ __/ '_/ /___/ .__/_,_/_/ /_/_ version 1.1.0 /_/ Using Scala version 2.10.4 (Java HotSpot(TM) 64-Bit Server VM, Java 1.7.0_45) Type in expressions to have them evaluated. Type :help for more information. 2014-09-07 22:31:21.064 java[1871:15527] Unable to load realm info from SCDynamicStore 14/09/07 22:31:21 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable Spark context available as sc. scala> val receipt = sc.textFile("/usr/local/Cellar/workspace/data/receipt/receipt.txt") receipt: org.apache.spark.rdd.RDD[String] = /usr/local/Cellar/workspace/data/receipt/receipt.txt MappedRDD[1] at textFile at <console>:12 scala> receipt.count res0: Long = 30
- 25. 25 scala> val words = receipt.flatMap(_.split(" ")) words: org.apache.spark.rdd.RDD[String] = FlatMappedRDD[2] at flatMap at <console>:14 scala> words.count res1: Long = 161 scala> words.distinct.count res2: Long = 72 scala> val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _).map{case(x,y) => (y,x)}.sortByKey(false).map{case(i,j) => (j, i)} wordCounts: org.apache.spark.rdd.RDD[(String, Int)] = MappedRDD[12] at map at <console>:16 scala> wordCounts.take(12) res5: Array[(String, Int)] = Array(("",82), (with,2), (Card,2), (new,2), (---------------- ---------------,2), (Frappuccino(R),2), (roast,2), (1,2), (and,2), (New,1), (Topped,1), (Starbucks,1))
- 26. 26
- 27. 27 What’s next… • With Spark 1.1 • Sort-based shuffling • MLLib: correlations, sampling, feature extraction, decision trees • GraphX: label propagation
- 28. 28 Using AtScale to build up a dimensional model based on the data that is stored within Impala / Hive
- 29. 29 Slice and filter the Impala model using Tableau
- 30. 30 Spark and Cloudera
- 31. Why Cloudera? Expertise • Deep engineering investment – only distribution vendor with engineering contributions to Spark and actual technical know-how • Field team, support, training and services with experience in many Spark use cases • Driving roadmap for Spark Experience •Most customers running Spark across all distributions put together • Range from few nodes to 800+ nodes • Longest field presence – first vendor to support and still only two vendors with official support Partnerships • Intel partnership brings 15 Spark developers focused on Cloudera customer use cases • Business relationship with Databricks to do joint development on Spark 31 ©2014 Cloudera, Inc. All rights reserved.
- 32. Spark Takes Over From MapReduce Stage 1 • Crunch on Spark • Search on Spark Stage 2 • Hive on Spark • Pig on Spark Stage 3 • MR equivalence • Sqoop on Spark Cloudera led multi-organization effort: MapR, Intel, Databricks, IBM 32 ©2014 Cloudera, Inc. All rights reserved.
- 33. Spark is Great but… • Opaque API limitations • Debugging and troubleshooting • Complex configuration CLOUDERA UNIVERSITY Spark Training 33 ©2014 Cloudera, Inc. All rights reserved.
- 34. Questions & Next Steps Download Now – www.cloudera.com/download Spark Training - www.cloudera.com/content/cloudera/en/training/cour ses/spark-training.html 34 ©2014 Cloudera, Inc. All rights reserved.
- 35. 35 ©2014 Cloudera, Inc. All rights reserved. Thank You
Notes de l'éditeur
- Cloudera’s enterprise data hub (powered by Hadoop) is a data management platform that provides a unique offering that’s unified, compliance-ready, accessible, and open. This enterprise data hub bring everything together in one unified layer. No copying of data. Simply one single transparent view that allows you to easily meet auditing and compliance goals. It offers a single, unified solution for: Storage & serialization Data ingest & egress Security & governance Metadata Resource management It’s compliance-ready for security and governance and includes: Authentication, authorization, encryption, audit, RBAC, lineage Single interface with integrated controls It’s accessible through: Multiple frameworks Familiar tools and skills And it’s completely open: 100% open source Apache licensed platform Extensible to 3rd party frameworks Zero lock-in platform As mentioned, Cloudera’s enterprise data hub has multiple different frameworks integrated into the platform for robust querying. One of the newest and most exciting querying frameworks is Spark, an open source, flexible data processing framework for machine learning and stream processing. Before we dive into Spark, we need to understand why Spark is necessary. And that requires an understanding of MapReduce
- Key idea: add “variables” to the “functions” in functional programming
- This is for a 29 GB dataset on 20 EC2 m1.xlarge machines (4 cores each)
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