Tcloud Computing Hadoop Family and Ecosystem Service 2013.Q2

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The presentation is designed for those interested in Hadoop technology, and can enhance your knowledge in Hadoop, such as community history, current development status, features of services, distributed computing framework and scenario of big data development in Enterprise.

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Tcloud Computing Hadoop Family and Ecosystem Service 2013.Q2

  1. 1. Will Y Lin Hadoop Product Family and Ecosystem
  2. 2. Agenda • What is BigData? • What is the problem? • Hadoop – Introduction to Hadoop – Hadoop components – What sort of problems can be solved with Hadoop? • Hadoop ecosystem • Conclusion
  3. 3. What is BigData? A set of files A database A single file
  4. 4. Big data Expands on 4 fronts Velocity Volume Variety Veracity MB GB TB PB batch periodic near Real-Time Real-Time http://whatis.techtarget.com/definition/3Vs
  5. 5. The Data-Driven World • Modern systems have to deal with far more data than was the case in the past – Organizations are generating huge amounts of data – That data has inherent value, and cannot be discarded • Examples: – Yahoo – over 170PB of data – Facebook – over 30PB of data – eBay – over 5PB of data • Many organizations are generating data at a rate of terabytes per day
  6. 6. What is the problem • Traditionally, computation has been processor-bound • For decades, the primary push was to increase the computing power of a single machine – Faster processor, more RAM • Distributed systems evolved to allow developers to use multiple machines for a single job – At compute time, data is copied to the compute nodes
  7. 7. What is the problem • Getting the data to the processors becomes the bottleneck • Quick calculation – Typical disk data transfer rate: • 75MB/sec – Time taken to transfer 100GB of data to the processor: • approx. 22 minutes!
  8. 8. What is the problem • Failure of a component may cost a lot • What we need when job fail? – May result in a graceful degradation of application performance, but entire system does not completely fail – Should not result in the loss of any data – Would not affect the outcome of the job
  9. 9. Hadoop Solutions The most common problems Hadoop can solve
  10. 10. Threat Analysis/Trade Surveillance • Challenge: – Detecting threats in the form of fraudulent activity or attacks • Large data volumes involved • Like looking for a needle in a haystack • Solution with Hadoop: – Parallel processing over huge datasets – Pattern recognition to identify anomalies • – i.e., threats • Typical Industry: – Security, Financial Services
  11. 11. Recommendation Engine • Challenge: – Using user data to predict which products to recommend • Solution with Hadoop: – Batch processing framework • Allow execution in in parallel over large datasets – Collaborative filtering • Collecting ‘taste’ information from many users • Utilizing information to predict what similar users like • Typical Industry – ISP, Advertising
  12. 12. Walmart Case Revenue ? FridayFriday BeerBeer DiapersDiapers
  13. 13. Hadoop!
  14. 14. • Apache Hadoop project – inspired by Google's MapReduce and Google File System papers. • Open sourced, flexible and available architecture for large scale computation and data processing on a network of commodity hardware • Open Source Software + Hardware Commodity – IT Costs Reduction – inspired by
  15. 15. Hadoop Concepts • Distribute the data as it is initially stored in the system • Individual nodes can work on data local to those nodes • Users can focus on developing applications.
  16. 16. Hadoop Components • Hadoop consists of two core components – The Hadoop Distributed File System (HDFS) – MapReduce Software Framework • There are many other projects based around core Hadoop – Often referred to as the ‘Hadoop Ecosystem’ – Pig, Hive, HBase, Flume, Oozie, Sqoop, etc MapReduce Runtime (Dist. Programming Framework) Hadoop Distributed File System (HDFS) Zookeeper (Coordination) Hbase (Column NoSQL DB) Sqoop/Flume (Data integration) Oozie (Job Workflow & Scheduling) Pig/Hive (Analytical Language) Hue (Web Console) Mahout (Data Mining)
  17. 17. Hadoop Components: HDFS • HDFS, the Hadoop Distributed File System, is responsible for storing data on the cluster • Two roles in HDFS – Namenode: Record metadata – Datanode: Store data MapReduce Runtime (Dist. Programming Framework) Hadoop Distributed File System (HDFS) Zookeeper (Coordination) Hbase (Column NoSQL DB) Sqoop/Flume (Data integration) Oozie (Job Workflow & Scheduling) Pig/Hive (Analytical Language) Hue (Web Console) Mahout (Data Mining)
  18. 18. How Files Are Stored: Example • NameNode holds metadata for the data files • DataNodes hold the actual blocks • Each block is replicated three times on the cluster
  19. 19. HDFS: Points To Note • When a client application wants to read a file: • It communicates with the NameNode to determine which blocks make up the file, and which DataNodes those blocks reside on • It then communicates directly with the DataNodes to read the data
  20. 20. Hadoop Components: MapReduce • MapReduce is a method for distributing a task across multiple nodes • It works like a Unix pipeline: – cat input | grep | sort | uniq -c | cat > output – Input | Map | Shuffle & Sort | Reduce | Output MapReduce Runtime (Dist. Programming Framework) Hadoop Distributed File System (HDFS) Zookeeper (Coordination) Hbase (Column NoSQL DB) Sqoop/Flume (Data integration) Oozie (Job Workflow & Scheduling) Pig/Hive (Analytical Language) Hue (Web Console) Mahout (Data Mining)
  21. 21. Features of MapReduce • Automatic parallelization and distribution • Automatic re-execution on failure • Locality optimizations • MapReduce abstracts all the ‘housekeeping’ away from the developer – Developer can concentrate simply on writing the Map and Reduce functions MapReduce Runtime (Dist. Programming Framework) Hadoop Distributed File System (HDFS) Zookeeper (Coordination) Hbase (Column NoSQL DB) Sqoop/Flume (Data integration) Oozie (Job Workflow & Scheduling) Pig/Hive (Analytical Language) Hue (Web Console) Mahout (Data Mining)
  22. 22. Example : word count • Word count is challenging over massive amounts of data – Using a single compute node would be too time-consuming – Number of unique words can easily exceed the RAM • MapReduce breaks complex tasks down into smaller elements which can be executed in parallel • More nodes, more faster
  23. 23. Word Count Example Key: offset Value: line Key: word Value: count Key: word Value: sum of count 0:The cat sat on the mat 22:The aardvark sat on the sofa
  24. 24. The Hadoop Ecosystems
  25. 25. Growing Hadoop Ecosystem • The term ‘Hadoop’ is taken to be the combination of HDFS and MapReduce • There are numerous other projects surrounding Hadoop – Typically referred to as the ‘Hadoop Ecosystem’ • Zookeeper • Hive and Pig • HBase • Flume • Other Ecosystem Projects – Sqoop – Oozie – Hue – Mahout
  26. 26. The Ecosystem is the System • Hadoop has become the kernel of the distributed operating system for Big Data • No one uses the kernel alone • A collection of projects at Apache
  27. 27. Relation Map MapReduce Runtime (Dist. Programming Framework) Hadoop Distributed File System (HDFS) Zookeeper (Coordination) Hbase (Column NoSQL DB) Sqoop/Flume (Data integration) Oozie (Job Workflow & Scheduling) Pig/Hive (Analytical Language) Hue (Web Console) Mahout (Data Mining)
  28. 28. Zookeeper – Coordination Framework MapReduce Runtime (Dist. Programming Framework) Hadoop Distributed File System (HDFS) Zookeeper (Coordination) Hbase (Column NoSQL DB) Sqoop/Flume (Data integration) Oozie (Job Workflow & Scheduling) Pig/Hive (Analytical Language) Hue (Web Console) Mahout (Data Mining)
  29. 29. What is ZooKeeper • A centralized service for maintaining – Configuration information – Providing distributed synchronization • A set of tools to build distributed applications that can safely handle partial failures • ZooKeeper was designed to store coordination data – Status information – Configuration – Location information
  30. 30. Why use ZooKeeper? • Manage configuration across nodes • Implement reliable messaging • Implement redundant services • Synchronize process execution
  31. 31. ZooKeeper Architecture – All servers store a copy of the data (in memory) – A leader is elected at startup – 2 roles – leader and follower • Followers service clients, all updates go through leader • Update responses are sent when a majority of servers have persisted the change – HA support
  32. 32. Hbase – Column NoSQL DB MapReduce Runtime (Dist. Programming Framework) Hadoop Distributed File System (HDFS) Zookeeper (Coordination) Hbase (Column NoSQL DB) Sqoop/Flume (Data integration) Oozie (Job Workflow & Scheduling) Pig/Hive (Analytical Language) Hue (Web Console) Mahout (Data Mining)
  33. 33. Structured-data vs Raw-data
  34. 34. I – Inspired by • Apache open source project • Inspired from Google Big Table • Non-relational, distributed database written in Java • Coordinated by Zookeeper
  35. 35. Row & Column Oriented
  36. 36. Hbase – Data Model • Cells are “versioned” • Table rows are sorted by row key • Region – a row range [start-key:end-key]
  37. 37. Architecture • Master Server (HMaster) – Assigns regions to regionservers – Monitors the health of regionservers • RegionServers – Contain regions and handle client read/write request
  38. 38. Hbase – workflow
  39. 39. When to use HBase • Need random, low latency access to the data • Application has a variable schema where each row is slightly different • Add columns • Most of columns are NULL in each row
  40. 40. Flume / Sqoop – Data Integration Framework MapReduce Runtime (Dist. Programming Framework) Hadoop Distributed File System (HDFS) Zookeeper (Coordination) Hbase (Column NoSQL DB) Sqoop/Flume (Data integration) Oozie (Job Workflow & Scheduling) Pig/Hive (Analytical Language) Hue (Web Console) Mahout (Data Mining)
  41. 41. What’s the problem for data collection • Data collection is currently a priori and ad hoc • A priori – decide what you want to collect ahead of time • Ad hoc – each kind of data source goes through its own collection path
  42. 42. (and how can it help?) • A distributed data collection service • It efficiently collecting, aggregating, and moving large amounts of data • Fault tolerant, many failover and recovery mechanism • One-stop solution for data collection of all formats
  43. 43. Flume: High-Level Overview • Logical Node • Source • Sink
  44. 44. Architecture • basic diagram – one master control multiple node
  45. 45. Architecture • multiple master control multiple node
  46. 46. An example flow
  47. 47. Flume / Sqoop – Data Integration Framework MapReduce Runtime (Dist. Programming Framework) Hadoop Distributed File System (HDFS) Zookeeper (Coordination) Hbase (Column NoSQL DB) Sqoop/Flume (Data integration) Oozie (Job Workflow & Scheduling) Pig/Hive (Analytical Language) Hue (Web Console) Mahout (Data Mining)
  48. 48. Sqoop • Easy, parallel database import/export • What you want do? – Insert data from RDBMS to HDFS – Export data from HDFS back into RDBMS
  49. 49. What is Sqoop • A suite of tools that connect Hadoop and database systems • Import tables from databases into HDFS for deep analysis • Export MapReduce results back to a database for presentation to end-users • Provides the ability to import from SQL databases straight into your Hive data warehouse
  50. 50. How Sqoop helps • The Problem – Structured data in traditional databases cannot be easily combined with complex data stored in HDFS • Sqoop (SQL-to-Hadoop) – Easy import of data from many databases to HDFS – Generate code for use in MapReduce applications
  51. 51. Sqoop - import process
  52. 52. Sqoop - export process • Exports are performed in parallel using MapReduce
  53. 53. Why Sqoop • JDBC-based implementation – Works with many popular database vendors • Auto-generation of tedious user-side code – Write MapReduce applications to work with your data, faster • Integration with Hive – Allows you to stay in a SQL-based environment
  54. 54. Sqoop - JOB • Job management options • E.g sqoop job –create myjob –import –connect xxxxxxx --table mytable
  55. 55. Pig / Hive – Analytical Language MapReduce Runtime (Dist. Programming Framework) Hadoop Distributed File System (HDFS) Zookeeper (Coordination) Hbase (Column NoSQL DB) Sqoop/Flume (Data integration) Oozie (Job Workflow & Scheduling) Pig/Hive (Analytical Language) Hue (Web Console) Mahout (Data Mining)
  56. 56. Why Hive and Pig? • Although MapReduce is very powerful, it can also be complex to master • Many organizations have business or data analysts who are skilled at writing SQL queries, but not at writing Java code • Many organizations have programmers who are skilled at writing code in scripting languages • Hive and Pig are two projects which evolved separately to help such people analyze huge amounts of data via MapReduce – Hive was initially developed at Facebook, Pig at Yahoo!
  57. 57. Hive – Developed by • What is Hive? – An SQL-like interface to Hadoop • Data Warehouse infrastructure that provides data summarization and ad hoc querying on top of Hadoop – MapRuduce for execution – HDFS for storage • Hive Query Language – Basic-SQL : Select, From, Join, Group-By – Equi-Join, Muti-Table Insert, Multi-Group-By – Batch query SELECT * FROM purchases WHERE price > 100 GROUP BY storeid
  58. 58. Pig • A high-level scripting language (Pig Latin) • Process data one step at a time • Simple to write MapReduce program • Easy understand • Easy debug A = load ‘a.txt’ as (id, name, age, ...) B = load ‘b.txt’ as (id, address, ...) C = JOIN A BY id, B BY id;STORE C into ‘c.txt’ – Initiated by
  59. 59. Hive vs. Pig Hive Pig Language HiveQL (SQL-like) Pig Latin, a scripting language Schema Table definitions that are stored in a metastore A schema is optionally defined at runtime Programmait Access JDBC, ODBC PigServer
  60. 60. • Input • For the given sample input the map emits • the reduce just sums up the values Hello World Bye World Hello Hadoop Goodbye Hadoop < Hello, 1> < World, 1> < Bye, 1> < World, 1> < Hello, 1> < Hadoop, 1> < Goodbye, 1> < Hadoop, 1> < Bye, 1> < Goodbye, 1> < Hadoop, 2> < Hello, 2> < World, 2> WordCount Example
  61. 61. WordCount Example In MapReduce public class WordCount { public static class Map extends Mapper<LongWritable, Text, Text, IntWritable> { private final static IntWritable one = new IntWritable(1); private Text word = new Text(); public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { String line = value.toString(); StringTokenizer tokenizer = new StringTokenizer(line); while (tokenizer.hasMoreTokens()) { word.set(tokenizer.nextToken()); context.write(word, one); } } } public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable> { public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException { int sum = 0; for (IntWritable val : values) { sum += val.get(); } context.write(key, new IntWritable(sum)); } } public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); Job job = new Job(conf, "wordcount"); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); job.setMapperClass(Map.class); job.setReducerClass(Reduce.class); job.setInputFormatClass(TextInputFormat.class); job.setOutputFormatClass(TextOutputFormat.class); FileInputFormat.addInputPath(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); job.waitForCompletion(true); } }
  62. 62. WordCount Example By Pig A = LOAD 'wordcount/input' USING PigStorage as (token:chararray); B = GROUP A BY token; C = FOREACH B GENERATE group, COUNT(A) as count; DUMP C;
  63. 63. WordCount Example By Hive CREATE TABLE wordcount (token STRING); LOAD DATA LOCAL INPATH ’wordcount/input' OVERWRITE INTO TABLE wordcount; SELECT count(*) FROM wordcount GROUP BY token;
  64. 64. Oozie – Job Workflow & Scheduling MapReduce Runtime (Dist. Programming Framework) Hadoop Distributed File System (HDFS) Zookeeper (Coordination) Hbase (Column NoSQL DB) Sqoop/Flume (Data integration) Oozie (Job Workflow & Scheduling) Pig/Hive (Analytical Language) Hue (Web Console) Mahout (Data Mining)
  65. 65. What is ? • A Java Web Application • Oozie is a workflow scheduler for Hadoop • Crond for Hadoop Job 1 Job 3 Job 2 Job 4 Job 5
  66. 66. Why • Why use Oozie instead of just cascading a jobs one after another • Major flexibility – Start, Stop, Suspend, and re-run jobs • Oozie allows you to restart from a failure – You can tell Oozie to restart a job from a specific node in the graph or to skip specific failed nodes
  67. 67. High Level Architecture • Web Service API • database store : – Workflow definitions – Currently running workflow instances, including instance states and variables Oozie Hadoop/Pig/HDFS DB WS API Tomcat web-app
  68. 68. How it triggered • Time – Execute your workflow every 15 minutes • Time and Data – Materialize your workflow every hour, but only run them when the input data is ready. 00:15 00:30 00:45 01:00 01:00 02:00 03:00 04:00 Hadoop Input Data Exists?
  69. 69. Exeample Workflow
  70. 70. Oozie use criteria • Need Launch, control, and monitor jobs from your Java Apps – Java Client API/Command Line Interface • Need control jobs from anywhere – Web Service API • Have jobs that you need to run every hour, day, week • Need receive notification when a job done – Email when a job is complete
  71. 71. Hue – Web Console MapReduce Runtime (Dist. Programming Framework) Hadoop Distributed File System (HDFS) Zookeeper (Coordination) Hbase (Column NoSQL DB) Sqoop/Flume (Data integration) Oozie (Job Workflow & Scheduling) Pig/Hive (Analytical Language) Hue (Web Console) Mahout (Data Mining)
  72. 72. Hue – developed by • Hadoop User Experience • Apache Open source project • HUE is a web UI for Hadoop • Platform for building custom applications with a nice UI library
  73. 73. Hue • HUE comes with a suite of applications – File Browser: Browse HDFS; change permissions and ownership; upload, download, view and edit files. – Job Browser: View jobs, tasks, counters, logs, etc. – Beeswax: Wizards to help create Hive tables, load data, run and manage Hive queries, and download results in Excel format.
  74. 74. Hue: File Browser UI
  75. 75. Hue: Beewax UI
  76. 76. Mahout – Data Mining MapReduce Runtime (Dist. Programming Framework) Hadoop Distributed File System (HDFS) Zookeeper (Coordination) Hbase (Column NoSQL DB) Sqoop/Flume (Data integration) Oozie (Job Workflow & Scheduling) Pig/Hive (Analytical Language) Hue (Web Console) Mahout (Data Mining)
  77. 77. What is • Machine-learning tool • Distributed and scalable machine learning algorithms on the Hadoop platform • Building intelligent applications easier and faster
  78. 78. Why • Current state of ML libraries – Lack Community – Lack Documentation and Examples – Lack Scalability – Are Research oriented
  79. 79. Mahout – scale • Scale to large datasets – Hadoop MapReduce implementations that scales linearly with data • Scalable to support your business case – Mahout is distributed under a commercially friendly Apache Software license • Scalable community – Vibrant, responsive and diverse
  80. 80. Mahout – four use cases • Mahout machine learning algorithms – Recommendation mining : takes users’ behavior and find items said specified user might like – Clustering : takes e.g. text documents and groups them based on related document topics – Classification : learns from existing categorized documents what specific category documents look like and is able to assign unlabeled documents to appropriate category – Frequent item set mining : takes a set of item groups (e.g. terms in query session, shopping cart content) and identifies, which individual items typically appear together
  81. 81. Use case Example • Predict what the user likes based on – His/Her historical behavior – Aggregate behavior of people similar to him
  82. 82. Conclusion Today, we introduced: • Why Hadoop is needed • The basic concepts of HDFS and MapReduce • What sort of problems can be solved with Hadoop • What other projects are included in the Hadoop ecosystem
  83. 83. Recap – Hadoop Ecosystem MapReduce Runtime (Dist. Programming Framework) Hadoop Distributed File System (HDFS) Zookeeper (Coordination) Hbase (Column NoSQL DB) Sqoop/Flume (Data integration) Oozie (Job Workflow & Scheduling) Pig/Hive (Analytical Language) Hue (Web Console) Mahout (Data Mining)
  84. 84. Questions?
  85. 85. Thank you!

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