Emerging Trends in Big Data

Transcription

1 Emerging Trends in Big Data TU Peter Linnell Big Data SUSE Apache Bigtop PMC plinnell@suse.com plinnell@apache.org

2 A little bit about me Scribus Founder and Core Team Member since 2001 Ex-Cloudera Kitchen Team baking Hadoop OpenSUSE Community member since 2006 OpenSUSE Board Member Apache Bigtop Founder and PMC Packager and contributor for many Open Source apps Day Job SUSE Systems Engineer in Silicon Valley High Performance Computing / Big Data Fan 2

3 Dilbert on Big Data 3

4 Hype Cycle 4

5 Linux is the Foundation for Big Data Scale Low Cost Commodity Hardware No Lock In Coopetition 5

6 6

7 Big Data The Jargon List Hadoop Core Hadoop is a Data Operating System Apache Hadoop is an open source software ecosystem, built around the core Hadoop technology. NoSQL A way of storing data, mostly in memory for quickly searching for data. Data has a temperature: Cold Data stored nearby Hot / Fast in memory or intelligent chaching Live Data Accessible to Big Data Tools Dead Data = Offline Data ACID - Atomicity, Consistency, Isolation, Durability Sharding see Wikipedia it is too complicated :-) 7

8 Big Data Challenges Existing data workflows are siloed Data is siloed Formats, proprietary applications Sensitive Data Concerns Regulatory Blockages Budget Constraints Planning Lead Times 8

9 Big Data Challenges 9 Data Scrubbing is the step never mentioned but indeed can be one of the biggest challenges. Big Data likes memory aka storage. Jobs can run longer than some typical mainframe or batch jobs. Hadoop turns the computing notion of bringing data to processing power on its head. You bring the compute power to where the data resides.

10 Examples of Big Data volumes 10 Scientific measurements (i. e. particle collision results from the Large Hadron Collider at the CERN) Financial data like stock information, share-price statistical data, stock related press coverage, etc. Medical data: genome database, patient's files in hospitals, information about pharmaceutical Indexed web or social media content Environmental Records - Weather Webserver Access-logs Sales data

11 Five main use cases for Big Data Transparency: insights into ongoing business operations Decision-testing: What happened (will happen) when (if) we made (make) this decision? Individualization in real time: tailoring offerings and services to customer wishes in real time in order to increase customer satisfaction and reduce customer churn Intelligent process control and automation Innovative data-driven business models From Big Data in Action

12 How to distinguish between several kinds of Big Data? 12 Amount of data: large (n terabytes) or very large (n petabytes) or gigantic (n exabytes)? Structured data (i. e. relational, column separated) or unstructured data (i. e. documents, webpages)? How complex is the data model? Transactional or non-transactional? Full data integrity required ACID? Usage patterns: Just lots of reads or also many inserts, updates and deletions? Usage performance: Realtime, short delays, long delays? Combination of several questions from above

13 Hadoop vs SQL (RDBMS) No predefined schema Schema defined in advance Fast Loading Data transformed Simpler Data Structures Fast Reading Flexible and Agile Standards/Governance The real innovation is the capability to explore original raw data 13

14 When to pick Hadoop vs RMDBS Scalablity is important Speed is important Structured or Unstructured ACID Transactions Interactive Analytics Complex Data Process A sports car is faster, but a truck can carry more. 14

15 Apache Hadoop Strengths Huge data volumes Unstructured data Reliable Scalable Lowest cost Open source No hardware lock in Batch processing 15

16 Apache Hadoop Weakenesses Not very efficient at small scale Real time is challenging at the moment (WIP) Requires skilled engineers and operations Less mature than SQL Weakly defined user roles in data access model (WIP) 16

17 What About NoSQL/NewSQL? Can be a cost effective replacement or supplement for traditional proprietary databases. There are several e.g MongoDB, Accumulo, Cassandra trying to solve different problems. Each has strengths and weaknesses to evaluate. 17

18 Linux Challenges Scalability We're hitting the limit of physics with current technology. The need for better fault tolerance in the O/S. Now helped by live kernel patching in Linux 4.1. The future will bring us exascale challenges. Think 3-7 years down the road Java scalability? Stutter affects Hadoop 18

19 Emerging Trends in Big Data Streaming accessing data in near real time for capture and analysis. Fast Data - in memory or intelligent caching. E.g. Spark, SAP HANA, HP Haven. Connectors are becoming ubiquitous Machine learning is becoming more accessible. Despite lesser performance, Cloud is becoming a more usable option for production. 19

20 Evaluation Thoughts Is Big Data a solution in search of a problem? Evaluate the need for real time data vs. near real time. Do we have right questions to ask? How can Big Data workflows be integrated with our existing infrastructure? What other agencies might have useful data? Pilot Pilot Pilot... 20

21 Evaluation Thoughts Pilot Pilot Pilot... 21

22 SUSE Big Data Partner Ecosystem Integrated solutions SAP HANA Teradata Aster Big Analytics Appliance Hadoop Distributions Intel Cloudera Hortonworks WANdisco Database 22 Intersystems CACHÉ

23 Bigtop 23 Packaging, QA testing and integration stack for Apache Hadoop components Made up of engineers from all the most of the Hadoop distros: Cloudera, Hortonworks and WANdisco,along with SUSE and independent contributors Almost unique among other Apache projects in that it integrates other projects as its goal All major Hadoop distros base their product on Bigtop

24 Why SUSE for Big Data? 24 SUSE has a decade plus of leadership in HPC/Supercomputing for Linux. Est 50% Top 500. Titan the biggest runs SLES. SLES12 has the most modern optimized kernel for Big Data work loads. We have Tier 1 support and relationships with all major open source Hadoop Distributors. Competition sees Big Data as an opportunity to sell proprietary solutions. We care about this market.

25 Why SUSE for Big Data? 25 Capable of supporting 64Tb, yes Tb of ram on one system. SLES12 has the most modern optimized kernel for Big Data work loads. Excellent deployment and management tools. Competition sees Big Data as an opportunity to sell proprietary solutions. We care about this market.

26 SUSE & Hortonworks Joint Flyer Partner Site Modern Data Architecture 26

27 SUSE Big Data Lab Big Data Cluster in Provo UT for: Benchmarking Software certification Integration / test Reference architectures Demo system Remotely accessible 27

28 Learn More Visit our web site Read our whitepapers Deploying Hadoop on SLES Deploy and Manage Hadoop with SUSE Manager Contact us 28

29 Questions? 29

30 30 Corporate Headquarters (Worldwide) Join us on: Maxfeldstrasse Nuremberg Germany

31 Appendix

32 Hadoop Core Components 32

33 Typical Hadoop Distribution 33

34 How Hadoop Works at Its Core Metadata ops Client Namenode Metadata (name, replicas, ): /home/foo/data, 3,... Read Block ops Rack 1 Rack 2 Replication Blocks Datanodes Datanodes Write Client 34

35 Hadoop is only one part But an important part 35 The compute layer of big data Supports the running of applications on large clusters of commodity hardware. Provides a distributed file system (HDFS) that stores data on the compute nodes. Enables applications to work with thousands of computers and petabytes of data. Lots of momentum IBM, Microsoft, Oracle, SAP, EMC, HP, Teradata, have built solutions on Hadoop or at least connectors to Hadoop Ecosystem of Hadoop players: Intel, Cloudera, HortonWorks, WANdisco, MapR, Greenplum Apache support

36 NameNode 36 The NameNode (NN) stores all metadata Information about file locations in HDFS Information about file ownership and permissions Names of the individual blocks Location of the blocks Metadata is stored on disk and read when the NameNode daemon starts

37 NameNode2 37 File name is fsimage Block locations are not stored in fsimage Changes to the metadata are made in RAM Changes are also written to a log file on disk called edits Each Hadoop cluster has a single NameNode The Secondary NameNode is not a fail-over NameNode The NameNode is a single point of failure (SPOF)

38 Secondary NameNode (master) 38 The Secondary NameNode (2NN) is not-a fail-over NameNode! It performs memory/intensive administrative functions for the NameNode. Secondary NameNode periodically combines a prior file system snapshot and editlog into a new snapshot New snapshot is transmitted back to the NameNode Secondary NameNode should run on a separate machine in a large installation It requires as much RAM as the NameNode

39 DataNode 39 DataNode (slave) JobTracker (master) / exactly one per cluster TaskTracker (slave) / one or more per cluster

40 Running Jobs 40 A client submits a job to the JobTracker JobTracker assigns a job ID Client calculates the input and splits for the job Client adds job code and configuration to HDFS The JobTracker creates a Map task for each input split TaskTrackers send periodic heartbeats to JobTracker These heartbeats also signal readiness to run tasks JobTracker then assigns tasks to these TaskTrackers

41 Running Jobs 41 The TaskTracker then forks a new JVM to run the task This isolates the TaskTracker from bugs or faulty code A single instance of task execution is called a task attempt Status info periodically sent back to JobTracker Each block is stored on multiple different nodes for redundancy Default is three replicas

42 Anatomy of a File Write 1. Client connects to the NameNode 2. NameNode places an entry for the file in its metadata, returns the block name and list of DataNodes to the client 3. Client connects to the first DataNode and starts sending data 4. As data is received by the first DataNode, it connects to the second and starts sending data 5. Second DataNode similarly connects to the third 6. Ack packets from the pipeline are sent back to the client 7. Client reports to the NameNode when the block is written 42

43 Hadoop Core Operations Review Metadata ops Client Namenode Metadata (name, replicas, ): /home/foo/data, 3,... Read Block ops Rack 1 Rack 2 Replication Blocks Datanodes Datanodes Write Client 43

44 Expanding on Core Hadoop 44

45 Hive, Hbase and Sqoop Hive High level abstraction on top of MapReduce Allows users to query data using HiveQL, a language very similar to standard SQL HBase A distributed, sparse, column oriented data store Sqoop 45 The Hadoop ingestion engine the basis of connectors like Teradata, Informatica, DB2 and many others.

46 Oozie 46 Work flow scheduler system to manage Apache Hadoop jobs Workflow jobs are Directed Acyclical Graphs (DAGs) of actions Coordinator jobs are recurrent Workflow jobs triggered by time (frequency) and data availabilty Integrated with the rest of the Hadoop stack Supports several types of Hadoop jobs out of the box (such as Java map-reduce, Streaming map-reduce, Pig, Hive, Sqoop and Distcp) Also supports system specific jobs (such as Java programs and shell scripts)

47 Flume 47 Distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data Simple and flexible architecture based on streaming data flows Robust and fault tolerant with tunable reliability mechanisms and many fail-over and recovery mechanisms Uses a simple extensible data model that allows for online analytic application

48 Mahout 48 The Apache Mahout machine learning library's goal is to build scalable machine learning libraries Currently Mahout supports mainly three use cases: Recommendation mining takes users' behavior and from that tries to find items users might like Clustering, for example, takes text documents and groups them into groups of topically related documents Classification learns from existing categorized documents what documents of a specific category look like and is able to assign unlabeled documents to the (hopefully) correct category

49 Whirr Set of libraries for launching Hadoop instances on clouds A cloud-neutral way to run services You don't have to worry about the idiosyncrasies of each provider. A common service API The details of provisioning are particular to the service. Smart defaults for services 49 You can get a properly configured system running quickly, while still being able to override settings as needed

50 Giraph 50 Iterative graph processing system built for high scalability Currently used at Facebook to analyze the social graph formed by users and their connections

51 Apache Pig 51 Platform for analyzing large data sets that consist of a high-level language for expressing data analysis programs Language layer currently consists of a textual language called Pig Latin, which has the following key properties: Complex tasks comprised of multiple interrelated data transformations are explicitly encoded as data flow sequences, making them easy to write, understand, and maintain. Extensibility. Users can create their own functions to do special-purpose processing.

52 Ambari Project goal is to develop software that simplifies Hadoop cluster management Provisioning a Hadoop Cluster Managing a Hadoop Cluster Monitoring a Hadoop Cluster 52 Ambari leverages well known technology like Ganglia and Nagios under the covers. Provides an intuitive, easy-to-use Hadoop management web UI backed by its RESTful APIs

53 HUE Hadoop User Experience 53 Graphical front end to Hadoop tools for launching, editing and monitoring jobs Provides short cuts to various command line shells for working directly with components Can be integrated with authentication services like Kerberos or Active Directory

54 R Statistical Language 54 Statistical Language Open Source Licensed Similar to Octave or Mathlab Not currently packaged for SLES or opensuse

55 Shark/Spark 55 Spark is a real time query framework developed at Berkeley AMP. Spark was initially developed for two applications where placing data in memory helps: iterative algorithms, which are common in machine learning, and interactive data mining. Shark uses Spark to process real time queries in Hive. Up to 100x faster than MapReduce in some cases. Going in to most Hadoop distros now or soon.

56 Zookeeper 56 An orchestration stack. Centralized service for: Maintaining configuration information Naming Providing distributed synchronization Delivering group services.

57 NoSQL Cassandra 57 Enterprise provider is Datastax Keyspace -> container for column families High Performance, Highly Scalable, Available - No SPOF Replication by hashing data between nodes Query by Column - Requires index SQL-Like Native support for Apache Hadoop Flexible Schema -> Change at runtime. No transactions, no JOINs

58 NoSQL (cont) Accumulo 58 Like Hbase, a BigTable clone. Join-Less Runs on top of Hadoop. MapReduce with hadoop. Used for scanning large two-dimensional tables Accumulo, HBase and Cassandra are part of the Hadoop ecosystem. HBase supported by the Hadoop provider. Hugely scalable NoSQL database developed at NSA. Only NoSQL DB with cell level locking and security..

59 NoSQL (cont) MongoDB 59 Enterprise provider MongoDB Inc, was known as 10gen Non-Relational DataStore for JSON Documents {"name":"alejandro"} {"name":"alejandro", "Age": 31, likes:["soccer","golf", "Beach"]} Schemaless, container vs table, document vs row Does not support JOINs or transactions (across multiple documents). Does not perform as memcached, not as functional as RDBMS. Sits in the middle.

60 NoSQL (cont - MongoDB) 60 Provides the "mongo" shell - JavaScript interpreter, tools and drivers for easy access to API. Support replication and sharding. Supports an aggregation framework, mapreduce, Hadoop plugin. Document size Max 16MB -> GridFS to store big data + metadata.

61 Web UI Ports for Users 61 Daemon Default Port Configuration parameter NameNode dfs.http.address DataNode dfs.datanode.http.address Secondary NameNode dfs.secondary.http.address Backup/Checkpoint Node dfs.backup.http.address JobTracker mapred.job.tracker.http.address TaskTracker mapred.task.tracker.http.address

62 tation techarticle/dm-1209hadoopbigdata/ 62

63 Resources SUSE Big Data website SUSE Big Data Flyer se_foundation_for_big_data_solution.pdf SUSE Big Data Contacts Business: Frank Rego Technical: Peter Linnell

64 64 Corporate Headquarters (Worldwide) Join us on: Maxfeldstrasse Nuremberg Germany

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