Emerging Trends in Big DataTU-20008

Peter LinnellBig Data Team @ SUSE Apache Bigtop PMC plinnell@suse.complinnell@apache.org

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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

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Dilbert on Big Data

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Hype Cycle

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Linux is the Foundation for Big Data

Scale

Low Cost

Commodity Hardware

No Lock In

“Coopetition”

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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 :-)

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Big Data Challenges

Existing data workflows are siloed

Data is siloed – Formats, proprietary applications

Sensitive Data Concerns

Regulatory Blockages

Budget Constraints

Planning Lead Times

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Big Data Challenges

● 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.

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Examples of Big Data volumes

• 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

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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” - http://en.sap.info/big-data-in-action/82754?source=email-en-sapinfo-newsletter-20121204

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How to distinguish between several kinds of Big Data?

• 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

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Hadoop vs SQL (RDBMS)

• No predefined schema

• Fast Loading

• Simpler Data Structures

• Flexible and Agile

• Schema defined in advance

• Data transformed

• Fast Reading

• Standards/Governance

The real innovation is the capability to explore original raw data

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When to pick Hadoop vs RMDBS

• Scalablity is important

• Structured or Unstructured

• Complex Data Process

• Speed is important

• ACID Transactions

• Interactive Analytics

A sports car is faster, but a truck can carry more.

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Apache Hadoop Strengths

Huge data volumes

Unstructured data

Reliable

Scalable

Lowest cost

Open source

No hardware lock in

Batch processing

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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)

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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.

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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. 1018

Java scalability ?

Stutter affects Hadoop

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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.

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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...

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Evaluation Thoughts

Pilot Pilot Pilot...

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SUSE Big Data Partner Ecosystem

• Integrated solutions‒ SAP HANA

‒ Teradata Aster Big Analytics Appliance

• Hadoop Distributions‒ Intel

‒ Cloudera

‒ Hortonworks

‒ WANdisco

• Database‒ Intersystems CACHÉ

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Bigtop

• 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

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Why SUSE for Big Data ?

• 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.

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Why SUSE for Big Data ?

• 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.

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SUSE & Hortonworks

Joint Flyer

Partner Site

Modern Data Architecture

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SUSE Big Data Lab

• Benchmarking

• Software certification

• Integration / test

• Reference architectures

• Demo system

• Remotely accessible

Big Data Cluster in Provo UT for:

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Learn More

Visit our web site www.suse.com/solutions/platform.html#big_data

Read our whitepapers Deploying Hadoop on SLESDeploy and Manage Hadoop with SUSE Manager

Contact us bigdata@suse.com

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Questions ?

bigdata@suse.com

Corporate HeadquartersMaxfeldstrasse 590409 NurembergGermany

+49 911 740 53 0 (Worldwide)www.suse.com

Join us on:www.opensuse.org

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Appendix

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Hadoop Core Components

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Typical Hadoop Distribution

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How Hadoop Works at Its Core

Namenode

Datanodes

Rack 1 Rack 2

Datanodes

Client

Client

Write

Replication

Read

Metadata ops

Block ops

Blocks

Metadata (name, replicas, …):/home/foo/data, 3,...

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Hadoop is only one partBut an important part

• 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

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NameNode

• 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

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NameNode2

• 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)

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Secondary NameNode (master)

• 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

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DataNode

• DataNode (slave)

• JobTracker (master) / exactly one per cluster

• TaskTracker (slave) / one or more per cluster

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Running Jobs

• 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

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Running Jobs

• 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

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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

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Hadoop Core Operations – Review

Namenode

Datanodes

Rack 1 Rack 2

Datanodes

Client

Client

Write

Replication

Read

Metadata ops

Block ops

Blocks

Metadata (name, replicas, …):/home/foo/data, 3,...

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Expanding on Core Hadoop

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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 ‒ The Hadoop ingestion engine – the basis of connectors

like Teradata, Informatica, DB2 and many others.

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Oozie

• 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)

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Flume

• 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

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Mahout

• 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

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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‒ You can get a properly configured system running quickly, while

still being able to override settings as needed

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Giraph

• Iterative graph processing system built for high scalability

• Currently used at Facebook to analyze the social graph formed by users and their connections

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Apache Pig

• 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.

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Ambari

• Project goal is to develop software that simplifies Hadoop cluster management

• Provisioning a Hadoop Cluster

• Managing a Hadoop Cluster

• Monitoring a Hadoop Cluster‒ 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

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HUE – Hadoop User Experience

• 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

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R Statistical Language

● Statistical Language – Open Source Licensed

● Similar to Octave or Mathlab

● Not currently packaged for SLES or openSUSE

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Shark/Spark

• 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.

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Zookeeper

• An orchestration stack.

• Centralized service for: ‒ Maintaining configuration information

‒ Naming

‒ Providing distributed synchronization

‒ Delivering group services.

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NoSQL

Cassandra

• 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

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NoSQL (cont)

Accumulo

• 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..

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NoSQL (cont)

MongoDB

• 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.

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NoSQL (cont - MongoDB)

• 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.

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Web UI Ports for Users

• Daemon Default Port Configuration parameter

• NameNode 50070 dfs.http.address

• DataNode 50075 dfs.datanode.http.address

• Secondary NameNode 50090 dfs.secondary.http.address

• Backup/Checkpoint Node 50105 dfs.backup.http.address

• JobTracker 50030 mapred.job.tracker.http.address

• TaskTracker 50060 mapred.task.tracker.http.address

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http://bigdatauniversity.com/

https://ccp.cloudera.com/display/DOC/Documentation

http://thecloudtutorial.com/hadoop-tutorial.html

http://www.saphana.com/community/learn

http://developer.yahoo.com/hadoop/tutorial/

http://www.ibm.com/developerworks/data/library/techarticle/dm-1209hadoopbigdata/

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Resources

• SUSE Big Data website‒ https://www.suse.com/solutions/platform.html#big_data

• SUSE Big Data Flyer‒ http://www.novell.com/docrep/2013/03/suse_linux_enterpri

se_foundation_for_big_data_solution.pdf

• SUSE Big Data Contacts‒ Business: Frank Rego frego@suse.com

‒ Technical: Peter Linnell plinnell@suse.com

Corporate HeadquartersMaxfeldstrasse 590409 NurembergGermany

+49 911 740 53 0 (Worldwide)www.suse.com

Join us on:www.opensuse.org

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