Things Every Oracle DBA Needs To Know About The Hadoop Ecosystem

Zohar Elkayam
www.realdbamagic.com
Twitter: @realmgic
Things Every Oracle DBA
Needs to Know about the
Hadoop Ecosystem

Who am I?
• Zohar Elkayam, CTO at Brillix
• Programmer, DBA, team leader, database trainer, public
speaker, and a senior consultant for over 18 years
• Oracle ACE Associate
• Part of ilOUG – Israel Oracle User Group
• Involved with Big Data projects since 2011
• Blogger – www.realdbamagic.com and www.ilDBA.co.il
2 http://brillix.co.il

About Brillix
• We offer complete, integrated end-to-end solutions based on best-of-
breed innovations in database, security and big data technologies
• We provide complete end-to-end 24x7 expert remote database
services
• We offer professional customized on-site trainings, delivered by our
top-notch world recognized instructors
3

Some of Our Customers
http://brillix.co.il4

Agenda
• What is the Big Data challenge?
• A Big Data Solution: Apache Hadoop
• HDFS
• MapReduce and YARN
• Hadoop Ecosystem: HBase, Sqoop, Hive, Pig and other tools
• Another Big Data Solution: Apache Spark
• Where does the DBA fits in?

The Big Data Challenge

Volume
• Big data comes in one size: Big.
• Size is measured in Terabyte (1012), Petabyte (1015),
Exabyte (1018), Zettabyte (1021)
• The storing and handling of the data becomes an issue
• Producing value out of the data in a reasonable time is an
issue

Variety
• Big Data extends beyond structured data, including semi-structured
and unstructured information: logs, text, audio and videos
• Wide variety of rapidly evolving data types requires highly flexible
stores and handling
Un-Structured Structured
Objects Tables
Flexible Columns and Rows
Structure Unknown Predefined Structure
Textual and Binary Mostly Textual

Velocity
•The speed in which data is being generated and
collected
•Streaming data and large volume data movement
•High velocity of data capture – requires rapid
ingestion
•Might cause a backlog problem

Value
Big data is not about the size of the data,
It’s about the value within the data

So, We Define Big Data Problem…
• When the data is too big or moves too fast to handle in a
sensible amount of time
• When the data doesn’t fit any conventional database
structure
• When we think that we can still produce value from that
data and want to handle it
• When the technical solution to the business need becomes
part of the problem

Big Data in Practice
•Big data is big: technological framework and
infrastructure solutions are needed
•Big data is complicated:
• We need developers to manage handling of the data
• We need devops to manage the clusters
• We need data analysts and data scientists to produce
value

Possible Solutions: Scale Up
• Older solution: using a giant server with a lot of resources
(scale up: more cores, faster processers, more memory) to
handle the data
• Process everything on a single server with hundreds of CPU
cores
• Use lots of memory (1+ TB)
• Have a huge data store on high end storage solutions
• Data needs to be copied to the processes in real time, so
it’s no good for high amounts of data (Terabytes to
Petabytes)

Another Solution: Distributed Systems
•A scale-out solution: let’s use distributed systems:
use multiple machine for a single job/application
•More machines means more resources
• CPU
• Memory
• Storage
•But the solution is still complicated: infrastructure
and frameworks are needed

Distributed Infrastructure Challenges
• We need Infrastructure that is built for:
• Large-scale
• Linear scale out ability
• Data-intensive jobs that spread the problem across clusters of server
nodes
• Storage: efficient and cost-effective enough to capture and store
terabytes, if not petabytes, of data
• Network infrastructure that can quickly import large data sets and
then replicate it to various nodes for processing
• High-end hardware is too expensive - we need a solution that uses
cheaper hardware

Distributed System/Frameworks Challenges
•How do we distribute our workload across the
system?
•Programming complexity – keeping the data in sync
•What to do with faults and redundancy?
•How do we handle security demands to protect
highly-distributed infrastructure and data?

A Big Data Solution:
Apache Hadoop
20

Apache Hadoop
•Open source project run by Apache Foundation (2006)
•Hadoop brings the ability to cheaply process large
amounts of data, regardless of its structure
•It Is has been the driving force behind the growth of
the big data industry
•Get the public release from:
• http://hadoop.apache.org/core/

Original Hadoop Components
•HDFS (Hadoop Distributed File System) – distributed
file system that runs in clustered environments
•MapReduce – programming paradigm for running
processes over clustered environments
•Hadoop main idea: let’s distribute the data to many
servers, and then bring the program to the data

Hadoop Benefits
•Designed for scale out
•Reliable solution based on unreliable hardware
•Load data first, structure later
•Designed for storing large files
•Designed to maximize throughput of large scans
•Designed to leverage parallelism
•Solution Ecosystem

What Hadoop Is Not?
• Hadoop is not a database – it does not a replacement for
DW, or for other relational databases
• Hadoop is not for OLTP/real-time systems
• Very good for large amounts, not so much for smaller sets
• Designed for clusters – there is no Hadoop monster server
(single server)

Hadoop Limitations
•Hadoop is scalable but it’s not fast
•Some assembly may be required
•Batteries are not included (DIY mindset) – some
features needs to be developed if they’re not available
•Open source license limitations apply
•Technology is changing very rapidly

Original Hadoop 1.0 Components
• HDFS (Hadoop Distributed File System) – distributed file
system that runs in a clustered environment
• MapReduce – programming technique for running
processes over a clustered environment

Hadoop 2.0
• Hadoop 2.0 changed the Hadoop conception and
introduced a better resource management concept:
• Hadoop Common
• HDFS
• YARN
• Multiple data processing
frameworks including
MapReduce, Spark and
others

HDFS is...
• A distributed file system
• Designed to reliably store data using commodity hardware
• Designed to expect hardware failures and still stay
resilient
• Intended for larger files
• Designed for batch inserts and appending data (no
updates)

Files and Blocks
•Files are split into 128MB blocks (single unit of
storage)
• Managed by NameNode and stored on DataNodes
• Transparent to users
•Replicated across machines at load time
• Same block is stored on multiple machines
• Good for fault-tolerance and access
• Default replication factor is 3

HDFS is Good for...
•Storing large files
• Terabytes, Petabytes, etc...
• Millions rather than billions of files
• 128MB or more per file
•Streaming data
• Write once and read-many times patterns
• Optimized for streaming reads rather than random reads

HDFS is Not So Good For...
• Low-latency reads / Real-time application
• High-throughput rather than low latency for small chunks of
data
• HBase addresses this issue
• Large amount of small files
• Better for millions of large files instead of billions of small files
• Multiple Writers
• Single writer per file
• Writes at the end of files, no-support for arbitrary offset

Using HDFS in Command Line

How Does HDFS Look Like (GUI)

Interfacing with HDFS

MapReduce is...
• A programming model for expressing distributed
computations at a massive scale
• An execution framework for organizing and performing
such computations
• MapReduce can be written in Java, Scala, C, Payton, Ruby
and others
• Concept: Bring the code to the data, not the data to the
code

The MapReduce Paradigm
• Imposes key-value input/output
• We implement two main functions:
• MAP - Takes a large problem and divides into sub problems and
performs the same function on all sub-problems
Map(k1, v1) -> list(k2, v2)
• REDUCE - Combine the output from all sub-problems (each key goes to
the same reducer)
Reduce(k2, list(v2)) -> list(v3)
• Framework handles everything else (almost)

Divide and Conquer

YARN
•Takes care of distributed processing and coordination
•Scheduling
• Jobs are broken down into smaller chunks called tasks
• These tasks are scheduled to run on data nodes
•Task Localization with Data
• Framework strives to place tasks on the nodes that host
the segment of data to be processed by that specific task
• Code is moved to where the data is

YARN
•Error Handling
• Failures are an expected behavior so tasks are
automatically re-tried on other machines
•Data Synchronization
• Shuffle and Sort barrier re-arranges and moves data
between machines
• Input and output are coordinated by the framework

Submitting a Job
•Yarn script with a class argument command launches
a JVM and executes the provided Job
$ yarn jar HadoopSamples.jar mr.wordcount.StartsWithCountJob
/user/sample/hamlet.txt
/user/sample/wordcount/

Resource Manage: UI

Application View

Hadoop Main Problems
• Hadoop MapReduce Framework (not MapReduce
paradigm) had some major problems:
• Developing MapReduce was complicated – there was more
than just business logics to develop
• Transferring data between stages requires the intermediate
data to be written to disk (and than read by the next step)
• Multi-step needed orchestration and abstraction solutions
• Initial resource management was very painful – MapReduce
framework was based on resource slots

Extending Hadoop
The Hadoop Ecosystem

Improving Hadoop: Distributions
• Core Hadoop is complicated so some tools and solution
frameworks were added to make things easier
• There are over 80 different Apache projects for big data
solution which uses Hadoop (and growing!)
• Hadoop Distributions collects some of these tools and
release them as a complete integrated package
• Cloudera
• HortonWorks
• MapR
• Amazon EMR
47

Common HADOOP 2.0 Technology Eco System

Improving Programmability
•MapReduce code in Java is sometime tedious, so
different solutions came to the rescue
• Pig: Programming language that simplifies Hadoop
actions: loading, transforming and sorting data
• Hive: enables Hadoop to operate as data warehouse using
SQL-like syntax
• Spark and other frameworks

Pig
• Pig is an abstraction on top of Hadoop
• Provides high level programming language designed for data
processing
• Scripts converted into MapReduce code, and executed on the
Hadoop Clusters
• Makes ETL/ELT processing and other simple MapReduce
easier without writing MapReduce code
• Pig was widely accepted and used by Yahoo!, Twitter, Netflix,
and others
• Often replaced by more up-to-date tools like Apache Spark

Hive
• Data Warehousing Solution built on top of Hadoop
• Provides SQL-like query language named HiveQL
• Minimal learning curve for people with SQL expertise
• Data analysts are target audience
• Early Hive development work started at Facebook in 2007
• Hive is an Apache top level project under
Hadoop
• http://hive.apache.org

Hive Provides
•Ability to bring structure to various data formats
•Simple interface for ad hoc querying, analyzing and
summarizing large amounts of data
•Access to files on various data stores such as HDFS
and HBase
•Also see: Apache Impala (mainly in Cloudera)
52

Databases and DB Connectivity
•HBase: Online NoSQL Key/Value wide-column
oriented datastore that is native to HDFS
•Sqoop: a tool designed to import data from and export
data to relational databases (HDFS, Hbase, or Hive)
•Sqoop2: Sqoop centralized service (GUI, WebUI,
REST)
53

HBase
• HBase is the closest thing we had to
database in the early Hadoop days
• Distributed key/value with wide-column oriented NoSQL
database, built on top of HDFS
• Providing Big Table-like capabilities
• Does not have a query language: only get, put, and scan
commands
• Often compared with Cassandra
(non-Hadoop native Apache project)

When Do We Use HBase?
•Huge volumes of randomly accessed data
•HBase is at its best when it’s accessed in a
distributed fashion by many clients (high
consistency)
•Consider HBase when we are loading data by key,
searching data by key (or range), serving data by key,
querying data by key or when storing data by row that
doesn’t conform well to a schema.
55

When NOT To Use HBase
•HBase doesn’t use SQL, don’t have an optimizer,
doesn’t support transactions or joins
•HBase doesn’t have data types
•See project Apache Phoenix for better data structure
and query language when using HBase
56

Sqoop and Sqoop2
• Sqoop is a command line tool for moving data
from RDBMS to Hadoop. Sqoop2 is a centralized
tool for running sqoop.
• Uses MapReduce load the data from relational database to HDFS
• Can also export data from HBase to RDBMS
• Comes with connectors to MySQL, PostgreSQL, Oracle, SQL
Server and DB2.
$bin/sqoop import --connect
'jdbc:sqlserver://10.80.181.127;username=dbuser;password=dbpasswd;database=tpch'
--table lineitem --hive-import
$bin/sqoop export --connect
'jdbc:sqlserver://10.80.181.127;username=dbuser;password=dbpasswd;database=tpch'
--table lineitem --export-dir /data/lineitemData

Improving Hadoop – More Useful Tools
•For improving coordination: Zookeeper
•For improving scheduling/orchestration: Oozie
•Data Storing in memory: Apache Impala
•For Improving log collection: Flume
•Text Search and Data Discovery: Solr
•For Improving UI and Dashboards: Hue and Ambari

Improving Hadoop – More Useful Tools (2)
•Data serialization: Avro and Parquet
•Data governance: Atlas
•Security: Knox and Ranger
•Data Replication: Falcon
•Machine Learning: Mahout
•Performance Improvement: Tez
•And there are more…

Is Hadoop the Only Big Data Solution?
• No – There are other solutions:
• Apache Spark and Apache Mesos frameworks
• NoSQL systems (Apache Cassandra, CouchBase, MongoDB
and many others)
• Stream analysis (Apache Kafka, Apache Storm, Apache Flink)
• Machine learning (Apache Mahout, Spark MLlib)
• Some can be integrated with Hadoop, but some are
independent

Another Big Data Solution: Apache Spark
•Apache Spark is a fast, general engine for
large-scale data processing on a cluster
•Originally developed by UC Berkeley in 2009 as a
research project, and is now an open source Apache
top level project
•Main idea: use the memory resources of the cluster
for better performance
•It is now one of the most fast-growing project today

The Spark Stack

Okay, So Where Does the DBA Fits In?
• Big Data solutions are not databases. Databases are
probably not going to disappear, but we feel the change
even today: DBA’s must be ready for the change
• DBA’s are the perfect candidates to transition into Big Data
Experts:
• Have system (OS, disk, memory, hardware) experience
• Can understand data easily
• DBA’s are used to work with developers and other data users

What DBAs Needs Now?
•DBA’s will need to know more programming: Java,
Scala, Python, R or any other popular language in the
Big Data world will do
•DBA’s needs to understand the position shifts, and
the introduction of DevOps, Data Scientists, CDO etc.
•Big Data is changing daily: we need to learn, read, and
be involved before we are left behind…

Summary
• Big Data is here – it’s complicated and RDBMS does not fit
anymore
• Big Data solutions are evolving Hadoop is an example for
such a solution
• Spark is very popular Big Data solution
• DBA’s need to be ready for the change: Big Data solutions
are not databases and we make ourselves ready

Thank You
Zohar Elkayam
twitter: @realmgic
Zohar@Brillix.co.il
www.realdbamagic.com

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