1. MYSQL AND HADOOP
INTEGRATION

2. STRUCTURED AND
UNSTRUCTURED DATA
•Structured Data (Deductive Logic)
• Analysis of defined relationships
• Defined Data Architecture
• SQL Compliant for fast processing with certainty
• Precision, Speed
•Unstructured Data (Inductive Logic)
• Hypothesis testing against unknown relationships
• Unknown (being less than 100% certainty)
• Iterative analysis to a level-of-certainty
• Open standards and tools
• Extremely high rate of change in processing/tooling options
• Volume, Speed

3. STRUCTURED DATA:
RDBMS
•Capabilities
• Defined Data Architecture/Structured Schema
• Data Integrity
• ACID Compliant
• Atomicity - Requires that each transaction is "all or nothing"
• Consistency - Any transaction will bring the database from one valid state
to another
• Isolation - All transactions are consistent as if they were issued serially
• Durability - Once the transaction is committed it persists
• Real-time processing and analysis against known relationships
•Limitations
• Comparatively static data architecture
• Requires defined data architecture for all data stored
• Relatively smaller, defined, more discrete data sets

4. UNSTRUCTURED DATA:
NOSQL
•Capabilities
• Key Value lookup
• Designed for fast single row lookup
• Loose Schema designed for fast lookup
• MySQL NoSQL Interface
• Used to augment Big Data solutions
•Limitations
• Not designed for Analytics
• Does not support 2 – 3 of the V for Big Data
• On its own, NoSQL is not considered Big Data

5. UNSTRUCTURED DATA:
HADOOP
•Capabilities
• High data load with different data formats
• Allows discovery and hypothesis testing against large data sets
• Near Real-time processing and analysis against unknown
relationships
•Limitations
• Not ACID compliant
• No transactional consistency
• High latency system
• Not designed for real time lookups
• Limited BI tool integration

6. WHAT IS HADOOP?
•What is Hadoop?
• Fastest growing, commercial Big Data Technology
• Basic Composition:
• Mapper
• Reducer
• Hadoop File System (HDFS)
• Approx 30 tools/subcomponents in the eco-system
• Primarily produced so developers and admin’s do not have to write raw
map/reduce code in Java
•Systems Architecture:
• Linux
• Commodity x86 Servers
• JBOD (standard block size 64-128MB)
• Virtualization not recommended due to high I/O requirements
• Open Source Project:
• http://hadoop.apache.org/

7. HADOOP: QUICK HISTORY
•Map Reduce Theory Paper:
• Published 2004
• Jeffrey Dean and Sanjay Ghemawat
• Foundation for GFS (Google File System)
• Problem:
• Ingest and search large data sets
•Hadoop:
• Doug Cutting, Cloudera (Yahoo)
• Lucene (1999) – indexing large files
• Nutch (2004) – search massive amounts of web data
• Hadoop (2007) – first release 2007, started in 2005

8. WHY IS HADOOP SO
POPULAR?
•Store everything regardless
• Analyze Now, or analyze Later
•Schema On-Read methodology
• Allows you to store all the data and determine how to use it later
•Low cost, scale out infrastructure
• Low cost hardware and large storage pools
• Allows for more of a load-it and forget-it approach
•Usage
• Sentiment analysis
• Marketing campaign analysis
• Customer churn modeling
• Fraud detection
• Research and Development
• Risk Modeling

9. HADOOP 1.0

10. MAP/REDUCE
•Programming and execution
framework
•Taken from functional programming
• Map – operate on every element
• Reduce – combine and aggregate results
•Abstracts storage, concurrency,
execution
• Just write two Java functions
• Contrast with MPI

11. HDFS
•Based on GFS
•Distributed, fault-tolerant filesystem
•Primarily designed for cost and scale
• Works on commodity hardware
• 20PB / 4000 node cluster at Facebook

12. HDFS ASSUMPTIONS
•Failures are common
• Massive scale means more failures
• Disks, network, node
•Files are append-only
•Files are large (GBs to TBs)
•Accesses are large and sequential

13. HDFS PRIMER
•Same concepts as the FS on your
laptop
• Directory tree
• Create, read, write, delete files
•Filesystems store metadata and data
• Metadata: filename, size, permissions, …
• Data: contents of a file

14. HDFS ARCHITECTURE
DataNode
NameNode
DataNode DataNode DataNode
Rack
1
Rack
2

15. MAP REDUCE AND HDFS
SUMMARY
•GFS and MR co-design
• Cheap, simple, effective at scale
•Fault-tolerance baked in
• Replicate data 3x
• Incrementally re-execute computation
• Avoid single points of failure
•Held the world sort record
(0.578TB/min)

16. SQOOP
• Performs
bidirectional data
transfers between
Hadoop and
almost any SQL
database with a
JDBC driver

17. FLUME
• Streaming data
collection and
aggregation
• Massive
volumes of data,
such as RPC
services, Log4J,
Syslog, etc.
Client
Client
Client
Client
Agent
Agent
Agent

18. HIVE
• Relational
database
abstraction using
a SQL like dialect
called HiveQL
• Statements are
executed as one
or more Map
Reduce Jobs
SELECT
s.word, s.freq, k.freq
FROM shakespeare
JOIN ON (s.word=
k.word)
WHERE s.freq >= 5;

19. PIG
• High-level scripting
language for executing
one or more
MapReduce jobs
• Created to simplify
authoring of
MapReduce jobs
• Can be extended with
user defined functions
emps = LOAD
'people.txt’ AS
(id,name,salary);
rich = FILTER emps BY
salary > 200000;
sorted_rich = ORDER
rich BY salary DESC;
STORE sorted_rich
INTO ’rich_people.txt';

20. HBASE
• Low-latency,
distributed,
columnar key-
value store
• Based on BigTable
• Efficient random
reads/writes on
HDFS
• Useful for frontend
applications

21. OOZIE
• Workflow engine
and scheduler
built specifically
for large-scale job
orchestration on a
Hadoop cluster

22. HUE
• Hue is an open source
web-based application for
making it easier to use
Apache Hadoop.
•Hue features
• File Browser for
HDFS
• Job
Designer/Browser for
MapReduce
• Query editors for
Hive, Pig and
Cloudera Impala
• Oozie

23. ZOOKEEPER
• Zookeeper is a distributed
consensus engine
• Provides well-defined
concurrent access
semantics:
• Leader election
• Service discovery
• Distributed locking / mutual
exclusion
• Message board / mailboxes

24. CASCADING
•Next gen software abstraction layer for
Map/Reduce
•Create and execute complex data
processing workflows
• Specifically for a Hadoop cluster using any JVM-based language
• Java
• Jruby
• Clojure
•Generally acknowledged as a better
alternative to Hive/Pig

25. MYSQL AND BIG DATA

26. CHARACTERISTICS OF BIG
DATA
•Big Data covers 4 dimensions
• Volume - 90% of all the data stored in the world has been
produced in the last 2 years
• Velocity – The ability to perform advanced analytics on
Terabytes or Petabytes of data in minutes to hours compared
to days
• Variety – Any data type from structured to unstructured data
including image files, social media, relational database
content, and text data from weblogs or sensors
• Veracity - 1 in 3 business leaders don’t trust the information
they use to make decisions. How do we ensure the results
are accurate and meaning?

27. BIG DATA CHALLENGES
• Loading web logs into MySQL
• How do you parse and keep all the Data?
• What about the variability of the Query String Parameters?
• What if the web log format changes?
• Integration of other data sources
• Social Media – Back in the early days even Facebook didn’t keep all
the data. How do we know what is important in the stream?
• Video and Image data – How do we store that type of data so we
can extract the metadata information?
• Sensor Data – Imagine all the different devices producing data and
the different formats of the data.

28. Web Servers
Ecommerce
Database
Order Processing
CRM
Operational Data
Store
Enterprise Data
Warehouse

29. Web Servers
Ecommerce
Database
Order Processing
CRM
Operational Data
Store
Enterprise Data
Warehouse

30. LIFE CYCLE OF BIG DATA
•Acquire
• Data captured at source
• Part of ongoing operational processes (Web Log, RDBMS)
•Organize
• Data transferred from operational systems to Big Data Platform
•Analyze
• Data processed in batch by Map/Reduce
• Data processed by Hadoop Tools (Hive, Pig)
• Can Pre-condition data that is loaded back into RDBMS
•Decide
• Load back into Operational Systems
• Load into BI Tools and ODS

31. MYSQL INTEGRATION WITH
THE BIG DATA LIFE CYCLE
ACQUIRE ORGANIZE
DECIDE ANALYZE
Applier
NoSQL
Sensor Logs
Web Logs
BI Tools

32. LIFE CYCLE OF BIG DATA:
MYSQL
•Acquire
• MySQL as a Data Source
• MySQL’s NoSQL
• New NoSQL API’s
• Ingest high volume, high velocity data, with veracity
• ACID guarantees not compromised
• Data Pre-processing or Conditioning
• Run Real-time analytics against new data
• Pre-process or condition data before loading into Hadoop
• For example healthcare records can be anonymized

33. LIFE CYCLE OF BIG DATA:
MYSQL
•Organize
• Data transferred in batches from MySQL tables to Hadoop using
Apache Sqoop or MySQL Applier
• With Applier, users can also invoke real-time change data
capture processes to stream new data from MySQL to HDFS as
it is committed by the client.
•Analyze
• Multi-structured, multi-sourced data consolidated and processed
• Run Map/Reduce Jobs and or Hadoop Tools (Hive, Pig, others)
•Decide
• Results loaded back to MySQL via Apache Sqoop
• Provide new data for real-time operational processes
• Provide broader, normalized data sets for BI Tool analytics

34. TOOLS: MYSQL APPLIER
•Overview
• Provides real-time replication of events between MySQL and
Hadoop
•Usage
• MySQL Applier for Hadoop uses an API (libhdfs, precompiled
with Hadoop) to connect to MySQL master
• Reads the binary log and then:
• Fetches the row insert events occurring on the master
• Decodes events, extracts data inserted into each field of the
row
• Uses content handlers to get it in required format
• Appends it to a text file in HDFS

35. TOOLS: MYSQL APPLIER
•Capabilities
• Streaming real-time updates from MySQL into Hadoop for
immediate analysis
• Addresses performance issues from bulk loading
• Exploits existing replication protocol
• Provides row-based replication
• Consumable by other tools
• Possibilities for update/delete
•Limitations
• DDL not handled
• Only row inserts

36. TOOLS: MYSQL APPLIER
Events
HDFS
MySQL Applier for Hadoop
Binlog
API
libhdfs
Binary Log
Decode
Row
Timestamp
Primary Key
Data

37. TOOLS: MYSQL NOSQL
•Overview
• NoSQL interfaces directly to the InnoDB and MySQL Cluster (NDB)
storage engines
• Bypass the SQL layer completely
• Without SQL parsing and optimization, Key-Value data can be written
directly to MySQL tables up to 9x faster, while maintaining ACID
guarantees.
•Usage
• Key Value Definition/Lookup
• Designed for fast single row lookup
• Loose Schema designed for fast lookup
• Data Pre-processing or Conditioning
• Run Real-time analytics against new data
• Pre-process or condition data before loading into Hadoop
• For example healthcare records can be anonymized

38. TOOLS: MYSQL NOSQL
•Capabilities
• Ingest high volume, high velocity data, with veracity
• ACID guarantees are not compromised
• Single stack for RDMBS and NoSQL
• High volume KVP processing
• Single-node processing:
• 70k transactions per second
• Clustered processing:
• 650k ACID-compliant writes per sec
• 19.5M writes per sec
• Auto-sharding across distributed clusters of commodity nodes
• Shared-nothing, fault-tolerant architecture for 99.999% uptime
•Limitations
• <specify>

39. WHAT’S NEXT

40. MAHOUT
•Scalable machine learning algorithms
•Primary focus:
• Collaborative filtering
• Clustering
• Classification

41. SPARK
•In-memory cluster computing
• Allows user programs to load data into a cluster's memory
and query it repeatedly
•Streaming Architecture

42. MESOS
•Cluster manager that manages
resources across distributed systems
•Allows finite control over system
resources
• Stateful versus stateless (i.e. traditional virtualization
architecture)

43. SENTRY
•Granular role-based access control
to data
•Addresses both data and metadata

44. Questions?