MYSQL AND HADOOP INTEGRATION

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

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

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

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

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/

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

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

HADOOP 1.0

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

HDFS

•Based on GFS

•Distributed, fault-tolerant filesystem

•Primarily designed for cost and scale• Works on commodity hardware• 20PB / 4000 node cluster at Facebook

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

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

HDFS ARCHITECTURE

DataNode

NameNode

DataNode DataNode DataNode

Rack 1

Rack 2

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)

SQOOP

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

FLUME

• Streaming data collection and aggregation

• Massive volumes of data, such as RPC services, Log4J, Syslog, etc.

Client

Client

Client

Client

Agent

Agent

Agent

HIVE

• Relational database abstraction using a SQL like dialect called HiveQL

• Statements are executed as one or more Map Reduce Jobs

SELECTs.word, s.freq, k.freq

FROM shakespeare JOIN ON (s.word= k.word)WHERE s.freq >= 5;

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';

HBASE

• Low-latency, distributed, columnar key-value store

• Based on BigTable

• Efficient random reads/writes on HDFS

• Useful for frontend applications

OOZIE

• Workflow engine

and scheduler

built specifically

for large-scale job

orchestration on a

Hadoop cluster

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

ZOOKEEPER

• Zookeeper is a distributed

consensus engine

• Provides well-defined

concurrent access

semantics:

• Leader election

• Service discovery

• Distributed locking / mutual

exclusion

• Message board / mailboxes

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

MYSQL AND BIG DATA

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?

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.

Web ServersEcommerce

Database

Order Processing

CRM

Operational Data Store

Enterprise Data Warehouse

Web ServersEcommerce

Database

Order Processing

CRM

Operational Data Store

Enterprise Data Warehouse

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

MYSQL INTEGRATION WITH THE BIG DATA LIFE CYCLEACQUIRE ORGANIZE

DECIDE ANALYZE

Applier

NoSQL

Sensor Logs

Web Logs

BI Tools

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

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

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

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

TOOLS: MYSQL APPLIER

EventsHDFS

MySQL Applier for Hadoop

Binlog API

libhdfs

Binary Log

Decode Row

Timestamp Primary Key

Data

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

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>

WHAT’S NEXT

MAHOUT

•Scalable machine learning algorithms

•Primary focus:• Collaborative filtering• Clustering• Classification

SPARK

•In-memory cluster computing • Allows user programs to load data into a cluster's memory

and query it repeatedly

•Streaming Architecture

MESOS

•Cluster manager that manages resources across distributed systems

•Allows finite control over system resources

• Stateful versus stateless (i.e. traditional virtualization architecture)

SENTRY

•Granular role-based access control to data

•Addresses both data and metadata

Questions?