HADOOPDB: AN ARCHITECTURAL HYBRID OF
MAPREDUCE AND DBMS TECHNOLOGIES FOR
ANALYTICAL WORKLOADS
1
By: Muhammad Mudassar
MS-IT-8
WHAT IS GOING ON
Data analysis techniques are changing
 Enterprises moving to cheaper commodity
hardware
 MPP (Massively Parallel Processing) architecture
inside “Clods”
 Analytical data is exploding
 What technology for data analysis?

Parallel databases
 MapReduce-based systems

2
THE TWO TECHNOLOGIES

Parallel Databases
High performance and
efficiency
 Bad scores in fault
tolerance and run in
heterogeneous
environment
 Few known
deployments over 100
nodes


MapReduce-based
systems
Designed to scale over
1000 of nodes
 Fault tolerant and
capable to run in
heterogeneous
environment
 Biggest issue with
MapReduce is
performance

3
HADOOPDB
A hybrid system to handle demands of data
intensive applications
 Advantages

Scalability of MapReduce
 Performance and efficiency of parallel databases


Completely build on open source free to use
components
PostgreSQL as database layer
 Hadoop MapReduce is used


Amazon’s EC2 cloud is used
4
DESIRED PROPERTIES




Performance
 A primary characteristic that commercial database
systems use to distinguish themselves
Fault tolerance
 Measured differently for analytical DBMS and
transactional DBMS.
 For analytical DBMS query restart is to be avoided
Ability to run in heterogeneous environment
 Nearly impossible to get homogeneous performance
from 100 or 1000 nodes
Flexible query interface
 Allow user to write user defined functions (UDFs) and
queries that should be parallelized automatically.
5
ARCHITECTURE OF HADOOPDB
6
THE HADOOP FRAMEWORK



Hadoop consists of 2 layers
 Data storage layers which is Hadoop Distributed File System
(HDFS)
 Data processing or the MapReduce framework
HDFS
 Block-structure file system managed by NameNode
 Data handled by DataNodes
MapReduce framework
 Master-slave architecture based on JobTracker &
TaskTracker
 JobTracker manages job like assignment keeping track of jobs
and load balancing
 TaskTrackers perform assigned Map or Reduce tasks assigned
to them
7
THE HADOOPDB’S COMPONENTS

1.
HadoopDB extends Hadoop framework with four
components
Database connector


2.
Catalog

3.
Interface between DBMS and TaskTacker
Database is similar to data blocks in HDFS
Maintain information about database
 Database location, driver class meta data like replica
location partitioning property
Data Loader



Globally partition the data on given key
Break single node data into chunks
Load the chunks to the database
8
THE HADOOPDB’S COMPONENTS
1.
SQL to MapReduce to SQL (SMS) Planner


HadoopDB provide front end to process SQL queries
SMS planner extends Hive






Parser transforms query to abstract syntax tree
Get table schema information from catalog
Logical plan generator creates query plan
Optimizer breaks up plan to Map or Reduce phases
Executable plan generated for one or more MapReduce jobs
SMS tries to push maximum work to database layer
9
EVALUATING HADOOPDB

Compare HadoopDB to
Hadoop
 Parallel databases (Vertica, DBMS-X)


Features

Performance
HadoopDB is expected to approach
performance of parallel databases

Scalability
HadoopDB would be scalable
10
DATA LOAD
11
QUERIES RESULTS
12
SCALABILITY
HadoopDB and
Hadoop take
advantage of run time
scheduling by
splitting data
 Parallel databases
restart entire query
on node failure or wait
for slowest node

13
CONCLUSION

HadoopDB





Is a Hybrid system
Scales better then parallel databases
Fault tolerant
Approaches the performance of parallel databases
Free and opensource
14