Distributed RDF data store on
HBase.
Project By:
• Anuj Shetye
• Vinay Boddula
Project Overview
Introduction
Motivation
HBase
Our work
Evaluation
Related work.
Future work and conclusion.
Introduction
As RDF datasets goes on increasing, therefore size of RDF
is much larger than traditional graph
Cardinality of vertex and edges is much larger.
Therefore large data stores are required for following reasons
Fast and efficient querying .
Scalability issues.
Motivation
Research has been done to map RDF dataset onto relational
databases
example: Virtuoso, Jena SDB.
But dataset is stored centrally i.e. on one server.
Examples:
Jena SDB map RDF triple in relational database.
– Scalability
Some try to store RDF data as a large graph but on single
node example Jena TDB– Scalability
Hbase is an open source
distributed sorted map
datastore.
modelled on google big table.
Contd...
Hbase is a
No SQL datbase.
High Scalability , Highly Fault Tolerant.
Fast Read/Write
Dynamic Database
Hadoop and other apps integrated.
Column family oriented data layout.
Max datasize : ~1 PB.
Read/write limits millions of queries per second.
Who uses Hbase/Bigtable
Adobe, Facebook, Twitter, Yahoo, Gmail, Google maps etc.
Hadoop EcoSystem
Src : cloudera
Our Project
Our project to create a distributed data storage capability for RDF
schema using Hbase .
We developed a system which takes the Ntriple file of an RDF
graph as an input and stores the triples in Hbase as a Key value
pair using Map reduce jobs.
The schema is simple
we create column families of each predicates
subjects as Row keys
objects as the values
System Architecture
MR
Job
I/p File
Mapper
MR job
MR
Job
Hbase Data
store
Data Model
Logical view as ‘Records’
Row key
Data
Anuj
hasAdvisor : {‘Dr. Miller’}
workedFor: {‘UGA’}
Vinay
hasAdvisor : {‘Dr.Ramaswamy’}
hasPapers : {‘Paper 1’,’Paper 2’}
workedFor: {‘IBM’ , ‘UGA’}
Data Model contd..
Physical Model
hasAdvisor Column family
Row Key
Column key Timestam
p
value
Anuj
hasAdvisor
T1
Dr. Miller
Vinay
hasAdvisor
T2
Dr.Ramaswamy
hasPaper Column family
Row Key
Column key Timestamp value
Vinay
hasPaper
T2
Paper1
Vinay
hasPaper
T1
Paper2
workedFor Column family
Row Key
Column key
Timestamp
value
Anuj
workedFor
T1
‘UGA’
Vinay
workedFor
T3
‘UGA’
Vinay
workedFor
T2
‘IBM’
Two major issues can be solved using Hbase
Data insertion
Data updation
Versioning possible (Timestamps).
Bulk loading of data.
Two types
complete bulk load (hbase File Formatter, our
approach )
Incremental bulk load
Evaluations
We talk about it during the demo
Related Work.
CumulusRDF: Linked Data Management on Nested KeyValue Stores appeared in SSWS 2011 works on distributed
key value indexing on data stores they used Casandra as the
data store.
Apache Casandra is currently capable of storing rdf data and
has an adapter to store data in a distributed management
system.
Future Work and Conclusion
Our future work lies in developing an efficient interface for
sparql as querying with SQL like HIVE is slower in Hbase.
The testing of the system was done on single node, therefore
testing it on multiple nodes would be an ultimate test of
efficiency .
Questions ??