Storing RDF Data in Hadoop And Retrieval

Pankil DoshiAsif Mohammed

Mohammad Farhan HusainDr. Latifur Khan

Dr. Bhavani Thuraisingham

Goal

• To build efficient storage using Hadoop for Peta-bytes of data

• To build an efficient query mechanism• Possible outcomes

– Open Source Framework for RDF– Integration with Jena

Possible Approaches

• Store RDF data in HDFS and query through Map-Reduce programming– Our current approach

• Store RDF data in HDFS and process query outside of Hadoop– Done in BIOMANTA [1] project, no details however

• Hbase– Currently being worked on by another team in

Semantic Web lab

Dataset And Queries

• LUBM [2]– Dataset generator– 14 benchmark queries– Generates data of some

imaginary universities– Used for query

execution performance comparison by many researches

Our Clusters

• 4 node cluster in Semantic Web lab• 10 node cluster in SAIAL lab

– 4 GB main memory– Intel Pentium IV 3.0 GHz processor– 640 GB hard drive

• OpenCirrus HP labs test bed– Sponsor: Andy Seaborne, HP Labs

Tasks Completed/In Progress

• Setup Hadoop cluster• Generate, preprocess & insert data• Devise algorithm to produce map-reduce code

for a SPARQL query• Code for 14 queries• Cascading output of one job to another job as

input without using hard disk

Two Storage Approaches

1. Multiple File Approach:• Dumping files as generated by LUBM generator, possibly merging some• Each Line on file Contains Subject, Predicate and Object

2. Predicate Based Approach:• Dividing Files based on Predicate• File name will be “Predicate “ name• Each line then contains only Subject and Object• On-an Average there are about 20 different type of Predicate

Common Preprocessing :-Adding Prefixeshttp://www.University10Department5:.... == U10D5:….

D0U0:Graduate20 ub:type lehigh:GraduateStudentD0U0:Graduate20 ub:memberOf lehigh:University0

D0U0:Graduate20 lehigh:GraduateStudent………

D0U0:Graduate20 lehigh:University0……

Example Of Predicate Based File division:

Filename : type

Filename : memberOf

Filename: type_GraduateStudentD0U0:Graduate20…

Filename: memberOf_UniversityD0U0:Graduate20 lehigh:University0…

Sample Query:-

PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX ub: <http://www.lehigh.edu/~zhp2/2004/0401/univ-bench.owl#> SELECT ?X WHERE {

?X rdf:type ub:Publication . ?X ub:publicationAuthor D0U0:AssistantProfessor0

}

Map Function :-Look from which file (key) the data (value) is coming and filter it according to conditions. For example:• If data is from file “type_Publication” output the pair• If data is from file “publicationAuthor_*” look for D0U0:AssistantProfessor0 as object

Reduce Function :-Look for all the required values according to condition and output the key as the result

Ex: Filter those results having both ub:Publication & D0U0:AssistantProfessor0

AlgorithmSELECT ?X, ?Y WHERE {1. ?X rdf:type ub:Chair . 2. ?Y rdf:type ub:Department . 3. ?X ub:worksFor ?Y . 4. ?Y ub:subOrganizationOf <http://www.University0.edu>} Y

1X

4Y

3X,Y

2YX

Y

Y

|E| = 4

Job 1 map output keys:1.Y – 2, 3, 4 (3 joins)Job 1 joins: 31 join left, so need more job

Variable Nodes Joins

X 1, 3 1-3

Y 2, 3, 4 2-3, 3-4, 4-2

Algorithm (contd.)

A(2, 3, 4)

X, Y

B(1)X

X

Job 2 map output key:1.X – A, B (1 Join)Job 2 joins: 1No joins left, no more jobs needed

Variable Nodes Joins

X A, B A-B

Some Query Results

Horizontal axis: Number of TriplesVertical axis: Time in milliseconds

Query Preprocessing

• Original query 2:?X rdf:type ub:GraduateStudent . ?Y rdf:type ub:University . ?Z rdf:type ub:Department . ?X ub:memberOf ?Z . ?Z ub:subOrganizationOf ?Y . ?X ub:undergraduateDegreeFrom ?Y

• Rewritten:?X rdf:type ub:GraduateStudent . ?X ub:memberOf_Department ?Z . ?Z ub:subOrganizationOf_University ?Y . ?X ub:undergraduateDegreeFrom_University ?Y

Parallel Experiment with Pig

• Script for query 2:/* Load statements */GS = LOAD ‘type_GraduateStudent‘ AS (gs_subject:chararray);MO = LOAD ‘memberOf_Department‘ AS (mo_subject:chararray, mo_object:chararray);SOF = LOAD ‘subOrganizationOf_University‘ AS (sof_subject:chararray, sof_object:chararray); UDF = LOAD ‘undergraduateDegreeFrom_University‘ AS (udf_subject:chararray, udf_object:chararray); /* Joins */ MO_UDF_GS = JOIN GS BY gs_subject, UDF BY udf_subject, MO BY mo_subject PARALLEL 8; MO_UDF_GS = FOREACH MO_UDF_GS GENERATE mo_subject, udf_object, mo_object;MO_UDF_GS_SOF = JOIN SOF BY (sof_subject, sof_object), MO_UDF_GS BY (mo_object, udf_object);MO_UDF_GS_SOF = FOREACH MO_UDF_GS_SOF GENERATE mo_subject, udf_object, mo_object; /* Store query answer */STORE MO_UDF_GS_SOF INTO ‘Query2' USING PigStorage('\t');

Parallel Experiment with Pig

• 2 jobs created for query 2• For 330 mln triples, answers in 20 mins

– Direct MapReduce approach takes 10 mins

Future Works• Run all 14 queries for 100 mln, 200 mln, … , 1 bln

triples and compare with Jena In-Memory, RDB, SDB, TDB models

• Cascading output of one job to another job as input without using hard disk

• Generic map reduce code• Proof of algorithm• Modification of algorithm for queries with

optional triple patterns• Indexing, summary statistics

References

• [1] BIOMANTA: http://www.biomanta.org/• [2] LUBM:

http://swat.cse.lehigh.edu/projects/lubm/