LODOP - Multi-Query Optimization for Linked Data Profiling Queries

LODOPMulti-Query Optimization for Linked Data Profiling Queries

Anja Jentzsch (@anjeve), Benedikt Forchhammer, Felix Naumann Hasso Plattner Institute, Potsdam, Germany

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1st International Workshop on Dataset Profiling & Federated Search for Linked Data (PROFILES2014), ESWC 2014

2014/05/26

LODOP - Multi-Query Optimization for Linked Data Profiling Queries. A. Jentzsch. PROFILES2014, ESWC2014.

1. Challenges of Linked Data Profiling 2. Profiling Tasks 3. LODOP 4. Multi-Query Optimizations

OUTLINE


LINKED DATA PROFILING

• Metadata often not available • e.g. statistical information on predicates, classes, vocabularies, value

patterns, property co-occurrence, … • Data registries, VoiD, and Semantic Sitemaps provide only basic

information. e.g., description, author & license information, estimated triple and link count !

• Use cases requiring metadata • Query optimization • Data cleansing • Data integration • Schema induction !

• Data profiling: methods for computing metrics / metadata for datasets


TRADITIONAL VS LINKED DATA PROFILING

• State of the art data profiling • Based on columns • Assumes well-defined semantics • Expects regular data !

• Heterogeneity on the Web of Data • Diverse sources • Diverse structures • Diverse views


CHALLENGES OF LD PROFILING

• Heterogeneity • Nested graphs Makes reasoning difficult • Loose structure Things have different predicate sets • Incomplete Missing property definitions • Poorly formatted Property types used inconsistently • Inconsistent Multiple representations claim opposite things !

• Existing (relational) data profiling tools don’t work !• Volume of data

• Requires parallelization


LODOP - CONTRIBUTIONS

• Implementation of 15 profiling tasks as Apache Pig scripts (56 scripts) • System for executing, benchmarking and optimizing data profiling

scripts with Apache Pig on Hadoop • Development and evaluation of 3 multi-script optimization rules !• Apache Pig:

• Platform for analyzing large datasets • High-level language: Pig Latin • Scripts executed on Hadoop / MapReduce


PROFILING TASKS

• Groupings • e.g. by resource, class, property type, language, vocabulary, … !

• Tasks • Number of triples • Average number of triples per resource • Average number of triples per object URI • Average number of triples per context URL • Number of property types • Average number of property values • Number of resources • Number of inlinks / outlinks

• Number of context URLs • Number of context PLDs • Property co-occurrence • Inverse Properties • URI-Literal ratio • Property value ranges • Average value length


DATASETS STATISTICS

!!!!!!!!!!!* source: BTC 2012 dataset ** WDC = Web Data Commons *** EUNIS = European Environment Agency !

Statistics for 1M triples! DBpedia*! Freebase*! WDC RDFa**!

EUNIS Species***!

Number of resources! 169,035! 226,834! 168,736! 65,843!

Avg. number of triples per resource! 5.9! 4.4! 5.9! 15.2!

Number of classes! 19,585! 1,928! 61! 1!

Number of property types! 7,844! 2,748! 477! 16!

Number of URIs! 519,692! 642,183! 174,317! 407,418!

Number of inlinks! 207,712! 192,179! 35,329! 78,377!

Number of literals! 480,279! 357,817! 825,564! 592,582!

Avg. number of property values! 127.5 363.9! 2096.2! 62,500.0!


PERFORMANCE EVALUATION


PERFORMANCE EVALUATION

• 10-15s scheduling overhead per MapReduce job (~3.4 jobs per script)

• Earlier MapReduce jobs have longer runtimes • Earlier jobs handle more data ⇒ more HDFS activity

• Most scripts scale linearly • Most scripts reduce amount of data in workflow • Exceptions e.g. property co-occurrence scripts


OPTIMIZATION GOALS

• Optimize concurrent execution of multiple scripts • Reduce number of operators • Reduce data flow between operators


NUMBER OF INSTANCES (PIG)


LODOP - SYSTEM OVERVIEW


MULTI-QUERY OPTIMIZATION

1. Merging identical operators 2. Combining FILTER operators 3. Combining FOREACH operators


• Merging all logical plans into one master plan • Allows parallel execution • Reduces runtime to 25-30% of sequential execution !

STEP 0: MASTER PLAN


1. MERGING IDENTICAL OPERATORS

Number of property types per class!

URI Literal Ratio per class!




1. Identify and compare sibling operators

2. Merge matching siblings





1. Identify and compare sibling operators

2. Merge matching siblings



• Number of operators reduced from 365 to 267 • Number of MapReduce jobs reduced from 176 to 140 • Frees up cluster resources • Prerequisite step for other optimisations • Restricts parallelism





2. COMBINING FILTER OPERATORS



1. Create combined FILTER operator

2. Rearrange original FILTER operators

3. Remove redundant operators














3. COMBINING FOREACH OPERATORS


1. Create combined FOREACH operator

2. Replace with simple projections

3. Remove redundant projection



1. Create combined FOREACH operator

2. Replace with simple projections

3. Remove redundant projection





ALL OPTIMIZATIONS


SUMMARY

• Optimizations reduce • Number of operations • Number of MapReduce jobs • Data flow between operators → less HDFS I/O → Improved execution time

• Reduces execution time by 70% • … but rules should not be applied in all cases

• More advanced (cost-based) approach is needed


FUTURE WORK

• Additional logical optimization rules • Ignore projections if it allows further merging of operators

• Advanced optimization strategies • Cost-based approach could use previous profiling results (e.g.

cardinalities) → on-the-go • Materialization of intermediate results

• Materialize common subsets, e.g. only triples with typed object values for later scripts


http://github.com/bforchhammer/lodop/ !

@anjeve [email protected]

http://github.com/bforchhammer/lodop/

mailto:[email protected]

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LODOP - Multi-Query Optimization for Linked Data Profiling Queries