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Benchmarks

Rigorous benchmarking supports the team’s development activities by providing rapid feedback on complex changes and a regular benchmark process allows the team to discover and address performance regressions during day-to-day development, well in advance of any releases.

The crux-bench sub-project contains a number of benchmarking suites that are run nightly on standard AWS infrastructure (m5.xlarge instances via CloudFormation) to support regression testing of new development and validate all performance improvement changes.

These suites cover a wide range of analysis at a variety of scale factors, including: graph query performance, bitemporal query performance, ingestion performance (throughput & latency), and disk usage.

Officially published benchmark results are forthcoming, but in the meantime please feel welcome to explore crux-bench independently and get in touch if you need any assistance.

Query benchmarks dashboard example
Figure 1. An example screenshot of the query benchmarks dashboard (May 2021)
Ingest benchmarks dashboard example
Figure 2. An example screenshot of the ingest benchmarks dashboard (May 2021)

For an example of independent benchmarking by the community, see the "Crux Connector" performance analysis by the Linux Foundation’s Egeria project, which compares Crux against an existing JanusGraph Connector.

TPC-H

Decision Support Benchmark

http://www.tpc.org/tpch/
The TPC-H is a decision support benchmark. It consists of a suite of business oriented ad-hoc queries and concurrent data modifications. The queries and the data populating the database have been chosen to have broad industry-wide relevance. This benchmark illustrates decision support systems that examine large volumes of data, execute queries with a high degree of complexity, and give answers to critical business questions.

See the TPC-H test fixture for details.

WatDiv SPARQL Tests

Waterloo SPARQL Diversity Test Suite
https://dsg.uwaterloo.ca/watdiv/

WatDiv has been developed to measure how an RDF data management system performs across a wide spectrum of SPARQL queries with varying structural characteristics and selectivity classes.

Benchmarking is performed nightly against the WatDiv test suite. These tests demonstrate comprehensive RDF subgraph matching. Note that Crux does not natively implement the RDF specification and only a simplified subset of the RDF tests have been translated for use in Crux. Crux generates Datalog, which shares many of the same properties as SPARQL, directly from the SPARQL definitions.

Subgraph matching is a fundamental kind of graph query and is a common mechanism for computing query results in SPARQL: RDF triples in both the queried RDF data and the query pattern are interpreted as nodes and edges of directed graphs, and the resulting query graph is matched to the data graph using variables as wildcards.

The diversity and scale of subgraph matching in the WatDiv tests provides a particularly helpful measure of end-to-end performance across many dimensions.

An example of a subgraph matching query in SPARQL from the WatDiv test suite
SELECT ?v0 ?v3 ?v4 ?v8 WHERE {
	?v0	sorg:legalName	?v1 .
	?v0	gr:offers	?v2 .
	?v2	sorg:eligibleRegion	wsdbm:Country5 .
	?v2	gr:includes	?v3 .
	?v4	sorg:jobTitle	?v5 .
	?v4	foaf:homepage	?v6 .
	?v4	wsdbm:makesPurchase	?v7 .
	?v7	wsdbm:purchaseFor	?v3 .
	?v3	rev:hasReview	?v8 .
	?v8	rev:totalVotes	?v9 .
}
WatDiv query visualisation
Figure 3. A diagram of the graph of the same query

DataScript

Crux’s test suite incorporates a large number of DataScript's query tests, demonstrating extensive functional coverage of DataScript’s Datalog implementation.

LUBM Web Ontology Language (OWL) Tests

Lehigh University Benchmark
http://swat.cse.lehigh.edu/projects/lubm/

The Lehigh University Benchmark is developed to facilitate the evaluation of Semantic Web repositories in a standard and systematic way. The benchmark is intended to evaluate the performance of those repositories with respect to extensional queries over a large data set that commits to a single realistic ontology. It consists of a university domain ontology, customizable and repeatable synthetic data, a set of test queries, and several performance metrics.

Racket Datalog

Several Datalog tests from the Racket Datalog examples have been translated and re-used within Crux’s query tests.

  • tutorial.rkt

  • path.rkt

  • revpath.rkt

  • bidipath.rkt

  • sym.rkt

Datalog Research

Several Datalog examples from a classic Datalog paper have been translated and re-used within Crux’s query tests.

What you Always Wanted to Know About Datalog (And Never Dared to Ask)

https://www.semanticscholar.org/paper/What-you-Always-Wanted-to-Know-About-Datalog-(And-Ceri-Gottlob/630444d76e5aa81867344cb11aaddaab8dc8174c
Stefano Ceri, Georg Gottlob, Letizia Tanca, Published in IEEE Trans. Knowl. Data Eng. 1989
DOI:10.1109/69.43410

Specifically:

  • "sgc"

  • 3 examples of "stratified Datalog"