This graph databse benchmarking framework allows to benchmark and compare different graph databse systems. Currently the framework supports OrientDB, Neo4j, Sparksee, HyperGraphDB and Tinkerpop (with Neo4J and TinkerGraph as backend).
The purpose of this framework is to examine the performance of each graph database in terms of execution time. The benchmark is composed of four workloads, Clustering, Massive Insertion, Single Insertion and Query Workload. Every workload has been designed to simulate common operations in graph database systems.
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Clustering Workload (CW): CW consists of a well-known community detection algorithm for modularity optimization, the Louvain Method. We adapt the algorithm on top of the benchmarked graph databases and employ cache techniques to take advantage of both graph database capabilities and in-memory execution speed. We measure the time the algorithm needs to converge.
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Massive Insertion Workload (MIW): we create the graph database and configure it for massive loading, then we populate it with a particular dataset. We measure the time for the creation of the whole graph.
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Single Insertion Workload (SIW): we create the graph database and load it with a particular dataset. Every object insertion (node or edge) is committed directly and the graph is constructed incrementally. We measure the insertion time per block, which consists of one thousand edges and the nodes that appear during the insertion of these edges.
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Query Workload (QW): we execute three common queries:
- FindNeighbours (FN): finds the neighbours of all nodes.
- FindAdjacentNodes (FA): finds the adjacent nodes of all edges.
- FindShortestPath (FS): finds the shortest path between the first node and 100 randomly picked nodes.
Here we measure the execution time of each query.
For the evaluation, the framework supports both, synthetic and real world data. The MIW, SIW and QW benchmarks should be executed using real data derived from the SNAP dataset collection (Enron Dataset, Amazon dataset, Youtube dataset and LiveJournal dataset).
The CW benchmark should be executed using synthetic data generated with the LFR-Benchmark generator. This generator produces networks with power-law degree distribution and implanted communities within the network. The synthetic data can be downloaded form here.
This project is a fork of https://github.com/socialsensor/graphdb-benchmarks.