在线时间:8:00-16:00
迪恩网络APP
随时随地掌握行业动态
扫描二维码
关注迪恩网络微信公众号
开源软件名称:gStore开源软件地址:https://gitee.com/PKUMOD/gStore开源软件介绍:gStore- Native graph database system for large-scale knowledge graph applicationThe latest version: The latest version is 0.9.1, updated on November 25, 2021. Help document: gStore User Guide - 0.9.1 Version-English Homepage: Product trial: Discussion group: http://www.gstore.cn/pcsite/index.html#/medical(only chinese) https://github.com/pkumod/gStore/issues Development team: Data Management Laboratory (PKUMOD) of Wangxuan Institute of Computer Technology of Peking University. License: Contact us: Technical problems: [email protected] , [email protected] Business issues:[email protected] , [email protected] IntroductiongStore is a graph-based RDF data management system(or what is commonly called a "triple store") that maintains the graph structure of the original RDF data. Its data model is a labeled, directed multi edge graph, where each vertex corresponds to a subject or an object. We represent a given SPARQL query by a query graph Q. Query processing involves finding subgraph matches of Q over the RDF graph G, instead of joining tables in relational data management system. gStore incorporates an index over the RDF graph (called VS-tree) to speed up query processing. VS-tree is a height balanced tree with a number of associated pruning techniques to speed up subgraph matching. (NOTICE: Homomorphism is used here, instead of isomorphism) FatureThe important features of gStore are as follows:
PublicationThe first essay to come up with Gstore System is gStore_VLDB.
Change log0.9.1(beta):2021-11-25 New features in gStore 0.9.1 are listed as follows:
0.9(beta):2021-02-10 New features in version 0.9 include:
The version is a beta version, you can get it by : git clone https://github.com/pkumod/gStore.git 0.8(Stable) The version is a stable version ,you can get it by git clone -b 0.8 https://github.com/pkumod/gStore.git Getting StartedCompile from SourceThis system is really user-friendly and you can pick it up in several minutes. Remember to check your platform where you want to run this system by viewing System Requirements. After all are verified, please get this project's source code. There are several ways to do this:
Then you need to compile the project, for the first time you need to type The first strategy is suggested to get the source code because you can easily acquire the updates of the code by typing
Deploy via DockerYou can easily deploy gStore via Docker. We provide both of Dockerfile and docker image. Please see our Docker Deployment Doc(EN) or Docker部署文档(中文) for details. RunTo run gStore, please type PerformanceThe formal experiment report is in EXPERIMENT. PreparationWe have compared the performance of gStore with several other database systems, such as Jena, Sesame, Virtuoso and so on. Contents to be compared are the time to build database, the size of the built database, the time to answer single SPARQL query and the matching case of single query's results. In addition, if the memory cost is very large(>20G), we will record the memory cost when running these database systems.(not accurate, just for your reference) To ensure all database systems can run correctly on all datasets and queries, the format of datasets must be supported by all database systems and the queries should not contain update operations, aggregate operations and operations related with uncertain predicates. Notice that when measuring the time to answer queries, the time of loading database index should not be included. To ensure this principle, we load the database index first for some database systems, and warm up several times for others. Datasets used here are WatDiv, Lubm, Bsbm and DBpedia. Some of them are provided by websites, and others are generated by algorithms. Queries are generated by algorithms or written by us. The experiment environment is a CentOS server, whose memory size is 82G and disk size is 7T. We use full_test to do this test. ResultThis program produces many logs placed in result.log/, load.log/ and time.log/. You can see that all results of all queries are matched by viewing files in result.log/, and the time cost and space cost of gStore to build database are larger than others by viewing files in load.log/. More precisely, there is an order of magnitude difference between gStore and others in the time/space cost of building database. Through analysing time.log/, we can find that gStore behave better than others on very complicated queries(many variables, circles, etc). For other simple queries, there is not much difference between the time of these database systems. Generally speaking, the memory cost of gStore when answering queries is higher than others. More complicated the query is and more large the dataset is, more apparent the phenomenon is. You can find more detailed information in test report. Notice that some questions in the test report have already be solved now. Advanced HelpIf you want to understand the details of the gStore system, or you want to try some advanced operations(for example, using the API, server/client), please see the chapters below.
Submit questionsBugs are recorded in BUG REPORT. You are welcome to submit any advice or errors in the Github Issues part of this repository and official website forum, if not requiring in-time reply. However, if you want to urgent on us to deal with your reports, please email to [email protected],[email protected] to submit your suggestions and report bugs. A full list of our whole team is in Mailing List. We have written a series of short essays addressing recurring challenges in using gStore to realize applications, which are placed in Recipe Book. Sometimes you may find some strange phenomena(but not wrong case), or something hard to understand/solve(don't know how to do next), then do not hesitate to visit the Frequently Asked Questions page. Graph database engine is a new area and we are still trying to go further, and we hope more and more people will support or even join us. You can support in many ways:
People who inspire us or contribute to this project will be listed in the Thanks List chapter. |
请发表评论