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JasmineGraph

1. Introduction

JasmineGraph is a C/C++ based distributed graph database server. It has been developed following the Acacia graph database server architecture. JasmineGraph can be run on single computer as well as on a compute cluster.

2. Building and Running JasmineGraph on Docker

JasmineGraph can be run inside a docker image. After cloning the project, build the image as follows:

docker build -t jasminegraph .

Run the image by providing the appropriate volume mount paths and parameters:

docker run -v "/var/run/docker.sock:/var/run/docker.sock:rw" -v "/root/.ssh:/home/user/.ssh" -v "/tmp:/tmp" -v "/var/tmp/jasminegraph-localstore:/var/tmp/jasminegraph-localstore" -v "/var/tmp/jasminegraph-aggregate:/var/tmp/jasminegraph-aggregate" -v "/home/user/Documents/jasminegraph/metadb:/home/ubuntu/software/jasminegraph/metadb" -v "/home/user/Documents/MSc/jasminegraph/performancedb:/home/ubuntu/software/jasminegraph/performancedb" -p 7777:7777 -p 7778:7778 jasminegraph --MODE 1 --MASTERIP <docker0 interface ip> --WORKERS 4 --WORKERIP <docker0 interface ip> --ENABLE_NMON false

Run JasmineGraph in Kubernetes environment by providing appropriate parameters:

./start-k8s.sh --META_DB_PATH "$(pwd)/metadb" \
    --PERFORMANCE_DB_PATH "$(pwd)/performancedb" \
    --DATA_PATH "$(pwd)/data" \
    --LOG_PATH "$(pwd)/logs" \
    --AGGREGATE_PATH "$(pwd)/aggregate" \
    --NO_OF_WORKERS 2 \
    --ENABLE_NMON false \
    --MAX_COUNT 4

Note: NO_OF_WORKERS, MAX_COUNT, and ENABLE_NMON are optional arguments, and it defaults to 2, 4 and false respectively.

Remove all resources created by JasmineGraph in Kubernetes environment:

./start-k8s.sh clean

3. Contact Information

Please contact [Miyuru Dayarathna](miyurud at yahoo dot co dot uk) for further information. Please let us know about bug reports or any further improvements you wish to have in JasmineGraph.

Open Source License

JasmineGraph is licensed under the Apache License, Version 2.0. Task Scheduler which is integrated in JasmineGraph is using MIT License.

References

More details of JasmineGraph's approach for managment and mining of large graph data is available from the following list of papers.

• Chinthaka Weerakkody, Miyuru Dayarathna, Sanath Jayasena, and Toyotaro Suzumura. 2022. "Guaranteeing Service Level Agreements for Triangle Counting via Observation-based Admission Control Algorithm.," in 2022 IEEE 15th International Conference on Cloud Computing (CLOUD), Barcelona, Spain, 2022 pp. 283-288.
• Damitha Senevirathne, Isuru Wijesiri, Suchitha Dehigaspitiya, Miyuru Dayarathna, Sanath Jayasena, and Toyotaro Suzumura. 2020. "Memory Efficient Graph Convolutional Network based Distributed Link Prediction," 2020 IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA, pp. 2977-2986.
• Anuradha Karunarathna, Dinika Senarath, Shalika Madhushanki, Chinthaka Weerakkody, Miyuru Dayarathna, Sanath Jayasena, and Toyotaro Suzumura. 2020. "Scalable Graph Convolutional Network based Link Prediction on a Distributed Graph Database Server.," IEEE 13th International Conference on Cloud Computing (CLOUD), Beijing, China, 2020, pp. 107-115.
• Miyuru Dayarathna, Sathya Bandara, Nandula Jayamaha, Mahen Herath, Achala Madhushan, Sanath Jayasena, and Toyotaro Suzumura. 2017. "An X10-Based Distributed Streaming Graph Database Engine.," 2017 IEEE 24th International Conference on High Performance Computing (HiPC), Jaipur, 2017, pp. 243-252.
• Miyuru Dayarathna, Isuru Herath, Yasima Dewmini, Gayan Mettananda, Sameera Nandasiri, Sanath Jayasena, and Toyotaro Suzumura. 2016 "Acacia-RDF: An X10-Based Scalable Distributed RDF Graph Database Engine.," 2016 IEEE 9th International Conference on Cloud Computing (CLOUD), San Francisco, CA, 2016, pp. 521-528. doi: 10.1109/CLOUD.2016.0075
• Miyuru Dayarathna and Toyotaro Suzumura. 2014. "Towards scalable distributed graph database engine for hybrid clouds." In Proceedings of the 5th International Workshop on Data-Intensive Computing in the Clouds (DataCloud '14). IEEE Press, Piscataway, NJ, USA, 1-8.