Package: gs-jython
A One-Stop Large-Scale Graph Computing System from AlibabaGraphScope is a unified distributed graph computing platform that provides a one-stop environment for performing diverse graph operations on a cluster of computers through a user-friendly Python interface. GraphScope makes multi-staged processing of large-scale graph data on compute clusters simple by combining several important pieces of Alibaba technology: includingGRAPE,MaxGraph, andGraph-Learn(GL) for analytics, interactive, and graph neural networks (GNN) computation, respectively, and theVineyardstore that offers efficient in-memory data transfers.Visit our website atgraphscope.ioto learn more.Getting StartedWe provide aPlaygroundwith a managed JupyterLab.Try GraphScopestraight away in your browser!GraphScope supports run in standalone mode or on clusters managed byKuberneteswithin containers. For quickly getting started,
let's begin with the standalone mode.Installation for Standalone ModeGraphScope pre-compiled package is distributed as a python package and can be easily installed withpip.pip3installgraphscopeNote thatgraphscoperequiresPython>= 3.6 andpip>= 19.0. The package is built for and tested on the most popular Linux (Ubuntu 18.04+ / CentOS 7+) and macOS (version 10.15+) distributions. For Windows users, you may want to install Ubuntu on WSL2 to use this package.Next, we will walk you through a concrete example to illustrate how GraphScope can be used by data scientists to effectively analyze large graphs.Demo: Node Classification on Citation Networkogbn-magis a heterogeneous network composed of a subset of the Microsoft Academic Graph. It contains 4 types of entities(i.e., papers, authors, institutions, and fields of study), as well as four types of directed relations connecting two entities.Given the heterogeneousogbn-magdata, the task is to predict the class of each paper. Node classification can identify papers in multiple venues, which represent different groups of scientific work on different topics. We apply both the attribute and structural information to classify papers. In the graph, each paper node contains a 128-dimensional word2vec vector representing its content, which is obtained by averaging the embeddings of words in its title and abstract. The embeddings of individual words are pre-trained. The structural information is computed on-the-fly.Loading a graphGraphScope models graph data as property graph, in which the edges/vertices are labeled and have many properties.
Takingogbn-magas example, the figure below shows the model of the property graph.This graph has four kinds of vertices, labeled aspaper,author,institutionandfield_of_study. There are four kinds of edges connecting them, each kind of edges has a label and specifies the vertex labels for its two ends. For example,citesedges connect two vertices labeledpaper. Another example iswrites, it requires the source vertex is labeledauthorand the destination is apapervertex. All the vertices and edges may have properties. e.g.,papervertices have properties like features, publish year, subject label, etc.To load this graph to GraphScope with our retrieval module, please use these code:importgraphscopefromgraphscope.datasetimportload_ogbn_magg=load_ogbn_mag()We provide a set of functions to load graph datasets fromogbandsnapfor convenience. Please find all the available graphshere. If you want to use your own graph data, please referthis docto load vertices and edges by labels.Interactive queryInteractive queries allow users to directly explore, examine, and present graph data in anexploratorymanner in order to locate specific or in-depth information in time.
GraphScope adopts a high-level language calledGremlinfor graph traversal, and providesefficient executionat scale.In this example, we use graph traversal to count the number of papers two given authors have co-authored. To simplify the query, we assume the authors can be uniquely identified by ID2and4307, respectively.# get the endpoint for submitting Gremlin queries on graph g.interactive=graphscope.gremlin(g)# count the number of papers two authors (with id 2 and 4307) have co-authoredpapers=interactive.execute("g.V().has('author', 'id', 2).out('writes').where(__.in('writes').has('id', 4307)).count()").one()Graph analyticsGraph analytics is widely used in real world. Many algorithms, like community detection, paths and connectivity, centrality are proven to be very useful in various businesses.
GraphScope ships with a set ofbuilt-in algorithms, enables users easily analysis their graph data.Continuing our example, below we first derive a subgraph by extracting publications in specific time out of the entire graph (using Gremlin!), and then run k-core decomposition and triangle counting to generate the structural features of each paper node.Please note that many algorithms may only work onhomogeneousgraphs, and therefore, to evaluate these algorithms over a property graph, we need to project it into a simple graph at first.# extract a subgraph of publication within a time rangesub_graph=interactive.subgraph("g.V().has('year', inside(2014, 2020)).outE('cites')")# project the projected graph to simple graph.simple_g=sub_graph.project(vertices={"paper":[]},edges={"cites":[]})ret1=graphscope.k_core(simple_g,k=5)ret2=graphscope.triangles(simple_g)# add the results as new columns to the citation graphsub_graph=sub_graph.add_column(ret1,{"kcore":"r"})sub_graph=sub_graph.add_column(ret2,{"tc":"r"})In addition, users can write their own algorithms in GraphScope.
Currently, GraphScope supports users to write their own algorithms
in Pregel model and PIE model.Graph neural networks (GNNs)Graph neural networks (GNNs) combines superiority of both graph analytics and machine learning. GNN algorithms can compress both structural and attribute information in a graph into low-dimensional embedding vectors on each node. These embeddings can be further fed into downstream machine learning tasks.In our example, we train a GCN model to classify the nodes (papers) into 349 categories,
each of which represents a venue (e.g. pre-print and conference).
To achieve this, first we launch a learning engine and build a graph with features
following the last step.# define the features for learningpaper_features=[]foriinrange(128):paper_features.append("feat_"+str(i))paper_features.append("kcore")paper_features.append("tc")# launch a learning engine.lg=graphscope.graphlearn(sub_graph,nodes=[("paper",paper_features)],edges=[("paper","cites","paper")],gen_labels=[("train","paper",100,(0,75)),("val","paper",100,(75,85)),("test","paper",100,(85,100))])Then we define the training process, and run it.# Note: Here we use tensorflow as NN backend to train GNN model. so please# install tensorflow.importgraphscope.learningfromgraphscope.learning.examplesimportGCNfromgraphscope.learning.graphlearn.python.model.tf.trainerimportLocalTFTrainerfromgraphscope.learning.graphlearn.python.model.tf.optimizerimportget_tf_optimizer# supervised GCN.deftrain(config,graph):defmodel_fn():returnGCN(graph,config["class_num"],config["features_num"],config["batch_size"],val_batch_size=config["val_batch_size"],test_batch_size=config["test_batch_size"],categorical_attrs_desc=config["categorical_attrs_desc"],hidden_dim=config["hidden_dim"],in_drop_rate=config["in_drop_rate"],neighs_num=config["neighs_num"],hops_num=config["hops_num"],node_type=config["node_type"],edge_type=config["edge_type"],full_graph_mode=config["full_graph_mode"],)graphscope.learning.reset_default_tf_graph()trainer=LocalTFTrainer(model_fn,epoch=config["epoch"],optimizer=get_tf_optimizer(config["learning_algo"],config["learning_rate"],config["weight_decay"]),)trainer.train_and_evaluate()config={"class_num":349,# output dimension"features_num":130,# 128 dimension + kcore + triangle count"batch_size":500,"val_batch_size":100,"test_batch_size":100,"categorical_attrs_desc":"","hidden_dim":256,"in_drop_rate":0.5,"hops_num":2,"neighs_num":[5,10],"full_graph_mode":False,"agg_type":"gcn",# mean, sum"learning_algo":"adam","learning_rate":0.0005,"weight_decay":0.000005,"epoch":20,"node_type":"paper","edge_type":"cites",}train(config,lg)A python script with the entire process is availabehere, you may try it out by yourself.Processing Large Graph on Kubernetes ClusterGraphScope is designed for processing large graphs, which are usually hard to fit in the memory of a single machine.
WithVineyardas the distributed in-memory data manager, GraphScope supports run on a cluster managed by Kubernetes(k8s).To continue this tutorial, please ensure that you have a k8s managed cluster and know the credentials for the cluster.
(e.g., address of k8s API server, usually stored a~/.kube/configfile.)Alternatively, you can set up a local k8s cluster for testing withKind. We provide a script for setup this environment.# for usage, type -h./scripts/install_deps.sh--k8sIf you did not install thegraphscopepackage in the above step, you can install a subset of the whole package with client functions only.pip3installgraphscope-clientNext, let's revisit the example by running on a cluster instead.The figure shows the flow of execution in the cluster mode. When users run code in the python client, it will:Step 1. Create a session or workspace in GraphScope.Step 2 - Step 5. Load a graph, query, analysis and run learning task on this graph via Python interface. These steps are the same to local mode, thus users process huge graphs in a distributed setting just like analysis a small graph on a single machine.(Note thatgraphscope.gremlinandgraphscope.graphlearnneed to be changed tosess.gremlinandsess.graphlearn, respectively.sessis the name of theSessioninstance user created.)Step 6. Close the session.Creating a sessionTo use GraphScope in a distributed setting, we need to establish a session in a python interpreter.For convenience, we provide several demo datasets, and an optionmount_datasetto mount the dataset in the graphscope cluster. The datasets will be mounted to location you assigned in the pods. If you want to use your own data on k8s cluster, please refer tothis.importgraphscopesess=graphscope.session(mount_dataset="/dataset")For macOS, the session needs to establish with the LoadBalancer service type (which is NodePort by default).importgraphscopesess=graphscope.session(mount_dataset="/dataset",k8s_service_type="LoadBalancer")A session tries to launch acoordinator, which is the entry for the back-end engines. The coordinator manages a cluster of resources (k8s pods), and the interactive/analytical/learning engines ran on them. For each pod in the cluster, there is a vineyard instance at service for distributed data in memory.Loading a graph and processing computation tasksSimilar to the standalone mode, we can still use the functions to load a graph easily.fromgraphscope.datasetimportload_ogbn_mag# Note we have mounted the demo datasets to /dataset,# There are several datasets including ogbn_mag_small,# User can attach to the engine container and explore the directory.g=load_ogbn_mag(sess,"/dataset/ogbn_mag_small/")Here, thegis loaded in parallel via vineyard and stored in vineyard instances in the cluster managed by the session.Next, we can conduct graph queries with Gremlin, invoke various graph algorithms, or run graph-based neural network tasks like we did in the standalone mode.
We do not repeat code here, but a.ipynbprocessing the classification task on k8s is available on thePlayground.Closing the sessionAnother additional step in the distribution is session close. We close the session after processing all graph tasks.sess.close()This operation will notify the backend engines and vineyard
to safely unload graphs and their applications,
Then, the coordinator will dealloc all the applied resources in the k8s cluster.Please note that we have not hardened this release for production use and it lacks important security features such as authentication and encryption, and thereforeit is NOT recommended for production use (yet)!DevelopmentBuilding on localTo build graphscope Python package and the engine bineries, you need to install some dependencies and build tools../scripts/install_deps.sh--dev# With argument --cn to speed up the download if you are in China../scripts/install_deps.sh--dev--cnThen you can build GraphScope with pre-configuredmakecommands.# to make graphscope whole package, including python package + engine binaries.makegraphscope# or make the engine components# make gie# make gae# make gleBuilding Docker imagesGraphScope ships with aDockerfilethat can build docker images for releasing. The images are built on abuilderimage with all dependencies installed and copied to
aruntime-baseimage. To build images with latest version of GraphScope, go to the root directory and run this command.# by default, the built image is tagged as graphscope/graphscope:SHORTSHAmakegraphscope-imageBuilding client libraryGraphScope python interface is separate with the engines image.
If you are developing python client and not modifying the protobuf files, the engines
image doesn't require to be rebuilt.You may want to re-install the python client on local.makeclientNote that the learning engine client has C/C++ extensions modules and setting up the build
environment is a bit tedious. By default the locally-built client library doesn't include
the support for learning engine. If you want to build client library with learning engine
enabled, please referBuild Python Wheels.TestingTo verify the correctness of your developed features, your code changes should pass our tests.You may run the whole test suite with commands:maketestDocumentationDocumentation can be generated using Sphinx. Users can build the documentation using:# build the docsmakegraphscope-docs# to open preview on localopendocs/_build/latest/html/index.htmlThe latest version of online documentation can be found athttps://graphscope.io/docsLicenseGraphScope is released underApache License 2.0. Please note that third-party libraries may not have the same license as GraphScope.PublicationsWenfei Fan, Tao He, Longbin Lai, Xue Li, Yong Li, Zhao Li, Zhengping Qian, Chao Tian, Lei Wang, Jingbo Xu, Youyang Yao, Qiang Yin, Wenyuan Yu, Jingren Zhou, Diwen Zhu, Rong Zhu.GraphScope: A Unified Engine For Big Graph Processing. The 47th International Conference on Very Large Data Bases (VLDB), industry, 2021.Jingbo Xu, Zhanning Bai, Wenfei Fan, Longbin Lai, Xue Li, Zhao Li, Zhengping Qian, Lei Wang, Yanyan Wang, Wenyuan Yu, Jingren Zhou.GraphScope: A One-Stop Large Graph Processing System. The 47th International Conference on Very Large Data Bases (VLDB), demo, 2021ContributingAny contributions you make aregreatly appreciated!Join in theSlack channelfor discussion.Please report bugs by submitting a GitHub issue.Please submit contributions using pull requests.