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key_features.html
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<meta property="og:description" content="Ease-of-use: Python Interface: The Python interface of GraphScope offers an intuitive and user-friendly way for data scientists to develop, test and deploy complex graph computation workflows quick..." />
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<p class="caption" role="heading"><span class="caption-text">Overview</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="overview/intro.html">Introducing GraphScope</a></li>
<li class="toctree-l1"><a class="reference internal" href="overview/getting_started.html">Getting Started</a></li>
<li class="toctree-l1"><a class="reference internal" href="loading_graphs.html">Loading Graphs</a></li>
<li class="toctree-l1"><a class="reference internal" href="overview/graph_analytics_workloads.html">Graph Analytics Workloads</a></li>
<li class="toctree-l1"><a class="reference internal" href="overview/graph_interactive_workloads.html">Graph Interactive Workloads</a></li>
<li class="toctree-l1"><a class="reference internal" href="overview/graph_learning_workloads.html">Graph Learning Workloads</a></li>
<li class="toctree-l1"><a class="reference internal" href="overview/design_of_graphscope.html">Design of GraphScope</a></li>
<li class="toctree-l1"><a class="reference internal" href="overview/positioning.html">Positioning of GraphScope</a></li>
<li class="toctree-l1"><a class="reference internal" href="overview/glossary.html">Glossary</a></li>
<li class="toctree-l1"><a class="reference internal" href="frequently_asked_questions.html">Frequently Asked Questions</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Installation & Deployment</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="deployment/install_on_local.html">Install GraphScope on Local</a></li>
<li class="toctree-l1"><a class="reference internal" href="deployment/deploy_graphscope_on_self_managed_k8s.html">Deploy on K8s Cluster</a></li>
<li class="toctree-l1"><a class="reference internal" href="deployment/deploy_with_existing_vineyard_cluster.html">Deploy with Existing Vineyard Cluster</a></li>
<li class="toctree-l1"><a class="reference internal" href="deployment/persistent_storage_of_graphs_on_k8s.html">Persistent storage of graphs on the Kubernetes cluster</a></li>
<li class="toctree-l1"><a class="reference internal" href="deployment/deploy_graphscope_with_helm.html">Deploy GraphScope with <code class="docutils literal notranslate"><span class="pre">helm</span></code></a></li>
<li class="toctree-l1"><a class="reference internal" href="deployment/install_in_offline_env.html">Install GraphScope in offline environment</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">GraphScope Flex</span></p>
<ul>
<li class="toctree-l1 has-children"><a class="reference internal" href="flex/interactive_intro.html">GraphScope Interactive</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of GraphScope Interactive</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l2"><a class="reference internal" href="flex/interactive/overview.html">Overview</a></li>
<li class="toctree-l2"><a class="reference internal" href="flex/interactive/installation.html">Installation</a></li>
<li class="toctree-l2"><a class="reference internal" href="flex/interactive/getting_started.html">Getting Started</a></li>
<li class="toctree-l2"><a class="reference internal" href="flex/interactive/custom_graph_data.html">Using Custom Graph</a></li>
<li class="toctree-l2"><a class="reference internal" href="flex/interactive/stored_procedures.html">Stored Procedures</a></li>
<li class="toctree-l2"><a class="reference internal" href="flex/interactive/configuration.html">Engine Configuration</a></li>
<li class="toctree-l2"><a class="reference internal" href="flex/interactive/data_model.html">Data Model</a></li>
<li class="toctree-l2"><a class="reference internal" href="flex/interactive/data_import.html">Data Import Configuration</a></li>
<li class="toctree-l2 has-children"><a class="reference internal" href="flex/interactive/dev_guide.html">GraphScope Interactive Development guide</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of GraphScope Interactive Development guide</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l3"><a class="reference internal" href="flex/interactive/development/cypher_procedure.html">Turning Cypher Queries into Stored Procedures with GraphScope Interactive</a></li>
<li class="toctree-l3"><a class="reference internal" href="flex/interactive/development/cpp_procedure.html">Create c++ Stored Procedures on GraphScope Interactive</a></li>
<li class="toctree-l3"><a class="reference internal" href="flex/interactive/development/admin_service.html">GraphScope Interactive Admin Service Documentation</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="flex/interactive/glossary.html">Glossary</a></li>
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<p class="caption" role="heading"><span class="caption-text">Graph Analytical Engine</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="analytical_engine/getting_started.html">Getting Started</a></li>
<li class="toctree-l1"><a class="reference internal" href="analytical_engine/deployment.html">Deployment for GAE</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="analytical_engine/design_of_gae.html">Design of Analytical Engine</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of Design of Analytical Engine</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
<li class="toctree-l2"><a class="reference internal" href="analytical_engine/overview_and_architecture.html">Overview and Architecture</a></li>
<li class="toctree-l2"><a class="reference internal" href="analytical_engine/key_concepts.html">Key Concepts</a></li>
<li class="toctree-l2"><a class="reference internal" href="analytical_engine/vertex_centric_models.html">Vertex-Centric Model</a></li>
<li class="toctree-l2"><a class="reference internal" href="analytical_engine/programming_model_pie.html">Programming Model: PIE</a></li>
<li class="toctree-l2"><a class="reference internal" href="analytical_engine/ingress.html">Ingress: Incrementalize Graph Algorithms</a></li>
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<section class="tex2jax_ignore mathjax_ignore" id="key-features">
<h1>Key Features<a class="headerlink" href="#key-features" title="Permalink to this heading">#</a></h1>
<section id="ease-of-use-python-interface">
<h2>Ease-of-use: Python Interface<a class="headerlink" href="#ease-of-use-python-interface" title="Permalink to this heading">#</a></h2>
<p>The <a class="reference external" href="https://graphscope.io/docs/latest/reference/python_index.html">Python interface</a> of GraphScope offers an intuitive and user-friendly way for data scientists to develop, test and deploy complex graph computation workflows quickly and correctly. The <a class="reference external" href="https://graphscope.io/docs/latest/reference/session.html#session-object">session</a> abstraction encapsulates the environment where data manipulation and graph computation operations are executed or evaluated. The Python clients interact with the GraphScope graph computation service cluster via sessions.</p>
<p>GraphScope also provides <a class="reference external" href="https://graphscope.io/docs/reference/networkx/index.html">NetworkX-compatible APIs</a> to support the creation, manipulation, query and analysis of graphs, offering an easy transition for users with prior experiences in the NetworkX library. The built-in graph analysis algorithms in GraphScope have compatible interfaces with the corresponding NetworkX counterparts, and can be well parallelized with a performance boost of several orders of magnitudes.</p>
</section>
<section id="graph-traversal-support-in-gremlin-and-cypher">
<h2>Graph Traversal Support, in Gremlin and Cypher<a class="headerlink" href="#graph-traversal-support-in-gremlin-and-cypher" title="Permalink to this heading">#</a></h2>
<p>GraphScope Interactive Engine(GIE) leverages Gremlin, a graph traversal language developed by <a class="reference external" href="https://tinkerpop.apache.org/">Apache TinkerPop</a>, to offer a powerful and intuitive way of querying graphs interactively, and supports automatic query parallelization. GIE implements TinkerPop’s <a class="reference external" href="https://tinkerpop.apache.org/docs/current/reference/#gremlin-server">Gremlin Server</a> interface such that the system can seamlessly interact with the TinkerPop ecosystem, including development tools such as <a class="reference external" href="https://tinkerpop.apache.org/docs/current/tutorials/the-gremlin-console/">Gremlin Console</a> and language wrappers such as <a class="reference external" href="https://pypi.org/project/gremlinpython/">Gremlin-Python</a> and <a class="reference external" href="https://tinkerpop.apache.org/docs/current/reference/#gremlin-java">Gremlin-Java</a>.</p>
<p><a class="reference external" href="https://neo4j.com/developer/cypher/">Cypher</a> is a declarative programming language for graph queries. It is designed to make it easy for users to specify the graph patterns they are interested in. We are currently working on adding support for the Cypher language to provide users with more flexibility and options on graph queries.</p>
</section>
<section id="high-performant-built-in-algorithms">
<h2>High Performant Built-in Algorithms<a class="headerlink" href="#high-performant-built-in-algorithms" title="Permalink to this heading">#</a></h2>
<p>GraphScope is equipped with various built-in graph algorithms, consisting of 20 graph analytics algorithms and 8 GNN models. The built-in algorithms are highly optimized, and offer superior performance. For example, we have performed a comparison with state-of-the-art graph processing systems on LDBC Graph Analytics Benchmark, and the results show GraphScope outperforms other graph systems (see more detailed results <a class="reference internal" href="#%5Bhttps://graphscope.io/docs/latest/performance_and_benchmark.html"><span class="xref myst">here</span></a>).</p>
</section>
<section id="extensible-algorithm-library-for-graph-analytics">
<h2>Extensible Algorithm Library for Graph Analytics<a class="headerlink" href="#extensible-algorithm-library-for-graph-analytics" title="Permalink to this heading">#</a></h2>
<p>GraphScope Analytical Engine(GAE) provides a rich set of commonly used algorithms, including connectivity and path analysis, community detection and centrality computations. This <a class="reference external" href="https://github.com/alibaba/GraphScope/tree/main/analytical_engine/apps">directory</a> includes a full list of the built-in algorithms, which is continuously growing.</p>
<p>GAE also provides developers the flexibility to customize their own algorithms with different programming models and programming languages. Currently, the programing models that GAE supports include the sub-graph based <a class="reference external" href="https://dl.acm.org/doi/10.1145/3282488">PIE</a> model and the vertex-centric <a class="reference external" href="https://dl.acm.org/doi/10.1145/1807167.1807184">Pregel</a> model. Meanwhile, GAE provides a multi-language SDK, and users can choose to write their own algorithms in C++, Java or Python.</p>
</section>
<section id="gnn-training-inference">
<h2>GNN Training & Inference<a class="headerlink" href="#gnn-training-inference" title="Permalink to this heading">#</a></h2>
<p>GraphScope Learning Engine (GLE) offers users an easy-to-use approach to take advantage of high-performance GNN training and inference on large-scale graphs. GLE provides multiple commonly-used (negative)sampling operators to facilitate graph sampling, and allows users to define graph queries (e.g., N-hop sampling queries) in a Gremlin-style Graph Query Language (<a class="reference external" href="https://graph-learn.readthedocs.io/en/latest/zh_CN/gl/graph/gsl.html">GSL</a>). It also comes with a wide range of GNN models, like GCN, GAT, GraphSAGE, and SEAL, and includes commonly-utilized algorithm modules (e.g., DeepWalk and TransE). Furthermore, GLE provides a set of paradigms and processes to ease the development of customized models. GLE is compatible with <a class="reference external" href="https://github.com/pyg-team/pytorch_geometric">PyG</a>, e.g., this <a class="reference external" href="https://github.com/alibaba/graph-learn/tree/66229a6dcf7b45a340a39ff9d0796ec11bf78d79/graphlearn/examples/pytorch/gcn">example</a> shows that a PyG model can be trained using GLE with very minor modifications. Users can flexibly choose <a class="reference external" href="https://github.com/tensorflow/tensorflow">TensorFlow</a> or <a class="reference external" href="https://github.com/pytorch/pytorch">PyTorch</a> as the training backend.</p>
<p>To support online inference on dynamic graphs, we propose Dynamic Graph Service (<a class="reference external" href="https://graph-learn.readthedocs.io/en/latest/en/dgs/intro.html">DGS</a>) in GLE to facilitate real-time sampling on dynamic graphs. The sampled subgraph can be fed into the serving modules (e.g., <a class="reference external" href="https://github.com/tensorflow/serving">TensorFlow Serving</a>) to obtain the inference results. This <a class="reference external" href="https://graph-learn.readthedocs.io/en/latest/en/dgs/tutorial.html#prepare-data">document</a> is organized to provide a detailed, step-by-step tutorial specifically demonstrating the use of GLE for offline training and online inference.</p>
</section>
<section id="cloud-native-design">
<h2>Cloud Native Design<a class="headerlink" href="#cloud-native-design" title="Permalink to this heading">#</a></h2>
<p>Cloud native is the software approach of building, deploying, and managing modern applications in cloud computing environments. GraphScope provides a set of Dockerfiles and users could easily build docker images of GraphScope. In addition, users could deploy GraphScope on a Kubernetes (k8s) cluster, or on managed Kubernetes service of cloud service providers (e.g., EKS for AWS and ACK for Alibaba Cloud). GraphScope also provides essential tools to allow users to handling situations like monitoring, failover, scaling and rolling update.</p>
</section>
<section id="across-engine-workflow-orchestration">
<h2>Across-Engine Workflow Orchestration<a class="headerlink" href="#across-engine-workflow-orchestration" title="Permalink to this heading">#</a></h2>
<p>GraphScope allows users to orchestrate complex graph computation workflows that combine tasks of various workloads, such as graph query, graph analytics and graph learning. With Vineyard that offers efficient in-memory data management, graph computation tasks backed up by different engines (GAE, GIE and GLE) can be seamlessly orchestrated into customized workflows. Users only need to concentrate on the workflow logics, complex operations such as storing and transferring intermediate results, computation parallelization and workflow pipelining in a distributed cluster are automatically handled by GraphScope. This <a class="reference external" href="https://nbviewer.org/github/alibaba/GraphScope/blob/main/tutorials/01_node_classification_on_citation.ipynb">tutorial</a> demonstrates an example of across-engine workflow orchestration in GraphScope.</p>
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<ul>
<li><a class="reference internal" href="#">Key Features</a><ul>
<li><a class="reference internal" href="#ease-of-use-python-interface">Ease-of-use: Python Interface</a></li>
<li><a class="reference internal" href="#graph-traversal-support-in-gremlin-and-cypher">Graph Traversal Support, in Gremlin and Cypher</a></li>
<li><a class="reference internal" href="#high-performant-built-in-algorithms">High Performant Built-in Algorithms</a></li>
<li><a class="reference internal" href="#extensible-algorithm-library-for-graph-analytics">Extensible Algorithm Library for Graph Analytics</a></li>
<li><a class="reference internal" href="#gnn-training-inference">GNN Training & Inference</a></li>
<li><a class="reference internal" href="#cloud-native-design">Cloud Native Design</a></li>
<li><a class="reference internal" href="#across-engine-workflow-orchestration">Across-Engine Workflow Orchestration</a></li>
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