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breadcrumbs__item--active"><span class="breadcrumbs__link" itemprop="name">Integrate into GraphScope</span><meta itemprop="position" content="4"></li></ul></nav><div class="tocCollapsible_e53W theme-doc-toc-mobile tocMobile_jOtr"><button type="button" class="clean-btn tocCollapsibleButton_jouK">On this page</button></div><div class="theme-doc-markdown markdown"><h1>Integrate into GraphScope</h1> |
| <p><a href="https://graphscope.io/" target="_blank" rel="noopener noreferrer">GraphScope</a> is a unified distributed graph |
| computing platform that provides a one-stop environment for performing |
| diverse graph operations on a cluster through a user-friendly Python |
| interface. As an important application case of GraphAr, we have |
| integrated it into GraphScope.</p> |
| <p>GraphScope works on a graph G fragmented via a partition strategy picked |
| by the user and each worker maintains a fragment of G. Given a query, it |
| posts the same query to all the workers and computes following the BSP |
| (Bulk Synchronous Parallel) model. More specifically, each worker first |
| executes processing against its local fragment, to compute partial |
| answers in parallel. And then each worker may exchange partial results |
| with other processors via synchronous message passing.</p> |
| <p>To integrate GraphAr into GraphScope, we implemented |
| <em>ArrowFragmentBuilder</em> and <em>ArrowFragmentWriter</em>. <em>ArrowFragmentBuilder</em> |
| establishes the fragments for workers of GraphScope through reading GraphAr |
| format data in parallel. Conversely, <em>ArrowFragmentWriter</em> can take the |
| GraphScope fragments and save them as GraphAr format files. If you're interested in |
| knowing more about the implementation, please refer to the <a href="https://github.com/v6d-io/v6d/commit/0eda2067e45fbb4ac46892398af0edc84fe1c27b" target="_blank" rel="noopener noreferrer">source |
| code</a>.</p> |
| <h2 class="anchor anchorWithStickyNavbar_qibj" id="performance-report">Performance Report<a href="#performance-report" class="hash-link" aria-label="Direct link to Performance Report" title="Direct link to Performance Report"></a></h2> |
| <h3 class="anchor anchorWithStickyNavbar_qibj" id="parameter-settings">Parameter settings<a href="#parameter-settings" class="hash-link" aria-label="Direct link to Parameter settings" title="Direct link to Parameter settings"></a></h3> |
| <p>The time performance of <em>ArrowFragmentBuilder</em> and <em>ArrowFragmentWriter</em> |
| in GraphScope is heavily dependent on the partitioning of the graph into |
| GraphAr format files, that is, the <em>vertex chunk size</em> and <em>edge chunk size</em>, which |
| are specified in the vertex information file and in the edge information |
| file, respectively.</p> |
| <p>Generally speaking, fewer chunks are created if the file size is large. |
| On small graphs, this can be disadvantageous as it reduces the degree of |
| parallelism, prolonging disk I/O time. On the other hand, having too |
| many small files increases the overhead associated with the file system |
| and the file parser.</p> |
| <p>We have conducted micro benchmarks to compare the time performance for |
| reading/writing GraphAr format files by |
| <em>ArrowFragmentBuilder</em>/<em>ArrowFragmentWriter</em>, across different <em>vertex |
| chunk size</em> and <em>edge chunk size</em> configurations. The settings we |
| recommend for <em>vertex chunk size</em> and <em>edge chunk size</em> are <strong>2^18</strong> and |
| <strong>2^22</strong>, respectively, which lead to efficient performance in most |
| cases. These settings can be used as the reference values when |
| integrating GraphAr into other systems besides GraphScope.</p> |
| <h3 class="anchor anchorWithStickyNavbar_qibj" id="time-performance-results">Time performance results<a href="#time-performance-results" class="hash-link" aria-label="Direct link to Time performance results" title="Direct link to Time performance results"></a></h3> |
| <p>Here we report the performance results of <em>ArrowFragmentBuilder</em>, and |
| compare it with loading the same graph through the default loading |
| strategy of GraphScope (through reading the csv files in parallel) . The |
| execution time reported below includes loading the graph data from the |
| disk into memory, as well as building GraphScope fragments from such |
| data. The experiments are conducted on a cluster of 4 AliCloud |
| ecs.r6.6xlarge instances (24vCPU, 192GB memory), and using |
| <a href="https://snap.stanford.edu/data/com-Friendster.html" target="_blank" rel="noopener noreferrer">com-friendster</a> (a |
| simple graph) and <a href="https://ldbcouncil.org/benchmarks/snb/" target="_blank" rel="noopener noreferrer">ldbc-snb-30</a> |
| (a multi-labeled property graph) as datasets.</p> |
| <table><thead><tr><th>Dataset</th><th>Workers</th><th>Default Loading</th><th>GraphAr Loading</th></tr></thead><tbody><tr><td>com-friendster</td><td>4</td><td>282s</td><td>54s</td></tr><tr><td>ldbc-snb-30</td><td>4</td><td>196s</td><td>40s</td></tr></tbody></table></div><footer class="theme-doc-footer docusaurus-mt-lg"><div class="theme-doc-footer-edit-meta-row row"><div class="col"><a href="https://github.com/apache/incubator-graphar/edit/main/docs/libraries/cpp/examples/graphscope.md" target="_blank" rel="noopener noreferrer" class="theme-edit-this-page"><svg fill="currentColor" height="20" width="20" viewBox="0 0 40 40" class="iconEdit_z10d" aria-hidden="true"><g><path d="m34.5 11.7l-3 3.1-6.3-6.3 3.1-3q0.5-0.5 1.2-0.5t1.1 0.5l3.9 3.9q0.5 0.4 0.5 1.1t-0.5 1.2z m-29.5 17.1l18.4-18.5 6.3 6.3-18.4 18.4h-6.3v-6.2z"></path></g></svg>Edit this page</a></div><div class="col lastUpdated_lw5U"></div></div></footer></article><nav class="pagination-nav docusaurus-mt-lg" aria-label="Docs 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