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Kinetica keeps topology and attributes in the same engine: <code>NODE</code>, <code>EDGE</code>, <code>WEIGHTS</code>, and <code>RESTRICTIONS</code> are annotated column references on existing tables, so a graph traversal can filter on any column without copying or syncing anything."},{q:"What is the Double-Link Structure (DLS) and why does it matter for streaming graphs?",a:'Standard graph storage uses Compressed Sparse Row (CSR), where edges are packed contiguously by source node. An insert on a busy node forces every downstream cell to shift in memory — an O(n) cost per edge. DLS uses fixed-size linked cells: each edge costs two pointer writes, an O(1) operation, so the graph holds its shape under continuous upserts instead of waiting on a batch rebuild. The architecture is documented in <a href="https://arxiv.org/abs/2201.02136" target="_blank" rel="noopener">arXiv:2201.02136</a>.'},{q:"Can graph traversals filter on columns that aren't part of the graph?",a:"Yes — and that's the point of the hybrid architecture. Pass a SQL <code>SELECT</code> as the <code>RESTRICTIONS</code> argument to <code>QUERY_GRAPH</code> and the predicate is evaluated by the distributed OLAP engine for every candidate node during the walk. Failed nodes are pruned mid-traversal. The filter can reference any column on any table in the database, including columns the graph has never seen."},{q:"What graph algorithms ship with Kinetica out of the box?",a:"Three endpoint families, all callable from standard SQL: <strong>Solve</strong> (<code>shortest_path</code>, <code>multiple_routing</code>/TSP, <code>backhaul_routing</code>, <code>page_rank</code>, <code>probability_rank</code>, <code>centrality</code>, <code>closeness</code>, <code>stats_all</code>, <code>inverse_shortest_path</code>), <strong>Match</strong> (<code>match_supply_demand</code>, <code>map_matching</code>, <code>charging_stations</code>, <code>pickup_dropoff</code>, <code>loops</code>, <code>similarity</code>, <code>clusters</code> via Louvain/RSB, <code>pattern</code>, <code>batch_solves</code> — four of which are patented), and <strong>Query</strong> (label-aware hop-based traversals with OLAP restrictions)."},{q:"How big a graph can Kinetica hold, and how fast can it traverse it?",a:"On a 500K-edge Seattle road network, a multi-source <code>SHORTEST_PATH</code> over five lon/lat pairs returns in roughly 250 ms. On a single node with 1 TB RAM + 0.5 TB ZRAM, Kinetica has held a 4.3 B-edge / 2.8 B-vertex graph and run 3-hop source-to-many queries in 1.2–1.8 seconds."}],at={items:tt},wt=()=>(w("Graph | Kinetica"),e.jsxs("div",{className:"hex-tile-bg",children:[e.jsx(Y,{}),e.jsx(le,{}),e.jsx(ge,{}),e.jsx(Ne,{}),e.jsx($e,{}),e.jsx(ze,{}),e.jsx(qe,{}),e.jsx(et,{}),e.jsx(R,{items:at.items})]}));

7export{wt as FeaturesGraphAnalyticsPage,le as GraphAnalyticsCategory,et as GraphAnalyticsFinalCta,Y as GraphAnalyticsHero,ze as GraphAnalyticsInProduction,Ne as GraphAnalyticsLiveGraph,qe as GraphAnalyticsNumbers,ge as GraphAnalyticsQuery,$e as GraphAnalyticsToolkit};