Graph Traversals
Effortlessly Explore Complex Relationships with ArangoDB's Advanced Graph Traversal: Uncover Patterns, Analyze Networks, and Extract Valuable Insights from Interconnected Data, All in Real-Time!
“ArangoDB’s multi-model approach and the possibility to have joins and graph traversals natively in AQL is very good. Sometimes it’s handy to combine joins and traversals in the same query.”
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Graph today
(no credit card required), and experience the shortest time to value for a hosted graph DB.
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Questions to ask Legacy Graph DB Vendors
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Case Studies
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Pruned Traversal
ArangoDB enables pruned traversals, allowing users to define specific conditions to stop traversing through certain vertices. This functionality ensures optimized performance by avoiding unnecessary traversal through irrelevant nodes, enabling more focused and efficient analysis of relevant connections and
relationships in the graph.
Why it matters: This leads to optimized resource utilization and improved traversal speeds, crucial for handling large-scale graph data sets effectively.
K-Shortest Path Queries
ArangoDB has the ability to calculate K-Shortest Paths in graphs, enabling users to uncover multiple shortest paths between two vertices. This feature is crucial for detecting alternative relationships and pathways within the interconnected data, offering a broader perspective and providing richer context during data analysis.
Why it matters: It enhances the depth of graph analysis, enabling exploration of diverse relationship pathways,
vital for multifaceted data interpretation.
Vertex-Centric Indices
Vertex-centric indices accelerate graph traversals. This feature allows for indexing based on vertex attributes and edge types, which significantly enhances traversal performance, especially in scenarios with complex graph structures. These indices greatly reduce lookup times and optimize query execution.
Why it matters: It enhances the depth of graph analysis, enabling exploration of diverse relationship pathways,
vital for multifaceted data interpretation.
Weighted Graph Traversal
ArangoDB supports traversing weighted graphs, allowing users to define weights on edges to represent the cost of traversal between vertices. This feature enables more nuanced analyses by considering the significance or cost associated with relationships, offering the ability to find the most cost-effective paths in the graph.
Why it matters: It provides a mechanism to incorporate real-world constraints and considerations into graph analysis, making the results more practical and actionable for decision-making processes.
Pattern Matching
ArangoDB supports advanced pattern matching during graph traversals. This enables users to perform more precise and complex analyses by identifying specific patterns within the graph structures, allowing more nuanced insights and understanding of the underlying interconnected data.
Why it matters: This facilitates the discovery of
intricate data patterns and relationships, crucial for extracting meaningful insights from complex datasets.
Get started with
Graph today
(no credit card required), and experience the shortest time to value for a hosted graph DB.
Download The Graph DB Buyer’s Guide
Questions to ask Legacy Graph DB Vendors
Read the
Case Studies
Learn why companies across industries are switching to ArangoDB for Graph.