Satsuma .NET Graph Library vs. Other .NET Graph Tools: Performance ComparisonThis article compares the Satsuma .NET Graph Library with several other .NET graph libraries, focusing on performance characteristics, algorithmic coverage, memory usage, ease of integration, and real-world suitability. The goal is a practical, experiment-backed guide to help developers choose a graph library for tasks such as shortest paths, maximum flow, matching, and large-scale graph processing in .NET applications.
Executive summary
- Satsuma .NET Graph Library is a high-performance, focused library implementing a compact set of efficient graph algorithms (shortest paths, max flow, matching, min-cost flow, spanning trees) with an emphasis on speed and low memory overhead for typical combinatorial problems.
- Competing .NET tools (e.g., QuickGraph, Microsoft.Msagl, Neo4j .NET drivers, and general-purpose numeric libraries like Math.NET combined with custom graph code) each have trade-offs: broader feature sets or visualization capabilities but often higher memory use, less optimized algorithm implementations, or architectural constraints.
- For raw algorithmic performance on classical combinatorial graph problems in-memory, Satsuma usually outperforms or matches alternatives, especially on sparse graphs and when low-allocation behavior matters. For graph databases, visualization, or enterprise features, other tools may be more appropriate.
Libraries compared
- Satsuma .NET Graph Library (Satsuma)
- QuickGraph (community .NET graph library)
- Microsoft Automatic Graph Layout (MSAGL) — focused on layout and visualization
- Neo4j .NET drivers (for working with a graph database)
- Custom implementations using Math.NET Numerics or other general-purpose libraries
- Google’s OR-Tools (has .NET bindings; includes graph algorithms, flows, routing)
Comparison criteria
- Algorithm coverage — which classical algorithms are implemented and ready to use.
- Time performance — runtime speed on representative workloads (shortest paths, max flow, matching).
- Memory usage — allocations and peak memory for large graphs.
- API ergonomics and integration with .NET projects.
- Scalability — behavior on large graphs (millions of edges/nodes).
- Use-case fit — when to pick Satsuma vs. alternatives.
Algorithm coverage
- Satsuma: includes Dijkstra, Bellman–Ford (or equivalent shortest path), maximum flow (Push-Relabel), min-cost flow, min-cost max-flow, maximum bipartite matching, minimum spanning tree, strongly connected components, topological sort, and utilities for directed/undirected multigraph handling. Focused on core combinatorial algorithms.
- QuickGraph: broad collection of algorithms and graph types, good for general-purpose tasks; community-maintained so coverage and performance vary.
- MSAGL: primarily layout, not optimized for algorithmic performance beyond what’s needed for rendering.
- Neo4j drivers: rely on database-side algorithms (APOC, built-in algorithms); client-side driver overhead and network I/O make it unsuitable for raw in-memory algorithm benchmarks.
- Math.NET + custom: flexible but requires implementing graph structures/algorithms; performance depends entirely on implementation quality.
- OR-Tools: excellent for optimization problems and routing; some graph algorithms are present but the library is larger and not focused purely on classical graph primitives.
Benchmark methodology (recommended reproducible approach)
To meaningfully compare: run controlled benchmarks on the same machine (.NET runtime specified), using identical graph inputs and measuring wall-clock time and memory:
- Graph types: sparse random graphs (E ≈ 4V), dense graphs (E ≈ V(V−1)/2 for smaller V), grid graphs, and real-world network snapshots (road networks, social graphs).
- Tasks: single-source shortest paths (Dijkstra), all-pairs shortest paths (where feasible), maximum flow between chosen node pairs, maximum bipartite matching, and minimum-cost flow.
- Measurements: median of multiple runs, GC collections counted, peak working set, and allocations (use dotnet-counters, BenchmarkDotNet, or PerfView).
- Implementation notes:
- Warm up JIT before measurements.
- Use release builds without debugger attached.
- Avoid logging/console I/O in timed sections.
- Use equivalent algorithmic variants where possible (e.g., priority queue implementation matters).
Representative benchmark results (summary of observed patterns)
Note: exact numbers depend on machine, .NET version, and graph instances. Below are typical, empirically observed patterns from multiple community reports and benchmark runs.
- Single-source shortest paths (sparse graphs, V=100k, E≈400k):
- Satsuma: fastest or among fastest, low allocations, optimized adjacency representation.
- QuickGraph: slower than Satsuma; more allocations and indirections.
- Math.NET + custom: varies; well-implemented C# optimized code can approach Satsuma but often lags due to less tuned data structures.
- Maximum flow (medium graphs V≈10k, E≈50k):
- Satsuma’s Push-Relabel implementation: very competitive, often faster than generic implementations in other libraries.
- OR-Tools or specialized C++ libs (accessed via interop): can outperform Satsuma for some dense instances, but interop overhead and complexity increase.
- Neo4j: not comparable for in-memory performance due to network/database I/O.
- Memory usage:
- Satsuma: low memory footprint due to compact storage types and minimal per-edge allocations.
- QuickGraph: higher per-edge/object overhead (edge objects, delegates).
- Custom implementations: can be optimized but risk mistakes leading to higher allocations.
- Large-scale behavior:
- Satsuma handles sparse large graphs well; performance degrades predictably with density.
- Graph databases scale horizontally for massive graphs but with different performance characteristics (query latency, transactional overhead).
API ergonomics & integration
- Satsuma: minimal, focused API oriented to algorithm calls on graph objects. Easy to integrate into algorithm-heavy applications. Less emphasis on visualization or persistence — you manage serialization or database integration separately.
- QuickGraph: flexible graph types, LINQ-friendly in places, but API surface is bigger and sometimes less consistent.
- MSAGL: integrates well into UI apps for visualization; not intended as a high-performance algorithm library.
- Neo4j drivers: integrate with Neo4j DB; excellent if you need ACID storage, complex queries, and graph database features. Not ideal for tight-loop algorithmic processing on in-memory graphs.
- OR-Tools: large API for optimization; good if you need linear programming, routing, and advanced solvers beyond classic graph algorithms.
When to choose Satsuma
- You need high-performance implementations of core graph algorithms (shortest path, flow, matching) executed in-memory.
- You work with large sparse graphs and must minimize allocations and GC pressure.
- Your application is algorithm-heavy (route planning, network flows, combinatorial optimization) and you want reliable, tested implementations without writing them yourself.
When to consider alternatives
- You need graph visualization or interactive layout (use MSAGL).
- You require a graph database with persistence, transactions, and querying (use Neo4j or other graph DBs).
- You need a very wide range of algorithms and language integrations supported by a large community (QuickGraph or OR-Tools for optimization-heavy workloads).
- You prefer native C/C++ libraries for absolute maximum performance and are willing to use interop.
Practical tips to maximize performance with Satsuma
- Use the library’s compact graph representations and avoid per-edge object wrappers.
- Preallocate capacities where supported to reduce reallocation.
- Prefer integer-based node/edge indices when possible to reduce boxing.
- Run GPU/parallel variants only if the library or your code supports them — Satsuma focuses on efficient single-machine CPU implementations.
- Profile hot paths with BenchmarkDotNet / dotnet-counters / PerfView rather than guessing.
Example micro-benchmark (conceptual)
Use BenchmarkDotNet to compare Dijkstra across libraries. Example structure (pseudo):
[MemoryDiagnoser] public class DijkstraBench { private Graph satsumaGraph; private Graph quickGraph; private int source; [GlobalSetup] public void Setup() { // build identical graphs for both libraries } [Benchmark] public void SatsumaDijkstra() => Satsuma.Algorithms.Dijkstra(satsumaGraph, source); [Benchmark] public void QuickGraphDijkstra() => QuickGraph.Algorithms.Dijkstra(quickGraph, source); } Run with release configuration and analyze allocations and time.
Limitations and caveats
- Benchmarks depend heavily on graph structures, input distributions, and machine characteristics. Always benchmark with your own data.
- Satsuma’s focus on algorithmic performance means fewer utilities for persistence, visualization, and metadata; you may need to write glue code.
- Some libraries are community-maintained and may have varying release/update frequency; check activity if long-term maintenance matters.
Conclusion
For focused in-memory algorithmic work—shortest paths, flows, and matching on large sparse graphs—Satsuma .NET Graph Library is a strong choice, often offering the best balance of speed and memory efficiency among .NET-native options. If your needs include visualization, persistent graph storage, or advanced optimization beyond classical graph algorithms, consider MSAGL, Neo4j, OR-Tools, or QuickGraph as complements or alternatives.
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