开发者 Semyon Sinchenko 利用 Apache DataFusion 实现了一套 Map-Reduce 图计算框架，突破了传统图算法库的内存限制，在仅 10GB 内存约束下成功处理十亿级规模的图数据。[1] 该方案通过将数据溢出到磁盘、采用面向批量扫描而非随机访问的算法设计，实现了在极限资源条件下的大规模图处理。[1]
在 PageRank 算法测试中，使用图 500-26 数据集（包含 10 亿条有向边），仅耗用 5GB 内存，完成 15 次迭代用时约 30 分钟。[1] 弱连通分量（WCC）识别任务处理了 twitter_mpi 数据集，该数据集包含 20 亿条边（CSV 格式原始大小 30GB），使用 10GB 内存在 22 次迭代内完成计算。[1] 图对称化后，WCC 前处理阶段边数增至 32.28 亿条，但首次迭代后又降至 8.4 亿条。[1]
NetworkX 和 Igraph 等现有图算法库无法处理这一规模的数据，通常需要借助 Apache Spark 和 GraphFrames 等分布式框架。[1] 本方案的计算结果与 Graphalytics 提供的基准真值实现了 100% 匹配，验证了其精度和可靠性。[1]
A developer has successfully implemented graph computing algorithms capable of processing billions of edges within severe memory constraints by leveraging Apache DataFusion.[1] The work demonstrates that large-scale graph analysis previously thought to require distributed systems like Apache Spark can be accomplished on a single machine with careful algorithm design.
The developer implemented a Map-Reduce framework for graph computation that spills data to disk and employs batch-scan-optimized algorithms rather than random-access approaches.[1] Using the graph500-26 dataset containing 1 billion directed edges, the PageRank algorithm completed 15 iterations in approximately 30 minutes while consuming just 5GB of memory.[1] In a more demanding test, the weakly connected components (WCC) algorithm processed the twitter_mpi dataset of 2 billion edges—stored as a 30GB CSV file—using 10GB of memory, with 22 iterations required for convergence.[1] The WCC computation involved preprocessing that expanded the edge count to 3.228 billion edges due to graph symmetrization, which then decreased to 840 million edges after the first iteration.[1]
Traditional graph libraries such as NetworkX and Igraph cannot handle graphs at these scales, and practitioners typically resort to Apache Spark with GraphFrames for such workloads.[1] The results achieved validation against benchmark ground truth provided by Graphalytics with 100% accuracy.[1]