dosubot[bot] commented on issue #2869:
URL: 
https://github.com/apache/incubator-hugegraph/issues/2869#issuecomment-3279510805

   <!-- Answer -->
   你的查询在 HugeGraph 1.5.0 下性能大幅下降,主要瓶颈集中在多层 
HugeVertexStep、TraversalFilterStep、AndStep 等遍历和过滤环节。代码实现上,这些步骤会先构造后端查询(如 
GraphTransaction.constructEdgesQuery),再用 FilterIterator 在内存中做二次过滤,复杂条件和多层 
inE/outE 会导致大量数据在 JVM 内部处理,极易拖慢查询,尤其是在 50 亿点/边的大规模数据下,分布式锁、session 
管理和后端通信开销也显著增加[代码分析](https://github.com/apache/incubator-hugegraph/blob/a53af864b31f803780a1db31cb20d3d94d5d34fc/hugegraph-server/hugegraph-core/src/main/java/org/apache/hugegraph/traversal/algorithm/HugeTraverser.java)。
   
   优化建议:
   - JVM 堆建议 32GB 以上,memory.mode 设为 off-heap,vertex/edge 
cache_capacity、cache_expire 调大,避免频繁后端 
IO,提升缓存命中率[配置建议](https://app.dosu.dev/documents/7efdc24e-edec-4811-afdd-d39a113f3dfe)。
   - 检查所有 where/has/filter 条件是否有二级或范围索引覆盖,避免全表扫描。
   - 查询语句建议加 limit、分页,减少一次性拉取和多层 outE/inE 链式遍历。
   - hstore 后端参数(分片数、线程池、batch size、rocksdb 缓存等)需根据硬件和数据量调优,避免锁竞争和 session 过多。
   - 用 Arthas 等工具定位慢查询瓶颈是在计算层还是存储层,关注 FilterIterator、validateVertex/Edge 
等环节的内存和 CPU 占用。
   
   如需更细致定位,建议补充 schema 索引结构和 hstore 详细配置。
   
   
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