dosubot[bot] commented on issue #3163:
URL: https://github.com/apache/hugegraph/issues/3163#issuecomment-5323228176

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   @cichengzi — this is essentially the same issue you raised in 
[#3059](https://github.com/apache/hugegraph/issues/3059), and the guidance 
there still applies. Here's a consolidated summary:
   
   **Why memory keeps growing:**
   
   1. **Off-heap RocksDB allocations** (memtables, block cache) are invisible 
to JVM GC. The default `rocksdb.total_memory_size` is **48GB**, which alone can 
exceed your machine's RAM. Set it to a realistic value (e.g., 8–10GB on a 32GB 
machine). 
[[1]](https://github.com/apache/hugegraph/issues/3059#issuecomment-4679589186)
   
   2. **glibc allocator fragmentation** — RocksDB's allocation pattern with the 
default `ptmalloc` allocator causes freed memory to never be returned to the 
OS. Replacing it with **jemalloc** is often the single most impactful fix 
[[1]](https://github.com/apache/hugegraph/issues/3059#issuecomment-4679589186):
   
      ```bash
      export LD_PRELOAD=/path/to/libjemalloc.so
      ```
   
   3. **Transaction listener leak** — repeatedly creating graphs accumulates 
orphaned event listeners in `CachedGraphTransaction` that are never cleaned up, 
causing persistent heap growth 
[[1]](https://github.com/apache/hugegraph/issues/3059#issuecomment-4679589186).
   
   4. **Accumulated task results** — completed tasks leave `~task` and 
`~taskresult` vertices in the backend that get reloaded into memory and are 
never automatically cleaned up. Periodically delete old task results, and 
reduce `task.result_size_limit` 
[[1]](https://github.com/apache/hugegraph/issues/3059#issuecomment-4679589186).
   
   5. **Vertex/edge caches** — HugeGraph's caches evict by capacity/TTL only, 
not by memory pressure, so they stay full even when heap is stressed. Reduce 
`vertex.cache_capacity`, `edge.cache_capacity`, and set a shorter 
`cache_expire` 
[[1]](https://github.com/apache/hugegraph/issues/3059#issuecomment-4679589186).
   
   **Key RocksDB settings to tune** 
[[1]](https://github.com/apache/hugegraph/issues/3059#issuecomment-4679589186):
   
   | Setting | Default | Recommendation |
   |---|---|---|
   | `rocksdb.total_memory_size` | 48GB | 8–10GB on 32GB machines |
   | `rocksdb.write_buffer_size` | 128MB | Reduce to 64MB |
   | `rocksdb.max_write_buffer_number` | 6 | Reduce to 3 |
   
   **Diagnosis tip:** Compare JVM heap (via `jstat -gc`) against total process 
RSS (`/proc/<pid>/status`). If heap is fine but RSS is very high, the problem 
is RocksDB native memory / allocator fragmentation. If heap itself is high, 
look at task results and caches first 
[[1]](https://github.com/apache/hugegraph/issues/3059#issuecomment-4679589186).
   
   For large Gremlin queries specifically, avoid returning huge result sets 
through the task result mechanism — it's not designed as a bulk download API. 
Write results incrementally to external storage instead 
[[1]](https://github.com/apache/hugegraph/issues/3059#issuecomment-4679589186).
   
   
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