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apache / incubator-hugegraph

A graph database that supports more than 100+ billion data, high performance and scalability (Include OLTP Engine & REST-API & Backends)
https://hugegraph.apache.org
Apache License 2.0
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Optimizing Shortest Path Calculation #2496

Closed ZeeJJ123 closed 2 months ago

ZeeJJ123 commented 3 months ago

Problem Type (问题类型)

others (please edit later)

Before submit

Environment (环境信息)

Your Question (问题描述)

I noticed someone mentioned that the s-t shortest path calculation is slow. I'm trying to understand the issue and aim to enhance the algorithm.

Vertex/Edge example (问题点 / 边数据举例)

No response

Schema [VertexLabel, EdgeLabel, IndexLabel] (元数据结构)

No response

dosubot[bot] commented 3 months ago

Hey @ZeeJJ123, great to see you diving into HugeGraph's challenges again! Hope you're doing well.

Based on the information you've provided, there are several strategies you can employ to optimize the shortest path calculation performance in Apache HugeGraph 1.0.0 with a RocksDB backend:

  1. Graph Schema Optimization: Design your graph schema efficiently. Use appropriate property keys and edge labels to minimize traversal paths. Although the provided context does not include schema details, ensuring a well-structured schema can significantly impact performance.

  2. Indexing: Utilize HugeGraph's indexing capabilities to speed up query performance. Create appropriate index labels for vertices and edges that are frequently accessed or queried. This can drastically reduce the search space for finding the shortest path.

  3. Query Optimization: Write efficient graph queries. Use filters and conditions to narrow down the search space as much as possible before executing shortest path algorithms.

  4. Hardware Considerations: While specific hardware details are not provided, using SSDs over HDDs can offer better I/O performance for RocksDB, which is I/O intensive. If possible, prioritize SSDs for your storage.

  5. Memory Management: Allocate sufficient memory to the Java Virtual Machine (JVM) that runs HugeGraph, as RocksDB and the graph processing itself can be memory-intensive. Adjust the JVM settings based on your available system resources.

  6. Parallelism and Concurrency: Leverage HugeGraph's support for parallel computing. If your setup allows, configure the system to use multiple cores or CPUs to process the graph data in parallel, reducing the overall computation time for finding shortest paths.

  7. RocksDB Tuning: Tune RocksDB settings according to your workload. RocksDB offers various configuration options that can be adjusted to optimize performance for specific scenarios, such as read-heavy or write-heavy workloads. Adjusting parameters like write_buffer_size, max_write_buffer_number, min_write_buffer_number_to_merge, and level0_file_num_compaction_trigger can help.

  8. Network Configuration: If your setup involves distributed nodes, ensure the network configuration is optimized for low latency and high throughput. The provided context includes RPC configuration options, which suggest the possibility of a distributed setup. Adjust RPC_CLIENT_CONNECT_TIMEOUT, RPC_CLIENT_READ_TIMEOUT, and RPC_CLIENT_RETRIES to optimize network calls.

  9. Use of Caching: If applicable, implement caching mechanisms for frequently accessed paths or subgraphs to reduce computation times for repeated queries.

  10. Monitoring and Profiling: Continuously monitor and profile your HugeGraph instance to identify performance bottlenecks. Use tools like VisualVM or JProfiler to monitor JVM performance, and consider HugeGraph's built-in metrics for monitoring graph-specific operations.

Unfortunately, without more specific details about your hardware, graph schema, and data, it's difficult to provide more targeted advice. If you can provide more information, I may be able to offer more specific suggestions.

Sources

#### About Dosu This response is meant to be useful and save you time. It is **not meant to be a precise solution**, but rather a starting point for your own research. Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant. If you want to continue the conversation, start your reply with @dosu-bot.
imbajin commented 3 months ago

@dosu-bot refer the context here #2359, also can't you be @/summoned to answer questions in historical issues? (u could tell us any rules about your answer mechanism)

@ZeeJJ123 u could give the robot more context/details/link about your problem, and continue to supplement and adjust corrections in the reply, which will align faster

dosubot[bot] commented 3 months ago

To enhance the shortest path calculation in Apache HugeGraph version 1.0.0 with a RocksDB backend, consider these strategies:

  1. Graph Schema Optimization: Minimize vertex and edge types based on intended queries and use appropriate property types to reduce storage and improve access speed.

  2. Indexing: Implement secondary indexing for vertices and edges that are frequently accessed or queried to significantly speed up query processing.

  3. Query Optimization: Fetch only the necessary data by using conditions and limits to reduce the amount of data processed and transferred.

  4. Hardware: Opt for SSDs over HDDs for storage to improve I/O performance. Scale your hardware both vertically (more powerful machines) and horizontally (more machines) as needed.

  5. Memory Management: Allocate sufficient memory to the Java heap and to RocksDB cache to improve performance, adjusting based on usage.

  6. Parallelism: If supported by HugeGraph, leverage parallel query execution to distribute the workload across multiple cores or nodes.

  7. RocksDB Tuning: Adjust RocksDB settings such as block_cache_size, write_buffer_size, and max_write_buffer_number to optimize read and write performance.

  8. Network Configuration: Ensure low-latency, high-bandwidth network connectivity between nodes in a distributed setup.

  9. Caching: Implement caching mechanisms for frequently accessed data to reduce database hits, either at the application level or using external solutions.

  10. Monitoring: Use tools like Prometheus and Grafana for monitoring metrics related to HugeGraph and RocksDB to identify bottlenecks and performance issues.

  11. RocksDB Backend Specific: Consider enabling compression in RocksDB to save space and potentially improve I/O performance for certain workloads.

The effectiveness of these optimizations can vary based on your specific setup, including node count, storage type, operating system, and dataset size. It's crucial to benchmark and monitor the impact of any changes.

For more detailed information, refer to the RpcOptions.java file in the repository.

Providing more context or details about your graph schema, hardware, and data could enable more targeted advice.

Sources

#### About Dosu This response is meant to be useful and save you time. It is **not meant to be a precise solution**, but rather a starting point for your own research. Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant. If you want to continue the conversation, start your reply with @dosu-bot.
ZeeJJ123 commented 3 months ago

@dosubot, The shortest path is calculated through both forward and backward traversal. Please reference all related files. We are considering whether Dijkstra's algorithm with a priority queue could improve efficiency. Analyze this strategy in comparison to our current implementation.

dosubot[bot] commented 3 months ago

Implementing Dijkstra's algorithm with a priority queue for the shortest path calculation in Apache HugeGraph could potentially enhance efficiency, especially for dense graphs. The algorithm's efficiency, characterized by its O((V+E) log V) time complexity, where V represents vertices and E represents edges, hinges on the graph's structure, the vertices and edges count, and the edge weights distribution. The priority queue optimizes vertex selection, crucial for performance improvement.

However, Dijkstra's algorithm is suitable for graphs with non-negative edge weights. If your graph includes negative edge weights, consider alternatives like Bellman-Ford or Johnson's algorithm.

Hardware optimizations, such as preferring SSDs over HDDs and ensuring ample memory allocation, significantly impact Dijkstra's algorithm performance. SSDs enhance data access speeds vital for the algorithm's frequent read operations, while adequate memory allocation reduces disk I/O operations by keeping more graph data in memory, facilitating faster priority queue operations.

For a precise performance comparison, conducting a benchmark test with a data subset is advisable, comparing Dijkstra's algorithm with a priority queue against the current forward and backward traversal method.

Relevant files for implementing Dijkstra's algorithm in Apache HugeGraph include:

This analysis aims to guide your decision on implementing Dijkstra's algorithm with a priority queue. For further questions or clarification, feel free to reach out.

Source

Sources

#### About Dosu This response is meant to be useful and save you time. It is **not meant to be a precise solution**, but rather a starting point for your own research. Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant. If you want to continue the conversation, start your reply with @dosu-bot.
github-actions[bot] commented 2 months ago

Due to the lack of activity, the current issue is marked as stale and will be closed after 20 days, any update will remove the stale label