PERFORMANCE on Apache HugeGraph

HugeGraph BenchMark Performance

Mon, 01 Jan 0001 00:00:00 +0000

1 Test environment

1.1 Hardware information

CPU	Memory	网卡	磁盘
48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz	128G	10000Mbps	750GB SSD

1.2 Software information

1.2.1 Test cases

Testing is done using the graphdb-benchmark, a benchmark suite for graph databases. This benchmark suite mainly consists of four types of tests:

Massive Insertion, which involves batch insertion of vertices and edges, with a certain number of vertices or edges being submitted at once.
Single Insertion, which involves the immediate insertion of each vertex or edge, one at a time.
Query, which mainly includes the basic query operations of the graph database:
- Find Neighbors, which queries the neighbors of all vertices.
- Find Adjacent Nodes, which queries the adjacent vertices of all edges.
- Find Shortest Path, which queries the shortest path from the first vertex to 100 random vertices.
Clustering, which is a community detection algorithm based on the Louvain Method.

1.2.2 Test dataset

Tests are conducted using both synthetic and real data.

HugeGraph-Loader Performance

Mon, 01 Jan 0001 00:00:00 +0000

Use Cases

When the number of graph data to be batch inserted (including vertices and edges) is at the billion level or below, or the total data size is less than TB, the HugeGraph-Loader tool can be used to continuously and quickly import graph data.

Performance

The test uses the edge data of website.

RocksDB single-machine performance

When label index is turned off, 228k edges/s.
When label index is turned on, 153k edges/s.

Cassandra cluster performance

When label index is turned on by default, 63k edges/s.

HugeGraph 0.4.4 Benchmark

Mon, 01 Jan 0001 00:00:00 +0000

1 测试环境

1.1 硬件信息

CPU	Memory	网卡	磁盘
48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz	128G	10000Mbps	750GB SSD

1.2 软件信息

1.2.1 测试用例

测试使用graphdb-benchmark，一个图数据库测试集。该测试集主要包含4类测试：

Massive Insertion，批量插入顶点和边，一定数量的顶点或边一次性提交
Single Insertion，单条插入，每个顶点或者每条边立即提交
Query，主要是图数据库的基本查询操作：
- Find Neighbors，查询所有顶点的邻居
- Find Adjacent Nodes，查询所有边的邻接顶点
- Find Shortest Path，查询第一个顶点到100个随机顶点的最短路径
Clustering，基于Louvain Method的社区发现算法

1.2.2 测试数据集

测试使用人造数据和真实数据

MIW、SIW和QW使用SNAP数据集
- Enron Dataset
- Amazon dataset