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| 顶点/边的单条插入 |
| 顶点/边的批量插入 |
| 顶点/边的查询 |
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| HugeGraph的每个发布版本的RESTful API的性能测试情况可以参考： |
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| <h1 class="title">HugeGraph-API Performance</h1> |
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| <ul> |
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| <li>1: <a href="#pg-dbfafc29a5e930415f78f72c47ee67f2">v0.5.6 Stand-alone(RocksDB)</a></li> |
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| <li>2: <a href="#pg-fd5b165e28a07f1c35ab177b10e15dc8">v0.5.6 Cluster(Cassandra)</a></li> |
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| <li>3: <a href="#pg-0005aca20e30ef2795411939ccbf0fd9">v0.4.4</a></li> |
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| <li>4: <a href="#pg-d4233a3feb070643ef5d54a6f9c12cab">v0.2</a></li> |
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| <p>HugeGraph API性能测试主要测试HugeGraph-Server对RESTful API请求的并发处理能力，包括：</p> |
| <ul> |
| <li>顶点/边的单条插入</li> |
| <li>顶点/边的批量插入</li> |
| <li>顶点/边的查询</li> |
| </ul> |
| <p>HugeGraph的每个发布版本的RESTful API的性能测试情况可以参考：</p> |
| <ul> |
| <li><a href="/docs/performance/api-preformance/hugegraph-api-0.5.6-rocksdb/">v0.5.6 stand-alone</a></li> |
| <li><a href="/docs/performance/api-preformance/hugegraph-api-0.5.6-cassandra/">v0.5.6 cluster</a></li> |
| <li><a href="/docs/performance/api-preformance/hugegraph-api-0.4.4/">v0.4.4</a></li> |
| <li><a href="/docs/performance/api-preformance/hugegraph-api-0.2/">v0.2</a></li> |
| </ul> |
| <blockquote> |
| <p>之前的版本只提供HugeGraph所支持的后端种类中性能最好的API性能测试，从0.5.6版本开始，分别提供了单机和集群的性能情况</p> |
| </blockquote> |
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| <div class="td-content" style="page-break-before: always"> |
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| <h1 id="pg-dbfafc29a5e930415f78f72c47ee67f2">1 - v0.5.6 Stand-alone(RocksDB)</h1> |
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| <h3 id="1-测试环境">1 测试环境</h3> |
| <p>被压机器信息</p> |
| <table> |
| <thead> |
| <tr> |
| <th>CPU</th> |
| <th>Memory</th> |
| <th>网卡</th> |
| <th>磁盘</th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr> |
| <td>48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz</td> |
| <td>128G</td> |
| <td>10000Mbps</td> |
| <td>750GB SSD,2.7T HDD</td> |
| </tr> |
| </tbody> |
| </table> |
| <ul> |
| <li>起压力机器信息：与被压机器同配置</li> |
| <li>测试工具：apache-Jmeter-2.5.1</li> |
| </ul> |
| <p>注：起压机器和被压机器在同一机房</p> |
| <h3 id="2-测试说明">2 测试说明</h3> |
| <h4 id="21-名词定义时间的单位均为ms">2.1 名词定义（时间的单位均为ms）</h4> |
| <ul> |
| <li>Samples – 本次场景中一共完成了多少个线程</li> |
| <li>Average – 平均响应时间</li> |
| <li>Median – 统计意义上面的响应时间的中值</li> |
| <li>90% Line – 所有线程中90%的线程的响应时间都小于xx</li> |
| <li>Min – 最小响应时间</li> |
| <li>Max – 最大响应时间</li> |
| <li>Error – 出错率</li> |
| <li>Throughput – 吞吐量</li> |
| <li>KB/sec – 以流量做衡量的吞吐量</li> |
| </ul> |
| <h4 id="22-底层存储">2.2 底层存储</h4> |
| <p>后端存储使用RocksDB，HugeGraph与RocksDB都在同一机器上启动，server相关的配置文件除主机和端口有修改外，其余均保持默认。</p> |
| <h3 id="3-性能结果总结">3 性能结果总结</h3> |
| <ol> |
| <li>HugeGraph单条插入顶点和边的速度在每秒1w左右</li> |
| <li>顶点和边的批量插入速度远大于单条插入速度</li> |
| <li>按id查询顶点和边的并发度可达到13000以上，且请求的平均延时小于50ms</li> |
| </ol> |
| <h3 id="4-测试结果及分析">4 测试结果及分析</h3> |
| <h4 id="41-batch插入">4.1 batch插入</h4> |
| <h5 id="411-压力上限测试">4.1.1 压力上限测试</h5> |
| <h6 id="测试方法">测试方法</h6> |
| <p>不断提升并发量，测试server仍能正常提供服务的压力上限</p> |
| <h6 id="压力参数">压力参数</h6> |
| <p>持续时间：5min</p> |
| <h6 id="顶点的最大插入速度">顶点的最大插入速度：</h6> |
| <center> |
| <img src="/docs/images/API-perf/v0.5.6/rocksdb/vertex_batch.png" alt="image"> |
| </center> |
| <p>####### 结论：</p> |
| <ul> |
| <li>并发2200，顶点的吞吐量是2026.8，每秒可处理的数据：2026.8*200=405360/s</li> |
| </ul> |
| <h6 id="边的最大插入速度">边的最大插入速度</h6> |
| <center> |
| <img src="/docs/images/API-perf/v0.5.6/rocksdb/edge_batch.png" alt="image"> |
| </center> |
| <p>####### 结论：</p> |
| <ul> |
| <li>并发900，边的吞吐量是776.9，每秒可处理的数据：776.9*500=388450/s</li> |
| </ul> |
| <h4 id="42-single插入">4.2 single插入</h4> |
| <h5 id="421-压力上限测试">4.2.1 压力上限测试</h5> |
| <h6 id="测试方法-1">测试方法</h6> |
| <p>不断提升并发量，测试server仍能正常提供服务的压力上限</p> |
| <h6 id="压力参数-1">压力参数</h6> |
| <ul> |
| <li>持续时间：5min</li> |
| <li>服务异常标志：错误率大于0.00%</li> |
| </ul> |
| <h6 id="顶点的单条插入">顶点的单条插入</h6> |
| <center> |
| <img src="/docs/images/API-perf/v0.5.6/rocksdb/vertex_single.png" alt="image"> |
| </center> |
| <p>####### 结论：</p> |
| <ul> |
| <li>并发11500，吞吐量为10730，顶点的单条插入并发能力为11500</li> |
| </ul> |
| <h6 id="边的单条插入">边的单条插入</h6> |
| <center> |
| <img src="/docs/images/API-perf/v0.5.6/rocksdb/edge_single.png" alt="image"> |
| </center> |
| <p>####### 结论：</p> |
| <ul> |
| <li>并发9000，吞吐量是8418，边的单条插入并发能力为9000</li> |
| </ul> |
| <h4 id="43-按id查询">4.3 按id查询</h4> |
| <h5 id="431-压力上限测试">4.3.1 压力上限测试</h5> |
| <h6 id="测试方法-2">测试方法</h6> |
| <p>不断提升并发量，测试server仍能正常提供服务的压力上限</p> |
| <h6 id="压力参数-2">压力参数</h6> |
| <ul> |
| <li>持续时间：5min</li> |
| <li>服务异常标志：错误率大于0.00%</li> |
| </ul> |
| <h6 id="顶点的按id查询">顶点的按id查询</h6> |
| <center> |
| <img src="/docs/images/API-perf/v0.5.6/rocksdb/vertex_id_query.png" alt="image"> |
| </center> |
| <p>####### 结论：</p> |
| <ul> |
| <li>并发14000，吞吐量是12663，顶点的按id查询的并发能力为14000，平均延时为44ms</li> |
| </ul> |
| <h6 id="边的按id查询">边的按id查询</h6> |
| <center> |
| <img src="/docs/images/API-perf/v0.5.6/rocksdb/edge_id_query.png" alt="image"> |
| </center> |
| <p>####### 结论：</p> |
| <ul> |
| <li>并发13000，吞吐量是12225，边的按id查询的并发能力为13000，平均延时为12ms</li> |
| </ul> |
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| </div> |
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| <div class="td-content" style="page-break-before: always"> |
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| <h1 id="pg-fd5b165e28a07f1c35ab177b10e15dc8">2 - v0.5.6 Cluster(Cassandra)</h1> |
| |
| <h3 id="1-测试环境">1 测试环境</h3> |
| <p>被压机器信息</p> |
| <table> |
| <thead> |
| <tr> |
| <th>CPU</th> |
| <th>Memory</th> |
| <th>网卡</th> |
| <th>磁盘</th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr> |
| <td>48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz</td> |
| <td>128G</td> |
| <td>10000Mbps</td> |
| <td>750GB SSD,2.7T HDD</td> |
| </tr> |
| </tbody> |
| </table> |
| <ul> |
| <li>起压力机器信息：与被压机器同配置</li> |
| <li>测试工具：apache-Jmeter-2.5.1</li> |
| </ul> |
| <p>注：起压机器和被压机器在同一机房</p> |
| <h3 id="2-测试说明">2 测试说明</h3> |
| <h4 id="21-名词定义时间的单位均为ms">2.1 名词定义（时间的单位均为ms）</h4> |
| <ul> |
| <li>Samples – 本次场景中一共完成了多少个线程</li> |
| <li>Average – 平均响应时间</li> |
| <li>Median – 统计意义上面的响应时间的中值</li> |
| <li>90% Line – 所有线程中90%的线程的响应时间都小于xx</li> |
| <li>Min – 最小响应时间</li> |
| <li>Max – 最大响应时间</li> |
| <li>Error – 出错率</li> |
| <li>Throughput – 吞吐量</li> |
| <li>KB/sec – 以流量做衡量的吞吐量</li> |
| </ul> |
| <h4 id="22-底层存储">2.2 底层存储</h4> |
| <p>后端存储使用15节点Cassandra集群，HugeGraph与Cassandra集群位于不同的服务器，server相关的配置文件除主机和端口有修改外，其余均保持默认。</p> |
| <h3 id="3-性能结果总结">3 性能结果总结</h3> |
| <ol> |
| <li>HugeGraph单条插入顶点和边的速度分别为9000和4500</li> |
| <li>顶点和边的批量插入速度分别为5w/s和15w/s，远大于单条插入速度</li> |
| <li>按id查询顶点和边的并发度可达到12000以上，且请求的平均延时小于70ms</li> |
| </ol> |
| <h3 id="4-测试结果及分析">4 测试结果及分析</h3> |
| <h4 id="41-batch插入">4.1 batch插入</h4> |
| <h5 id="411-压力上限测试">4.1.1 压力上限测试</h5> |
| <h6 id="测试方法">测试方法</h6> |
| <p>不断提升并发量，测试server仍能正常提供服务的压力上限</p> |
| <h6 id="压力参数">压力参数</h6> |
| <p>持续时间：5min</p> |
| <h6 id="顶点的最大插入速度">顶点的最大插入速度：</h6> |
| <center> |
| <img src="/docs/images/API-perf/v0.5.6/cassandra/vertex_batch.png" alt="image"> |
| </center> |
| <p>####### 结论：</p> |
| <ul> |
| <li>并发3500，顶点的吞吐量是261，每秒可处理的数据：261*200=52200/s</li> |
| </ul> |
| <h6 id="边的最大插入速度">边的最大插入速度</h6> |
| <center> |
| <img src="/docs/images/API-perf/v0.5.6/cassandra/edge_batch.png" alt="image"> |
| </center> |
| <p>####### 结论：</p> |
| <ul> |
| <li>并发1000，边的吞吐量是323，每秒可处理的数据：323*500=161500/s</li> |
| </ul> |
| <h4 id="42-single插入">4.2 single插入</h4> |
| <h5 id="421-压力上限测试">4.2.1 压力上限测试</h5> |
| <h6 id="测试方法-1">测试方法</h6> |
| <p>不断提升并发量，测试server仍能正常提供服务的压力上限</p> |
| <h6 id="压力参数-1">压力参数</h6> |
| <ul> |
| <li>持续时间：5min</li> |
| <li>服务异常标志：错误率大于0.00%</li> |
| </ul> |
| <h6 id="顶点的单条插入">顶点的单条插入</h6> |
| <center> |
| <img src="/docs/images/API-perf/v0.5.6/cassandra/vertex_single.png" alt="image"> |
| </center> |
| <p>####### 结论：</p> |
| <ul> |
| <li>并发9000，吞吐量为8400，顶点的单条插入并发能力为9000</li> |
| </ul> |
| <h6 id="边的单条插入">边的单条插入</h6> |
| <center> |
| <img src="/docs/images/API-perf/v0.5.6/cassandra/edge_single.png" alt="image"> |
| </center> |
| <p>####### 结论：</p> |
| <ul> |
| <li>并发4500，吞吐量是4160，边的单条插入并发能力为4500</li> |
| </ul> |
| <h4 id="43-按id查询">4.3 按id查询</h4> |
| <h5 id="431-压力上限测试">4.3.1 压力上限测试</h5> |
| <h6 id="测试方法-2">测试方法</h6> |
| <p>不断提升并发量，测试server仍能正常提供服务的压力上限</p> |
| <h6 id="压力参数-2">压力参数</h6> |
| <ul> |
| <li>持续时间：5min</li> |
| <li>服务异常标志：错误率大于0.00%</li> |
| </ul> |
| <h6 id="顶点的按id查询">顶点的按id查询</h6> |
| <center> |
| <img src="/docs/images/API-perf/v0.5.6/cassandra/vertex_id_query.png" alt="image"> |
| </center> |
| <p>####### 结论：</p> |
| <ul> |
| <li>并发14500，吞吐量是13576，顶点的按id查询的并发能力为14500，平均延时为11ms</li> |
| </ul> |
| <h6 id="边的按id查询">边的按id查询</h6> |
| <center> |
| <img src="/docs/images/API-perf/v0.5.6/cassandra/edge_id_query.png" alt="image"> |
| </center> |
| <p>####### 结论：</p> |
| <ul> |
| <li>并发12000，吞吐量是10688，边的按id查询的并发能力为12000，平均延时为63ms</li> |
| </ul> |
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| </div> |
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| <div class="td-content" style="page-break-before: always"> |
| |
| <h1 id="pg-0005aca20e30ef2795411939ccbf0fd9">3 - v0.4.4</h1> |
| |
| <h3 id="1-测试环境">1 测试环境</h3> |
| <p>被压机器信息</p> |
| <table> |
| <thead> |
| <tr> |
| <th>机器编号</th> |
| <th>CPU</th> |
| <th>Memory</th> |
| <th>网卡</th> |
| <th>磁盘</th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr> |
| <td>1</td> |
| <td>24 Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz</td> |
| <td>61G</td> |
| <td>1000Mbps</td> |
| <td>1.4T HDD</td> |
| </tr> |
| <tr> |
| <td>2</td> |
| <td>48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz</td> |
| <td>128G</td> |
| <td>10000Mbps</td> |
| <td>750GB SSD,2.7T HDD</td> |
| </tr> |
| </tbody> |
| </table> |
| <ul> |
| <li>起压力机器信息：与编号 1 机器同配置</li> |
| <li>测试工具：apache-Jmeter-2.5.1</li> |
| </ul> |
| <p>注：起压机器和被压机器在同一机房</p> |
| <h3 id="2-测试说明">2 测试说明</h3> |
| <h4 id="21-名词定义时间的单位均为ms">2.1 名词定义（时间的单位均为ms）</h4> |
| <ul> |
| <li>Samples – 本次场景中一共完成了多少个线程</li> |
| <li>Average – 平均响应时间</li> |
| <li>Median – 统计意义上面的响应时间的中值</li> |
| <li>90% Line – 所有线程中90%的线程的响应时间都小于xx</li> |
| <li>Min – 最小响应时间</li> |
| <li>Max – 最大响应时间</li> |
| <li>Error – 出错率</li> |
| <li>Throughput – 吞吐量</li> |
| <li>KB/sec – 以流量做衡量的吞吐量</li> |
| </ul> |
| <h4 id="22-底层存储">2.2 底层存储</h4> |
| <p>后端存储使用RocksDB，HugeGraph与RocksDB都在同一机器上启动，server相关的配置文件除主机和端口有修改外，其余均保持默认。</p> |
| <h3 id="3-性能结果总结">3 性能结果总结</h3> |
| <ol> |
| <li>HugeGraph每秒能够处理的请求数目上限是7000</li> |
| <li>批量插入速度远大于单条插入，在服务器上测试结果达到22w edges/s，37w vertices/s</li> |
| <li>后端是RocksDB，增大CPU数目和内存大小可以增大批量插入的性能。CPU和内存扩大一倍，性能增加45%-60%</li> |
| <li>批量插入场景，使用SSD替代HDD，性能提升较小，只有3%-5%</li> |
| </ol> |
| <h3 id="4-测试结果及分析">4 测试结果及分析</h3> |
| <h4 id="41-batch插入">4.1 batch插入</h4> |
| <h5 id="411-压力上限测试">4.1.1 压力上限测试</h5> |
| <h6 id="测试方法">测试方法</h6> |
| <p>不断提升并发量，测试server仍能正常提供服务的压力上限</p> |
| <h6 id="压力参数">压力参数</h6> |
| <p>持续时间：5min</p> |
| <h6 id="顶点和边的最大插入速度高性能服务器使用ssd存储rocksdb数据">顶点和边的最大插入速度（高性能服务器，使用SSD存储RocksDB数据）：</h6> |
| <center> |
| <img src="/docs/images/API-perf/v0.4.4/best.png" alt="image"> |
| </center> |
| <h6 id="结论">结论：</h6> |
| <ul> |
| <li>并发1000，边的吞吐量是是451，每秒可处理的数据：451*500条=225500/s</li> |
| <li>并发2000，顶点的吞吐量是1842.4，每秒可处理的数据：1842.4*200=368480/s</li> |
| </ul> |
| <p><strong>1. CPU和内存对插入性能的影响（服务器都使用HDD存储RocksDB数据，批量插入）</strong></p> |
| <center> |
| <img src="/docs/images/API-perf/v0.4.4/cpu-memory.png" alt="image"> |
| </center> |
| <h6 id="结论-1">结论：</h6> |
| <ul> |
| <li>同样使用HDD硬盘，CPU和内存增加了1倍</li> |
| <li>边：吞吐量从268提升至426，性能提升了约60%</li> |
| <li>顶点：吞吐量从1263.8提升至1842.4，性能提升了约45%</li> |
| </ul> |
| <p><strong>2. SSD和HDD对插入性能的影响（高性能服务器，批量插入）</strong></p> |
| <center> |
| <img src="/docs/images/API-perf/v0.4.4/ssd.png" alt="image"> |
| </center> |
| <h6 id="结论-2">结论：</h6> |
| <ul> |
| <li>边：使用SSD吞吐量451.7,使用HDD吞吐量426.6，性能提升5%</li> |
| <li>顶点：使用SSD吞吐量1842.4，使用HDD吞吐量1794，性能提升约3%</li> |
| </ul> |
| <p><strong>3. 不同并发线程数对插入性能的影响（普通服务器，使用HDD存储RocksDB数据）</strong></p> |
| <center> |
| <img src="/docs/images/API-perf/v0.4.4/threads-batch.png" alt="image"> |
| </center> |
| <h6 id="结论-3">结论：</h6> |
| <ul> |
| <li>顶点：1000并发，响应时间7ms和1500并发响应时间1028ms差距悬殊，且吞吐量一直保持在1300左右，因此拐点数据应该在1300 ，且并发1300时，响应时间已达到22ms，在可控范围内，相比HugeGraph 0.2（1000并发：平均响应时间8959ms），处理能力出现质的飞跃；</li> |
| <li>边：从1000并发到2000并发，处理时间过长，超过3s，且吞吐量几乎在270左右浮动，因此继续增大并发线程数吞吐量不会再大幅增长，270 是一个拐点，跟HugeGraph 0.2版本（1000并发：平均响应时间31849ms）相比较，处理能力提升非常明显；</li> |
| </ul> |
| <h4 id="42-single插入">4.2 single插入</h4> |
| <h5 id="421-压力上限测试">4.2.1 压力上限测试</h5> |
| <h6 id="测试方法-1">测试方法</h6> |
| <p>不断提升并发量，测试server仍能正常提供服务的压力上限</p> |
| <h6 id="压力参数-1">压力参数</h6> |
| <ul> |
| <li>持续时间：5min</li> |
| <li>服务异常标志：错误率大于0.00%</li> |
| </ul> |
| <center> |
| <img src="/docs/images/API-perf/v0.4.4/threads-single.png" alt="image"> |
| </center> |
| <h6 id="结论-4">结论：</h6> |
| <ul> |
| <li>顶点： |
| <ul> |
| <li>4000并发：正常，无错误率，平均耗时小于1ms， 6000并发无错误，平均耗时5ms，在可接受范围内；</li> |
| <li>8000并发：存在0.01%的错误，已经无法处理，出现connection timeout错误，顶峰应该在7000左右</li> |
| </ul> |
| </li> |
| <li>边： |
| <ul> |
| <li>4000并发：响应时间1ms，6000并发无任何异常，平均响应时间8ms，主要差异在于 IO network recv和send以及CPU）；</li> |
| <li>8000并发：存在0.01%的错误率，平均耗15ms，拐点应该在7000左右，跟顶点结果匹配；</li> |
| </ul> |
| </li> |
| </ul> |
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| </div> |
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| <div class="td-content" style="page-break-before: always"> |
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| <h1 id="pg-d4233a3feb070643ef5d54a6f9c12cab">4 - v0.2</h1> |
| |
| <h3 id="1-测试环境">1 测试环境</h3> |
| <h4 id="11-软硬件信息">1.1 软硬件信息</h4> |
| <p>起压和被压机器配置相同，基本参数如下：</p> |
| <table> |
| <thead> |
| <tr> |
| <th>CPU</th> |
| <th>Memory</th> |
| <th>网卡</th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr> |
| <td>24 Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz</td> |
| <td>61G</td> |
| <td>1000Mbps</td> |
| </tr> |
| </tbody> |
| </table> |
| <p>测试工具：apache-Jmeter-2.5.1</p> |
| <h4 id="12-服务配置">1.2 服务配置</h4> |
| <ul> |
| <li>HugeGraph版本：0.2</li> |
| <li>后端存储：使用服务内嵌的cassandra-3.10，单点部署；</li> |
| <li>后端配置修改：修改了cassandra.yaml文件中的以下两个属性，其余选项均保持默认</li> |
| </ul> |
| <pre tabindex="0"><code> batch_size_warn_threshold_in_kb: 1000 |
| batch_size_fail_threshold_in_kb: 1000 |
| </code></pre><ul> |
| <li>HugeGraphServer 与 HugeGremlinServer 与cassandra都在同一机器上启动，server 相关的配置文件除主机和端口有修改外，其余均保持默认。</li> |
| </ul> |
| <h4 id="13-名词解释">1.3 名词解释</h4> |
| <ul> |
| <li>Samples – 本次场景中一共完成了多少个线程</li> |
| <li>Average – 平均响应时间</li> |
| <li>Median – 统计意义上面的响应时间的中值</li> |
| <li>90% Line – 所有线程中90%的线程的响应时间都小于xx</li> |
| <li>Min – 最小响应时间</li> |
| <li>Max – 最大响应时间</li> |
| <li>Error – 出错率</li> |
| <li>Troughput – 吞吐量Â</li> |
| <li>KB/sec – 以流量做衡量的吞吐量</li> |
| </ul> |
| <p><em>注：时间的单位均为ms</em></p> |
| <h3 id="2-测试结果">2 测试结果</h3> |
| <h4 id="21-schema">2.1 schema</h4> |
| <table> |
| <thead> |
| <tr> |
| <th>Label</th> |
| <th>Samples</th> |
| <th>Average</th> |
| <th>Median</th> |
| <th>90%Line</th> |
| <th>Min</th> |
| <th>Max</th> |
| <th>Error%</th> |
| <th>Throughput</th> |
| <th>KB/sec</th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr> |
| <td>property_keys</td> |
| <td>331000</td> |
| <td>1</td> |
| <td>1</td> |
| <td>2</td> |
| <td>0</td> |
| <td>172</td> |
| <td>0.00%</td> |
| <td>920.7/sec</td> |
| <td>178.1</td> |
| </tr> |
| <tr> |
| <td>vertex_labels</td> |
| <td>331000</td> |
| <td>1</td> |
| <td>2</td> |
| <td>2</td> |
| <td>1</td> |
| <td>126</td> |
| <td>0.00%</td> |
| <td>920.7/sec</td> |
| <td>193.4</td> |
| </tr> |
| <tr> |
| <td>edge_labels</td> |
| <td>331000</td> |
| <td>2</td> |
| <td>2</td> |
| <td>3</td> |
| <td>1</td> |
| <td>158</td> |
| <td>0.00%</td> |
| <td>920.7/sec</td> |
| <td>242.8</td> |
| </tr> |
| </tbody> |
| </table> |
| <p>结论：schema的接口，在1000并发持续5分钟的压力下，平均响应时间1-2ms，无压力</p> |
| <h4 id="22-single-插入">2.2 single 插入</h4> |
| <h5 id="221-插入速率测试">2.2.1 插入速率测试</h5> |
| <h6 id="压力参数">压力参数</h6> |
| <p>测试方法：固定并发量，测试server和后端的处理速率</p> |
| <ul> |
| <li>并发量：1000</li> |
| <li>持续时间：5min</li> |
| </ul> |
| <h6 id="性能指标">性能指标</h6> |
| <table> |
| <thead> |
| <tr> |
| <th>Label</th> |
| <th>Samples</th> |
| <th>Average</th> |
| <th>Median</th> |
| <th>90%Line</th> |
| <th>Min</th> |
| <th>Max</th> |
| <th>Error%</th> |
| <th>Throughput</th> |
| <th>KB/sec</th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr> |
| <td>single_insert_vertices</td> |
| <td>331000</td> |
| <td>0</td> |
| <td>1</td> |
| <td>1</td> |
| <td>0</td> |
| <td>21</td> |
| <td>0.00%</td> |
| <td>920.7/sec</td> |
| <td>234.4</td> |
| </tr> |
| <tr> |
| <td>single_insert_edges</td> |
| <td>331000</td> |
| <td>2</td> |
| <td>2</td> |
| <td>3</td> |
| <td>1</td> |
| <td>53</td> |
| <td>0.00%</td> |
| <td>920.7/sec</td> |
| <td>309.1</td> |
| </tr> |
| </tbody> |
| </table> |
| <h6 id="结论">结论</h6> |
| <ul> |
| <li>顶点：平均响应时间1ms，每个请求插入一条数据，平均每秒处理920个请求，则每秒平均总共处理的数据为1*920约等于920条数据；</li> |
| <li>边：平均响应时间1ms，每个请求插入一条数据，平均每秒处理920个请求，则每秒平均总共处理的数据为1*920约等于920条数据；</li> |
| </ul> |
| <h5 id="222-压力上限测试">2.2.2 压力上限测试</h5> |
| <p>测试方法：不断提升并发量，测试server仍能正常提供服务的压力上限</p> |
| <h6 id="压力参数-1">压力参数</h6> |
| <ul> |
| <li>持续时间：5min</li> |
| <li>服务异常标志：错误率大于0.00%</li> |
| </ul> |
| <h6 id="性能指标-1">性能指标</h6> |
| <table> |
| <thead> |
| <tr> |
| <th>Concurrency</th> |
| <th>Samples</th> |
| <th>Average</th> |
| <th>Median</th> |
| <th>90%Line</th> |
| <th>Min</th> |
| <th>Max</th> |
| <th>Error%</th> |
| <th>Throughput</th> |
| <th>KB/sec</th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr> |
| <td>2000(vertex)</td> |
| <td>661916</td> |
| <td>1</td> |
| <td>1</td> |
| <td>1</td> |
| <td>0</td> |
| <td>3012</td> |
| <td>0.00%</td> |
| <td>1842.9/sec</td> |
| <td>469.1</td> |
| </tr> |
| <tr> |
| <td>4000(vertex)</td> |
| <td>1316124</td> |
| <td>13</td> |
| <td>1</td> |
| <td>14</td> |
| <td>0</td> |
| <td>9023</td> |
| <td>0.00%</td> |
| <td>3673.1/sec</td> |
| <td>935.0</td> |
| </tr> |
| <tr> |
| <td>5000(vertex)</td> |
| <td>1468121</td> |
| <td>1010</td> |
| <td>1135</td> |
| <td>1227</td> |
| <td>0</td> |
| <td>9223</td> |
| <td>0.06%</td> |
| <td>4095.6/sec</td> |
| <td>1046.0</td> |
| </tr> |
| <tr> |
| <td>7000(vertex)</td> |
| <td>1378454</td> |
| <td>1617</td> |
| <td>1708</td> |
| <td>1886</td> |
| <td>0</td> |
| <td>9361</td> |
| <td>0.08%</td> |
| <td>3860.3/sec</td> |
| <td>987.1</td> |
| </tr> |
| <tr> |
| <td>2000(edge)</td> |
| <td>629399</td> |
| <td>953</td> |
| <td>1043</td> |
| <td>1113</td> |
| <td>1</td> |
| <td>9001</td> |
| <td>0.00%</td> |
| <td>1750.3/sec</td> |
| <td>587.6</td> |
| </tr> |
| <tr> |
| <td>3000(edge)</td> |
| <td>648364</td> |
| <td>2258</td> |
| <td>2404</td> |
| <td>2500</td> |
| <td>2</td> |
| <td>9001</td> |
| <td>0.00%</td> |
| <td>1810.7/sec</td> |
| <td>607.9</td> |
| </tr> |
| <tr> |
| <td>4000(edge)</td> |
| <td>649904</td> |
| <td>1992</td> |
| <td>2112</td> |
| <td>2211</td> |
| <td>1</td> |
| <td>9001</td> |
| <td>0.06%</td> |
| <td>1812.5/sec</td> |
| <td>608.5</td> |
| </tr> |
| </tbody> |
| </table> |
| <h6 id="结论-1">结论</h6> |
| <ul> |
| <li>顶点： |
| <ul> |
| <li>4000并发：正常，无错误率，平均耗时13ms；</li> |
| <li>5000并发：每秒处理5000个数据的插入，就会存在0.06%的错误，应该已经处理不了了，顶峰应该在4000</li> |
| </ul> |
| </li> |
| <li>边： |
| <ul> |
| <li>1000并发：响应时间2ms，跟2000并发的响应时间相差较多，主要是 IO network rec和send以及CPU几乎增加了一倍）；</li> |
| <li>2000并发：每秒处理2000个数据的插入，平均耗时953ms，平均每秒处理1750个请求；</li> |
| <li>3000并发：每秒处理3000个数据的插入，平均耗时2258ms，平均每秒处理1810个请求；</li> |
| <li>4000并发：每秒处理4000个数据的插入，平均每秒处理1812个请求；</li> |
| </ul> |
| </li> |
| </ul> |
| <h4 id="23-batch-插入">2.3 batch 插入</h4> |
| <h5 id="231-插入速率测试">2.3.1 插入速率测试</h5> |
| <h6 id="压力参数-2">压力参数</h6> |
| <p>测试方法：固定并发量，测试server和后端的处理速率</p> |
| <ul> |
| <li>并发量：1000</li> |
| <li>持续时间：5min</li> |
| </ul> |
| <h6 id="性能指标-2">性能指标</h6> |
| <table> |
| <thead> |
| <tr> |
| <th>Label</th> |
| <th>Samples</th> |
| <th>Average</th> |
| <th>Median</th> |
| <th>90%Line</th> |
| <th>Min</th> |
| <th>Max</th> |
| <th>Error%</th> |
| <th>Throughput</th> |
| <th>KB/sec</th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr> |
| <td>batch_insert_vertices</td> |
| <td>37162</td> |
| <td>8959</td> |
| <td>9595</td> |
| <td>9704</td> |
| <td>17</td> |
| <td>9852</td> |
| <td>0.00%</td> |
| <td>103.4/sec</td> |
| <td>393.3</td> |
| </tr> |
| <tr> |
| <td>batch_insert_edges</td> |
| <td>10800</td> |
| <td>31849</td> |
| <td>34544</td> |
| <td>35132</td> |
| <td>435</td> |
| <td>35747</td> |
| <td>0.00%</td> |
| <td>28.8/sec</td> |
| <td>814.9</td> |
| </tr> |
| </tbody> |
| </table> |
| <h6 id="结论-2">结论</h6> |
| <ul> |
| <li>顶点：平均响应时间为8959ms，处理时间过长。每个请求插入199条数据，平均每秒处理103个请求，则每秒平均总共处理的数据为199*131约等于2w条数据；</li> |
| <li>边：平均响应时间31849ms，处理时间过长。每个请求插入499个数据，平均每秒处理28个请求，则每秒平均总共处理的数据为28*499约等于13900条数据；</li> |
| </ul> |
| |
| </div> |
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