# nebula-bench **Repository Path**: mirrors_vesoft-inc/nebula-bench ## Basic Information - **Project Name**: nebula-bench - **Description**: Collection of benchmark services and tools for NebulaGraph - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2021-01-12 - **Last Updated**: 2026-05-17 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # NebulaGraph Bench `NebulaGraph Bench` 用于测试 NebulaGraph 的基线性能数据，使用 LDBC v0.3.3 的标准数据集。 当前只适用于 NebulaGraph v2.0 以上版本。 主要功能: * 生产 LDBC 数据集并导入 NebulaGraph。 * 用 k6 进行压测。 ## 工具依赖 | NebulaGraph Bench | NebuaGraph | NebulaGraph Importer | K6 Plugin | ldbc_snb_datagen | NebulaGraph go | |:-----------------:|:-------------:|:---------------:|:------------:|:-------------------:|:--------------:| | [v0.2](https://github.com/vesoft-inc/nebula-bench/releases/tag/v0.2) | [v2.0.1](https://github.com/vesoft-inc/nebula-graph/releases/tag/v2.0.1) | [v2.0.0-ga](https://github.com/vesoft-inc/nebula-importer/releases/tag/v2.0.0-ga) | [v0.0.6](https://github.com/vesoft-inc/k6-plugin/releases/tag/v0.0.6) | [v0.3.3](https://github.com/ldbc/ldbc_snb_datagen_spark/tree/v0.3.3) | [v2.0.0-ga](https://github.com/vesoft-inc/nebula-go/releases/tag/v2.0.0-ga) | | [v1.0.0](https://github.com/vesoft-inc/nebula-bench/releases/tag/v1.0.0) | [v2.5.0](https://github.com/vesoft-inc/nebula-graph/releases/tag/v2.5.0) / [v2.5.1](https://github.com/vesoft-inc/nebula-graph/releases/tag/v2.5.1) | [v2.5.1](https://github.com/vesoft-inc/nebula-importer/releases/tag/v2.5.1) | [v0.0.7](https://github.com/vesoft-inc/k6-plugin/releases/tag/v0.0.7) | [v0.3.3](https://github.com/ldbc/ldbc_snb_datagen_spark/tree/v0.3.3) | [v2.5.1](https://github.com/vesoft-inc/nebula-go/releases/tag/v2.5.1) | | [v1.0.0](https://github.com/vesoft-inc/nebula-bench/releases/tag/v1.0.0) | [v2.6.0](https://github.com/vesoft-inc/nebula/releases/tag/v2.6.0) / [v2.6.1](https://github.com/vesoft-inc/nebula/releases/tag/v2.6.1) / [v2.6.2](https://github.com/vesoft-inc/nebula/releases/tag/v2.6.2) | [v2.6.0](https://github.com/vesoft-inc/nebula-importer/releases/tag/v2.6.0) | [v0.0.8](https://github.com/vesoft-inc/k6-plugin/releases/tag/v0.0.8) | [v0.3.3](https://github.com/ldbc/ldbc_snb_datagen_spark/tree/v0.3.3) | [v2.6.0](https://github.com/vesoft-inc/nebula-go/releases/tag/v2.6.0) | | [v1.1.0](https://github.com/vesoft-inc/nebula-bench/releases/tag/v1.1.0) | [v3.0.0](https://github.com/vesoft-inc/nebula/releases/tag/v3.0.0) / [v3.0.1](https://github.com/vesoft-inc/nebula/releases/tag/v3.0.1) | [v3.0.0](https://github.com/vesoft-inc/nebula-importer/releases/tag/v3.0.0) | [v0.0.9](https://github.com/vesoft-inc/k6-plugin/releases/tag/v0.0.9) | [v0.3.3](https://github.com/ldbc/ldbc_snb_datagen_spark/tree/v0.3.3) | [v3.0.0](https://github.com/vesoft-inc/nebula-go/releases/tag/v3.0.0) | | [v1.2.0](https://github.com/vesoft-inc/nebula-bench/releases/tag/v1.2.0) | [v3.1.0](https://github.com/vesoft-inc/nebula/releases/tag/v3.1.0) | [v3.1.0](https://github.com/vesoft-inc/nebula-importer/releases/tag/v3.1.0) | [v1.0.0](https://github.com/vesoft-inc/k6-plugin/releases/tag/v1.0.0) | [v0.3.3](https://github.com/ldbc/ldbc_snb_datagen_spark/tree/v0.3.3) | NONE | | master | nightly | [v3.2.0](https://github.com/vesoft-inc/nebula/releases/tag/v3.2.0) | [v1.1.6](https://github.com/vesoft-inc/k6-plugin/releases/tag/v1.1.6) | [v0.3.3](https://github.com/ldbc/ldbc_snb_datagen_spark/tree/v0.3.3) | NONE | ## 使用说明 ### 安装准备 #### Centos7 ```bash sudo yum install -y git \ make \ file \ libev \ libev-devel \ gcc \ wget \ python3 \ python3-devel \ java-1.8.0-openjdk \ maven ``` #### Ubuntu ```bash sudo apt-get install -y \ git \ wget \ python3-pip \ python \ openjdk-8-jdk \ maven export JAVA_HOME=/usr/lib/jvm/default-java/ ``` ```bash git clone https://github.com/vesoft-inc/nebula-bench.git cd nebula-bench pip3 install --user -r requirements.txt python3 run.py --help ``` 准备 NebulaGraph 的工具。 安装 golang，然后编译相关工具。 * [nebula-importer](https://github.com/vesoft-inc/nebula-importer) * [xk6-nebula](https://github.com/vesoft-inc/k6-plugin) ```bash /bin/bash scripts/setup.sh ``` 如果 go get 的包下载不下来，需要设置 golang 代理。 ```bash export GOPROXY=https://goproxy.cn ``` 编译后，二进制包在 `scripts` 文件夹中。 ### 生成 LDBC 数据 ```bash python3 run.py data ``` 会自动下载 Hadoop，然后使用 [ldbc_snb_datagen](https://github.com/ldbc/ldbc_snb_datagen_spark) 生成数据。 为了方便 importer 导入，将生成后的文件拆分了一个带头的 header 文件，再去掉原有文件第一行。 默认生成的文件在 `${PWD}/target/data/test_data/`。 更多命令 ```bash # generate sf10 ldbc data python3 run.py data -s 10 # change hadoop options export HADOOP_CLIENT_OPTS="-Xmx8G" python3 run.py data -s 100 # only generate, do not split the data python3 run.py data -og # split data, no need generate again. python3 run.py data -os ``` ### 导入数据到 NebulaGraph ```bash python3 run.py nebula importer ``` 会根据 header 文件，自动生成 importer 的配置文件，然后运行 importer 导入。 ```bash # after prepare the data, you could import the data to any NebulaGraph as you want. # space is mytest, graph address is 127.0.0.1:9669 python3 run.py nebula importer -s mytest -a 127.0.0.1:9669 # or using dotenv cp env .env # vi .env python3 run.py nebula importer # dry run, just create the import config file, and you can modify any configuration。 # by default, PARTITION_NUM is 24, REPLICA_FACTOR is 3. python3 run.py nebula importer --dry-run ``` ### nebula benchmark 使用带有 [xk6-nebula](https://github.com/HarrisChu/xk6-nebula) 插件的 [K6](https://github.com/k6io/k6) 来进行压测。 自动化的场景，在 `nebula_bench/scenarios/` 中。 ```bash # show help python3 run.py stress run --help # run all scenarios with 100 virtual users, every scenario lasts 60 seconds. python3 run.py stress run # run all scenarios with 10 virtual users, every scenario lasts 3 seconds. python3 run.py stress run --args='-u 10 -d 3s' # list all stress test scenarios python3 run.py stress scenarios # run go.Go1StepEdge scenarios with 10 virtual users, every scenario lasts 3 seconds. python3 run.py stress run -scenario go.Go1StepEdge --args='-u 10 -d 3s' # run go.Go1StepEdge scenarios with special test stage. # ramping up from 0 to 10 vus in first 10 seconds, then run 10 vus in 30 seconds, # then ramping up from 10 to 50 vus in 10 seconds. python3 run.py stress run -scenario go.Go1StepEdge --args='-s 10s:10 -s 30s:10 -s 10s:50' # use csv output python3 run.py stress run -scenario go.Go1StepEdge --args='-s 10s:10 -s 30s:10 -s 10s:50 -o csv=test.csv' ``` 更多 k6 参数，请参考。 ```bash scripts/k6 run --help ``` k6 config file, summary result and outputs are in `output` folder. e.g. ```bash # you should install jq to parse json. # how many checks jq .metrics.checks output/result_Go1Step.json # summary latency jq .metrics.latency output/result_Go1Step.json # summary error message awk -F ',' 'NR>1{print $NF}' output/output_Go1Step.csv |sort|uniq -c ``` 或者从标准输出看测试的结果。 ```bash ✓ IsSucceed █ setup █ teardown checks...............: 100.00% ✓ 113778 ✗ 0 data_received........: 0 B 0 B/s data_sent............: 0 B 0 B/s iteration_duration...: min=747.84µs avg=52.76ms med=40.77ms max=1.17s p(90)=98.68ms p(95)=147.15ms p(99)=263.03ms iterations...........: 113778 1861.550127/s latency..............: min=462 avg=49182.770298 med=37245 max=1160358 p(90)=93377 p(95)=142304.15 p(99)=258465.89 responseTime.........: min=662 avg=52636.793537 med=40659 max=1177651 p(90)=98556.5 p(95)=147036.15 p(99)=262869.63 rowSize..............: min=0 avg=9.212424 med=3 max=243 p(90)=25 p(95)=40 p(99)=70 vus..................: 100 min=0 max=100 vus_max..............: 100 min=100 max=100 ``` * `checks`，每次执行有一个检查点，默认是检查服务端返回的 `isSucceed`。 * `data_received` 和 `data_sent`，是 k6 工具自带的，对 NebulaGraph 用处不大。 * `iteration_duration`，每次执行的总时间。 * `latency`，服务端耗时，单位：微秒(us)。 * `responseTime`，客户端耗时, 单位：微秒(us)。 * `rowSize`，请求返回行数。 * `vus`，并发的用户数 大体来说 iteration_duration = responseTime + (客户端从 csv 读数据的耗时) responseTime = latency + (网络传输的耗时) + (客户端解码的耗时) 因为一个查询有一个检查点，所以上面代表执行了 113778 个 query，所有都成功了。 latency 的单位是 `us`。 ### 报告 ```bash # generate the `report.html` with summary json file in `output/20221109140449` folder. python3 run.py report export -f output/20221109140449 # export to a new file. python3 run.py report export -f output/20221109140449 -o new_report.html # launch a http server with port 5050, and could browser all result in `output` folder. python3 run.py report serve -p 5050 ``` ![report](doc/img/report.png) ## 更多 * 生成的数据文件，如果是 `aaa_xxYY_bbb` 格式，比如 `comment_hasTag_tag`，会认为是一个边类型，然后边的格式是 `XX_YY`。和 ldbc 保持一致 [ldbc_snb_interactive](https://github.com/ldbc/ldbc_snb_interactive/blob/main/cypher/queries/interactive-complex-1.cypher) * 否则是一个 Tag 类型。 * 不同的 Tag，有可能 Vertex ID 是一样的，比如 Forum 和 Post。暂时没有特殊处理。 LDBC schema ```bash CREATE TAG IF NOT EXISTS `Post`(`imageFile` string,`creationDate` datetime,`locationIP` string,`browserUsed` string,`language` string,`content` string,`length` int); CREATE TAG IF NOT EXISTS `Tag`(`name` string,`url` string); CREATE TAG IF NOT EXISTS `Tagclass`(`name` string,`url` string); CREATE TAG IF NOT EXISTS `Organisation`(`type` string,`name` string,`url` string); CREATE TAG IF NOT EXISTS `Place`(`name` string,`url` string,`type` string); CREATE TAG IF NOT EXISTS `Person`(`firstName` string,`lastName` string,`gender` string,`birthday` string,`creationDate` datetime,`locationIP` string,`browserUsed` string); CREATE TAG IF NOT EXISTS `Forum`(`title` string,`creationDate` datetime); CREATE TAG IF NOT EXISTS `Comment`(`creationDate` datetime,`locationIP` string,`browserUsed` string,`content` string,`length` int); CREATE EDGE IF NOT EXISTS `HAS_CREATOR`(); CREATE EDGE IF NOT EXISTS `REPLY_OF`(); CREATE EDGE IF NOT EXISTS `STUDY_AT`(`classYear` int); CREATE EDGE IF NOT EXISTS `WORK_AT`(`workFrom` int); CREATE EDGE IF NOT EXISTS `HAS_MODERATOR`(); CREATE EDGE IF NOT EXISTS `LIKES`(`creationDate` datetime); CREATE EDGE IF NOT EXISTS `HAS_MEMBER`(`joinDate` datetime); CREATE EDGE IF NOT EXISTS `IS_PART_OF`(); CREATE EDGE IF NOT EXISTS `CONTAINER_OF`(); CREATE EDGE IF NOT EXISTS `HAS_INTEREST`(); CREATE EDGE IF NOT EXISTS `IS_SUBCLASS_OF`(); CREATE EDGE IF NOT EXISTS `HAS_TAG`(); CREATE EDGE IF NOT EXISTS `IS_LOCATED_IN`(); CREATE EDGE IF NOT EXISTS `HAS_TYPE`(); CREATE EDGE IF NOT EXISTS `KNOWS`(`creationDate` datetime); CREATE TAG INDEX IF NOT EXISTS `person_first_name_idx` on `Person`(firstName(10)); CREATE EDGE INDEX IF NOT EXISTS `like_creationDate_idx` on `LIKES`(creationDate); ```