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NebulaGraph Bench

中文文档

NebulaGraph Bench is a tool to test the NebulaGraph benchmark by using LDBC dataset.

Currently, we use ldbc_snb_datagen v0.3.3. It only support NebulaGraph 2.0+ release.

The main features:

  • Generate the LDBC dataset and then import into NebulaGraph.
  • Run load test with k6.

Dependency

⚠️ Please note, the NebulaGraph Importer being used by NebulaGraph Bench is v3.x, the lastest branch of Importer v4.x is not yet supported.

NebulaGraph Bench NebuaGraph NebulaGraph Importer K6 Plugin ldbc_snb_datagen NebulaGraph go
v0.2 v2.0.1 v2.0.0-ga v0.0.6 v0.3.3 v2.0.0-ga
v1.0.0 v2.5.0 / v2.5.1 v2.5.1 v0.0.7 v0.3.3 v2.5.1
v1.0.0 v2.6.0 / v2.6.1 / v2.6.2 v2.6.0 v0.0.8 v0.3.3 v2.6.0
v1.1.0 v3.0.0 / v3.0.1 v3.0.0 v0.0.9 v0.3.3 v3.0.0
v1.2.0 v3.1.0 v3.1.0 v1.0.0 v0.3.3 NONE
master nightly v3.2.0 v1.1.6 v0.3.3 NONE

How to use

prepare

Centos7

sudo yum install -y git \
                    make \
                    file \
                    libev \
                    libev-devel \
                    gcc \
                    wget \
                    python3 \
                    python3-devel \
                    java-1.8.0-openjdk \
                    maven

Ubuntu

sudo apt-get install -y \
     git \
     wget \
     python3-pip \
     python \
     openjdk-8-jdk \
     maven 

export JAVA_HOME=/usr/lib/jvm/default-java/
git clone https://github.com/vesoft-inc/nebula-bench.git 
cd nebula-bench
pip3 install --user -r requirements.txt
python3 run.py --help

prepare NebulaGraph tools.

./scripts/setup.sh

After compilation, it would put binaries in scripts folder.

generate ldbc data

python3 run.py data

It would download the Hadoop automatically, build the datagen jar, and then generate ldbc data.

To import the data more easier, split the file header to a header.csv file. The result files in ${PWD}/target/data/test_data/

More information

# generate sf10 ldbc data
python3 run.py data  -s 10

# change hadoop options
export HADOOP_CLIENT_OPTS="-Xmx8G"
python3 run.py data -s 100

# or
# only generate, do not split the data
python3 run.py data -og
# split data, no need generate again.
python3 run.py data -os

import data into NebulaGraph

python3 run.py nebula importer

Render the import config file according to the header files, and then run nebula-importer.

# 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

NebulaGraph benchmark

Use k6 with xk6-nebula extension.

Scenarios are in nebula_bench/scenarios/.

# 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.Go1Step 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.Go1Step 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'

for more k6 args, please refer to k6 run help.

scripts/k6 run --help

k6 config file, summary result and outputs are in output folder. e.g.

# 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

or, just review the summary result in stdout. e.g.

     ✓ 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, one check per iteration, verify isSucceed by default.
  • data_received and data_sent, used by HTTP requests, useless for NebulaGraph.
  • iteration_duration, time consuming for every iteration.
  • latency, time consuming in NebulaGraph server, unit: (us).
  • responseTime, time consuming in client, unit: (us).
  • rowSize, response row size.
  • vus, concurrent virtual users.

In general

iteration_duration = responseTime + (time consuming for read data from csv)

responseTime = latency + (time consuming for network) + (client decode)

As one iteration has one check, it means run 113778 queries.

The unit of latency is us.

Report

# 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

and more

  • The file with aaa_xxYY_bbb format, like comment_hasTag_tag, should be an edge, and the edge name shoud be XX_YY. Keep the same format with ldbc_snb_interactive
  • Otherwise it should be a vertex tag.
  • Different entity types might have same ID (e.g. Forum and Post).

LDBC schema

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);