This article documents the details on how to reproduce the graph database benchmark result on RedisGraph. Currently, RedisGraph benchmark supports two use cases:
| Name | Description and Source | Vertices | Edges |
|---|---|---|---|
| graph500 | Synthetic Kronecker graph http://graph500.org | 2.4 M | 64 M |
| Twitter user-follower directed graph http://an.kaist.ac.kr/traces/WWW2010.html | 41.6 M | 1.47 B |
Graph benchmarking involves 3 phases: data retrieval, data loading/insertion, and query execution.
The easiest way to get and install the benchmark code is to use:
git clone https://github.com/RedisGraph/graph-database-benchmark.git
cd graph-database-benchmark/benchmark/redisgraph
pip install --user -r requirements.txtVariables:
DATABASE_HOST(default:127.0.0.1)DATABASE_PORT(default:6379)BULK_DATA_DIR(default:/tmp/bulk_data)DATASET_NAME(default:dependent on the use case)EDGE_FILE(default:dependent on the use case)NODE_FILE(default:dependent on the use case)
The easiest way to get the datasets required for the benchmark is to use one of the helper scripts:
./scripts/get_graph500_dataset.shor./scripts/get_twitter_dataset.sh
Sample output:
./scripts/get_graph500_dataset.sh./scripts/redisgraph_load_graph500.shSample output:
+python bulk_insert.py graph500_22 -n data/graph500_22_unique_node -r data/graph500_22
graph500_22_unique_node [####################################] 100%
2396019 nodes created with label 'graph500_22_unique_node'
graph500_22 [####################################] 100%
67108863 relations created for type 'graph500_22'
Construction of graph 'graph500_22' complete: 2396019 nodes created, 67108863 relations created in 1410.983482 seconds
+python graph_query.py --name "graph500_22" --query "create index on :graph500_22_unique_node(id)"./scripts/redisgraph_load_twitter.shSample output:
Change graph500-22-seed and twitter_rv.net-seed path to your seed file path. Results will be stored in "result_redisgraph" output directory.
# 300 seeds, depth 1, 10 iterations per query
python kn.py -g graph500_22 -s graph500_22_seed -c 300 -d 1 -p redisgraph -l graph500_22_unique_node -t 22 -i 10
# 300 seeds, depth 2, 10 iterations per query
python kn.py -g graph500_22 -s graph500_22_seed -c 300 -d 2 -p redisgraph -l graph500_22_unique_node -t 22 -i 10
# 10 seeds, depth 3, 10 iterations per query
python kn.py -g graph500_22 -s graph500_22_seed -c 10 -d 3 -p redisgraph -l graph500_22_unique_node -t 22 -i 10
# 10 seeds, depth 6, 10 iterations per query
python kn.py -g graph500_22 -s graph500_22_seed -c 10 -d 6 -p redisgraph -l graph500_22_unique_node -t 22 -i 10# 300 seeds, depth 1, 3 iterations per query
nohup python kn.py -g twitter_rv -s twitter_rv_net_seed -c 300 -d 1 -p redisgraph -l twitter_rv_net_unique_node -t 22 -i 3
# 300 seeds, depth 2, 3 iterations per query
nohup python kn.py -g twitter_rv -s twitter_rv_net_seed -c 300 -d 2 -p redisgraph -l twitter_rv_net_unique_node -t 22 -i 3
# 10 seeds, depth 3, 3 iterations per query
nohup python kn.py -g twitter_rv -s twitter_rv_net_seed -c 10 -d 3 -p redisgraph -l twitter_rv_net_unique_node -t 22 -i 3
# 10 seeds, depth 6, 3 iterations per query
nohup python kn.py -g twitter_rv -s twitter_rv_net_seed -c 10 -d 6 -p redisgraph -l twitter_rv_net_unique_node -t 22 -i 3# clone the repo
git clone https://github.com/RedisGraph/graph-database-benchmark.git
# make sure you're at this project folder
cd graph-database-benchmark/benchmark/redisgraph
docker build -t graph-database-benchmark-redisgraph .# run a 50 seeds, depth 1, 10 iterations per query benchmark
docker run --net=host tigergraph_redisgraph:latest -g graph500_22 -s graph500_22_seed -c 50 -d 1 -p redisgraph -l graph500_22_unique_node -t 50 -i 10 --stdoutYou should be able to see a latency report similar to the presented bellow and use is to produce a latency percentile distribution chart, like the one presented here:
Sample Latency by percentile distribuition chart:
Full output:
Aggregate reports
Finalizing report
Value Percentile TotalCount 1/(1-Percentile)
0.5940 0.000000000000 1 1.00
0.7650 0.050000000000 26 1.05
0.8490 0.097500000000 50 1.11
0.9250 0.142625000000 72 1.17
0.9820 0.185493750000 93 1.23
1.0400 0.226219062500 114 1.29
1.1010 0.264908109375 135 1.36
1.1610 0.301662703906 151 1.43
1.2050 0.336579568711 169 1.51
1.2520 0.369750590275 186 1.59
1.2830 0.401263060762 201 1.67
1.3230 0.431199907724 216 1.76
1.3500 0.459639912337 230 1.85
1.3940 0.486657916720 245 1.95
1.4410 0.512325020884 257 2.05
1.4970 0.536708769840 269 2.16
1.5280 0.559873331348 280 2.27
1.5540 0.581879664781 291 2.39
1.5950 0.602785681542 302 2.52
1.6250 0.622646397465 314 2.65
1.6500 0.641514077591 321 2.79
1.6740 0.659438373712 330 2.94
1.6920 0.676466455026 339 3.09
1.7370 0.692643132275 347 3.25
1.7680 0.708010975661 355 3.42
1.7890 0.722610426878 362 3.61
1.8150 0.736479905534 369 3.79
1.8400 0.749655910258 375 3.99
1.8710 0.762173114745 382 4.20
1.9090 0.774064459007 388 4.43
1.9390 0.785361236057 393 4.66
1.9810 0.796093174254 399 4.90
2.0000 0.806288515541 404 5.16
2.0380 0.815974089764 409 5.43
2.0780 0.825175385276 413 5.72
2.0980 0.833916616012 418 6.02
2.1150 0.842220785212 422 6.34
2.1540 0.850109745951 426 6.67
2.1660 0.857604258654 429 7.02
2.1980 0.864724045721 433 7.39
2.2280 0.871487843435 436 7.78
2.2340 0.877913451263 439 8.19
2.2620 0.884017778700 443 8.62
2.3260 0.889816889765 445 9.08
2.3470 0.895326045277 448 9.55
2.3780 0.900559743013 451 10.06
2.3820 0.905531755862 453 10.59
2.3900 0.910255168069 456 11.14
2.4190 0.914742409666 458 11.73
2.4270 0.919005289182 460 12.35
2.4760 0.923055024723 462 13.00
2.5160 0.926902273487 464 13.68
2.5560 0.930557159813 466 14.40
2.5860 0.934029301822 468 15.16
2.6030 0.937327836731 469 15.96
2.6190 0.940461444894 471 16.80
2.6430 0.943438372650 472 17.68
2.7050 0.946266454017 474 18.61
2.7100 0.948953131316 475 19.59
2.7190 0.951505474751 476 20.62
2.7220 0.953930201013 477 21.71
2.7370 0.956233690962 479 22.85
2.7570 0.958422006414 480 24.05
2.8370 0.960500906094 481 25.32
2.9370 0.962475860789 482 26.65
2.9480 0.964352067749 483 28.05
2.9970 0.966134464362 484 29.53
2.9970 0.967827741144 484 31.08
3.0960 0.969436354087 485 32.72
3.1140 0.970964536382 486 34.44
3.1490 0.972416309563 487 36.25
3.1490 0.973795494085 487 38.16
3.2490 0.975105719381 488 40.17
3.2950 0.976350433412 489 42.28
3.2950 0.977532911741 489 44.51
3.3650 0.978656266154 490 46.85
3.3650 0.979723452846 490 49.32
3.4610 0.980737280204 491 51.91
3.4610 0.981700416194 491 54.65
3.5940 0.982615395384 492 57.52
3.5940 0.983484625615 492 60.55
3.7870 0.984310394334 493 63.74
3.7870 0.985094874618 493 67.09
3.7870 0.985840130887 493 70.62
3.9060 0.986548124342 494 74.34
3.9060 0.987220718125 494 78.25
3.9060 0.987859682219 494 82.37
3.9730 0.988466698108 495 86.71
3.9730 0.989043363203 495 91.27
3.9730 0.989591195042 495 96.07
4.1220 0.990111635290 496 101.13
4.1220 0.990606053526 496 106.45
4.1220 0.991075750850 496 112.05
4.1220 0.991521963307 496 117.95
4.1220 0.991945865142 496 124.16
4.1620 0.992348571885 497 130.69
4.1620 0.992731143290 497 137.57
4.1620 0.993094586126 497 144.81
4.1620 0.993439856820 497 152.44
4.1620 0.993767863979 497 160.46
4.1690 0.994079470780 498 168.90
4.1690 0.994375497241 498 177.79
4.1690 0.994656722379 498 187.15
4.1690 0.994923886260 498 197.00
4.1690 0.995177691947 498 207.37
4.1690 0.995418807349 498 218.28
4.1690 0.995647866982 498 229.77
4.1690 0.995865473633 498 241.87
4.4130 0.996072199951 499 254.60
4.4130 0.996268589954 499 268.00
4.4130 0.996455160456 499 282.10
4.4130 0.996632402433 499 296.95
4.4130 0.996800782311 499 312.58
4.4130 0.996960743196 499 329.03
4.4130 0.997112706036 499 346.35
4.4130 0.997257070734 499 364.57
4.4130 0.997394217198 499 383.76
4.4130 0.997524506338 499 403.96
4.4130 0.997648281021 499 425.22
4.4130 0.997765866970 499 447.60
4.4130 0.997877573621 499 471.16
4.4130 0.997983694940 499 495.96
4.9050 0.998084510193 500 522.06
4.9050 1.000000000000 500
#[Mean = 1.5468, StdDeviation = 652.9673]
#[Max = 4.9050, TotalCount = 500.0000]
#[Buckets = 6, SubBuckets = 32768]
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Summary : avgKNSize=0.000000, avgQueryTime=1.546826[ms], totalRuntime=157.258749[ms]
Latency by percentile : q50=1.420000[ms], q99=2.362000[ms], q99.99=4.905000[ms], q99.9999=4.905000[ms],
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