RageDB Benchmarks for LDBC SNB

This repository contains queries, test data and Gatling tests for the LDBC Social Network Benchmark. Watch ths video for more information about the LDBC and their benchmarks.

Currently only queries used in the "Interactive Workload" have been written. You can read a paper on and get the latest reference

The data model and queries presented here are based on version 2.2.2-SNAPSHOT, commit b2ee1ca.

IMPORTANT NOTE: This is NOT an official LDBC Benchmark result.

Implementation Notes:

The LDBC SNB data model is a logical data model and implementers are able to make tweaks to their physical data models. We will follow along with TuGraph and make the following changes:

• Some relationships are further refined according to the connecting Node types:
  • HAS_TAG: FORUM_HAS_TAG, POST_HAS_TAG, COMMENT_HAS_TAG
  • HAS_CREATOR: POST_HAS_CREATOR, COMMENT_HAS_CREATOR
  • IS_LOCATED_IN: PERSON_IS_LOCATED_IN, ORGANISATION_IS_LOCATED_IN, POST_IS_LOCATED_IN, COMMENT_IS_LOCATED_IN
• There are two precomputed edge properties (similar to materialized views):
  • HAS_MEMBER.numPosts which maintains the number of posts the given person posted in the given forum and used in Complex Read 5
  • KNOWS.weight which maintains the weight between the pair of given persons, calculated using the formula in Complex Read 14

Running the Benchmark:

• Get the data (more instructions TBD)
• Install RageDB (more instructions TBD)
• Run the schema.lua file
• Run the import-sf01.lua file
• Run the pre-process-sf01.lua file
• Run the interactive-short-sf01.lua file to create the "stored" procedures for the short queries
• Run the interactive-sf01.lua file to create the "stored" procedures for the complex queries
• Run Gatling and select the test you wish to run

Results:

Results as of February 23, 2023, on LDBC SNB SF01:

4 Concurrent Threads:

Query	Total	OK	KO	q/s	min	99th	max	mean
IQ01	8228	8228	0	135	0	79	120	29
IQ02	34772	34772	0	570	0	9	17	7
IQ03	75386	75386	0	1236	1	4	20	3
IQ04	84504	84504	0	1385	0	7	21	3
IQ05	77	77	0	1.26	483	6338	6622	3144
IQ06	61515	61515	0	1008	1	6	27	4
IQ07	22428	22428	0	368	1	25	41	11
IQ08	210651	210651	0	3453	0	3	21	1
IQ09	225	225	0	3.7	0	1909	2421	1077
IQ11	147827	147827	0	2423	0	3	20	2
IQ12	91886	91886	0	1506	0	5	17	3
IQ13	234108	234108	0	3838	0	2	14	1
IS01	1988391	1988391	0	32597	0	1	46	0
IS02	123875	123875	0	2030	0	6	21	2
IS03	142017	142017	0	2328	0	9	24	2
IS04	2956709	2956709	0	48471	0	1	18	0
IS05	2745297	2745297	0	45005	0	1	23	0
IS06	2022301	2022301	0	33152	0	1	23	0
IS07	1330813	1330813	0	21816	0	1	25	0

Benchmark Parameters:

userCount: 4            // Concurrent requests
duration:  60 seconds   // Test duration
ragedb -c4              // This limits RageDB to 4 (out of the 8) cores

Physical Server:

NZXT, Inc MS-7D07
Intel® Core™ i7-10700K CPU @ 3.80GHz × 8
64 GB RAM
Ubuntu 22.04.1 LTS

Pre-Requisites for LDBC SNB Tests

1. Create the schema
2. Import the data
3. Create the stored procedures

See the top level ldbc folder for details.

Command Line Instructions

Assuming you have an ec2 instance in the same vpc and subnet as your RageDB Server and your cluster has an additional security group that allows incoming TCP 7243 from the security group your ec2 instance belongs to.

You can either edit src/test/scala/rage/TestParameters and change the default parameters:

getProperty("RAGE_URL", "http://127.0.0.1:7243")

to your RageDB server endpoint, then run:

mvn gatling:test

To run specific tests:

mvn gatling:test -Dgatling.simulationClass=ldbc.Interactive01

You can pass in test parameters for the simulation

mvn gatling:test -DUSERS=4 -DDURATION=30 -DRAGE_URL="http://127.0.0.1:7243" -DRAGE_DB=rage -Dgatling.simulationClass=ldbc.Interactive01

export SF=1 export LDBC_SNB_DATAGEN_MAX_MEM=32G export LDBC_SNB_DATAGEN_JAR=$(sbt -batch -error 'print assembly / assemblyOutputPath')

rm -rf out-sf${SF}/{factors,graphs/parquet/raw} tools/run.py
--cores $(nproc)
--memory ${LDBC_SNB_DATAGEN_MAX_MEM}
--
--format parquet
--scale-factor ${SF}
--mode raw
--output-dir out-sf${SF}
--generate-factors

./tools/run.py -- --format csv --scale-factor 0.003 --mode bi --generate-factors --conf