See the benchmarks (Click here)

Key Benchmark Takeaways

Get mission-critical performance even under extreme workloads, with response times staying under 140ms at p99, while competitors struggle with multi-second latencies. Reduce infrastructure costs and improve user experience with FalkorDB's superior performance profile, requiring fewer resources to handle peak workloads.

| Percentile | FalkorDB (ms) | Neo4j (ms) | Performance Difference | |------------------|---------------|------------|------------------------| | p50 (median) | 55.0 | 577.5 | 10.5x faster | | p90 | 108.0 | 4784.1 | 44.3x faster | | p99 | 136.2 | 46923.8 | 344.5x faster |

About the benchmarks

This benchmark provides comprehensive performance comparisons between FalkorDB and Neo4j graph databases. This benchmark specifically focuses on aggregate expansion operations, a common workload in graph database applications. The results indicate FalkorDB's particular strength in maintaining consistent performance under varying workload conditions, especially crucial for production environments where predictable response times are essential.

System Requirements

Prerequisites

Ubuntu
Redis server
build-essential, cmake, m4, automake
libtool, autoconf, python3
libomp-dev, libssl-dev
pkg-config
Rust toolchain
SDKman
unzip, zip

Installation Steps

install redis server

sudo apt-get install lsb-release curl gpg
curl -fsSL https://packages.redis.io/gpg | sudo gpg --dearmor -o /usr/share/keyrings/redis-archive-keyring.gpg
sudo chmod 644 /usr/share/keyrings/redis-archive-keyring.gpg
echo "deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb $(lsb_release -cs) main" | sudo tee /etc/apt/sources.list.d/redis.list
sudo apt-get update
sudo apt-get install redis

stop the redis server sudo systemctl stop redis-server
disable the redis server sudo systemctl disable redis-server
check the redis server status sudo systemctl status redis-server

install sdkman

install unzip sudo apt install unzip zip -y
curl -s "https://get.sdkman.io" | bash
load sdkman in the current shell source "$HOME/.sdkman/bin/sdkman-init.sh"

build falkordb from source

git clone --recurse-submodules -j8 https://github.com/FalkorDB/FalkorDB.git
sudo apt install build-essential cmake m4 automake libtool autoconf python3 libomp-dev libssl-dev
install rust curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
from FalkorDB root dir run make

build the benchmark from source

from ~/

install pkg-config sudo apt install pkg-config -y
git clone git@github.com:FalkorDB/benchmark.git
cd benchmark
sdk env install
download and unpack neo4j ./scripts/download-neo4j.sh
build the benchmark cargo build --release
enable autocomplete source <(./target/release/benchmark generate-auto-complete bash)
copy the falkor shared lib to cp ~/FalkorDB/bin/linux-x64-release/src/falkordb.so .

run the benchmark

Generate the docker compose for prometheus and grafana

./generate_docker_compose.sh

Run the docker compose

docker-compose up

The benchmark is a cli tool that can be used to run the benchmarks

➜  cargo run  --bin benchmark -- --help                                                                  git:(prometheus|✚7…3
    
Usage: benchmark <COMMAND>

Commands:
  generate-auto-complete
  load                    load data into the database
  generate-queries        generate a set of queries and store them in a file to be used with the run command
  run                     run the queries generated by the GenerateQueries command against the chosen vendor
  help                    Print this message or the help of the given subcommand(s)

Options:
  -h, --help     Print help
  -V, --version  Print version

load the data

cargo run --release --bin benchmark -- load --vendor falkor -s small
cargo run --release --bin benchmark -- load --vendor neo4j -s small

create a set of queries to be used with the run command

cargo run --release --bin benchmark -- generate-queries -s10000000 --dataset small --name=small-readonly --write-ratio 0.0

run the benchmarks

cargo run --release --bin benchmark run --vendor falkor --name small-readonly -p40 --mps 4000
cargo run --release --bin benchmark run --vendor neo4j --name small-readonly -p40 --mps 4000

run simulation to see that the benchmark itself can sustain spesific mps given a fix latency on that hardware

For example, simulate 40 clients that send at 5000 messages per seconds with latency of one millisecond per call.

`cargo run --release --bin benchmark run --vendor falkor --name small -p40 --mps 5000 --simulate 1'

Data

The data is based on https://www.kaggle.com/datasets/wolfram77/graphs-snap-soc-pokec licensed: https://creativecommons.org/licenses/by/4.0/

FAQ

System Requirements

Q: What are the minimum system requirements?
A: FalkorDB requires a Linux/Unix system with 4GB RAM minimum. For production environments, 16GB RAM is recommended.

Installation & Setup

Q: Can I run FalkorDB without Redis?
A: No, FalkorDB requires Redis 6.2 or higher as it operates as a Redis module.

Development

Q: Which query language does FalkorDB use?
A: FalkorDB uses the Cypher query language, similar to Neo4j, making migration straightforward.

Data Management

Q: Does FalkorDB support data persistence?
A: Yes, through Redis persistence mechanisms (RDB/AOF). Additional persistence options are in development.

Integration

Q: Does FalkorDB support common programming languages?
A: Yes, through FalkorDB has set of clients in all these programming langauges and more see official clients

Production Use

Q: Is FalkorDB production-ready?
A: Yes, FalkorDB is stable for production use, being a continuation of the battle-tested RedisGraph codebase.

Troubleshooting

Q: What should I do if I get "libgomp.so.1: cannot open shared object file"?
A: Install OpenMP:

Ubuntu: apt-get install libgomp1
RHEL/CentOS: yum install libgomp
OSX: brew install libomp

Migration

Q: Can I migrate from Neo4j to FalkorDB?
A: Yes, FalkorDB supports the Cypher query language, making migration from Neo4j straightforward. Migration tools are in development.

Grafana and Prometheus

Accessing grafana http://localhost:3000
Accessing prometheus http://localhost:9090
sum by (vendor, spawn_id) (rate(operations_total{vendor="falkor"}[1m])) redis
rate(redis_commands_processed_total{instance=~"redis-exporter:9121"}[1m])
redis_connected_clients{instance=~"redis-exporter:9121"}
topk(5, irate(redis_commands_total{instance=~"redis-exporter:9121"} [1m]))
redis_blocked_clients
redis_commands_total
redis_commands_failed_calls_total
redis_commands_latencies_usec_count
redis_commands_rejected_calls_total
redis_io_threaded_reads_processed
redis_io_threaded_writes_processed
redis_io_threads_active
redis_memory_max_bytes
redis_memory_used_bytes
redis_memory_used_peak_bytes
redis_memory_used_vm_total
redis_process_id

Benchmark

Install / Use

README

See the benchmarks (Click here)

Key Benchmark Takeaways

About the benchmarks

System Requirements

Prerequisites

Installation Steps

install redis server

install sdkman

build falkordb from source

build the benchmark from source

run the benchmark

Generate the docker compose for prometheus and grafana

Run the docker compose

load the data

create a set of queries to be used with the run command

run the benchmarks

run simulation to see that the benchmark itself can sustain spesific mps given a fix latency on that hardware

Data

FAQ

System Requirements

Installation & Setup

Development

Data Management

Integration

Production Use

Troubleshooting

Migration

Grafana and Prometheus

redis_process_id