# Benchmarking Memgraph against others

At Memgraph, we believe we are building the **fastest graph database on the market**—and much of that performance comes 
from our **in-memory architecture**.

Many disk-based graph databases must write data to disk during every transaction and retrieve it back during reads.
While these databases often rely on caching layers to speed up reads, **cache invalidation becomes a bottleneck** in 
**high-throughput write scenarios**. Every new write risks invalidating parts of the cache, leading to more disk reads
and slower performance.

In **read-only workloads**, the performance gap between Memgraph and other databases may appear smaller at first glance.
However, even in these scenarios, **on-disk graph databases can suffer from unpredictable latency** due to
**cache invalidation**, especially if the system has recently handled write operations. This can lead to a
**poor user experience**, with inconsistent query response times depending on whether data is fetched from cache or disk.

Memgraph, on the other hand, **consistently performs better** by keeping the entire dataset **in memory**,
ensuring **predictable latency and fast response times** regardless of previous operations or system state.

## Introducing mgbench

To support fair and realistic comparisons, we built **[mgbench](https://github.com/memgraph/memgraph/blob/master/tests/mgbench/README.md)**, a benchmarking 
framework located within our core Memgraph repository. Unlike simplistic benchmarks that run a query once and record latency, 
mgbench focuses on **repeatable, controlled testing** across different conditions and vendors.
### What sets mgbench apart?
- Eliminates noise from unrelated system activity  
- Differentiates between **cold** and **hot** database states
- Detects how **query repetition, data freshness** or **system-level caching** affect results  
- Supports **adding new vendors and workloads** easily  
- Provides both **latency and throughput metrics at scale**

Many teams make the mistake of testing queries in isolation. But in real production workloads, especially ones with 
**high write volume**, read metrics can be greatly affected. This is where Memgraph's in-memory model really shines, offering 
**consistent performance even under pressure**, where disk-based solutions tend to fall behind.
### Realistic + mixed workloads
For the reasons listed above, `mgbench` supports:
- [Realistic workloads](https://github.com/memgraph/memgraph/blob/master/tests/mgbench/README.md#workloads): Simulate 
    production-like distributions of reads, writes, and analytical queries
- [Mixed workloads](https://github.com/memgraph/memgraph/blob/master/tests/mgbench/README.md#workloads): Analyze how the 
    combination of different query types affects individual performance metrics.


> **Info**
>
> Ready to test for yourself? Try out [mgbench](https://github.com/memgraph/memgraph/blob/master/tests/mgbench/README.md) with
> your own data and workloads, and see how Memgraph performs under realistic production conditions. 🚀


## Visualizing results with Benchgraph

While `mgbench` gives you full control over benchmarking and produces raw results in JSON format, we know that
analyzing performance metrics at scale can be time-consuming. That’s why we created
[**Benchgraph**](https://github.com/memgraph/benchgraph)—a companion visualization tool for `mgbench`.

![](https://memgraph.com/docs/pages/deployment/benchmarking-memgraph/benchgraph-snippet.png)

Benchgraph is not required to use `mgbench`, but it offers a **convenient way to visualize and explore**
benchmark results across a variety of run conditions:

- **Cold vs. hot cache** scenarios  
- **Isolated query performance** vs. **realistic workload distributions**  
- **Latency and throughput trends** across vendors and dataset sizes
- **Per query comparison** across the whole workload

By turning JSON output into interactive dashboards, Benchgraph helps you identify trends and outliers quickly,
spot regressions, and better understand how each database behaves under pressure.


> **Info**
>
> For looking into the published benchmarks, check out our official [BenchGraph site](https://memgraph.com/benchgraph)!