GraphCanon updated 4w · GitHub synced 4w
Decision brief
DragonflyDB positions itself as an advanced cache and database solution that competes directly with established tools like Redis and Memcached while introducing key features such as efficient support for vector search.
Good fit when
- If your application requires high-performance vector search within a unified platform, DragonflyDB integrates this capability out-of-the-box.
- Preferring native C++ implementation can lead to significant performance benefits in scenarios where low-level operations and integration are crucial.
Avoid when
- When a smaller footprint is required due to limited resources or preference for lightweight solutions, older but more established tools like Memcached may be preferable.
- If your ecosystem already heavily relies on Redis-specific features that have been built over years of use and customization, DragonflyDB might not offer the same level of compatibility or feature set
- Pricing:
- unknown - The specific cost structure for using DragonflyDB is not documented in this repository content.
- Requirements:
- Min 4 GB RAM; DragonflyDB is most effective in environments capable of leveraging multi-threading and low-level optimization features
Observed Jul 11, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of 4w
- Provenance
- Not a fork · Organization account
- As of 4w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/dragonflydb/dragonflySimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
DragonflyDB is built in C++ and serves as an advanced cache, key-value store, and message broker with support for vector search.
Capability facts
- Languages
- c++, python
Source: github.language+pyproject.toml · Jul 23, 2026
Categories
Tags
README
Before moving on, please consider giving us a GitHub star ⭐️. Thank you!
Other languages: 简体中文 日本語 한국어 Português
Website • Docs • Quick Start • Community Discord • Dragonfly User Conference • Join the Dragonfly Community
GitHub Discussions • GitHub Issues • Contributing • AI Agents Guide • Dragonfly Cloud
The world's most efficient in-memory data store
Dragonfly is an in-memory data store built for modern application workloads.
Fully compatible with Redis and Memcached APIs, Dragonfly requires no code changes to adopt. Compared to legacy in-memory datastores, Dragonfly delivers 25X more throughput, higher cache hit rates with lower tail latency, and can run on up to 80% less resources for the same sized workload.
Contents
- Benchmarks
- Quick start
- Configuration
- Roadmap and status
- Design decisions
- Background
- Build from source
Benchmarks
We first compare Dragonfly with Redis on m5.large instance which is commonly used to run Redis
due to its single-threaded architecture. The benchmark program runs from another
load-test instance (c5n) in the same AZ using memtier_benchmark -c 20 --test-time 100 -t 4 -d 256 --distinct-client-seed
Dragonfly shows a comparable performance:
- SETs (
--ratio 1:0):
| Redis | DF |
|---|---|
| QPS: 159K, P99.9: 1.16ms, P99: 0.82ms | QPS:173K, P99.9: 1.26ms, P99: 0.9ms |
- GETs (
--ratio 0:1):
| Redis | DF |
|---|---|
| QPS: 194K, P99.9: 0.8ms, P99: 0.65ms | QPS: 191K, P99.9: 0.95ms, P99: 0.8ms |
The benchmark above shows that the algorithmic layer inside DF that allows it to scale vertically does not take a large toll when running single-threaded.
However, if we take a bit stronger instance (m5.xlarge), the gap between DF and Redis starts growing.
(memtier_benchmark -c 20 --test-time 100 -t 6 -d 256 --distinct-client-seed):
- SETs (
--ratio 1:0):
| Redis | DF |
|---|---|
| QPS: 190K, P99.9: 2.45ms, P99: 0.97ms | QPS: 279K , P99.9: 1.95ms, P99: 1.48ms |
- GETs (
--ratio 0:1):
| Redis | DF |
|---|---|
| QPS: 220K, P99.9: 0.98ms , P99: 0.8ms | QPS: 305K, P99.9: 1.03ms, P99: 0.87ms |
Dragonfly throughput capacity continues to grow with instance size, while single-threaded Redis is bottlenecked on CPU and reaches local maxima in terms of performance.
If we compare Dragonfly and R
For agents
This page has a .md twin and JSON over the API.