Dragonfly is a modern in-memory datastore, fully compatible with Redis and Memcached APIs. Dragonfly implements novel algorithms and data structures on top of a multi-threaded, shared-nothing architecture. As a result, Dragonfly reaches x25 performance
compared to Redis and supports millions of QPS on a single instance.
Dragonfly's core properties make it a cost-effective, high-performing, and easy-to-use Redis replacement.
Dragonfly is crossing 3.8M QPS on c6gn.16xlarge reaching x25 increase in throughput compared to Redis.
99th latency percentile of Dragonfly at its peak throughput:
| op |r6g | c6gn | c7g |
|-----|-----|------|----|
| set |0.8ms | 1ms | 1ms |
| get | 0.9ms | 0.9ms |0.8ms |
|setex| 0.9ms | 1.1ms | 1.3ms
*All benchmarks were performed using `memtier_benchmark` (see below) with number of threads tuned per server type and the instance type. `memtier` was running on a separate c6gn.16xlarge machine. For setex benchmark we used expiry-range of 500, so it would survive the end of the test.*
We compared memcached with Dragonfly on `c6gn.16xlarge` instance on AWS.
As you can see below Dragonfly dominates memcached for both write and read workloads
in terms of throughput with a comparable latency. For write workloads, Dragonfly has also better latency, due to contention on the [write path in memcached](docs/memcached_benchmark.md).
We maintain [binary releases](https://github.com/dragonflydb/dragonfly/releases) for x86 and arm64 architectures. You will need to install `libunwind8` lib to run the binaries.
*`bind` localhost to only allow locahost connections, Public IP ADDRESS , to allow connections **to that ip** address (aka from outside too)
*`requirepass` password for AUTH authentication, default: ""
*`maxmemory` Limit on maximum-memory (in bytes) that is used by the database.0 - means the program will automatically determine its maximum memory usage. default: 0
*`dir` - by default, dragonfly docker uses `/data` folder for snapshotting. the CLI uses: ""
The Prometheus exported metrics are compatible with the Grafana dashboard [see here](tools/local/monitoring/grafana/provisioning/dashboards/dashboard.json).
Dragonfly started as an experiment to see how an in-memory datastore could look like if it was designed in 2022. Based on lessons learned from our experience as users of memory stores and as engineers who worked for cloud companies, we knew that we need to preserve two key properties for Dragonfly: a) to provide atomicity guarantees for all its operations, and b) to guarantee low, sub-millisecond latency over very high throughput.
Our first challenge was how to fully utilize CPU, memory, and i/o resources using servers that are available today in public clouds. To solve this, we used [shared-nothing architecture](https://en.wikipedia.org/wiki/Shared-nothing_architecture), which allows us to partition the keyspace of the memory store between threads, so that each thread would manage its own slice of dictionary data. We call these slices - shards. The library that powers thread and I/O management for shared-nothing architecture is open-sourced [here](https://github.com/romange/helio).
To provide atomicity guarantees for multi-key operations, we used the advancements from recent academic research. We chose the paper ["VLL: a lock manager redesign for main memory database systems”](https://www.cs.umd.edu/~abadi/papers/vldbj-vll.pdf) to develop the transactional framework for Dragonfly. The choice of shared-nothing architecture and VLL allowed us to compose atomic multi-key operations without using mutexes or spinlocks. This was a major milestone for our PoC and its performance stood out from other commercial and open-source solutions.
Our second challenge was to engineer more efficient data structures for the new store. To achieve this goal, we based our core hashtable structure on paper ["Dash: Scalable Hashing on Persistent Memory"](https://arxiv.org/pdf/2003.07302.pdf). The paper itself is centered around persistent memory domain and is not directly related to main-memory stores.
Nevertheless, its very much applicable for our problem. It suggested a hashtable design that allowed us to maintain two special properties that are present in the Redis dictionary: a) its incremental hashing ability during datastore growth b) its ability to traverse the dictionary under changes using a stateless scan operation. Besides these 2 properties,
Dash is much more efficient in CPU and memory. By leveraging Dash's design, we were able to innovate further with the following features:
* Efficient record expiry for TTL records.
* A novel cache eviction algorithm that achieves higher hit rates than other caching strategies like LRU and LFU with **zero memory overhead**.
* A novel **fork-less** snapshotting algorithm.
After we built the foundation for Dragonfly and [we were happy with its performance](#benchmarks),
we went on to implement the Redis and Memcached functionality. By now, we have implemented ~130 Redis commands (equivalent to v2.8) and 13 Memcached commands.
And finally, <br>
<em>Our mission is to build a well-designed, ultra-fast, cost-efficient in-memory datastore for cloud workloads that takes advantage of the latest hardware advancements. We intend to address the pain points of current solutions while preserving their product APIs and propositions.
