Redis API, Dragonfly Performance
Dragonfly is an in-memory datastore designed to scale vertically and make extremely efficient use of the underlying hardware. It is fully compatible with the Redis ecosystem and requires no code changes to implement.
→ ~/ docker run --network=host --ulimit memlock=-1 docker.dragonflydb.io/dragonflydb/dragonfly
Featured Posts
Redis Analysis - Part 1: Threading model
December 09, 2021
Following my previous post, we are going start with the “hottest potato” - single-threaded vs multi-threaded argument.
June 23, 2022
I talked in my previous post about Redis eviction policies. In this post, I would like to describe the design behind Dragonfly cache.
Infrastructure should be boring
October 21, 2022
Infrastructure should be boring. Boring is good. Boring means that it just works, and you don’t have to worry about it. A year ago, we went on a quest to build a boring in-memory store.
Fully compatible with Redis
Dragonfly gives you so much more with a complete, modern engine architecture that’s fully compatible with the Redis and Memcached APIs. See why it’s the fastest memory store in the universe.
Ultra performant
25X the throughput of Redis
With non-contending, multi-threaded processes, Dragonfly is architected to deliver the performance that modern applications require: millions of operations per second, all from a single instance.
View the benchmarksQPS benchmark on AWS r6gn.16xlarge. Snapshot benchmark on AWS r6gd.16xlarge. Source
25x
More QPS than Redis
12x
Faster snapshotting than Redis
Highly Scalable
Simple Vertical Scaling
Dragonfly is architected to scale vertically on a single machine, saving teams the cost and complexity of managing a multi-node cluster. For in-memory datasets up to 1TB, Dragonfly offers the simplest and most reliable scale on the market.
1 TB
In-memory datasets on a single instance
30%
Less memory usage
Unparalleled efficiency
30-60% better memory utilization than Redis
Dragonfly utilizes an innovative hash table structure called dashtable to minimize memory overhead and tail latency. Dragonfly also utilizes bitpacking and denseSet techniques to compress the in-memory data, making it on average 30% more memory efficient than Redis. Lastly, Dragonfly uses consistent memory during the snapshotting, eliminating the need to over-provision memory that is typical with Redis.
Memory usage under BGSAVE. Filling with 5GB of data using debug populate 5000000 key 1024, sending the update traffic with memtier, and snapshotting with bgsave. Source
All-new architecture
A new in-memory data store, rearchitected for today
Memory Efficient
While classic chaining hash-tables are built upon a dynamic array of linked-lists, Dragonfly's dashtable is a dynamic array of flat hash-tables of constant size. This design allows for much better memory efficiency.
High Hit Ratio
Dragonfly utilizes a unique 'least frequenty recently used' cache policy. When compared to Redis' LRU cache policy, LFRU is resistant to fluctuations in traffic, does not require random sampling, has zero memory overhead per item, and has a very small run-time overhead.
High Throughput
Dragonfly's new in-memory engine, optimized for throughput, uses a thread-per-core architecture without locks to deliver stable and low latencies. By implementing true async interfaces, Dragonfly takes full advantage of the underlying hardware to deliver maximum performance.
Start building today
Dragonfly is fully compatible with the Redis ecosystem and requires no code changes to implement.