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https://github.com/yottaStore/yottaStore
A datastore aiming at linear scalability up to the yottabyte range. Inspired by dynamo and cassandra.
https://github.com/yottaStore/yottaStore
Last synced: about 2 months ago
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A datastore aiming at linear scalability up to the yottabyte range. Inspired by dynamo and cassandra.
- Host: GitHub
- URL: https://github.com/yottaStore/yottaStore
- Owner: yottaStore
- License: mit
- Created: 2022-05-13T16:58:00.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-04-11T11:02:56.000Z (over 1 year ago)
- Last Synced: 2024-05-22T15:31:20.197Z (4 months ago)
- Size: 1000 KB
- Stars: 81
- Watchers: 1
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-iouring - Yottastore
README
# Introduction
Yotta Store is a [next generation]() storage system aiming to **scale out to the yotta byte range**
and **scale up to millions of concurrent read and writes per record**.
The goal is to have **two orders of magnitude more throughput than DynamoDB**,
dollar per dollar, while maintaining a **sub-ms latency**.
Check our [benchmarks](docs/10_benchmarks/benchmarks.md)
Yotta Store is built on top of a 512 bit distributed machine, with a 4kib word size.
We try to design a system which can exploit the capabilities of
modern hardware and software, like NVMe disks or `io_uring`.
Read more in the [docs](docs/README.md)
- [Golang implementation](https://github.com/yottaStore/golang) (WIP) backed by Cloud Storage
- [Rust implementation](https://github.com/yottaStore/rust) (WIP) Backed by io_uring NVMe API
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## Main features
- Linear scalability, up to 10^9 nodes and 10 yotta bytes of addressable space.
- Anti fragility, the multi tenant setup increase reliability and availability with load.
- Self configuring and optimizing, thanks to machine learning
- Strong consistency guarantees, aiming at sub ms latency.
- Cheap transactions and indexes, at around `o(n)`.
- Storage decoupled from compute with a serverless architecture.
- Two orders of magnitude faster than DynamoDB, dollar per dollar.
## Techniques used
Yotta Store does not create any new technique, but uses existing ones in a novel combination:
| Problem | Solution | Description |
|----------------------------------|---------------------------------|---------------------------------------------------------------------------------------------------------------------------|
| Partitioning and replication | ReBaR: Rendezvous Based Routing | A generalization of consistent hashing, for agreeing k
choices in a stable way and with minimal information exchange |
| Highly available read and writes | YottaFS | A wait free userspace filesystem, designed around NVMe devices and CRDTs. |
| Hot records | YottaSelf | Hot records can be sandboxed in dedicated partitions. |
| Transactions | Modified Warp | Thanks to the modified warp algorithm no distributed consensus is needed. |
| Indexes, queries across keys | Partitioned indexes | Indexes are sharded and lazily built on the same partition seeing the changes. |
| Membership and failure detection | Gossip agreement protocol | Thanks to GAP and ReBaR the expected cost is `o(log(n))` deterministically. |
## Inspirations
- [Designing Data-Intensive Applications, Martin Kleppmann](https://www.oreilly.com/library/view/designing-data-intensive-applications/9781491903063/)
- [The Dynamo paper, many authors](https://www.allthingsdistributed.com/files/amazon-dynamo-sosp2007.pdf)
- [A pragmatic implementation of non-blocking linked-lists, Timothy L. Harris](https://timharris.uk/papers/2001-disc.pdf)
- [Warp: Lightweight Multi-Key Transactions for Key-Value Stores, many authors](https://arxiv.org/pdf/1509.07815.pdf)
- [Thread per core](https://helda.helsinki.fi/bitstream/handle/10138/313642/tpc_ancs19.pdf)
- [Kafka](https://docs.confluent.io/platform/current/kafka/design.html)