cdb
yottaStore
| cdb | yottaStore | |
|---|---|---|
| 1 | 9 | |
| 64 | 79 | |
| - | - | |
| 10.0 | 1.8 | |
| about 5 years ago | about 1 year ago | |
| Go | ||
| MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
cdb
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Looking for fast, space-efficient key-lookup
github.com/colinmarc/cdb is a Go implementation,
yottaStore
- Ask HN: Why are there no open source NVMe-native key value stores in 2023?
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How to deal with overflowing counters?
I'm building a database, and I'm working on the storage format. For each record I have a logical clock which increases by 1 every time there's a write operation on the record, and I would like to be able to compare the clocks of two writes to understand which happened first.
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Need help porting a wait free trie from C to Rust (and other silly questions)
If you're curious to know more, the tree is used for rendezvous based routing, as used by my datastore. I'm doing machine learning on a 200 TB dataset, using around 200 machines.
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Looking for fast, space-efficient key-lookup
I copied this approach from several papers, with some improvements, for my datastore.
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How to handle hundreds of routes?
I have a server with hundreds of routes, representing all the possible operations I can do on a datastore. How can I organize my code better?
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Async-rdma v0.4.0: A Rust lib for writing high-throughput, low-latency networking apps simply
Yes I'm building a database where storage and compute are decoupled. I use io_uring to do pseudo-RDMA, and I'm looking to add ePBF to make it even more effective.
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Avoid hash flooding without a secret key?
I'm currently building an implementation of the dynamo paper, yottastore. Imagine it as a huge, distributed, hash map.
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How to deal with a very big hash table?
I'm building an implementation of the dynamo paper, yottastore. Given a key, I need to find which NVMe block stores the data. To do that I hash the key to find the shard where I have an in memory array in which at position [hash] I can find a struct with:
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Golang is better than Rust for next generation in-memory database
I have to be honest, I'm very skeptical about your results and your code. I'm building a database, yottastore, both in javascript, golang and rust so I think I can share my opinion:
What are some alternatives?
cuckoofilter - Cuckoo Filter: Practically Better Than Bloom
solid_cache - A database-backed ActiveSupport::Cache::Store
goleveldb - LevelDB key/value database in Go.
async-rdma - Easy to use RDMA API in Rust async
bbolt - An embedded key/value database for Go.
KVRocks - RocksDB compatible key value store and MyRocks compatible storage engine designed for KV SSD
RoaringBitmap - A better compressed bitset in Java: used by Apache Spark, Netflix Atlas, Apache Pinot, Tablesaw, and many others
ssd-nvme-database - Columnar database on SSD NVMe
go - The Go programming language
uNVMe - KV and LBA SSD userspace NVMe driver
pebble - RocksDB/LevelDB inspired key-value database in Go
xnvme - Portable and high-performance libraries and tools for NVMe devices as well as support for traditional/legacy storage devices/interfaces.