cuckoofilter
yottaStore
cuckoofilter | yottaStore | |
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1 | 9 | |
1,066 | 79 | |
- | - | |
0.0 | 1.8 | |
about 1 month 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.
cuckoofilter
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Looking for fast, space-efficient key-lookup
- a cuckoo filter for fast lookup. This has around a 3% false positive rate. There are other implementations however that have a much lower rate. You can store the filter in the database as well in a different bucket so you don't have to rebuild the filter on startup.
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?
hyperloglog - HyperLogLog with lots of sugar (Sparse, LogLog-Beta bias correction and TailCut space reduction) brought to you by Axiom
async-rdma - Easy to use RDMA API in Rust async
golang-set - A simple, battle-tested and generic set type for the Go language. Trusted by Docker, 1Password, Ethereum and Hashicorp.
solid_cache - A database-backed ActiveSupport::Cache::Store
bloom - Bloom filters implemented in Go.
cdb - A native golang implementation of cdb (http://cr.yp.to/cdb.html)
gods - GoDS (Go Data Structures) - Sets, Lists, Stacks, Maps, Trees, Queues, and much more
KVRocks - RocksDB compatible key value store and MyRocks compatible storage engine designed for KV SSD
gota - Gota: DataFrames and data wrangling in Go (Golang)
ssd-nvme-database - Columnar database on SSD NVMe
go-datastructures - A collection of useful, performant, and threadsafe Go datastructures.
uNVMe - KV and LBA SSD userspace NVMe driver