pebble
RocksDB
pebble | RocksDB | |
---|---|---|
11 | 43 | |
4,448 | 27,389 | |
1.1% | 0.7% | |
9.8 | 9.8 | |
6 days ago | 6 days ago | |
Go | C++ | |
BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 only |
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pebble
- Fivefold Slower Compared to Go? Optimizing Rust's Protobuf Decoding Performance
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Hummock: A Storage Engine Designed for Stream Processing
To improve the compacting speed of L0 files, we took inspiration from the design of the CockroachDB storage engine pebble.
- RocksDB/LevelDB inspired key-value database in Go
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Is there a lightweight, stable and embedded database library?
https://github.com/cockroachdb/pebble ?
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Understanding Fantoms Underlying Tech & Lachesis Consensus Algorithm + The Most Recent Developments
Pebble is a LevelDB/RocksDB-inspired key-value database focused on performance. PebbleDB is replacing Fantom’s previous RPC software to match Fantom’s sheer throughput. PebbleDB is consistently faster than previous options and it has helped the Fantom network to consistently reach synchronization with the network quicker while reducing the duration of API requests.
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Looking for fast, space-efficient key-lookup
https://github.com/cockroachdb/pebble Pure go SSD native key-value store. You could think of it as map[[]byte][]byte on persistent storage.
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Open Source Databases in Go
Genji is a document-oriented, embedded, SQL database. It is build over Pebble which is a port of RocksDB in Go, by the authors of CockroachDB.
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CockroachDB: The Resilient Geo-Distributed SQL Database
Out of curiosity, why write https://github.com/cockroachdb/pebble and not just use https://github.com/dgraph-io/badger?
- Recommendation for Key/Value storage
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CGo-free sqlite database/sql driver 1.8.0 for darwin/amd64 released
There's the semi-recent pebble from the folks that make cockroach db. Haven't used it, but seems promising: https://github.com/cockroachdb/pebble
RocksDB
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How to choose the right type of database
RocksDB: A high-performance embedded database optimized for multi-core CPUs and fast storage like SSDs. Its use of a log-structured merge-tree (LSM tree) makes it suitable for applications requiring high throughput and efficient storage, such as streaming data processing.
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Fast persistent recoverable log and key-value store
[RocksDB](https://rocksdb.org/) isn’t a distributed storage system, fwiw. It’s an embedded KV engine similar to LevelDB, LMDB, or really sqlite (though that’s full SQL, not just KV)
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The Hallucinated Rows Incident
To output the top 3 rocks, our engine has to first store all the rocks in some sorted way. To do this, we of course picked RocksDB, an embedded lexicographically sorted key-value store, which acts as the sorting operation's persistent state. In our RocksDB state, the diffs are keyed by the value of weight, and since RocksDB is sorted, our stored diffs are automatically sorted by their weight.
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In-memory vs. disk-based databases: Why do you need a larger than memory architecture?
The in-memory version of Memgraph uses Delta storage to support multi-version concurrency control (MVCC). However, for larger-than-memory storage, we decided to use the Optimistic Concurrency Control Protocol (OCC) since we assumed conflicts would rarely happen, and we could make use of RocksDB’s transactions without dealing with the custom layer of complexity like in the case of Delta storage.
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Local file non relational database with filter by value
I was looking at https://github.com/facebook/rocksdb/ but it seems to not allow queries by value, as my last requirmenet.
- Rocksdb over network
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How RocksDB Works
Tuning RocksDB well is a very very hard challenge, and one that I am happy to not do day to day anymore. RocksDB is very powerful but it comes with other very sharp edges. Compaction is one of those, and all answers are likely workload dependent.
If you are worried about write amplification then leveled compactions are sub-optimal. I would try the universal compaction.
- https://github.com/facebook/rocksdb/wiki/Universal-Compactio...
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What are the advantages of using Rust to develop KV databases?
It's fairly challenging to write a KV database, and takes several years of development to get the balance right between performance and reliability and avoiding data loss. Maybe read through the documentation for RocksDB https://github.com/facebook/rocksdb/wiki/RocksDB-Overview and watch the video on why it was developed and that may give you an impression of what is involved.
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We’re the Meilisearch team! To celebrate v1.0 of our open-source search engine, Ask us Anything!
LMDB is much more sain in the sense that it supports real ACID transactions instead of savepoints for RocksDB. The latter is heavy and consumes a lot more memory for a lot less read throughput. However, RocksDB has a much better parallel and concurrent write story, where you can merge entries with merge functions and therefore write from multiple CPUs.
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Google's OSS-Fuzz expands fuzz-reward program to $30000
https://github.com/facebook/rocksdb/issues?q=is%3Aissue+clic...
Here are some bugs in JeMalloc:
What are some alternatives?
bolthold - BoltHold is an embeddable NoSQL store for Go types built on BoltDB
LevelDB - LevelDB is a fast key-value storage library written at Google that provides an ordered mapping from string keys to string values.
LMDB - Read-only mirror of official repo on openldap.org. Issues and pull requests here are ignored. Use OpenLDAP ITS for issues.
badger - Fast key-value DB in Go.
SQLite - Unofficial git mirror of SQLite sources (see link for build instructions)
pogreb - Embedded key-value store for read-heavy workloads written in Go
sled - the champagne of beta embedded databases
Redis - Redis Go client
ClickHouse - ClickHouse® is a free analytics DBMS for big data
goleveldb - LevelDB key/value database in Go.
TileDB - The Universal Storage Engine