lz4_flex
RocksDB
lz4_flex | RocksDB | |
---|---|---|
13 | 43 | |
411 | 27,424 | |
- | 0.8% | |
6.5 | 9.8 | |
26 days ago | 4 days ago | |
Rust | C++ | |
MIT License | GNU General Public License v3.0 only |
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lz4_flex
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Hetzner ARM cax11 vs Intel cx11 Benchmark
I run a benchmark based on my LZ4 implementation (de/compressor) between cax11 and cx11 since the instances cost the same. The code doesn't use any SIMD.
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lz4_flex 0.11: Gainzzzzz Unleashed!: Performance Improvements Detailed in Blogpost (LZ4 De/compression)
By the way, the PR removing bounds checks from extend_from_within_overlapping is up: https://github.com/PSeitz/lz4_flex/pull/141
- lz4_flex (fast LZ4 de/compression) 0.10 released, now with legacy frame support ~ also 1Mio downloads 🎉
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ZAP: a VERY fast zip alternative, written in rust!
Zap uses LZ4 as it's primary compression algorithm, although I might add ZSTD as an option too. The LZ4 crate i'm using goes over the compression better than I can in the span of a comment. Check it out here.
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lz4_flex 0.9 released
I wrote something here how hc could work: https://github.com/PSeitz/lz4_flex/issues/21
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lz4_flex 0.8 released with support for frame format and major performance improvements
Yes, the updated benchmarks are here: https://github.com/pseitz/lz4_flex#results-v080-17-05-2021-safe-decode-and-safe-encode-off
- Lz4_flex – fast LZ4 implementation in Rust
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LZ4, an Extremely Fast Compression Algorithm
I ported the block format to Rust matching the C implementation in performance and ratio.
https://github.com/pseitz/lz4_flex
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lz4_flex 0.7.2 reaches parity with cpp reference implementation on speed and ratio
Following this change count_same_bytes is unsound - it offsets the pointer by the value of candidate without any bounds checks, which may result in out-of-bounds access.
- lz4_flex 0.7 supports no_std (thanks @coolreader18), 32bit and is dependency-free
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?
zfs - OpenZFS on Linux and FreeBSD
LevelDB - LevelDB is a fast key-value storage library written at Google that provides an ordered mapping from string keys to string values.
LZ4 - Extremely Fast Compression algorithm
LMDB - Read-only mirror of official repo on openldap.org. Issues and pull requests here are ignored. Use OpenLDAP ITS for issues.
density - Superfast compression library
SQLite - Unofficial git mirror of SQLite sources (see link for build instructions)
7-Zip-zstd - 7-Zip with support for Brotli, Fast-LZMA2, Lizard, LZ4, LZ5 and Zstandard
sled - the champagne of beta embedded databases
squashfs-tools-ng - A new set of tools and libraries for working with SquashFS images
ClickHouse - ClickHouse® is a free analytics DBMS for big data
zstd - Zstandard - Fast real-time compression algorithm
TileDB - The Universal Storage Engine