tinysearch
RocksDB
tinysearch | RocksDB | |
---|---|---|
6 | 43 | |
2,658 | 27,424 | |
3.1% | 0.8% | |
6.7 | 9.8 | |
7 months ago | 6 days ago | |
Rust | C++ | |
Apache License 2.0 | GNU General Public License v3.0 only |
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.
tinysearch
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Adding search to static websites
When getting into performance territory you might want to work on the performance of the index, there are multiple options, you could implement fuse filters or Bloom Filters or XOR Filters like the ones suggested in the blog post. If you want to go for a further performance bump, server side is your best bet.
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Ask HN: What's the best way to add search to my website?
If your content is mostly static, you might want to consider pre-building an index and shipping it as a whole. You could look into something like
* https://stork-search.net/ (Rust/WASM)
* tinysearch: https://github.com/tinysearch/tinysearch (Rust/WASM)
* https://lunrjs.com/ (JS, simple, stable)
* http://elasticlunr.com/ - based on the former, slightly more sophisticated tuning options
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We’re the Meilisearch team! To celebrate v1.0 of our open-source search engine, Ask us Anything!
500kB sounds, like could be just shipped to the client lazily? https://github.com/tinysearch/tinysearch
- A tiny static full-text search engine using Rust and WebAssembly (2019)
- tinysearch
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Everything I Know – Wiki
Checkout https://github.com/mre/tinysearch
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?
knowledge - Everything I know
LevelDB - LevelDB is a fast key-value storage library written at Google that provides an ordered mapping from string keys to string values.
alfred-my-mind - Alfred workflow to search through my notes and bookmarks
LMDB - Read-only mirror of official repo on openldap.org. Issues and pull requests here are ignored. Use OpenLDAP ITS for issues.
reqwasm - HTTP requests library for WASM Apps
SQLite - Unofficial git mirror of SQLite sources (see link for build instructions)
wiki - some useful information
sled - the champagne of beta embedded databases
MeiliSearch - A lightning-fast search API that fits effortlessly into your apps, websites, and workflow
ClickHouse - ClickHouse® is a free analytics DBMS for big data
elasticlunr-rs - A partial port of elasticlunr to Rust. Intended to be used for generating compatible search indices.
TileDB - The Universal Storage Engine