goleveldb
Tile38
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goleveldb | Tile38 | |
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15 | 9 | |
5,849 | 8,697 | |
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0.0 | 0.0 | |
2 months ago | 23 days ago | |
Go | Go | |
BSD 2-clause "Simplified" License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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goleveldb
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Looking for fast, space-efficient key-lookup
Looks like a job for GoLevelDB.
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Open Source Databases in Go
goleveldb - Implementation of the LevelDB key/value database in Go.
- A Database for 2022
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An IndexedDB clone in pure Go
I wanted to get deeper insights into both indexeddb and leveldb, so decided to write an indexeddb wrapper around goleveldb.
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What type of software do you write at your workplace?
https://github.com/syndtr/goleveldb for heavy-duty local data storage.
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Embedded write-heavy on-disk cache, write-amplification
We're using go-leveldb for a reasonably high-load case here at my $dayjob.
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Best way to store logs?
I would used some embedded kv store like go-leveldb or bolt. Key is BigEnding timestamp + optional tail to allow duplicate timestamps.
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Memory leack (?) with pprof on graphql server
I'm using the leveldb https://github.com/syndtr/goleveldb for the moment because I'm developing the architecture. Mh, leveldb support the only the inmem db? :/
- IceFireDB:Distributed disk storage database based on Raft and Redis protocol.
- https://np.reddit.com/r/programming/comments/p7a56u/icefiredbdistributed_disk_storage_database_based/h9i9j44/
Tile38
- Show HN: TG – Fast geometry library in C
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PostgreSQL: No More Vacuum, No More Bloat
Experimental format to help readability of a long rant:
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According to the OP, there's a "terrifying tale of VACUUM in PostgreSQL," dating back to "a historical artifact that traces its roots back to the Berkeley Postgres project." (1986?)
2.
Maybe the whole idea of "use X, it has been battle-tested for [TIME], is robust, all the bugs have been and keep being fixed," etc., should not really be that attractive or realistic for at least a large subset of projects.
3.
In the case of Postgres, on top of piles of "historic code" and cruft, there's the fact that each user of Postgres installs and runs a huge software artifact with hundreds or even thousands of features and dependencies, of which every particular user may only use a tiny subset.
4.
In Kleppmann's DDOA [1], after explaining why the declarative SQL language is "better," he writes: "in databases, declarative query languages like SQL turned out to be much better than imperative query APIs." I find this footnote to the paragraph a bit ironic: "IMS and CODASYL both used imperative query APIs. Applications typically used COBOL code to iterate over records in the database, one record at a time." So, SQL was better than CODASYL and COBOL in a number of ways... big surprise?
Postgres' own PL/pgSQL [2] is a language that (I imagine) most people would rather NOT use: hence a bunch of alternatives, including PL/v8, on its own a huge mass of additional complexity. SQL is definitely "COBOLESQUE" itself.
5.
Could we come up with something more minimal than SQL and looking less like COBOL? (Hopefully also getting rid of ORMs in the process). Also, I have found inspiring to see some people creating databases for themselves. Perhaps not a bad idea for small applications? For instance, I found BuntDB [3], which the developer seems to be using to run his own business [4]. Also, HYTRADBOI? :-) [5].
6.
A usual objection to use anything other than a stablished relational DB is "creating a database is too difficult for the average programmer." How about debugging PostgreSQL issues, developing new storage engines for it, or even building expertise on how to set up the instances properly and keep it alive and performant? Is that easier?
I personally feel more capable of implementing a small, well-tested, problem-specific, small implementation of a B-Tree than learning how to develop Postgres extensions, become an expert in its configuration and internals, or debug its many issues.
Another common opinion is "SQL is easy to use for non-programmers." But every person that knows SQL had to learn it somehow. I'm 100% confident that anyone able to learn SQL should be able to learn a simple, domain-specific, programming language designed for querying DBs. And how many of these people that are not able to program imperatively would be able to read a SQL EXPLAIN output and fix deficient queries? If they can, that supports even more the idea that they should be able to learn something different than SQL.
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2: https://www.postgresql.org/docs/7.3/plpgsql-examples.html
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Your Data Fits in RAM
I actually worked on a project that did this. We used a database called "Tile38" [1] which used an R-Tree to make geospatial queries speedy. It was pretty good.
Our dataset was ~150 GiB, I think? All in RAM. Took a while to start the server, as it all came off disk. Could have been faster. (It borrowed Redis's query language, and its storage was just "store the commands the recreate the DB, literally", IIRC. Dead simple, but a lot of slack/wasted space there.)
Overall not a bad database. Latency serving out of RAM was, as one should/would expect, very speedy!
[1]: https://tile38.com/
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Redcon - Redis compatible server framework for Rust
I ported it from Go and use it for my Tile38 project.
- Path hints for B-trees can bring a performance increase of 150% – 300%
What are some alternatives?
badger - Fast key-value DB in Go.
bolt
vitess - Vitess is a database clustering system for horizontal scaling of MySQL.
bbolt - An embedded key/value database for Go.
go-mysql-elasticsearch - Sync MySQL data into elasticsearch
ledisdb - A high performance NoSQL Database Server powered by Go
buntdb - BuntDB is an embeddable, in-memory key/value database for Go with custom indexing and geospatial support
groupcache - groupcache is a caching and cache-filling library, intended as a replacement for memcached in many cases.
kingshard - A high-performance MySQL proxy
InfluxDB - Scalable datastore for metrics, events, and real-time analytics