postgres
buntdb
postgres | buntdb | |
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4 | 7 | |
23 | 4,401 | |
- | - | |
9.6 | 0.0 | |
6 days ago | about 1 month ago | |
C | Go | |
GNU General Public License v3.0 or later | MIT License |
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postgres
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PostgreSQL: No More Vacuum, No More Bloat
> .. and the delta code to be committed upstream is less than 2K LOC.
Then mainline it?
The last commit that /postgres/postgres and /orioledb/postgres share (1) is 6 months old for 15.2?
I mean, this is literally what I'm pointing out in my comment; you're chasing a moving target. Every change postgres makes, you have to merge in check it doesn't conflict, roll out a new patch set...
...and, you're already falling behind on it.
So, going forward, how do you plan to keep up to date...? ...because it looks to me, like a < 2K LOC isn't a fix for this problem; it hasn't solved it for you, as time goes forwards, it will continue not to be a solution to the problem.
The solution is mainlining those changes --> https://github.com/orioledb/postgres/issues/2
> Currently the changes needed to Postgres core are less than a 1000 lines of code. Due to the separate development schedules for Postgres and OrioleDB, these changes cannot be unstreamed in time for v.15.
^ They were < 1000 lines a year ago, apparently.
I think you can draw a clear pattern in how this going to go, going forward.
[1] - https://github.com/postgres/postgres/commit/78ec02d612a9b690... vs https://github.com/orioledb/postgres/commit/78ec02d612a9b690...
- OrioleDB β solving some PostgreSQL wicked problems
buntdb
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PostgreSQL: No More Vacuum, No More Bloat
Experimental format to help readability of a long rant:
1.
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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1: https://dataintensive.net/
2: https://www.postgresql.org/docs/7.3/plpgsql-examples.html
3: https://github.com/tidwall/buntdb
4: https://tile38.com/
5: https://www.hytradboi.com/
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Is there a nice embedded json db, like PoloDB (Rust) for Golang
https://github.com/tidwall/buntdb -> i think this one you might want
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Open Source Databases in Go
buntdb - Fast, embeddable, in-memory key/value database for Go with custom indexing and spatial support.
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Alternative to MongoDB?
BuntDB for NoSQL
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Path hints for B-trees can bring a performance increase of 150% β 300%
BuntDB [0] from @tidwall uses this package as a backing data structure. And BuntDB is in turn used by Tile38 [1]
[0] https://github.com/tidwall/buntdb
- The start of my journey learning Go. Any tips/suggestions would greatly appreciated!
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In-memory caching solutions
I've used BuntDB and had a great experience with it. It's basically just a JSON-based key-value store. I'm a huge fan of the developers other work (sjson, gjson, jj, etc) and stumbled on it while looking for a simple, embedded DB solution. It's not specifically a cache, though--just a simple DB, so you'd have to write the caching logic yourself.
What are some alternatives?
orioledb - OrioleDB β building a modern cloud-native storage engine (... and solving some PostgreSQL wicked problems) Β πΊπ¦
bolt
Tile38 - Real-time Geospatial and Geofencing
badger - Fast key-value DB in Go.
PostgreSQL - Mirror of the official PostgreSQL GIT repository. Note that this is just a *mirror* - we don't work with pull requests on github. To contribute, please see https://wiki.postgresql.org/wiki/Submitting_a_Patch
nutsdb - A simple, fast, embeddable, persistent key/value store written in pure Go. It supports fully serializable transactions and many data structures such as list, set, sorted set.
go-memdb - Golang in-memory database built on immutable radix trees
goleveldb - LevelDB key/value database in Go.
ledisdb - A high performance NoSQL Database Server powered by Go
bbolt - An embedded key/value database for Go.
diskv - A disk-backed key-value store.
vitess - Vitess is a database clustering system for horizontal scaling of MySQL.