groupcache
Tile38
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groupcache | Tile38 | |
---|---|---|
12 | 9 | |
12,717 | 8,897 | |
0.7% | - | |
0.0 | 7.0 | |
5 months ago | 13 days ago | |
Go | Go | |
Apache License 2.0 | MIT License |
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.
groupcache
- [imcache] A generic in-memory cache Go library. Feedback appreciated.
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DREAMEMO: An out-of-the-box, high-scalability, modular-design distributed cache
As shown in the title, DREAMEMO is a distributed cache with out-of-the-box, high-scalability, modular-design features.The groupcache implementation is referenced, and re-structured, specific module differentiation is as follows:
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Sourcehut will blacklist the Go module mirror
I remember one of the first real-world uses of Go being the groupcache package: https://github.com/golang/groupcache (to serve Chrome downloads, IIRC?)
> comes with a cache filling mechanism. Whereas memcached just says "Sorry, cache miss", often resulting in a thundering herd of database (or whatever) loads from an unbounded number of clients (which has resulted in several fun outages), groupcache coordinates cache fills such that only one load in one process of an entire replicated set of processes populates the cache, then multiplexes the loaded value to all callers.
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Distributed fault-tolerant persistent atomic counter in golang
I read that group cache (https://github.com/golang/groupcache) can be used to sync servers around a key.
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How can you ensure all Microservices have finished their tasks?
I've not tried this myself, but I've seen it suggested to use groupcache (https://github.com/golang/groupcache) to sync your servers.
- What is for you the project who represents the best the power of Golang ?
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go-generics-cache: An in-memory key:value store/cache library for Go Generics
https://github.com/golang/groupcache is managing distributed caching that addresses thundering herd problem of memcache.
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How to Create HTTP Cache Service in Golang?
How it goes sometimes. Check out https://github.com/golang/groupcache and of course the AWS golang SDK.
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Redis inside cluster
There is also groupcache, written by the same author as memcached, but better.
- Can you share some Go package that you think has high quality clean code?
Tile38
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Show HN: TG – Fast geometry library in C
[2] https://github.com/tidwall/tile38
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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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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.
- Tile38 - a geolocation data store, spatial index, and realtime geofence
- Path hints for B-trees can bring a performance increase of 150% – 300%
- How do I implement push notifications on a 10 mile radius from a certain user?
What are some alternatives?
BigCache - Efficient cache for gigabytes of data written in Go.
vitess - Vitess is a database clustering system for horizontal scaling of MySQL.
go-cache - An in-memory key:value store/cache (similar to Memcached) library for Go, suitable for single-machine applications.
go-mysql-elasticsearch - Sync MySQL data into elasticsearch
cache2go - Concurrency-safe Go caching library with expiration capabilities and access counters
ledisdb - A high performance NoSQL Database Server powered by Go
rqlite - The lightweight, distributed relational database built on SQLite.
goleveldb - LevelDB key/value database in Go.
kingshard - A high-performance MySQL proxy
go-memdb - Golang in-memory database built on immutable radix trees
InfluxDB - Scalable datastore for metrics, events, and real-time analytics