Distributed-Cache-System
A simple implementation of distributed cache system (by cruzelx)
consistent
Consistent hashing with bounded loads in Golang (by buraksezer)
| Distributed-Cache-System | consistent | |
|---|---|---|
| 3 | 2 | |
| 13 | 712 | |
| - | 0.0% | |
| 7.8 | 0.0 | |
| over 1 year ago | over 1 year ago | |
| Go | Go | |
| - | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
Distributed-Cache-System
Posts with mentions or reviews of Distributed-Cache-System.
We have used some of these posts to build our list of alternatives
and similar projects.
- An attempt on Distributed Cache System
-
An attempt to design a distributed cache system
I have worked on this project for quite a while. Started from scratch and learned and built the system. I would appreciate any comment or advice on the project. Here is the github link: https://github.com/cruzelx/Distributed-Cache-System
consistent
Posts with mentions or reviews of consistent.
We have used some of these posts to build our list of alternatives
and similar projects.
-
Cloud Scheduler, can there really be only 5000 number of jobs? Is there a limit at how far I can plan tasks into the future? Alternatives?
For example, for a user with the user id of 123-ddd-44232, you would devise a system in your language of choice to hash that ID to the number 2. You then always send the queue messages for that user to queue 2 with the user id in the payload (and not in the URL). This will ensure that FIFO still happens on a per-user basis, but also lets you scale your queues appropriately. Here's an example library in Go that achieves the hashing part with just a few lines of code. These types of libraries are available in most languages.
- Consistent hashing with bounded loads in Golang
What are some alternatives?
When comparing Distributed-Cache-System and consistent you can also consider the following projects:
go-hashlru - A simple thread-safe and fixed size LRU. Based on the Hashlru Algorithm :arrows_clockwise:
resgate - A Realtime API Gateway used with NATS to build REST, real time, and RPC APIs, where all your clients are synchronized seamlessly.
holster - A place to keep useful golang functions and small libraries
emitter-io - High performance, distributed and low latency publish-subscribe platform.
Dkron - Dkron - Distributed, fault tolerant job scheduling system https://dkron.io
ringpop-go - Scalable, fault-tolerant application-layer sharding for Go applications