In a Distributed System, achieving ACID is hard due to many challenges
Read more π https://lttr.ai/AYt45
#java #Microservices #Transactions #DistributedSystem #RelationalDatabaseSystems #CloselyTied
In a Distributed System, achieving ACID is hard due to many challenges
Read more π https://lttr.ai/AYt45
#java #Microservices #Transactions #DistributedSystem #RelationalDatabaseSystems #CloselyTied
Consistency (C):Consistency ensures that a transaction brings the database from one valid state to another.
Read more π https://lttr.ai/ATvUu
#java #Microservices #Transactions #DistributedSystem #RelationalDatabaseSystems #CloselyTied
Due to these challenges related to ACID, we have some alternatives to overcome them, especially when we talk about distributed systems.
Read more π https://lttr.ai/AOD5R
#java #Microservices #Transactions #DistributedSystem #RelationalDatabaseSystems #CloselyTied
Different isolation levels provide different levels of protection against issues such as dirty reads, non-repeatable reads, and phantom reads.
Read more π https://lttr.ai/ATj5U
#java #Microservices #Transactions #DistributedSystem #RelationalDatabaseSystems #CloselyTied
- Divulged into the conception and making of a visual book on software architecture in this episode: https://ter.li/ooqott
- Learned about new approaches and techniques to successfully run through a distributed system in this episode: https://ter.li/nza3zq
2/2
#DistributedSystem #SoftwareArchitecture #SoftwareDevelopment
Great workshop with GLACIATION consortium under #HorizonEurope! Discussed synergies for Braine software, including #edge #security, #distributedsystem for #dataprivacy, #datasovereignty & #energyefficient #microdatacentre for #bigdata & #AI.
Thanks @AidanOMahony &
#Dell ! π»π
Just found out I have no #introduction post yet. So :
I'm a techie that likes to tinker with stuff. I'm interested in #opensource, #AI, #ML, #computersystem, and #distributedsystem topics. Sometimes I write blogs about them.
I've signed up and have been using this little website sporadically since 2017. I thought it was a really cool idea that lacked users.
But recently it proved its value by accepting people who want to be free.
Hope to see you guys here more in the future!
I wonder if #EventModeling or #Eventsourcing provides a methodic approach or something similar to describe failure #scenarios for a #distributedsystem. I made already a mapping of the #k8s based #distributedsystem to #12factor and the #reactive principles as described in the #reactivemanifesto.
I also collected all possible failure events that came to my mind with a mindmap.
So, now I search for a good way to describe the failure scenarios.
Ideas and hints very welcome.
People are sometimes afraid of #p2pβs complexity because itβs a #distributedsystem.
But client-server is a many-to-one relationship, so servers are naturally bottlenecks. Scaling them quickly becomes a distributed system of shared state with complexity equal to or greater than any p2p design.
Funny observation: the design of #ActivityPub is very close to X.500 (ITU-T directory standard first published 1988).
#DistributedSystem: if it looks like a π¦ , talks like a π¦ , ...
My favorite distributed systems essay, Fred Hebert's "Queues Don't Fix Overload": https://ferd.ca/queues-don-t-fix-overload.html When working on a #DistributedSystem team, I usually link this a couple of times week. The key ideas:
1. Your system has a bottleneck somewhere.
2. Putting queues in front of the bottleneck means you crash less often, but harder.
3. The bottleneck needs to be able slow down system input. "Back-pressure" and load shedding are your friends!
Find that bottleneck.
Consistent Hashing
{ by @yt_tr1x@twitter.com } from @hashnode@twitter.com
#hashing #server #distributedsystem #binarysearch-algorithm https://tr1x.hashnode.dev/consistent-hashing