MICROSERVICES ARCHITECTURE | SCALE CUBE | PART - 4

Learn strategies to scale the microservices.
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[@glennmglazer]

One thing to be careful of when using data partitioning is that the expected load on each bucket is roughly equal. Consider partitioning by name in a country where there are only a few surnames: some buckets will get a huge amount of data and some none at all.

[@hyperborean72]

I believe what you call data partitioning in this video (splitting data from the same table)) is in fact called sharding. Partitioning is when a database is split into several databases - the concept essential to microservices that you touched upon in the previous video

[@negatifzeo5709]

Great video, thanks!

[@navpreetkaur4902]

thanks so much, very informative and easy to understand videos

[@arvindaaswani1303]

First like👍

[@celalcagriakgunduz7133]

Thank you for the cool video. All videos in the playlist except part 4 and 10 are private and not accessible. I just want to let you know if it is not by purpose

[@PK-xw3nn]

Hey Narendra,
Good video. But I do have some quick questions.
So when we scaled it further into S1(A-D), S2(E-H), S3(I-L), S4(M-P), S5(Q-T), S6(U-Z).
Do these scales also have their own DBs??
Because if it is, then isn't this Microservice is a BIG MESS?
And if it is, then why the heck these huge MNCs are still using it? How do they solve this issue?

[@karthikiyer9834]

Are all the microservices deployed on the same host(like using containers).? If yes then while horizontal scaling, will all the server machines have copies of all
the microservices?

[@Vruddhi_Tolia]

I have a doubt, this individual services have to call REST APIs to interact with other service and other service will access to database , then overall performance doesnot get slow?

[@InvincibleMan99]

It depends on usecase, as when one service calls another service, It can call it async, and response from that service does not have to wait for response from another service.

[@Emmanuel-px9lk]

Question: What are the advantages of using data partitioning over horizontal scaling ? To me, they both seem the same. One is just adding more nodes , the other is adding more nodes but handling specific requests per node

[@varunvats32]

In horizontal scaling, the DB will remain common, and that can become a bottleneck. Hence, we can think of moving towards data partitioning to divide the DB, to serve only specific requests.

[@NeerajPahuja]

@@varunvats32 - Is it same as Data Sharding? Is data sharding same as what you explained or is it a different thing?

[@aashish01yash]

They are both different, You would use Horizontal scaling to a point but as your application grows and you understand the data pattern more, it could happen that traffic for A-N is more compared to N-Z partition so you would focus on scaling for that specific partition only rather then scale the whole service.
Beyond a point you would have to trade off and employ other strategies like service do-composition.

[@aashish01yash]

@@NeerajPahuja Data partition would apply to data this applies to service

[@tenminutetokyo2643]

Microservices is a huge mess.