MICROSERVICES ARCHITECTURE | SCALE CUBE | PART - 4

Learn strategies to scale the microservices.
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[navpreetkaur]

thanks so much, very informative and easy to understand videos

[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.

[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?

[karthikiyer]

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?

[celalcagriakgunduz]

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-xwnn]

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?

[Emmanuel-pxlk]

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

[hyperborean]

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