Google recent paper on protective load balancing in TCP attempts to improve packet drops and latency by making the host change the flow path using the IPv6 Flow label. The Linux kernels gets the PLB support in Linux 6.2 this December, let us discuss with this is. 

0:00 Intro 
2:11 DCTCP
5:30 ECMP Routing
7:30 TCP PLB
11:20 Linux Kernel Support PLB

References
https://www.phoronix.com/news/TCP-PLB-Linux
https://git.kernel.org/pub/scm/linux/kernel/git/netdev/net-next.git/commit/?id=957ed5e7129f2ce85dd76e4cdce749388295467d
https://dl.acm.org/doi/pdf/10.1145/3544216.3544226
https://www.rfc-editor.org/rfc/rfc2460#appendix-A
https://en.wikipedia.org/wiki/Equal-cost_multi-path_routing
https://www.rfc-editor.org/rfc/rfc8257
https://en.wikipedia.org/wiki/IPv6_packet
https://www.ietf.org/id/draft-lapukhov-bgp-ecmp-considerations-09.html


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Google pushes TCP Protective Load Balancing to Linux Kernel 6.2

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What is OSI Model | Real World Examples

The Sidecar model is often used in Kubernetes deployments to add common tools like observability, service mesh and security into application pods.

eBPF is a kernel technology that lets us dynamically change the way that the kernel behaves - and it’s revolutionizing the way we instrument applications running under Kubernetes. Tools built with eBPF have the power to observe, secure and connect all the processes on a (virtual) machine, so there’s no need to inject a sidecar container into every pod.

Does eBPF mean we’ll be saying goodbye to sidecars? Attend this talk to find out!

PUBLICATION PERMISSIONS:
Original video was published with the Creative Commons Attribution license (reuse allowed). Link: https://www.youtube.com/watch?v=bnl25El7RYk

Hello eBPF! Goodbye Sidecars?

I’m a big believer that database systems share similar core fundamentals at their storage layer and understanding them allows one to compare different DBMS objectively. For example, How documents are stored in MongoDB is no different from how MySQL or PostgreSQL store rows.
Everything goes to disk, the trick is to fetch what you need from disk efficiently with as fewer I/Os as possible, the rest is API.

In this video I discuss the evolution of MongoDB internal architecture on how documents are stored and retrieved focusing on the index storage representation. I assume the reader is well versed with fundamentals of database engineering such as indexes, B+Trees, data files, WAL etc, you may pick up my database course to learn the skills.
Let us get started.

0:00 Intro
2:00 SQL vs NOSQL 
18:00 MongoDB first version MMAPV1 
26:30 MongoDB Wired Tiger 
38:00 Clustered Collections 

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MongoDB Internal Architecture

The User Interface/User Experience has great impact on the backend architecture and scalability. In this podcast I discuss three UI/UX that affected backend design and scalability. 

0:00 Intro
1:40 UI vs UX
4:30 Google Chrome OmniBox 
12:30 1 out of X Page
20:00 YouTube Notification

Resources
https://blog.apnic.net/2020/08/21/chromiums-impact-on-root-dns-traffic/


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How UI/UX can break the backend

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Shopify posted a blog on tips to for scalable payment system, one tip peeked my interest related to switching from UUID to ULID. I explore the reasoning behind this in this video.

https://shopify.engineering/building-resilient-payment-systems

0:00 Intro
1:30 idempotency
6:30 UUID vs ULID
9:50 Clustered Index
13:30 Why UUID4 Inserts are slow 
17:15 How ULID helps Shopify
22:00 Problem with tail pages
25:00 Does ULID help in all cases?


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