Ross

mostly cloud/distributed computing, software development, and cats. thoughts && opinions owned by the cats. meow. he/him

[Ross @__not__a_cat]

@engineering_bae I just did 7 rounds 🥴. Silver lining they were only 45 minute interviews instead of 1hr?

[Darren Shepherd @ibuildthecloud]

https://t.co/kGFxX3DVG0(...) is an admirable API name. You don't realize the confidence of that API.

Dev 1: "What should we call the method for executing a http request?"
Dev 2: "I dunno, Do?"
Dev 1: "Perfect"

[terminally onλine εngineer @tekbog]

has technology gone too far?

[ahmet alp balkan @ahmetb]

Goldmine paper from Google sharing the characteristics of their RPCs (API calls between their services). Especially interesting given everything is an RPC at Google. https://t.co/NO49EMBML0

My takeaways:
✱ The study is sampled from 700B RPCs in a single day, from 10K unique RPC methods (incl. stateless apps, DBs, KV stores, query engines)

✱ 10 most popular methods get 58% of all requests, 100 most popular ones get 91% of all requests!

✱ 100 slowest RPCs also happen to be the 40% of all RPCs! The, network disk “write” RPC alone is 28% of all RPCs.

✱ Major variation of latencies across the board, but avg latency of p90 of the slowest service is 10ms.

✱ RPC call trees are more “wide” than “deep” (they fan out significantly more).

✱ p50 of RPCs cause ≤13 rpcs, but p90 causes 105, and p99 causes 1155 other rpcs(!).

✱ req/resp sizes vary heavily on service type. p99 req/resp=196KB/563KB, p90 is 11KB/10KB.

✱ RPC overhead/tax (time takes to serialize, make the req, transit, decode the resp) is roughly 2% of RPC time on avg at Google’s fleet.

✱ 7% of CPU cycles are consumed by this RPC tax (this is significant given Google's fleet efficiency!), 3% of that is compression.

✱ 1.9% of RPCs result in errors (grpc_code!=OK responses). Most of these are benign (“Canceled” due to hedging/timeout, or natural “NotFound” errors).

[Detroit Lions @Lions]

Do you understand what we're capable of?

#AllGrit

[Alex Xu @alexxubyte]

The CAP theorem is one of the most famous terms in computer science, but I bet different developers have different understandings. Let’s examine what it is and why it can be confusing.

CAP theorem states that a distributed system can't provide more than two of these three guarantees simultaneously.

Consistency: consistency means all clients see the same data at the same time no matter which node they connect to.

Availability: availability means any client which requests data get a response even if some of the nodes are down.

Partition Tolerance: a partition indicates a communication break between two nodes. Partition tolerance means the system continues to operate despite network partitions.

The “2 of 3” formulation can be useful, but this simplification could be misleading.

- Picking a database is not easy. Justifying our choice purely based on the CAP theorem is not enough. For example, companies don't choose Cassandra for chat applications simply because it is an AP system. There is a list of good characteristics that make Cassandra a desirable option for storing chat messages. We need to dig deeper.

- “CAP prohibits only a tiny part of the design space: perfect availability and consistency in the presence of partitions, which are rare”. Quoted from the paper: CAP Twelve Years Later: How the “Rules” Have Changed.

- The theorem is about 100% availability and consistency. A more realistic discussion would be the trade-offs between latency and consistency when there is no network partition. See PACELC theorem for more details.

Is the CAP theorem really useful?
I think it is still useful as it opens our minds to a set of tradeoff discussions, but it is only part of the story. We need to dig deeper when picking the right database.

--
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[Alex Xu @alexxubyte]

Twitter has enforced very strict rate limiting. Some people cannot even see their own tweets.

Rate limiting is a very important yet often overlooked topic. Let's use this opportunity to take a look at what it is and the most popular algorithms.

A thread.

#RateLimitExceeded

[Dion Almaer @dalmaer]

CEOs: “hmm Elon is running Twitter great with a small percentage of the engineers. It’s going well… we should consider…”

On Blind:

[Karen Osborne ✨ @karenthology]

hey fellow millennials, the zoomers are killing things now, we can finally lay down our bloodsoaked crowbars and rest

[VALENTINA @vvaalb]

“i can’t wait for summer so i can wear nice fits”

the fits:

[Karandeep Singh @kdpsinghlab]

2023: HBO Launches Max
2024: HBO Launches Min
2025: HBO Launches Mean
2026: HBO Launches Median
2027: HBO Launches Standard Deviation
2028: HBO Launches Interquartile Range
2029: HBO Launches Regression to the Mean

[Gregor @ghohpe]

@mipsytipsy For some people "the stack" goes top-to-bottom (business function to device driver) and for others front-to-back. I am with you in the former camp but find the latter to be a useful skill.
I myself am a full-crap-developer as in: I deal with all the crap no matter where...

[👩‍💻 Paige Bailey @DynamicWebPaige]

̶ ̶ ̶P̶U̶B̶L̶I̶S̶H̶ ̶O̶R̶ ̶P̶E̶R̶I̶S̶H̶ ̶ ̶

🚀 SHIP OR RIP ☠️

[Ansh Nanda @anshnanda]

Tech bro just discovered “hanging out”

[Ross @__not__a_cat]

@lorenc_dan @KitMerker @_ediri

[Gators Daily 🐊 @GatorsDaily]

ok guys here is a test

which one will see u later
and which one will see u in a while

[Ross @__not__a_cat]

@KitMerker I ask what services had major incidents last week. What questions should I be asking?

[Ross @__not__a_cat]

@Sh1bumi This! You can create and configure and then it just shows you the sdk code in a given language to create the proposed resource.