engineering notes about software architecture from your favourite corps by engineers for engineers.
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As many of you know every week we send out newsletter and each one has a new image the top each week, this takes a bit of time to design and create with @LumaLabsAI. I am about to produce this in the fraction of the time with tons of artistic control.
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Architecture Notes Weekly: Trio out now!
Why pgvector might not cut it in production
How bloom filters fail at scale
Why LLMs can’t replace hands-on learning
Plus: slashing cloud costs and Postgres as a lakehouse system
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Building GenAI infra isn’t theory—it’s hard-won experience.
Learn from 60+ engineers who’ve scaled LLMs, vector stores & RAG pipelines at QCon SF 2025.
What worked. What failed. What to avoid.
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Architecture Notes Weekly: Generator out now!
How AWS serves 1 petabyte per second on old hard drives
The 14-hour outage that killed Slack, Signal, and Premier League
Building a key-value database from scratch with LSM trees
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Selling to enterprises? Features like SSO, Directory Sync (SCIM), and Audit Logs are must-haves — but building them in-house is expensive and time-consuming let @WorkOS do the heavy lifting. Here is how they saved one company almost 8 million.
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Arc Notes Weekly #90: Halt is out now!
1.Lessons from Canva’s outage on resilience
2.uv: The package manager simplifying Python scripting
3.Building your own data center for personal projects
4.Test-driven development with LLMs
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Arc Notes Weekly #89: Land
1. Discover how Threads scaled for 300M+ users.
2. Master advanced Git commands.
3. Explore innovations making SQLite 100x faster for serverless and edge computing.
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Capture and share deep session replays, from frontend screens to backend traces, metrics, and logs with @trymultiplayer's new Platform Debugger.
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Get a bird’s-eye view of your entire system with @trymultiplayer real-time insights. Auto-document your architecture effortlessly using integrated OpenTelemetry.
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Arc Notes Weekly #88: Asha ❤️
1. Optimize AI VRAM usage with Ollama’s advanced quantization techniques.
2. Enhancing System Architecture with Concurrency Diagrams
3. Explore Amazon’s new Aurora DSQL database and its potential impact.
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Arc Notes Weekly #84: Orb is here! ✨
1. See how Netflix boosted its homepage speed by cutting Time-to-Interactive by 50%.
2. Discover why OpenZFS deduplication, despite new updates, may still not be right for you.
3. Learn the value of designing ephemeral software for flexible and efficient systems.
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Arc Notes Weekly #83: Seagull is out now!
1. How Dropbox cut server costs by 25% with their in-house load balancer, Robinhood
2. Spotify’s method for scaling content annotations
3. Vercel’s performance boost with microfrontends
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Arc Notes Weekly #82: Axis is out now! ✨
1. How AI copilots are streamlining on-call engineering workflows
2. The impact of lightweight multi-agent orchestration on development
3. Cost-effective video scraping methods for structured data extraction
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What if you never had to manually document your system again? Multiplayer auto-documents everything from your logical architecture down to your dependencies and APIs. Don’t miss out on this game-changer. dub.sh/c6u6T8M
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Arc Notes Weekly #81: Rise is out now!
1. Netflix’s scalable data abstraction for temporal data
2. DoorDash’s Kafka platform that streamlines engineering
3. AI-powered drones aiding search and rescue
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Tired of chasing down the latest system updates? Multiplayer auto-documents your entire architecture and more, without lifting a finger. Don’t miss out on this game-changer. dub.sh/KioeYUE
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Arc Notes Weekly #80: Bloom is out now! 🌸
1. Redis to SQLite migration for faster performance
2. The power of “skin-shedding” code
3. How to manage tech FOMO in an ever-changing landscape
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Arc Notes Weekly #79: Changing Order is out now!
1. How Discord reduced WebSocket traffic by 40%
2. Challenges of concurrency in software development
3. Why tidying up code is a super power.
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architecturenotes.co/p/arc-n…
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The SQL standard defines 4 standard isolation levels these can and should be configured globally (insidious things can happen if we can't reliably reason about isolation levels).
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Phantom reads are another committed read phenomena, which occurs when you are most commonly dealing with aggregates, which results in you getting two different values if your database doesn't support range locks for these transactions.
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Similarly, a dirty read occurs when you perform a read, and another transaction updates the same row but doesn't commit the work, you perform another read, and you can access the uncommitted (dirty) value, which isn't a durable state change and is inconsistent with reality.
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Non-repeatable reads occur if you cannot get a consistent view of the data between two subsequent reads during your transaction. In specific modes, concurrent database modification is possible, and there can be scenarios where the value you just read can be modified.
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Secondly, the leaf nodes are linked, so if you need to do an index scan, you can do a single linear pass rather than traversing the entire tree up and down and loading more index data from the disk.
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The main difference B+ Trees show off is that intermediate nodes don't store any data on them. Instead, all the data references are linked to the leaf nodes, which allows for better caching of the tree structure.
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B as in balance. So you might wonder where you made a massive error to find yourself reading about B-Trees you hated from school. This tree structure gets its name balanced because the depth is uniform across the entire tree.
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So here is where most developers go – I have seen this problem before; we need some dictionary (hash map) and a way to get to the specific row we are looking for. These are called index leaf nodes.
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Indexes are a data structure that helps decrease the look-up time of requested data. Indexes achieve this with the additional costs of storage, memory, and keeping it up to date (slower writes), which allows us to skip the tedious task of checking every table row.
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It is often surprising how little is known about how databases operate at a surface level, considering they store almost all of the states in our applications. Things You Should Know About Databases.
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After 6 years of using GraphQL in production we aren't reaching for it as much as we once did.
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Locks are essential in modern systems to prevent simultaneous access to shared resources and ensure data integrity. 🔒 A deep dive into locks and their performance impact from @arcnotes
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Heroku's platform metrics give developers insights into the performance & health of their apps. But keeping this high-scale system running smoothly takes some serious engineering!
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The Twelve-Factor App methodology is a methodology for building software-as-a-service applications by Adam Wiggins. We cover how they have since evolved, and what we can learn from them today and how they changed the status quo of yesteryear.
architecturenotes.co/12-fact…
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Arc Notes Weekly #73: Energy is out now! ✨
1. Post Quantum Security
2. Monorepos @ Stripe
3. S3 starting to show its age?
Today 60,223 subscribers receive it today! Don't miss out!
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Coupled with that you would ideally prefer a good distribution within that cardinality. Running this as an experiment with accurate data is easy enough to do with a few lines of code.
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When selecting a shard key for this sharding type, it is essential to select one with high cardinality, so the number of possible values for that key is numerous. For example, a key with possible values of North, South, East, and West is of low cardinality since there are only 4.
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The same node houses entries that have the same shard key. A group of data that shares the same shard key is referred to as a logical shard. Multiple logical shards are included in a database node, also known as a physical shard.
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Shard Key is a piece of the primary key that tells how the data should be distributed. With a shard key, you can quickly find and change data by routing operations to the correct database.
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Data is divided into chunks depending on the ranges of a certain value in range-based sharding. This requires a look up table to see where the data should be stored. Maintaining the consistency of such a table and obviously picking the ranges here are critical.
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Now that we have discussed the potential server architectures, let us talk about data layout. You can see two types of strategies at play below.
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Often when you start to look into scaling your data system either need to scale for reads or writes. Replication is one way when you need extra read capacity.
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Sharding is an example of horizontal scaling, while vertical scaling is an example of just getting larger and larger machines to support the new workload.
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Engineers often get caught up in doing things the most involved way, but keeping things simple early on makes challenging things later on as your application evolves much easier. So if your problem goes away by getting machines with more resources, 9/10, that's the correct answer
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Your app is getting better. It has more features, more active users, and every day it collects more data. Your database is now causing the rest of your application to slow down.
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Arc Notes Weekly #72: Offspring is out now! ✨
1. Soft Delete Downsides
2. Programming with the GPU
3. Vercel's Infrastructure
Today 60,013 subscribers receive it today! Don't miss out!
architecturenotes.co/p/arc-n…
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Arc Notes Weekly #71: Codex is out now! 🌟
1. Counting in the Billions
2. Passkeys failed?
3. Corporate Open Source Over?
Today 59,238 subscribers receive it today! Check it out.
architecturenotes.co/email/e…
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