Functional Requirements

• FR1: Platform can ingest metrics (CPU, memory, latency, custom counters) from services.
• FR2: Users can query and visualize metrics on dashboards with filters, aggregations, and time ranges.
• FR3: Users can define alert rules with thresholds over time windows, e.g., “alert if P99 latency > 500ms for 5min”.
• FR4: Users can receive notifications when alerts fire, e.g., email, Slack.

Scale: 500K servers, 5M metrics per second, ~1GB/s raw ingestion.

Notes

• Introduction
• A Case Study: Designing a Document Editor

Design Pattern Catalog

• Creational Patterns: Abstract Factory; Builder; Factory Method; Prototype; Singleton.

• Structural Patterns: Adapter; Bridge; Composite; Decorator; Façade; Flyweight; Proxy.

• Behavioral Patterns: Chain of Responsibility; Command; Interpreter; Iterator; Mediator; Memento; Observer; State; Strategy; Template Method; Visitor.

• Conclusion

Design Patterns: Elements of Reusable Object-Oriented Software. Erich Gamma; Richard Helm; Ralph Johnson; John Vlissides. www.uml.org.cn . Aug 1, 1997.

Performance in Context

The execution of a web browser primarily involves three tasks: fetching resources, page layout and rendering, and JavaScript execution. That said, optimizing the last two tasks won’t do much good if the browser is blocked on the network, waiting for resources to arrive.

Of the top 100K, the median page on desktop requests 97 resources weighing 2,954KB in total, and makes 16 TCP connections . While the underlying transport (TCP) is optimized for large, streaming downloads, most network transfers in the browser are short and bursty.

Stability, Testing and the Multi-Process Architecture

In a single-threaded browser, devs use asynchronous APIs - which may sometimes hold the browser up. Instead of multiple threads, Chromium chose multiple processes: isolation, independent crashes, better asymptotic memory management, identifying expensive sites, etc.

The Architecture of Open Source Applications. The Performance of Open Source Applications. Tavish Armstrong. aosabook.org . Sep 26, 2013. Accessed Jan 19, 2026.

✅ High Performance Networking in Chrome. Many of the sites we use today are not just web pages, they are applications. How do we make the fastest browser? Notes .

From SocialCalc to EtherCalc. EtherCalc is an online spreadsheet system optimized towards simultaneous editing, using SocialCalc as its in-browser spreadsheet engine.

Personalizing Your Shell

Put all of your scripts in the ~/bin/ directory, and add ~/bin/ to your path. Beware of conflicting with system commands. One alternative is to prefix your commands with a comma.

Managing launchctl Services

launchctl list lists the running services, e.g.,

$ launchctl list | grep blog
1357    0       com.dchege711.blogserver

launchctl kickstart -k [service-target] restarts the service (-k kills it if it were running before), e.g., launchctl kickstart -k gui/501/com.dchege711.blogserver.

Conflict

MAXWELL LORD. What he failed to understand, my friend, is that it is human nature. To find things that are better than us – godly, wondrous divine things – and drag them down and pull them apart. Until there isn’t very much godly or divine about them at all.

THE CHRONICLER. Having foreseen his imminent capture and inevitable end, Parzurem had already fractured his mind into two arguments, given shape and structure by each other’s existence… moder and paeter. The moder pyxis found itself drawn by the gravitational fallout of the sundering of Urgrund, eventually falling into the orbit of Galactica, which would later come to be known as New Genesis. But the paeter pyxis drifted through an eternity, driven by Parzurem’s search for peace and ponderance, before finding its way to an infant planet in a far corner of the cosmos where it would be undisturbed for a brief age.

BISHOP 1. Your damned letter to the Emperor –
URI TUPKA. How could I not? When he commissioned a giant goat statue – a hundred feet tall and solid gold…?
BISHOP 1. And didn’t we warn you what would happpen? Didn’t we all warn you?
It was only to remind him. Scripture. Theodipus 6:19 – “And all men shall be equal to each other under the eyes of the gods, even as all the gods, like leaves of grass, shall be equal to each other.

Sample Use Cases

Meta

Binary Search on a Non-Decreasing \(f: \mathbb{R} \to \mathbb{R}\)

Given a number \(L\) and a non-decreasing function \(f: \mathbb{R} \to \mathbb{R}\), find the greatest \(x\) such that \(f(x) \le L\). To start, there are two numbers \(lo\) and \(hi\), such that \(f(lo) \le L < f(hi)\).

Algorithm

double binary_search(double lo, double hi, double lim) {
  while (hi - lo > precision) {
    const double mid = (hi + lo) / 2;
    if (lim < f(mid)) hi = mid; // Search in left half
    else lo = mid; // Search in right half
  }
  return lo;
}

Sample Problem

You are given two integer arrays:

• greeds, where greeds[i] represents the minimum size of a cookie that a child needs to to be satisfied.
• cookies, where cookies[j] represents the size of a cookie.

Assign cookies such that as many children as possible are satisfied. Each child can receive at most one cookie, and each cookie can be given to only one child.

A Greedy Algorithm

def num_satisfied_children(greeds: List[int], cookies: List[int]) -> int:
  greeds.sort()
  cookies.sort()

  count = 0
  g_idx, c_idx = 0, 0
  while g_idx < len(greeds) and c_idx < len(cookies):
    if cookies[c_idx] >= greeds[g_idx]:
      count += 1
      g_idx += 1

    c_idx += 1

  return count

The Event Loop

The event loop contains a collection of jobs to be run. Some jobs are added by application code, and others indirectly by asyncio. The event loop takes a job from its backlog and invokes it (“gives it control”). Once that job pauses or completes, it returns control to the event loop which then selects another job from the backlog and invokes it. When there are no jobs to be done, the event loop rests (doesn’t waster CPU cycles) and comes back when more jobs are enqueued.

Web Caching

Web caching was the original motivation for consistent hashing. With a web cache, if a browser requests a URL that is not in the cache, the page is downloaded from the server, and the result is sent to both the browser and the cache. On a second request, the page is served from the cache without contacting the server.

Notes

Sharding comes up when a single database won’t work (e.g., hit storage limits, write throughput limits, or read throughput limits) and you need to split your data across multiple independent servers.

For a user-centric social media app, sharding by user_id means all of a user’s posts, likes, and comments live on one shard. User-scoped queries are fast, but “trending posts across all users” become expensive because of hitting every shard and aggregating results. Mutating transations need to account for the distributed nature of the shards, making them complex and slow.

Notes

At a basic level, understand how services talk to each other and what happens when those connections fail or get slow.

For 90% of the use cases, default to HTTP over TCP.

WebSockets and Server-Sent Events (SSE) come up when you need real-time updates. SSE is unidirectional - the client makes an initial HTTP request to open the connection, and the server pushes data down that connection. WebSockets handle bidirectional communication where both sides send messages freely.

Notes

Indexes make database queries fast. Most relational databases use B-trees, which support exact lookups (find user with email X) and range queries (find all orders between date A and date B). Hash indexes are faster for exact matches but can’t do range queries.

There are external systems that go beyond what the primary DB provider has. Elasticsearch is the go-to for full-text search. PostGIS in Postgres is popular for geospatial queries. Such extensions typically sync from your primary DB and so the search index will lag slightly behind. However, the increased functionality is usually worth it.

Notes

Choosing what data to store and how to structure it directly affects performance, scalability, and maintenance.

Relational databases are useful when you have structured data with clear relationships and need strong consistency (transaction-based actions, enforcing foreign key constraints). NoSQL databases shine for flexible schemas or when you need to scale horizontally across many servers without complex joins.

Normalization refers to splitting data across tables to avoid duplication, e.g., you have a users table, and orders table, and a products table, where each order references a userId and productId. Normalized data is easy to keep consistent, e.g., updating a product name is limited to the products table. However, getting complete data needs joins, which get expensive on huge/many tables.

Notes

The CAP theorem states that you can only have 2 of 3 properties at once:

• Consistency: All nodes see the same data.
• Availability: Every request gets a response.
• Partition Tolerance: System works even when network connections fail between nodes.

In practice, network partitions are unavoidable in distributed system. Choosing consistency means some nodes will refuse to serve requests rather than return potentially stale data. Choosing availability means different nodes might temporarily have different data.

Notes

For read-heavy applications, storing frequently accessed data in fast memory (e.g., Redis) allows you to skip the DB entirely for some reads. A cache hit on Redis takes ~1ms compared to 20-50ms for a typical DB query, and this speedup is impactful in the order of millions of requests.

Caching requires a solution for invalidating stale data, e.g., user updates their profile in the DB. Strategies include invalidating the cache immediately after writes, using short TTLs, etc.