ceberly

default:
	gcc -O0 -g3 -Wall -Wextra -Wconversion -Wdouble-promotion \
		-Wno-unused-parameter -Wno-unused-function -Wno-sign-conversion \
		main.c

format:
	clang-format -i main.c

sancar

And upgradable read write lock for go

UpgradableRWMutex is an enhanced version of the standard sync.RWMutex. It has the all methods sync.RWMutex with exact same semantics. It gives more methods to give upgradable-read feature.

The new semantics for upgradable-read are as follows:

• Multiple goroutines can get read-lock together with a single upgradable-read-lock.
• Only one goroutine can have a write-lock and no read-lock/upgradable-read-lock can be acquired in this state.

debasishg

Cache Oblivious and Cache Aware Data Structure and Algorithms

1. Cache-Oblivious Algorithms and Data Structures - Erik Demaine (One of the earliest papers in cache oblivious data structures and algorithms that introduces the cache oblivious model in detail and examines static and dynamic cache oblivious data structures built between 2000-2003)

2. Cache Oblivious B-Trees - Bender, Demaine, Farch-Colton (This paper presents two dynamic search trees attaining near-optimal performance on any hierarchical memory. One of the fundamental papers in the field where both search trees discussed match the optimal search bound of Θ(1+log (B+1)N) memory transfers)

3. Cache Oblivious Search Trees via Binary Trees of Small Height - Brodal, Fagerberg, Jacob (The data structure discussed in this paper works on the version of [2] but avoids the use o

cpursley

PostgreSQL is Enough

• Simplify: move code into database functions
• Just Use Postgres for Everything
• PostgreSQL is the worlds’ best database
• Postgres is eating the database world
• Hacker News discussion

Background and Cron Jobs

debasishg

Basics

1. The Log-Structured Merge-Tree (LSM-Tree)
2. B-Tree vs Log-Structured Merge-Tree
3. Modern B-tree techniques
4. LSM-based Storage Techniques: A Survey

B-trees and CPU Caches

1. B-tree Indexes and CPU Caches by Goetz Graefe and Per-Åke Larson

IshaanAdarsh

Google Summer of Code 2023 Final Work Product

Introduction

Jarmos-san

# Set the working directory globally
ARG SOURCEDIR="app"

# Pin the Python version to use
# See the following article to learn more about choosing the right base image
# https://pythonspeed.com/articles/base-image-python-docker-images
ARG PYTHONVERSION="3.11-slim-bookworm"

# Createh the base image for generating the requirements file
FROM python:${PYTHONVERSION} AS builder

francbartoli

# From https://github.com/tiangolo/fastapi/issues/258

from typing import List

from fastapi import FastAPI
from starlette.responses import HTMLResponse
from starlette.websockets import WebSocket, WebSocketDisconnect


app = FastAPI()

antirez

# LVDB - LLOOGG Memory DB
# Copyriht (C) 2009 Salvatore Sanfilippo <antirez@gmail.com>
# All Rights Reserved

# TODO
# - cron with cleanup of timedout clients, automatic dump
# - the dump should use array startsearch to write it line by line
#   and may just use gets to read element by element and load the whole state.
# - 'help','stopserver','saveandstopserver','save','load','reset','keys' commands.
# - ttl with milliseconds resolution 'ttl a 1000'. Check ttl in dump!

ptarjan

Scaling your API with rate limiters

The following are examples of the four types rate limiters discussed in the accompanying blog post. In the examples below I've used pseudocode-like Ruby, so if you're unfamiliar with Ruby you should be able to easily translate this approach to other languages. Complete examples in Ruby are also provided later in this gist.

In most cases you'll want all these examples to be classes, but I've used simple functions here to keep the code samples brief.

Request rate limiter

This uses a basic token bucket algorithm and relies on the fact that Redis scripts execute atomically. No other operations can run between fetching the count and writing the new count.