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@debasishg
debasishg / gist:8172796
Last active December 31, 2025 22:20
A collection of links for streaming algorithms and data structures

General Background and Overview

  1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
  2. Models and Issues in Data Stream Systems
  3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
  4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
  5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t
@ryanlecompte
ryanlecompte / gist:5746241
Last active October 31, 2019 05:05
Bounded priority queue in Scala
import scala.collection.mutable
/**
* Bounded priority queue trait that is intended to be mixed into instances of
* scala.collection.mutable.PriorityQueue. By default PriorityQueue instances in
* Scala are unbounded. This trait modifies the original PriorityQueue's
* enqueue methods such that we only retain the top K elements.
* The top K elements are defined by an implicit Ordering[A].
* @author Ryan LeCompte ([email protected])
*/
@MohamedAlaa
MohamedAlaa / tmux-cheatsheet.markdown
Last active January 14, 2026 02:51
tmux shortcuts & cheatsheet

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname
@jboner
jboner / latency.txt
Last active January 12, 2026 20:16
Latency Numbers Every Programmer Should Know
Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference 0.5 ns
Branch mispredict 5 ns
L2 cache reference 7 ns 14x L1 cache
Mutex lock/unlock 25 ns
Main memory reference 100 ns 20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy 3,000 ns 3 us
Send 1K bytes over 1 Gbps network 10,000 ns 10 us
Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD
@daien
daien / simplex_projection.py
Created October 8, 2011 16:56
Compute Euclidean projections on the simplex or L1-ball
""" Module to compute projections on the positive simplex or the L1-ball
A positive simplex is a set X = { \mathbf{x} | \sum_i x_i = s, x_i \geq 0 }
The (unit) L1-ball is the set X = { \mathbf{x} | || x ||_1 \leq 1 }
Adrien Gaidon - INRIA - 2011
"""