- https://medium.com/making-instapaper/instapaper-outage-cause-recovery-3c32a7e9cc5f#.8wd4ufmw5
- https://blogs.dropbox.com/tech/2014/01/outage-post-mortem/
- https://groups.google.com/forum/#!topic/google-appengine/p2QKJ0OSLc8
- https://about.gitlab.com/2017/02/10/postmortem-of-database-outage-of-january-31/
- https://blog.cloudflare.com/todays-outage-post-mortem-82515/
- https://github.com/blog/1759-dns-outage-post-mortem
- https://blog.travis-ci.com/2017-02-07-jan-31-macos-outage-postmortem
- http://stackstatus.net/post/147710624694/outage-postmortem-july-20-2016
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#include <stdlib.h> | |
#include <stdio.h> | |
#include <stdbool.h> | |
#define N 10 | |
typedef struct { | |
int y; | |
int weight; | |
struct Edge *next; /* next in adj list */ |
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This note explains how to build Postgres from source and setup to debug it using LLDB on a Mac. I used this technique to research this article: | |
http://patshaughnessy.net/2014/10/13/following-a-select-statement-through-postgres-internals | |
1. Shut down existing postgres if necessary - you don’t want to mess up your existing DB or work :) | |
$ ps aux | grep postgres | |
pat 456 0.0 0.0 2503812 828 ?? Ss Sun10AM 0:11.59 postgres: stats collector process | |
pat 455 0.0 0.0 2649692 2536 ?? Ss Sun10AM 0:05.00 postgres: autovacuum launcher process | |
pat 454 0.0 0.0 2640476 304 ?? Ss Sun10AM 0:00.74 postgres: wal writer process | |
pat 453 0.0 0.0 2640476 336 ?? Ss Sun10AM 0:00.76 postgres: writer process |
- x : not worked
- o: worked (able to open in quicktime but need conversion)
- oo: worked well (preview worked but wrong aspect ratio)
- ooo: worked very well (preview worked, and aspect ratio’s right)
(Codecs are extracted from https://web.archive.org/web/20120722124832/http://opencv.willowgarage.com/wiki/QuickTimeCodecs )
- 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.
- Models and Issues in Data Stream Systems
- 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
- Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
- [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t