marianoguerra

10-Deadlock Avoidance for Streaming Computations with Filtering.pdf
10-EventScript: An Event-Processing Language Based on Regular Expressions with Actions.pdf
10-hancock-language-data-streams.pdf
11-Cayuga A General Purpose Event Monitoring System.pdf
11-Finding Frequent Items in Data Streams.pdf
11-Optimization of Sequence Queries in Database Systems.pdf
12-Chord: A Scalable Peer-to-peer Lookup Service for Internet Applications.pdf
12-Distributed Event Stream Processing with Non-deterministic Finite Automata.pdf
12-High-Performance Complex Event Processing over XML Streams.pdf
12-LSM-trie: An LSM-tree-based Ultra-Large Key-Value Store for Small Data.pdf

wenhuizhang

#Distributed System Course List

##Systems

• Cornell CS 614 - Advanced Course in Computer Systems - Ken Birman teaches this course. The readings cover more distributed systems research than is typical (which I am in favour of!). In fact, there's barely anything on traditional internal OS topics like filesystems or memory management. There's some worthwhile commentary at the bottom of the page.

• Princeton COS 518 - Advanced Operating Systems - short and snappy reading list of two papers per topic, covering some interesting stuff like buffering inside the operating system, and L4.

tlockney

What questions would you put on a phone screen for a distributed systems position?

These come from @tsantero with the last two additions being curteousy of @ifesdjeen in reply to this question from @SeanTAllen.

1. explain the life of an http request.
2. what does the FLP result teach us?
3. what is a byzantine failure?
4. explain CRDTs
5. explain linearizability.
6. how does DNS work?

dominictarr

(dominic: this list of papers was originally recommended to me by Brain Noguchi @bnoguchi, and was a great start to understanding distributed systems)

Here's a selection of papers that I think you would find helpful and interesting:

Time, Clocks, and the Ordering of Events in a Distributed System

The seminal paper about event ordering and concurrency. The important result is that events in a distributed system define a partially ordered set. The connection to what we're working on is fundamental, as this defines how to detect concurrent updates. Moreover, the chosen algorithm to turn the partially ordered set into a totally ordered set defines the conflict resolution algorithm.

http://research.microsoft.com/en-us/um/people/lamport/pubs/time-clocks.pdf

evancz

Architecture in Elm

This document is a collection of concepts and strategies to make large Elm projects modular and extensible.

We will start by thinking about the structure of signals in our program. Broadly speaking, your application state should live in one big foldp. You will probably merge a bunch of input signals into a single stream of updates. This sounds a bit crazy at first, but it is in the same ballpark as Om or Facebook's Flux. There are a couple major benefits to having a centralized home for your application state:

1. There is a single source of truth. Traditional approaches force you to write a decent amount of custom and error prone code to synchronize state between many different stateful components. (The state of this widget needs to be synced with the application state, which needs to be synced with some other widget, etc.) By placing all of your state in one location, you eliminate an entire class of bugs in which two components get into inconsistent states. We also think yo

staltz

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

---

This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

---

lttlrck

git branch -m old_branch new_branch         # Rename branch locally    
git push origin :old_branch                 # Delete the old branch    
git push --set-upstream origin new_branch   # Push the new branch, set local branch to track the new remote

debasishg

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

joost-de-vries

a semigroup is addable

a monoid is foldRightable or aggregatable
(ie addable, zero)

a functor is mappable
 
a monad is chainable or flatmappable

strong suit of monads:

edlich

VIM and Clojure:
The current steps might be (intended for vim beginners).

0A. Install Pathogen for easy vim installation (optional)
- https://github.com/tpope/vim-pathogen
- http://www.vim.org/scripts/script.php?script_id=2332

0B. Install new version of static runtime editing (optional, only if latest version is needed)
- https://github.com/guns/vim-clojure-static