egobrain

%%%-------------------------------------------------------------------
%%% @author egobrain <egobrain@linux-ympb>
%%% @copyright (C) 2012, egobrain
%%% @doc
%%%  Function for uploading files and properties,which were sent as a
%%%  multipart. Files are stored in tmp_folder with random name,
%%%  generated by tmp_filename function.
%%% @end
%%% Created : 25 Mar 2012 by egobrain <egobrain@linux-ympb>
%%%-------------------------------------------------------------------

tenderlove

(This is a translation of ko1's blog. All mistakes are mine.)

This is the 8th day of the Ruby VM Advent Calendar.

I'm slowly running out of breath.

Today is,

• Use of ObjectSpace.reachable_objects_from() - GC Advent Calendar

dherman

Looking at the preceding token when the lexer points to a '/'
character is insufficient to determine which context it's in. In
fact, you need to look at an arbitrary number of preceding tokens
to figure it out. This example demonstrates a case where we can
pump up the number of preceding tokens to an arbitrary size
before you can disambiguate your syntactic context.

pbailis

RapGenius has an interesting post about Heroku's randomized load balancing, complaining about how random placement degrades performance compared to prior omniscient approaches. RapGenius ran some simulations, including an experiments with a "Choice of Two" method:

Choice of two routing is the naive method from before, with the twist that when you assign a request to a random dyno, if that dyno is already busy then you reassign the request to a second random dyno, with no regard for whether the second dyno is busy

This differs subtly but substantially from the standard "Power of Two Choices" randomized load balancing:

each [request] is placed in the least loaded of d >= 2 [Dynos] chosen independently and uniformly at random

Take a look at the difference in queue lengths below, for 200 Dynos, 100

kevsmith

-module(bench).

-export([run_suite/3, run/1]).

run_suite(Name, Passes, Messages) ->
    Results = run_suite1(Passes, Messages, []),
    Avg = lists:sum(Results) / Passes,
    StdDev = math:sqrt(lists:sum([math:pow(X - Avg, 2) || X <- Results]) / Passes),
    Summary = [{avg, Avg}, {stddev, StdDev}, {min, lists:min(Results)},
               {max, lists:max(Results)}],

pbailis

A friend asked me for a few pointers to interesting, mostly recent papers on data warehousing and "big data" database systems, with an eye towards real-world deployments. I figured I'd share the list. It's biased and rather incomplete but maybe of interest to someone. While many are obvious choices (I've omitted several, like MapReduce), I think there are a few underappreciated gems.

###Dataflow Engines:

Dryad--general-purpose distributed parallel dataflow engine
http://research.microsoft.com/en-us/projects/dryad/eurosys07.pdf

Spark--in memory dataflow
http://www.cs.berkeley.edu/~matei/papers/2012/nsdi_spark.pdf

KonTrax

#### ALL CLASSES
# Unsorted at the moment
|===========================|======|==================|====================|=================|========|==================|
|           CLASS           | TYPE |    ATTRIBUTES    |     PROPERTIES     | FONT.PROPERTIES | LAYERS | LAYER.PROPERTIES |
|===========================|======|==================|====================|=================|========|==================|
| auto_complete             |      |                  | row_padding        |                 | layer0 | tint             |
|                           |      |                  | dark_content       |                 |        | opacity          |
|---------------------------|------|------------------|--------------------|-----------------|--------|------------------|
| auto_complete_label       |      |                  | fg                 |                 |        |                  |
|                           |      |                  | match_fg           |                 |        |                  

taliesinb

Equal
SameQ
MatchQ
MemberQ
Take
Drop
Head
Most
Rest
Part

cmeiklejohn

• CRDTs
  • What are CRDTs?
• Vector clocks
  • How do these related to dotted version vectors?
• Causal consistency
  • CAC proof
• Rack awareness and multi-data center deployments
  • What is this?
  • Why is this important?
• Wait-free synchronization

cemerick

(ns whatever.cljs
  (:require [cljs.compiler :refer (munge)])
  (:refer-clojure :exclude (munge defonce)))

(defmacro defonce
  [vname expr]
  (let [ns (-> &env :ns :name name munge)
        mname (munge (str vname))]
    `(when-not (.hasOwnProperty ~(symbol "js" ns) ~mname)
       (def ~vname ~expr))))