achille

Keybase proof

I hereby claim:

• I am achille on github.
• I am achille (https://keybase.io/achille) on keybase.
• I have a public key whose fingerprint is 381B A447 5045 5140 AC05 46D2 A1D0 FF17 221A 89BF

To claim this, I am signing this object:

achille

// compares MongoDB regular Indexes vs using MultiKey & Elematch

/* // regular index: 
{ "field0": 0,
  "field1": 1,
  "field2": 2 ... etc }
  
MultiKey index: 
{ "props": [
    { "field": "field0", "value": 0 },

achille

import cv2
import os

#call visualize(QTable)  tod display the qtable
#Press space to proceed, and q to exit (Frame render pauses execution)

def write(image, output_dir="visualize", name=None, is_test_image=False):
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)
    if name is None:

achille

//Create 1gb collection & enqueue
for(i=0;i<1000;i++){db.foo.insert({f:''.pad(1024*1024,true,'A')})}
enqueueWork("test.foo")

//Each worker calls dequeue() and works on it's own range
work = dequeue("test.foo")


function enqueueWork(ns,splitSizeBytes=320000000){
    split = db.runCommand({splitVector:ns, keyPattern:{_id: 1},

achille

"""
Vitter JS (1987) 'An efficient algorithm for sequential
random sampling.' ACM T. Math. Softw. 13(1): 58--67.

Copied from: https://gist.github.com/ldoddema/bb4ba2d4ad1b948a05e0
"""

from math import exp, log
import random
import numpy as np

achille

Howdy folks

This is an attempt at standardizing the intro threads, and instructions on how to include a student map in them. Note the map & question set may be added to existing Piazza threads.

Steps:

Create a Google Map

• Login to Google Maps, create a new Map
• Menu -> Your Places -> Maps -> Create Map

achille

/* Check for gaps or duplicates in keyspace */

function check_keyspace(ns) { 
    print("Checking: " + ns);
    str = JSON.stringify
    forwardCount=0;
    reverseCount=0;
    min = db.chunks.find(ns).pretty().sort({min: 1}).limit(1)[0]
    max = db.chunks.find(ns).pretty().sort({min:-1}).limit(1)[0]
    current = min

achille

/* 
 * Auto-tuning delete that allows for removal of large amounts of data 
 * without impacting performance. Configurable to a target load amount.
 *
 * How it works: 
 * TL;DR: Delete a small slice every second; Vary the size of each slice 
 *        based on how long the previous delete took; sleep; repeat. 
 *
 * TODO: Modify this to allow for deletion based on objectid's date 
 *       which is embedded in the first four bytes.

achille

import numpy as np
import scipy.optimize as spo
ir = lambda n: int(round(n))

# Constants
s_freq = 500   #server-server heartbeat frequency ms
c_freq = 10000 #client-server heartbeat frequency ms

# Simulation boundaries
threshold = 300000 # 5 minute max lag/skew

achille

// compactness() calculates how closely the resulting documents are located together
// It counts the size of the documents vs size of the unique pages they reside on
 
function compactness(collection, query, limit) {
    Object.size = function(o) { var size = 0, key;
        for (key in o) { if (o.hasOwnProperty(key)) size++; }
        return size; };
 
    count=0;
    size=0;