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Advanced Distributed System Design Course - Udi Dahan

Notes by Craig Phillips

Fallacies of Distributed Computing

• There are 11 fallacies of Distributed Computing:
  1. The network is reliable
  2. Latency isn’t a problem
  3. Bandwidth isn’t a problem
  4. The network is secure

5. The topology won’t change

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Filling an array or slice with a repeated pattern

Looking for an efficient pure GO approach to copy repeating patterns into a slice, for a toy project, I ran a few tests and discovered a neat approach to significantly improve performance. For the toy project, I am using this to fill a background buffer with a specific RGB color pattern, so improving this performance significantly improved my acheivable framerate.

All the test were run with a buffer of 73437 bytes, allocated as follows

var bigSlice = make([]byte, 73437, 73437)

Fill the slice with the value 65 by looping through each element and setting the value

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package main

import (
	"bytes"
	"crypto/aes"
	"crypto/cipher"
	"encoding/hex"
	"fmt"
)

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import cv2

cap = cv2.VideoCapture(0)

# Capture frame
ret, frame = cap.read()
if ret:
	cv2.imwrite('image.jpg', frame)

cap.release()

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#include <memory>
#include <iostream>

class objectA {
public:
   ~objectA() {
      std::cout << "A";
   }
};

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Given an unsigned integer, return the number of trailing zero bits

Test framework:

#include <stdio.h>
#include <assert.h>

unsigned trailing_zeros(unsigned n) {
    // fill in this function

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import numpy as np
import pandas as pd
import statsmodels.formula.api as sm #lin reg
import pylab as py
import matplotlib as mp
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import RandomForestRegressor

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import numpy as np
import pandas as pd
import statsmodels.formula.api as sm #lin reg
import pylab as plt
import matplotlib as mp


from sklearn.linear_model import Lasso
from sklearn.linear_model import LassoCV
from sklearn.linear_model import lasso_path, enet_path

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import numpy as np
import pandas as pd
import statsmodels.formula.api as sm #lin reg
import pylab as py
import matplotlib as mp
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import GradientBoostingClassifier, GradientBoostingRegressor