Credit: http://www.artinstructionblog.com/an-introduction-to-negative-drawing-with-mike-sibley

NEGATIVE SPACE by Leonardo Santamaria for Satellite Citi.

In software architecture realm, we usually come up with a basic design, implement a “layout” (infrastructure of your application) and then start augmenting it by adding features and providing better coverage for different use-cases.

Defining the negative space of your program means that not only you define what it should do – but also declare what it shouldn’t. By understanding what should be the limitations of your program you can simplify the design and easily reject feature requests that are crossing these borders.

In the world or micro-services it is very easy to create a new service and sometimes the right decision is to not support a new capability in “service A” but rather push that capability into a more qualifying “service B” or even spawn up a new dedicated service.

During my work at Netflix I ran a few times into a case where we needed to implement a new capability into existing services. That capability had a complex spec and was latency-sensitive, so we created new services in order to be able to:

- Keep the current services simple
- Add a new dedicated caching layer, but more important:
- Make our services resilient by applying the bulkhead design pattern.

So next time you architect a piece of software don’t forget to take into account the negative space!

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I searched online and found implementations such as this, this and even Guava but they are not helpful in our case since the order they are iterating is vertical: 1,7,13,17,2,8,14,18,3,9,4,…

So I decided to come up with my solution to the problem, and here’s what I got:

import java.util.Iterator; import java.util.LinkedList; import java.util.List; import java.util.NoSuchElementException; /** * Created by alfasin on 11/15/15. */ public class MultiIterator<E> implements Iterator { List<Iterator<E>> iterators = new LinkedList<>(); Iterator<E> current = null; public MultiIterator(Iterator<Iterator<E>> iterator) { // copy the iterators into a list while (iterator.hasNext()) { iterators.add(iterator.next()); } } @Override public boolean hasNext() { boolean result = false; if (iterators.isEmpty() && (current == null || !current.hasNext())) { return false; } if (current == null) { current = iterators.remove(0); } while (!current.hasNext() && !iterators.isEmpty()) { current = iterators.remove(0); } if (current.hasNext()) { result = true; } return result; } @Override public E next() { if (current == null) { try { current = iterators.remove(0); } catch (IndexOutOfBoundsException e) { throw new NoSuchElementException(); } } E result = current.next(); // if this method was called without checking 'hasNext' this line might raise NoSuchElementException which is fine iterators.add(current); current = iterators.remove(0); return result; } // test public static void main(String[] args) { List<Integer> a = new LinkedList<>(); a.add(1); a.add(7); a.add(13); a.add(17); List<Integer> b = new LinkedList<>(); b.add(2); b.add(8); b.add(14); b.add(18); List<Integer> c = new LinkedList<>(); c.add(3); c.add(9); List<Integer> d = new LinkedList<>(); d.add(4); d.add(10); d.add(15); List<Integer> e = new LinkedList<>(); e.add(5); e.add(11); List<Integer> f = new LinkedList<>(); f.add(6); f.add(12); f.add(16); f.add(19); List<Iterator<Integer>> iterators = new LinkedList<>(); iterators.add(a.iterator()); iterators.add(b.iterator()); iterators.add(c.iterator()); iterators.add(d.iterator()); iterators.add(e.iterator()); iterators.add(f.iterator()); MultiIterator<Integer> it = new MultiIterator<>(iterators.iterator()); while (it.hasNext()) { System.out.print(it.next() + ","); // prints: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19, } } }

A different approach would be to read all the elements, during initialization, into one list and then return an iterator of that list!

Let’s see how it works:

import java.util.Iterator; import java.util.LinkedList; import java.util.List; /** * Created by alfasin on 11/15/15. */ public class MultiIterator<E> { Iterator<Iterator<E>> iterator = null; List<E> elements = new LinkedList<>(); private MultiIterator(Iterator<Iterator<E>> iterator) { this.iterator = iterator; } private void copyElementsInOrder() { List<Iterator<E>> iterators = new LinkedList<>(); // copy the iterators into a list while (iterator.hasNext()) { iterators.add(iterator.next()); } // go over the list, round-robin, and grab one // element from each sub-iterator and add it to *elements* // empty sub-iterators will get dropped off the list while (!iterators.isEmpty()) { Iterator<E> subIterator = iterators.remove(0); if (subIterator.hasNext()) { elements.add(subIterator.next()); iterators.add(subIterator); } } } public static <E> Iterator<E> iterator(Iterator<Iterator<E>> iterator) { MultiIterator<E> instance = new MultiIterator<>(iterator); instance.copyElementsInOrder(); return instance.elements.iterator(); } // test public static void main(String[] args) { List<Integer> a = new LinkedList<>(); a.add(1); a.add(7); a.add(13); a.add(17); List<Integer> b = new LinkedList<>(); b.add(2); b.add(8); b.add(14); b.add(18); List<Integer> c = new LinkedList<>(); c.add(3); c.add(9); List<Integer> d = new LinkedList<>(); d.add(4); d.add(10); d.add(15); List<Integer> e = new LinkedList<>(); e.add(5); e.add(11); List<Integer> f = new LinkedList<>(); f.add(6); f.add(12); f.add(16); f.add(19); List<Iterator<Integer>> iterators = new LinkedList<>(); iterators.add(a.iterator()); iterators.add(b.iterator()); iterators.add(c.iterator()); iterators.add(d.iterator()); iterators.add(e.iterator()); iterators.add(f.iterator()); Iterator<Integer> it = MultiIterator.<Integer>iterator(iterators.iterator()); while (it.hasNext()) { System.out.print(it.next() + ","); // prints: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19, } } }

There are pros and cons to each one of these approaches:

The first approach is more complex because it requires checking and handling of many edge-cases (including catching an IndexOutOfBoundsException in case that our list of iterators is empty and throwing the proper NoSuchElementException instead).

The second approach is more concise on one hand, but on the other hand it requires copying, in-advance, all the elements (greedy) vs. the first approach which is lazy and should be preferred in cases of large datasets.

Note that the second approach implements a factory and as such it doesn’t implement the `Iterator`

interface!

**EDIT:**

Now with Java 9, it’s much more elegant to create such lists (using the new collections APIs). I re-did this exercise and here’s what I got:

class MultiIterator<E> implements Iterator { private LinkedList<Iterator<E>> iterators = new LinkedList<>(); private int current = 0; public MultiIterator(Iterator<Iterator<E>> iterator) { while(iterator.hasNext()) { iterators.add(iterator.next()); } } /** * Returns {@code true} if the iteration has more elements. * (In other words, returns {@code true} if {@link #next} would * return an element rather than throwing an exception.) * * @return {@code true} if the iteration has more elements */ @Override public boolean hasNext() { if (iterators.size() == 0) { return false; } if (iterators.get(current).hasNext()) { return true; } return false; } /** * Returns the next element in the iteration. * * @return the next element in the iteration * @throws NoSuchElementException if the iteration has no more elements */ @Override public E next() { E next; try { next = iterators.get(current).next(); // might throw IndexOutOfBoundsException if wasn't called with hasNext() Iterator<E> iter = iterators.remove(current); if (iter.hasNext()) { iterators.add(iter); } } catch (IndexOutOfBoundsException e) { throw new NoSuchElementException(); } return next; } public static void main(String[] args) { List<Integer> l1 = List.of(1, 2, 3, 4, 5, 6); List<Integer> l2 = List.of(5, 6, 7, 8); List<Integer> l3 = List.of(9, 10, 11, 12); Iterator<Iterator<Integer>> iterators = List.of(l1.iterator(), l2.iterator(), l3.iterator()).iterator(); MultiIterator<Integer> iters = new MultiIterator<>(iterators); while (iters.hasNext()) { System.out.println(iters.next()); } } }

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Over the past week I went through some Python code in Stackstorm (hosted in GitHub), and by doing so I learned a few cool new things. Maybe ‘new’ is not the right term, but these things were new to me and I wanted to share them with you.

The code I am referring to resides in a few places in Stackstorm, the first item:

@six.add_metaclass(abc.ABCMeta) class Access(object):

Two things here:

1. **six.add_metaclass** – makes *Access* a metaclass in a way which is compatible both with Python2 and Python3. In general, six package has a purpose of helping developers migrate their code from Python 2 to Python 3 by writing code which is compatible with both.

Hence the name: six = 2 * 3. To read more: https://pypi.python.org/pypi/six

2. **abc.ABCMeta** – abc is another builtin package, its purpose is to provide support for **A**bstract **B**ase **C**lasses. By making a class abstract you ensure that nobody can instantiate objects out of it.

This is useful for (at least) three purposes:

a. Maintain logic which is common to multiple classes in one place

b. force the users to implement methods which are annotated by *@abc.abstractmethod* (you can also force the inheriting classes to create specific class members by using the annotation *@abc.abstractproperty*). For more info in regards see the docs.

c. creating a singleton. I’m still not convinced about the usefulness of creating a singleton in Python (because we have modules), but assuming you want to do so, you can either create a metaclass and call its methods as ClassName.method() or extend a metaclass but *not* implement the abstractmethod or abstractproperty. By not implementing the abstract-methods/properties the inheriting class will become abstract as well. This technique is used in a few different places in StackStorm, if you want to see some examples, search for classes that inherit from Access.

3. This one took me a while to wrap my head around (requires a “functional programming” mind if you will):

def decorate(f): @functools.wraps(f) def callfunction(*args, **kwargs):

By calling **@functools.wraps(f)** we’re using a decorator inside a decorator. Not trivial…

To understand the main idea of ‘functools.wraps’, it’s easier to go through the following points/process (at least, easier for me…):

– When you decorate a function, you’re creating a new function that wraps the ‘original’ one, and return the wrapper

– The wrapper is used in order to be able to perform all kinds of tasks before/after the ‘original’ function is applied. Examples: preparing the arguments for the ‘original function’, timing how long does it take for the function to execute, add log printings before and after and etc.

– Say you have a wrapped function f, returned from a decorator, the user will then see the *description* (metadata) of the decorator instead of f, the original function.

– In order to avoid this confusion, @functools.wraps(f) comes into the picture: by annotating the wrapper function with @functools.wraps(f) – you’re making sure that the user of the wrapper will see the same description/metadata as the original function has. By ‘description’ I mean the function-signature (the name of the function and the names of the arguments it takes).

Hope this is helpful!

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class Sqrt { public static void main(String[] args) { System.out.println(sqrt(17)); // 4.123105985575862 } final static double DELTA = 0.0001; /** * Newton's method: * * X1 = X0 - f(x)/f'(x) * x^2 = num * f(x) = x^2 - num * f'(x) = 2*x * * X1 = X0 - (X0^2 - num) / 2*X0 * ... * Xn = Xn-1 - ((Xn-1)^2 - num)/2Xn-1 * * Stop condition: |num - (Xn)^2| < DELTA * */ public static double sqrt(double num) { double x = num / 2; // starting point while(Math.abs(num - x * x) > DELTA) { x = x - ((x * x - num) / (2 * x)); } return x; } }

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def is_number(s): try: float(s) return True except ValueError: return False def calc(expr): if is_number(expr): return float(expr) arr = expr.split('+') if len(arr) > 1: return sum(map(calc, arr)) arr = expr.split('-') if len(arr) > 1: return reduce(lambda x,y: x-y, map(calc, arr)) arr = expr.split('*') if len(arr) > 1: return reduce(lambda x,y: x*y, map(calc, arr), 1) arr = expr.split('/') if len(arr) > 1: return reduce(lambda x,y: x/y, map(calc, arr)) print calc("3+4-2 *2/ 2") # 5

Nice, isn’t it?

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I would recommend using Alfred, even without buying the power-pack it has very useful clipboard snippets:

This way you can save commands that you’re using frequently and find them by typing “keywords” and when you press “enter” it’s paste into your clipboard/terminal. Very useful – especially when you work remotely on nodes in the cloud where you don’t have all your aliases defined.

I think that only if you buy the power-pack – you can use the workflows as well:

The great thing about it is, that with one command/shortcut you can open multiple windows in your browser, open a terminal and connect somewhere, and basically do multiple operations simultaneously.

This is very useful in different scenarios, for example, when you are in emergency situation – it can both save time *and* make sure you’re not forgetting anything. For example, when I get paged for an incident I open the terminal and connect to our production bastion, open our public channel in hipchat to make sure we didn’t get any reports about the incident from other people in the company, open a tab in the browser with our dashboard and etc.

But Workflows are great not only in emergency situations, for instance, we have a service that, given a node-id – it provides all the information about the node (aws-account/region/instance-type/asg/etc) as well as provides links to Asgard and some other stuff. So in Alfred I have a workflow with a shortcut, and now all I have to do is type:

“loc <node-id>” (short for “locate”) and it opens that service in a new tab in my browser.

Alfred has many other features which I haven’t yet explored, but in case you decide to try it out here’s one very important tip:

under “advanced” tab there’s a “set sync folder” button. Set it to save your stuff under dropbox (or any other cloud-drive). This way you’re making sure that:

- you can use your Alfred setup with multiple computers
- it’s always backed-up – if anything happens to your computer – you haven’t lost anything (all your shortcuts, clipboard, workflows etc).

Even without using multiple screens I find this app very useful: it provides keyboard shortcuts to resize windows, center them in the screen, and when you’re using multiple monitors – move them from one screen to another and back really easily. And yes, I agree, it is annoying you have to pay for something that you get for free as a Windows user, but I think that Apple users (unlike Windows users) are used to pay for stuff they need/want…

Captures screenshots of all/part of the screen, menus, etc. And then lets you edit and save/embed them. This app is free and I’m using it all the time – the screenshots in this post were done by Skitch!

Thanks to JS who introduced me to Alfred & Sizeup!

**Disclaimer**: I’m not affiliated with any of the applications above, nor I am receiving any kind of commission/royalties/etc!

Mosh is a command-line tool that replaces SSH. The main advantage of this terminal application is that it keeps the connection active, so even if you close your laptop, pack it in your bag, travel somewhere, open your laptop again – and you’re still connected on the same session!

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Rusty calculator

================Lab minion Rusty works for Professor Boolean, a mad scientist. He’s been stuck in this dead-end job crunching numbers all day since 1969. And it’s not even the cool type of number-crunching – all he does is addition and multiplication. To make matters worse, under strict orders from Professor Boolean, the only kind of calculator minions are allowed to touch is the Unix dc utility, which uses reverse Polish notation.

Recall that reverse Polish calculators such as dc push numbers from the input onto a stack. When a binary operator (like “+” or “*”) is encountered, they pop the top two numbers, and then push the result of applying the operator to them.

For example:

2 3 * => 6

4 9 + 2 * 3 + => 13 2 * 3 + => 26 3 + => 29Each day, Professor Boolean sends the minions some strings representing equations, which take the form of single digits separated by “+” or “*”, without parentheses. To make Rusty’s work easier, write function called answer(str) that takes such a string and returns the lexicographically largest string representing the same equation but in reverse Polish notation.

All numbers in the output must appear in the same order as they did in the input. So, even though “32+” is lexicographically larger than “23+”, the expected answer for “2+3″ is “23+”.

Note that all numbers are single-digit, so no spaces are required in the answer. Further, only the characters [0-9+*] are permitted in the input and output.

The number of digits in the input to answer will not exceed 100.

Test cases

==========Inputs:

(string) str = “2+3*2″

Output:

(string) “232*+”Inputs:

(string) str = “2*4*3+9*3+5″

Output:

(string) “243**93*5++”

The solution (in Python) is surprisingly short:

def answer(normal): res = '' muls = normal.split('+') for mul in muls: if len(mul) > 1: local_mul = mul.split('*') res += ''.join(local_mul) res += (len(local_mul) - 1) * '*' else: res += mul res += (len(muls)-1) * '+' return res

That’s definitely **NOT** the way you should write your code…

Can you figure it out?

Hint: a similar technique was used here

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To me it happened on a Saturday around midnight, I googled something related to Python and suddenly the search page collapsed and became dark. There was a single sentence that said something like:

“You speak our language”.

and then it invited me to participate in a series of code-challenges.

As a veteran member of CodeEval I gladly stepped into the Matrix and started solving different challenges. All was good until I ran into the following:

Peculiar balance

================

Can we save them? Beta Rabbit is trying to break into a lab that contains the only known zombie cure – but there’s an obstacle. The door will only open if a challenge is solved correctly. The future of the zombified rabbit population is at stake, so Beta reads the challenge: There is a scale with an object on the left-hand side, whose mass is given in some number of units. Predictably, the task is to balance the two sides. But there is a catch: You only have this peculiar weight set, having masses 1, 3, 9, 27, … units. That is, one for each power of 3. Being a brilliant mathematician, Beta Rabbit quickly discovers that any number of units of mass can be balanced exactly using this set.

To help Beta get into the room, write a method called answer(x), which outputs a list of strings representing where the weights should be placed, in order for the two sides to be balanced, assuming that weight on the left has mass x units.

The first element of the output list should correspond to the 1-unit weight, the second element to the 3-unit weight, and so on. Each string is one of:“L” : put weight on left-hand side

“R” : put weight on right-hand side

“-” : do not use weightTo ensure that the output is the smallest possible, the last element of the list must not be “-“.

x will always be a positive integer, no larger than 1000000000.Test cases

==========

Inputs:

(int) x = 2

Output:

(string list) [“L”, “R”]

Inputs:

(int) x = 8

Output:

(string list) [“L”, “-“, “R”]

After playing a bit with numbers, I figured out my strategy:

Given a number x I’ll try to find the minimal integer y >=x so that y can be presented as a sum of *distinct* powers of 3.

I say “distinct” because the coefficients for any power of 3 can be either 0 or 1.

Example:

A good solution: 12 = 0*3^0 + 1*3^1 + 1*3^2

A bad solution: 12 = **3***3^0 + 0*3^1 + 1*3^2

The second solution is bad because one of the coefficients is 3.

So I need to find the smallest y that respects this rule. Once I’ve found such a y, and since I already know how to represent it as a series of distinct powers of 3 – it’s a good candidate for ‘R’.

Now I still need to figure out how to represent the difference *y-x* as a “series of distinct powers of 3″ and I’m done.

As you might already started suspecting: we cannot find such a representation for *any ***y-x**. And indeed, in case we fail, we’ll have to look for the “next y” and on and forth.

The code looked like this:

from itertools import combinations def answer(x): """ Main logic: find the closest number y >= x so such that: 1. y is a sum of powers of 3 2. x + z = y where z is sum of numbers, each of which is a power of 3 3. z will be decomposed to "L" and y will be decomposed to "R" in build_list() """ next = find_next_sum_of_powers(x) left = represent_as_powers(next - x) while list(set(left)) != left: # meaning we have the same item more than once in "left" # look for the next "sum of powers" and try to use it instead next = find_next_sum_of_powers(next + 1) left = represent_as_powers(next - x) right = represent_as_powers(next) return build_list(left, right) def find_next_sum_of_powers(x): """ Given a number x, find the closest number y such that y >= x and y is one of the numbers returned by get_list_of_sums() and there is no number z, which is also returned by get_list_of_sums() such that: y > z > x """ next = find_next_power(x) if x == next: return x if x <= next // 2: sums = [] for list_of_sums in get_list_of_sums(x): for sm in list_of_sums: if sm not in sums: if x == sm: return x if sm - x in sums: return sm sums.append(sm) else: return next def is_power_of_3(x): """ Checks if x is a power of 3 """ n = 0 b = 0 while n < x: n = 3 ** b b += 1 return n == x def get_list_of_sums(x): """ Generates a list of sums of powers of 3: 1, 3, 4, 9, 10, 12, 13... 4 = 3 + 1; 10 = 9 + 1; 12 = 9 + 3; 13 = 9 + 3 + 1; ... """ workset = [] for i in gen_next_power(): if i > x: break workset.append(i) res = [] for i in range(len(workset)+1): for j in combinations(workset, i): res.append(j) yield map(sum, res[1:]) def gen_next_power(): """ Generator for powers of 3 each iteration will get the next power """ pow = 0 while True: yield 3 ** pow pow += 1 def find_next_power(x): """ Given a number x find the closest power of 3, y such that y > x and there is no power of 3, z such that x < z < y """ if x == 1: return x t = 3 while t < x: t *= 3 return t def represent_as_powers(num): res = [] while num > 0: t = find_next_power(num) if t > num: t /= 3 res.append(t) num -= t return res[::-1] def build_list(left, right): stop = right[-1] res, i = [], 0 while 3 ** i <= stop : if 3 ** i in left: res.append("L") elif 3 ** i in right: res.append("R") else: res.append("-") i += 1 return res

Now, this works. Only problem is – this approach of trying different y’s could get really expensive.

In fact, if I limited the number “retries” to a small number – I could get almost all the test-cases to pass – all but one…

But I wasn’t ready to give up

So I started googling and found this post which provided a good direction but to be honest – I didn’t understand:

1. how balanced ternary representation helps to solve our problem

2. how to translate from ternary representation to balanced ternary

These questions are coupled, and it took me some extra-reading to figure it out and what I’d like to do now – is save *you* the time that I spent, by trying to explain it a little better than that post, wikipedia and other resources I ran into during my investigation.

Let’s start with the second question which is pure technical, by comparing numbers in different bases, we’ll represent “balanced” with 1,0,-1:

decimal 1 2 3 4 5

ternary 1 2 10 11 12

balanced 1 1-1 10 11 1-1-1

Trying to follow up on the “balanced” gave me a headache… but after playing with it for a bit, and reading some search results like this answer (which explains how to add numbers in balanced-ternary form), made things much clearer:

in “balanced” we’re still using ternary-base, and the numbers 1/0/-1 are simply the coefficients of the respective power of 3. Lets demonstrate:

2 = **1***3^1 **-1***3^0 = **1-1**

3 = **1***3^1 +** 0***3^0 = **10**

4 = **1***3^1 +** 1***3^0 = **11**

5** = ****1***3^2 **–****1***3^1 **–****1***3^0 = 9 – 3 – 1 = **1-1-1**

etc

Once you realize the answer to #2 it’s only another small step to understand the connection to the code challenge:

Given that number x, once we find its balanced-ternary representation we can express it as a… **sum of distinct powers of 3!**

Now we have only a small obstacle: this sum has positive and negative elements – how do we handle it?

The challenge was to balance weights, which means that instead of “subtracting” the left side we can “add” the weight to the right side – like balancing equations!

The meaning of this is that the balanced-ternary form is actually the answer we’re looking for, lets take the number 5 from the previous example:

5 =** ****1***3^2 **–****1***3^1 **–****1***3^0 = 9 – 3 – 1 = **1-1-1**

in the form of our answer – we’re looking at the result from right to left, which means that

1-1-1 = [-1, -1, 1] = **–****1***3^0 **–****1***3^1 + **1***3^2

and the way it helps us solving the problem is by using ‘R’ as 1*3^2 and balancing it with ‘L’ by adding to ‘L’ 1*3^0 + 1*3^1 which ends up as simply replacing letters in the reversed version of the balanced-ternary form

from: **[-1, -1, 1]**

to:** ****[L, L, R]**

Further, if we have a zero in the balanced-ternary form it means that neither **L** nor **R** should add the respective power of 3, so we can simply replace any zero with “-” (dash).

We’re now ready to look at the code:

def answer(x): return to_balanced(to_ternary(x)) def to_ternary(x): """ convert decimal into ternary """ res = "" while x > 0: res = str(x % 3) + res x /= 3 return int(res) def to_balanced(t): """ convert ternary number into balanced-ternary and then to L/-/R representation """ res = map(int, str(t))[::-1] # iterating the digit from right --> left for i in range(0, len(res)): # zeros will remain zeros and ones remain ones # the only thing we need to convert is 2 into +- (== +1-1) if res[i] == 2: res[i] = -1 res = inc(res, i + 1) # convert from (1,0,-1) representation to (R,-,L) return ['-' if x == 0 else ('R' if x == 1 else 'L') for x in res] def inc(arr, ind): """ calculate the carryover """ if len(arr) == ind: arr += [1] # prepend the last carryover elif arr[ind] == -1: arr[ind] = 0 elif arr[ind] == 0: arr[ind] = 1 elif arr[ind] == 1: arr[ind] = -1 arr = inc(arr, ind + 1) else: # arr[ind] == 2 arr[ind] = 0 arr = inc(arr, ind + 1) return arr

Needless to say that this solution worked perfectly.

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Last week a friend asked me to help him prepare for a job-interview at FB. The first thing I did was pointing him to Cracking the Coding Interview which provides an excellent overview of all the main subjects an Engineer might be asked during an interview. My recommendation was to take the time and go through each and every chapter, and try to solve the questions. It’s very important not to “cheat” and try hard to solve them by yourself – otherwise you won’t improve!

Another important thing is, in case you weren’t able to solve a question and you checked out the answer – DO NOT try to memories it! instead, go on and continue solving other questions, and after a few days, try again to solve that same question. You might be surprised to find out that even though you saw the answer – it’s not always trivial to re-write it. Further, this is a good exercise that sharpens another important skill: being able to write code that expresses the algorithm we have in mind.

There’s plenty of good information available online, just Google it and you’ll find excellent blog posts such as this one, and many others.

Another smart thing my friend did was to go over job-interview websites and forums and copy questions that other candidates wrote they were asked during their interviews in FB. The following is a list of such questions:

- Calculate the square root of a double
- Given n intervals [si, fi], find the maximum number of overlapping intervals.
- Print all the paths from root to every leaf in a binary tree.
- Print the sum of all the numbers at every vertical level in a binary tree
- Given a set of n jobs with [start time, end time, cost] find a subset so that no 2 jobs overlap and the cost is maximum ?
- Given 1 trillion messages on FB and each message has at max 10 words, how do you build the index table and how many machines do you need on the cluster to store the index table ?
- Given an input array and another array that describes a new index for each element, mutate the input array so that each element ends up in their new index. Discuss the runtime of the algorithm and how you can be sure there won’t be any infinite loops.
- Find all the anagrams in an array of strings
- Combinations(n, k) – print all combinations of k out of n items
- Printing a binary tree L-R,
- Implementing a comparator function to sort files based on a certain naming convention.
- Implement a LRU cache
- Design FB newsfeed
- How would you query for all the Places near a given coordinate? The focus is on how to scale this to a large number of places while keeping response time to within acceptable user expectations
- Given a list of words, group anagrams.
- Find all 3 items that sum to 0 in an array.
- Write a function that calculates input strings with operators +,-,*,/ eg. “5+5*6″ should output 35
- Printing the nodes of a linked list in reverse
- Finding the longest palindrome in a given string
- Finding maximum subarray sum (similar to Kadane’s Algorithm) with the constraint that two numbers in the array that form the max sum cannot be next to each other
- How do you use Facebook app and what are the problems with it? How would you fix it?
- Design a Facebook travel app
- designing a system to do spam detection work and describing it in a huge flowchart, as might be done in an early but detailed product planning session. Be prepared to think on large scales.
- Write a palindrome-checking function
- Print a tree, level by level.
- Write all solutions for a^3+b^3 = c^3 + d^3, where a, b, c, d lie between [0, 10^5]
- Print a list in reverse
- Compute maximum profit for buying selling a stock given an array of prices for n days.
- Check if two binary trees are Isomorphic
- How would you come to an agreement if you and another senior level engineer couldn’t agree on a technical design?
- Write a function that takes 2 arguments: a binary tree and an integer n, it should return the n-th element in the inorder traversal of the binary tree.
- Add up two big integers represented in arrays.
- Design a system to support Facebook status update.
- Design the recommendation system for search keywords
- Given 2 very large numbers, each of which is so large it can only be represented as an array of integers, write a function to multiply them.
- Write a function to display a JSON string in pretty format.
- Write a function to check if polygon is simple based on given list of points
- Function to compute the number of ways to climb a flight of n steps. Taking 1, 2, or 3 steps at a time. Do it in Linear time and constant space. Example:
n = 3. 1 1 1 1 2 2 1 3 Ans = 4

- Interweave a linked list. Do it in Linear time and constant space.
Input: A->B->C->D->E Output: A->E->B->D->C

- Given a dictionary based simple password, create all possible (special character) passwords based on a provided mapping.
Input: face Map: {a -> @, 4, A} Output: f@ce, f4ce, fAce

- Get numeric number out of a roman string, linear time
Given: mapping I = 1, V = 5, X = 10, L = 50, C = 100, D = 500, M = 1000 Input/Output: II = 2, III = 3, IV = 4 VI, VII, VIII IX = 9 XL = 40, XLIX = 49

- Merge ‘k’ sorted arrays, each array may have max ‘n’ elements
- What would you change in Facebook?
- Given a sorted array, write a program to decide if two elements sum up to a third.
- Determine the 10 most frequent words given a terabyte of strings

Good Luck!

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Yesterday, one of my colleagues reminded me that I once said that I’m going to do the minimum to maintain project X. Project X is not its real name but maybe I should back up a few steps and start with providing some background:

one of the things that happens at Netflix pretty often is that teams change, responsibilities shift and as a result projects are handed off between teams. Project X is such project, it was written long time ago (more than two years back which is basically forever in our industry) by a guy that now manages another team, and was handed off to a team that currently doesn’t exist, and when that team was disassembled I was given this project.

So yesterday, the guy who wrote project X reminded my that I said I’m planning to do the minimum to maintain it. I was surprised because one of the things that doesn’t fit me is to come up with such a statement, and then I remembered: WALP.

Some people will describe it like this, but I just rather say that I’ll do the maximum to be in a condition where I have to do the minimum in order to maintain an existing project. Sounds confusing ? well, not really…

Project X was written in Python and had two major components (class-files) which became monolithic: functions that perform multiple tasks (which goes against SRP) with hundreds lines of code, a few levels of nested loops – complicated to follow and understand, a nightmare to debug and horror to test.

So, in order to be able to “do the minimum to maintain it” one of the first things I did was refactoring these two classes: breaking the logic into smaller functions, minimizing the number of arguments, working according to DRY and SRP, renaming variables and functions, adding unit-tests and while I was “shaking the tree” a few bugs were discovered and fixed as well.

There wasn’t much “fun” in it, it’s not like writing a project from scratch which is really tempting to most engineers but usually is a bad idea. It’s tempting because you think “I’ll get it right this time” or “I can do much better than that” and let’s be honest: when you’re maintaining someone else’s code – it will never “belong” to you, that other guy will always get the credit for writing this project – even if the code changed so much that it doesn’t look anything like it originally did…

Back to the story, the refactoring paid off, and it wasn’t even in the long term:

* a few bugs were found and fixed

* bugs that were discovered later on, were easier to debug and fix

* I had to write a few additional features, all were implemented very quickly, easily, without any pain and were easy to test

So yes, I AM lazy, and I’m willing to work really hard to sustain it…

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