Technical Interview Cheat Sheet

Interview Preparation Grid

Evaluated skills:

Analytical skills
Coding skills
Technical knowledge / Computer Science fundamentals
Experience
Culture fit / Communication skills

Behavioral Questions

Be specific, not arrogant
Limit Details
Focus on yourself, not your team
Give structured answers: Nugget first, situation, action, result

Tell Me About Yourself

Current Role (Headline Only)
College
Post College & Onwards
Current Role (Details)
Outside of Work
Wrap Up

What Questions to Ask?

TODO

Algorithms and Data Structures

Big-O

Big O - upper bound
Big Omega - lower bound
Big Theta - upper and lower bound - that's what's usually used but called Big O.

Amortized time

An ArrayList is implemented with an array. When the array hits capacity, the ArrayList class will create a new array with double the capacity and copy all the elements over to the new array.

For most inserts it takes O(1) time. For those inserts where the array is doubling it takes X time. X + X/2 + X/4 + Y/8 + ... + 1 is roughly 2X.

Therefore X insertions take O(X) time. The amortized time for each insertion is O(1).

Divide and Conquer

Usually recursive - ex. Merge Sort.

Master Method (allows to estimate time complexity of a recursive algorithm):

T(n) <= a*T(n/b) + O(n^d)

QuickSort - uses partitioning around pivot, works in-place, n*log(n) running time on average.

Randomized Selection - allows finding n-th order statistic of an array - uses quick sort algorithm but iterates only to 1 subarray, and thus O(n) time (master method case 2).

Guiding principles for algorithms analysis:

Worst case analysis (others are average case, best case)
Drop constant factors, lower-order terms
Asymptotic analysis (focus on running time for large input)

Graphs and Trees

Minimum cut - Karger's basic algorithm iteratively contracts randomly chosen edges until only two nodes remain; those nodes represent a cut in the original graph. By iterating this basic algorithm a sufficient number of times, a minimum cut can be found with high probability.

BFS (Breadth-first search) - uses queue. Can be used to find shortest path, connected components.

DFS (Depth-first search) - uses stack or recursion. Can be used for topological ordering of DAGs. Strongly connected components of directed graphs - 2 passes - first on reverted graph, second on straight graph (Kosaraju's two-pass algorithm). The first pass can be run on the straight graph as well, and then the node ordering should be reversed (because topological sort of the reversed graph is the reverse of the topological sort of the straight graph).

graph_dfs_bfs.py

graph_topological_sort.py

Dijkstra's algorithm - choose edge with lowest score (sum of current vertex length of shortest path and edge length), and add it to the set of explored vertexes.

runs in O(m*log(n)) time if heap data structure is used.

graph_dijkstra.py

Bellman-Ford algorithm - uses dynamic programming to compute shortest path from s to all other nodes in the graph.

allows negative links.
matrix D[i,v] contains shortest paths from s to v with at most i edges. D[0,s] = 0, D[0,v] = inf for all other nodes.
D[i, v] = min{D[i - 1, v], min{D[i - 1, w] + cost(w,v)}[for all w that have edges to v]}.
running time omplexity is O(n * m), space complexity is O(n ^ 2).
used in Internet for routing (BGP protocol), but instead of computing distances to all destinations, it propagates distances to all sources from a given destination. Routers maintain routing tables that store distances to all possible destinations/subnetworks. They push updated routes to their neighbors periodically. To avoid routing loops table stores not only shortest distances but entire paths.

Floyd-Warshall algorithms - finds shortest paths between all pairs of nodes.

Uses dynamic programming - matrix Dk stores shortest paths between nodes i and j, allowing to use only intermediary nodes with index less than k;
D0 contains direct paths between nodes i and j. D(k + 1)[i,j] = min(D(k)[i,j], D(k)[i,k] + D(k)[k,j]).
runs in O(n ^ 3), where n is number of nodes.

Number of edges in connected undirected graph with n nodes:

min: n - 1
max: n * (n - 1) / 2

https://classroom.udacity.com/courses/cs215

Eulerian path - path that visits every edge once. Might exist if a graph has exactly 2 nodes with odd degree, or all node have even degrees (in which case there may be an Eulerian cycle).

Euler's Formula - in a connected planar graph: n - m + r = 2, where n is the number of nodes, m is the number of edges, r is the number of regions.

The number of edges in a planar graph is O(n), where n is number of nodes.

Heap data structure - a tree where values in all nodes are larger (smaller for min-heap) that all values in respective subnodes. Time complexity is log(n) for inserting an element, and for extracting min/max element.

Two heaps can be used to maintain median of sequence of numbers (1 min heap and 1 max heap that should be rebalanced in case they become unbalanced).

Binary search tree - all operations are log(n), better than sorted array for inserting/deleting, but worse for getting i-th order statistic, min/max, rank, successor/predecessor.

Find min or max - takes log(n) time. For min, follow left child until there is none.
Find successor or predecessor - takes log(n) time. For predecessor, if there is left node, find max of it; if there is no left node, follow the parent until node is right node of the parent.
Insertion and deletion - takes log(n) time. For insertion just follow search algorithm until you find NULL and insert new element there. For deletion, in case a node has only left or right subtree, put it in place of deleted node; in case both left and right are present, find the predecessor of deleted node, put it in place of deleted node, then follow deletion algorithm for predecessor.
Select and rank (find n-th order statistic) - takes log(n) time, requires keeping size of subtree i.e. how many nodes are contained in the tree.

Red-black tree - makes binary search tree relatively balanced.

Invariants: root is always black, no consecutive red nodes, root-to-NULL paths all have same number of block nodes.
Invariants are maintained by recoloring nodes and rotations, when nodes are inserted or deleted.

AVL trees - makes binary search tree relatively balanced. Stores in each node the height of the subtrees rooted at this node.

Invariant: the height of the left subtree and the height of the right subtree differ by no more than one.
Invariants are maintained via rotations: if left subtree is heavier: LEFT RIGHT SHAPE -> LEFT LEFT SHAPE -> BALANCED.

Binary tree traversals:

In-Order Traversal means to "visit" (often, print) the left branch, then the current node, and finally, the right branch.
Pre-order traversal visits the current node before its child nodes (hence the name "pre-order").
Post-order traversal visits the current node after its child nodes (hence the name "post-order").

trie.py

radix_sort.py

Dynamic Programming

Sequence Alignment:

Given 2 strings A and B find the alignment with lowest penalty.
Penalties are given for mismatched characters and for gaps.
Fill in the matrix M(i,j) where i is prefix length of A, j is prefix length of B, M(i,j) is penalty of best alignment of prefixes.
M(0,j) = j*gap_penalty, analogous for M(i,0). M(i,j) = min(M(i-1,j) + gap_penalty, M(i,j-1) + gap_penalty, M(i-1,j-1) + penalty(A[i],B[j])).
The best alignment can be reconstructed from the matrix M.

Greedy Algorithms

Minimum Spanning Tree - a tree with minimum sum of edge costs that spans all vertices.

There are 2 greedy algorithms for finding a minimum spanning tree:

Prim's algorithms - pick random vertex, choose edge with smallest weight that comes from tree to outside. Add vertex to tree. Repeat.
Kruskal's algorithm - pick edge with smallest weight, add it to MST if it doesn't create the cycle. Repeat. Uses Union-find data structure to check if edge adds a cycle.

Union-Find - array where values are references to parent. find() returns the root of tree, union() merges 2 roots. It provides near-constant-time operations (bounded by the inverse Ackermann function) for both operations.

union_find.py

Optimal Caching - when cache is full, replace furthest-in-the-future element, i.e. the one that will be requested latest in the future. Replacing least recently used (LRU) is a good approximation to the most optimal algorithms.

Scheduling Jobs - pick the job with highest ratio of w/l, where w is job's weight, l is job's length.

NP-Completeness

P is the class of decision problems which can be solved in polynomial time by a deterministic Turing machine.
NP is the class of decision problems which can be solved in polynomial time by a non-deterministic Turing machine. Equivalently, it is the class of problems which can be verified in polynomial time by a deterministic Turing machine.
NP-hard is the class of decision problems to which all problems in NP can be reduced to in polynomial time by a deterministic Turing machine.
NP-complete is the intersection of NP-hard and NP. Equivalently, NP-complete is the class of decision problems in NP to which all problems in NP can be reduced to in polynomial time by a deterministic Turing machine.

A reduction from X to Y is simply an algorithm A which solves X by making use of some other algorithm B which solves problem Y. This reduction is called a "polynomial time reduction" if all parts of A other than B have a polynomial time complexity. As a trivial example, the problem of finding the smallest element in an array is constant-time reducible to the sorting problem, since you can sort the array and then return the first element of the sorted array.

Examples of NP-complete problems:

Knapsack problem - fill the knapsack with items sum of weights of which don't exceed the limit, providing maximum sum of values of items.
Subset sum problem - given the integers or natural numbers w1, w2, ... wn, does any subset of them sum to precisely W.
Travelling salesman problem - given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city and returns to the origin city?
Graph coloring problem - can be solved using backtracking. In social networks, creating groups of people none of whom are friends - mixer party. All planar graphs can be colored with 4 colors, such that no two adjacent nodes have same color (4-clor map theorem).
Boolean satisfiability problem (SAT) - determining if there exists an interpretation that satisfies a given Boolean formula.

To show that some problem A is NP-complete, try reducing some other NP-complete problem to A.

Approaches for solving NP-complete problems:

Approximation: Instead of searching for an optimal solution, search for a solution that is at most a factor from an optimal one.
Randomization: Use randomness to get a faster average running time, and allow the algorithm to fail with some small probability. Note: The Monte Carlo method is not an example of an efficient algorithm in this specific sense, although evolutionary approaches like Genetic algorithms may be.
Restriction: By restricting the structure of the input (e.g., to planar graphs), faster algorithms are usually possible.
Parameterization: Often there are fast algorithms if certain parameters of the input are fixed.
Heuristic: An algorithm that works "reasonably well" in many cases, but for which there is no proof that it is both always fast and always produces a good result. Metaheuristic approaches are often used.

Checklist for Solving Algorithm and Data Structures Problems

Come up with at least 2 examples.
Come up with and state the brute force solution.
Estimate time and space complexities before writing code - worst case, average case; time, space.
For optimization
- try different data structures - graph, tree, hashmap, stack, queue.
- try different algorithm approaches - recursion, divide and conquer, greedy, dynamic programming, line sweep.
Run the code through all examples.
Check corner cases.

Python Cheat Sheet

String functions https://docs.python.org/3/library/stdtypes.html#textseq:

string.split(s[, sep[, maxsplit]]), string.rsplit(s[, sep[, maxsplit]]) - maxsplit means the result will have at most maxsplit+1 elements.
string.find(s, sub[, start[, end]]), string.rfind(s, sub[, start[, end]]) - return the lowest index in s where the substring sub is found such that sub is wholly contained in s[start:end].
string.replace(s, old, new[, maxreplace]).
str.partition(sep), str.rpartition(sep) - partition the string by separator and return 3-tuple containing the part before the separator, the separator itself, and the part after the separator.
str.isdigit()
str.isalpha()
str.isalnum()

Python Cookbook https://www.amazon.com/Python-Cookbook-Third-David-Beazley/dp/1449340377:

(1.1) x, y = (4, 5) - Unpacking a Sequence into Separate Variables
(1.2) first, *middle, last = [1, 2, 3, 4] - Unpacking Elements from Iterables of Arbitrary Length
(1.3) deq = collections.deque(maxlen=5) - Keeping the Last N Items
(1.4) heapq.nlargest(3, [1, 8, 2, 23, 7]) - Finding the Largest or Smallest N Items
(1.5) heapq.heapify(); heapq.heappush(); heapq.heappop() - Implementing a Priority Queue
(1.6) collections.defaultdict(list) - Mapping Keys to Multiple Values in a Dictionary
(1.7) collections.OrderedDict() - Keeping Dictionaries in Order
(1.8) zip(dict1.values(), dict1.keys()) - Calculating with Dictionaries
(1.9) dict1.keys() & dict2.keys() - Finding Commonalities in Two Dictionaries
(1.10) set() combined with generator function - Removing Duplicates from a Sequence while Maintaining Order
(1.11) slice_name = slice(2,5); list1[slice_name] - Naming a Slice
(1.12) collections.Counter() - Determining the Most Frequently Occurring Items in a Sequence
(1.13) sorted(items, key=itemgetter('fname')) - Sorting a List of Dictionaries by a Common Key
(1.14) sorted(items, key=lambda i: i.field1) - Sorting Objects Without Native Comparison Support
(1.15) itertools.groupby() - Grouping Records Together Based on a Field
(1.16) [item for item in [-1, 2, 3] if n > 0] - Filtering Sequence Elements
(1.17) { key:value for key,value in dict1.items() if key in set1 } - Extracting a Subset of a Dictionary
(1.18) collections.namedtuple('MyEntity', ['field1', 'field2']) - Mapping Names to Sequence Elements
(1.20) collections.ChainMap or dict1.update(dict2) - Combining Multiple Mappings into a Single Mapping

Google's Python style guide: https://google.github.io/styleguide/pyguide.html.

Combinatorics

https://www.coursera.org/learn/combinatorics

Tuples: n ^ k - strings of length k from alphabet of size n, where characters can be repeated. Distribute n assignments among k people: k ^ n (look from a different point of view).
Permutations: n!/(n - k)! - strings of length k from alphabet of size n, where characters can not be repeated.
Combinations: (n choose k) = n! / ((n - k)! * k!) - form teams of size k from n people.
Combinations with repetitions: (k + n - 1) choose (n - 1) - make a salad consisting of k units, which can be chosen out of n types of ingredients, ingredients of each type are unlimited (we don't have to use all ingredients), order doesn't matter. Distribute n candies among k children, every child can receive from 0 to n candies: (k + n - 1) choose (k - 1) (look from a different point of view).

Binomial Theorem: (x + y) ^ n = sum((n choose k) * a^(n-k) * b^k), for all 0 <= k <= n.

Operating Systems

http://pages.cs.wisc.edu/~remzi/OSTEP/

Virtualization

CPU Virtualization (time sharing)

Basics:

The process is the major OS abstraction of a running program. At any point in time, the process can be described by its state: the contents of memory in its address space, the contents of CPU registers (including the program counter and stack pointer, among others), and information about I/O (such as open files which can be read or written).
The process API consists of calls programs can make related to processes. Typically, this includes creation, destruction, and other useful calls.
Processes exist in one of many different process states, including ready, running, and blocked. Different events (e.g., getting scheduled or descheduled, or waiting for an I/O to complete) transition a process from one of these states to the other.
A process list contains information about all processes in the system. Each entry is found in what is sometimes called a process control block (PCB), which is really just a structure that contains information about a specific process.
- State (ready, running, blocked)
- PC (program counter).
- Stack pointer.
- Frame pointer.
- Register context.
- Open files.

Process API:

Each process has a a process ID (PID).
The fork() system call is used in UNIX systems to create a new process. The creator is called the parent; the newly created process is called the child. As sometimes occurs in real life, the child process is a nearly identical copy of the parent.
The wait() system call allows a parent to wait for its child to complete execution.
The exec() family of system calls allows a child to break free from its similarity to its parent and execute an entirely new program.
A UNIX shell commonly uses fork(), wait(), and exec() to launch user commands; the separation of fork and exec enables features like input/output redirection, pipes, and other cool features, all without changing anything about the programs being run.
Process control is available in the form of signals, which can cause jobs to stop, continue, or even terminate.
Which processes can be controlled by a particular person is encapsulated in the notion of a user; the operating system allows multiple users onto the system, and ensures users can only control their own processes.
A superuser can control all processes (and indeed do many other things); this role should be assumed infrequently and with caution for security reasons.

Limited Direct Execution:

The CPU should support at least two modes of execution: a restricted user mode and a privileged (non-restricted) kernel mode.
Typical user applications run in user mode, and use a system call to trap into the kernel to request operating system services.
The trap instruction saves register state carefully, changes the hardware status to kernel mode, and jumps into the OS to a pre-specified destination: the trap table.
When the OS finishes servicing a system call, it returns to the user program via another special return-from-trap instruction, which reduces privilege and returns control to the instruction after the trap that jumped into the OS.
The trap tables must be set up by the OS at boot time, and make sure that they cannot be readily modified by user programs. All of this is part of the limited direct execution protocol which runs programs efficiently but without loss of OS control.
Once a program is running, the OS must use hardware mechanisms to ensure the user program does not run forever, namely the timer interrupt. This approach is a non-cooperative approach to CPU scheduling.
Sometimes the OS, during a timer interrupt or system call, might wish to switch from running the current process to a different one, a low-level technique known as a context switch.

Each process has a kernel stack (or more generally, each thread has its own stack) - https://www.cs.umb.edu/~eoneil/cs444_f06/class10.html

Just like there has to be a separate place for each process to hold its set of saved registers (in its process table entry), each process also needs its own kernel stack, to work as its execution stack when it is executing in the kernel.

For example, if a process is doing a read syscall, it is executing the kernel code for read, and needs a stack to do this. It could block on user input, and give up the CPU, but that whole execution environment held on the stack (and in the saved CPU state in the process table entry) has to be saved for its later use. Another process could run meanwhile and do its own syscall, and then it needs its own kernel stack, separate from that blocked reader's stack, to support its own kernel execution.

Since threads can also do system calls, each needs a kernel stack as well.

In Linux, the process/thread table entry and kernel stack are bundled up in one block of memory for each thread. Other OS's organize the memory differently, but still have both of these for each process/thread.

Sometimes the kernel stack is completely empty, notably when the process is executing user code.
Then when it does a system call, the kernel stack starts growing, and later shrinking back to nothing at the system call return.

CPU Scheduling:

Shortest Job First (SJF) - run shorter jobs before longer ones. Good for turnaround time (time since arrival to completion), bad for responsiveness. Preemptive Shortest Job First (PSJF) - same as SJF but jobs that arrive later can preempt already running jobs.

Round Robin - run jobs one by one in a circle. Good for responsiveness, bad for turnaround.

The Multi-Level Feedback Queue:

It has multiple levels of queues, and uses feedback to determine the priority of a given job. Balance of responsiveness and turnaround.

Rule 1: If Priority(A) > Priority(B), A runs (B doesn’t).
Rule 2: If Priority(A) = Priority(B), A & B run in round-robin fashion using the time slice (quantum length) of the given queue.
Rule 3: When a job enters the system, it is placed at the highest priority (the topmost queue).
Rule 4: Once a job uses up its time allotment at a given level (regardless of how many times it has given up the CPU), its priority is reduced (i.e., it moves down one queue).
Rule 5: After some time period S, move all the jobs in the system to the topmost queue.

Memory Virtualization (space sharing)

Every process has an illusion of private memory. The OS builds this abstraction of a private, potentially large address space for multiple running processes (all sharing memory) on top of a single, physical memory.

pointer = malloc(size) - allocate memory.
free(pointer) - free memory.
sizeof(pointer) - size of allocated slot.

They are not system calls, but instead library functions.

System calls:

brk, which is used to change the location of the program's break: the location of the end of the heap. Automatically called by malloc() and free() if necessary.

Address Translation

In older operating systems (in newer paging is used, scroll down): the hardware provides the base and bounds registers; each CPU thus has an additional pair of registers, part of the memory management unit (MMU) of the CPU. When a user program is running, the hardware will translate each address, by adding the base value to the virtual address generated by the user program. The hardware must also be able to check whether the address is valid, which is accomplished by using the bounds register and some circuitry within the CPU.

Base and bounds registers can only be modified in kernel mode by the OS during context switches.

Segmentation - there is base and bounds registers for each segment of memory: code, heap and stack.

Free Space Management

When malloc() and free() are called, the library updates the data structure called free list, where free chunks of memory are recorded. Most allocators store a little bit of extra information in a header block which is kept in memory, usually just before the handed-out chunk of memory.

Head denotes the head of free list, i.e. the linked list of free chunks of memory.

Strategies for finding free memory:

The best fit strategy is quite simple: first, search through the free list and find chunks of free memory that are as big or bigger than the requested size. Then, return the one that is the smallest in that group of candidates; this is the so called best-fit chunk (it could be called smallest fit too).

Worst fit tries to thus leave big chunks free instead of lots of small chunks that can arise from a best-fit approach.

The first fit method simply finds the first block that is big enough and returns the requested amount to the user.

Paging

Physical memory is divided into physical frames: fixed-size chunks of memory. OS with the help of hardware maintains a page translation table in memory that maps virtual page numbers (VPN) to physical frame numbers (PFN).

Paging has many advantages over previous approaches (such as segmentation). First, it does not lead to external fragmentation, as paging (by design) divides memory into fixed-sized units. Second, it is quite flexible, enabling the sparse use of virtual address spaces.

Translation Look-aside Buffers (TLBs)

Hardware provides a small, dedicated on-chip TLB as an address-translation cache, most memory references will hopefully be handled without having to access the page table in main memory. Thus, in the common case, the performance of the program will be almost as if memory isn't being virtualized at all, an excellent achievement for an operating system, and certainly essential to the use of paging in modern systems.

If the number of pages a program accesses in a short period of time exceeds the number of pages that fit into the TLB, the program will generate a large number of TLB misses, and thus run quite a bit more slowly. We refer to this phenomenon as exceeding the TLB coverage, and it can be quite a problem for certain programs. Some OSes support larger page sizes which increases effective coverage of the TLB. It's often utilized by DBMS.

Entry in TLB:

VPN	PFN	valid bit	protection bits	ASID (address space identifier, similar to PID but fewer bits)
10	100	1	rwx	1

Page Table Optimizations

Linear is faster but uses more memory. Multi-level uses less memory but slower.

The trade-offs multi-level tables present are in time and space — the bigger the table, the faster a TLB miss can be serviced, as well as the converse — and thus the right choice of structure depends strongly on the constraints of the given environment.

Swapping

Processes can access more memory than is physically present within a system. To do so requires more complexity in page-table structures, as a present bit (of some kind) must be included to tell us whether the page is present in memory or not. When not, the operating system page-fault handler runs to service the page fault, and thus arranges for the transfer of the desired page from disk to memory, perhaps first replacing some pages in memory to make room for those soon to be swapped in.

These actions all take place transparently to the process. As far as the process is concerned, it is just accessing its own private, contiguous virtual memory. Behind the scenes, pages are placed in arbitrary (non-contiguous) locations in physical memory, and sometimes they are not even present in memory, requiring a fetch from disk. While we hope that in the common case a memory access is fast, in some cases it will take multiple disk operations to service it; something as simple as performing a single instruction can, in the worst case, take many milliseconds to complete.

To keep a small amount of memory free, most operating systems thus have some kind of high watermark (HW) and low watermark (LW) to help decide when to start evicting pages from memory. The background thread, sometimes called the swap daemon or page daemon, then goes to sleep, happy that it has freed some memory for running processes and the OS to use.

Linux Address Space

Concurrency

A critical section is a piece of code that accesses a shared resource, usually a variable or data structure.
A race condition arises if multiple threads of execution enter the critical section at roughly the same time; both attempt to update the shared data structure, leading to a surprising (and perhaps undesirable) outcome.
An indeterminate program consists of one or more race conditions; the output of the program varies from run to run, depending on which threads ran when. The outcome is thus not deterministic, something we usually expect from computer systems.
To avoid these problems, threads should use some kind of mutual exclusion primitives; doing so guarantees that only a single thread ever enters a critical section, thus avoiding races, and resulting in deterministic program outputs.

Locks

Hardware provides primitives for implementing locks by OS:

old = testAndSet(var, old_ptr, new_val) - updates the variable and returns the old value atomically.
compareAndSwap()
fetchAndAdd()

They can be used to implement a simple spin-lock - the one that continuously tests the lock. A better implementation is to use a queue with waiting thread IDs. A guard is used as a spin lock, then the lock is acquired if it's available, otherwise thread is parked to the queue, when unlocking another thread is unparked from the queue.

Condition Variables

To wait for a condition to become true, a thread can make use of what is known as a condition variable. A condition variable is an explicit queue that threads can put themselves on when some state of execution (i.e., some condition) is not as desired (by waiting on the condition); some other thread, when it changes said state, can then wake one (or more) of those waiting threads and thus allow them to continue (by signaling on the condition).

Condition Variables can be used to solve Producer/Consumer problem. Producer sends a signal when adding item to queue, consumer sends signal when item removed from queue.

Semaphores

int sem_wait(sem_t *s) {
  decrement the value of semaphore s by one
  wait if value of semaphore s is negative
}  

int sem_post(sem_t *s) {
  increment the value of semaphore s by one
  if there are one or more threads waiting, wake one
}

A semaphore initialized with value 1 behaves like a lock.
A semaphore initialized with value 0 behaves like a condition variable.

Semaphores can be used to solve thread throttling problem: https://github.com/blockchain-etl/blockchain-etl-common/blob/master/blockchainetl_common/executors/bounded_executor.py

Event-Based Concurrency

Pseudocode for an event loop:

while (1) {
    events = getEvents();
    for (e in events)
    processEvent(e);
}

OS provides system calls select() or poll() to get the list of ready I/O descriptors:

int select(int nfds,
    fd_set *restrict readfds,
    fd_set *restrict writefds,
    fd_set *restrict errorfds,
    struct timeval *restrict timeout);

select() examines the I/O descriptor sets whose addresses are passed in readfds, writefds, and errorfds to see if some of their descriptors are ready for reading, are ready for writing, or have an exceptional condition pending, respectively. The first nfds descriptors are checked in each set, i.e., the descriptors from 0 through nfds-1 in the descriptor sets are examined. On return, select() replaces the given descriptor sets with subsets consisting of those descriptors that are ready for the requested operation. select() returns the total number of ready descriptors in all the sets.

select() must be used in combination with asynchronous I/O system calls. An example system call for async read:

int aio_read(struct aiocb *aiocbp);

Persistence

inode - a data structure in a file system that describes a file-system object, e.g. a file or a directory.

Below is an example of information stored in an inode:

struct stat {
    dev_t st_dev; // ID of device containing file
    ino_t st_ino; // inode number
    mode_t st_mode; // protection
    nlink_t st_nlink; // number of hard links
    uid_t st_uid; // user ID of owner
    gid_t st_gid; // group ID of owner
    dev_t st_rdev; // device ID (if special file)
    off_t st_size; // total size, in bytes
    blksize_t st_blksize; // blocksize for filesystem I/O
    blkcnt_t st_blocks; // number of blocks allocated
    time_t st_atime; // time of last access
    time_t st_mtime; // time of last modification
    time_t st_ctime; // time of last status change
};

ln file file2 - creates a hard link by incrementing hard link counter in the inode.

ln -s file file2 - creates a soft link by creating another inode with type soft link.

Read timeline for /foo/bar

Network

sockets_echo_server.py

sockets_echo_client.py

Tracing System Calls

Tracing system calls on MacOS:

For a given pid:

sudo dtruss -p <pid>

Start a process with tracing:

pip install ethereum-etl
# trace open file system calls
sudo dtruss ethereumetl export_all --start 2019-01-01 --end 2019-01-02 -w 1 2>&1 | grep open

List open files for process starting with pname:

lsof -c pname

Scalability and System Design

Checklist:

List all assumptions - needed to scope the project
Draft high-level solution
List limitations
List hard problems - what will take most effort

https://github.com/donnemartin/system-design-primer

https://www.quora.com/Is-there-any-book-to-prepare-for-System-Design-and-Architecture-interview-questions

You should be familiar with the speed of everything your computer can do, including the relative performance of RAM, disk, SSD and your network.

Numbers everyone Should Know http://static.googleusercontent.com/media/research.google.com/en/us/people/jeff/stanford-295-talk.pdf

Operation	Time
L1 cache reference	0.5 ns
Branch mispredict	5 ns
L2 cache reference	7 ns
Mutex lock/unlock	100 ns
Main memory reference	100 ns
Compress 1K bytes with Zippy	10,000 ns
Send 2K bytes over 1 Gbps network	20,000 ns
Read 1 MB sequentially from memory	250,000 ns (0.25 ms) 4 GB/s
Round trip within same datacenter	500,000 ns (0.5 millis)
Disk seek	10,000,000 ns
Read 1 MB sequentially from network	10,000,000 ns (0.01 s) 100 MB/s
Read 1 MB sequentially from disk	30,000,000 ns (0.03 s) 33 MB/s
Send packet CA->Netherlands->CA	150,000,000 ns (0.15 s)

https://github.com/donnemartin/system-design-primer

DNS

A root name server is a name server for the root zone of the DNS.

OS, or DNS server, or resolver typically includes a file with IPs of 13 root servers hardcoded into it:

HOSTNAME	IP ADDRESSES	MANAGER
a.root-servers.net	198.41.0.4, 2001:503:ba3e::2:30	VeriSign, Inc.
b.root-servers.net	199.9.14.201, 2001:500:200::b	University of Southern California (ISI)
c.root-servers.net	192.33.4.12, 2001:500:2::c	Cogent Communications
d.root-servers.net	199.7.91.13, 2001:500:2d::d	University of Maryland
e.root-servers.net	192.203.230.10, 2001:500:a8::e	NASA (Ames Research Center)
f.root-servers.net	192.5.5.241, 2001:500:2f::f	Internet Systems Consortium, Inc.
g.root-servers.net	192.112.36.4, 2001:500:12::d0d	US Department of Defense (NIC)
h.root-servers.net	198.97.190.53, 2001:500:1::53	US Army (Research Lab)
i.root-servers.net	192.36.148.17, 2001:7fe::53	Netnod
j.root-servers.net	192.58.128.30, 2001:503:c27::2:30	VeriSign, Inc.
k.root-servers.net	193.0.14.129, 2001:7fd::1	RIPE NCC
l.root-servers.net	199.7.83.42, 2001:500:9f::42	ICANN
m.root-servers.net	202.12.27.33, 2001:dc3::35	WIDE Project

The use of anycast (1 IP address corresponds to multiple machines) addressing permits the actual number of root server instances to be much larger, and is 997 as of 11 July 2019.

Since 2016, the root zone has been overseen by the Internet Corporation for Assigned Names and Numbers (ICANN) which delegate the management to a subsidiary acting as the Internet Assigned Numbers Authority (IANA). Distribution services are provided by Verisign.

Round-robin DNS - DNS server responds with list of IP addresses, which is ordered differently for every request. It allows to do load balancing of requests, e.g. to scale load balancers themselves.

Misc

Load Balancing
Caching
Data Partitioning (using Directory Partitioning)
Indexes
Proxies
Queues
Redundancy and Replication
SQL vs NoSQL
CAP Theorem
Consistent Hashing
API Gateway (centralized authentication/authorization, logging/monitoring, traffic control (rate limiting), API version control)

Key characteristics of distributed systems:

Scalability is the capability of a system, process or a network to grow and manage increased demand.
Reliability
Availability
Efficiency
Manageability

Aspects:

Capacity Estimations (Storage, Memory, Requests/Sec, Bandwidth)
Scalability and Performance
Bottlenecks
Database Schema Design
CI/CD/CT
Logging and Monitoring
Security and Privacy

Database types:

RDBMS (MySQL, Postgres)
Wide-column store (Bigtable, Cassandra)
Document store (Firestore, DynamoDB, MongoDB)
Key-value store (Redis, Memcache)
Columnar database (BigQuery, Redshift)
Graph database (Neo4j, JanusGraph)

Jeff Dean's Design Principles

Jeff Dean Building Software System https://www.youtube.com/watch?v=modXC5IWTJI

Design for Growth

Pattern: Single Master, 1000s of Workers

Pattern: Tree Distribution of Requests

Pattern: Elastic Systems

The Google Stack

http://malteschwarzkopf.de/research/assets/google-stack.pdf

BigTable Design

https://static.googleusercontent.com/media/research.google.com/en//archive/bigtable-osdi06.pdf

BigQuery Design

Google Docs Design

https://stackoverflow.com/questions/5772879/how-do-you-write-a-real-time-webbased-collaboration-tool-such-as-google-docs

Google Docs works via operational transformation:

The basic idea of operational transformation is to transform (or adjust) the parameters of an editing operation according to the effects of previously executed concurrent operations so that the transformed operation can achieve the correct effect and maintain document consistency.

https://coursehunter-club.net/t/educative-io-design-gurus-grokking-the-system-design-interview-part-1/579

http://blog.gainlo.co/index.php/category/system-design-interview-questions/

Designing Dropbox

Files are broken down into chunks of 4MB, only updated chunks are synchronized between clients.

In GFS there is a master (with Shadow masters), which keeps metadata, and Chunk servers, which keep the file chunks, replicated 3 times.

Designing Messenger

For 500M concurrent active users, we need 10K chat servers each holding 50K connections with users.

Designing Typeahead Suggestion

Use Trie with precalculated top 10 hits for every node of the Trie.

Designing API rate limiter

Fixed window is easy just store (startTimestamp, count). Sliding window need to quantize the window into discrete intervals and track counts in each interval, then sum last N intervals on throttle query.

Designing Twitter Search

400mil status updates daily, each status is 300 bytes, 120GB of storage daily, 1800 days in 5 years, ~200TB of storage for 5 years, with buffer 250TB, with replication 500TB, 4TB of storage per server, 125 servers to hold the data.

Index contains only words and status ids, 500K words in English language, each word 5 characters, 2.5MB to store all words.

730 statuses in 5 years, need 5 bytes for status id, for 2 years 292B statuses, ~1.4TB of data, if each status message is 15 words, ~21TB total memory for index, if server has 100GB of memory, need 210 servers for index.

Partition by status id to avoid hot word searches, aggregator queries all servers and combines the results.

Designing Web Crawler

Start with seed urls, use breadth-first search to crawl pages, have dedup servers for urls and for page content. Crawler has separate queues for domain buckets, to avoid overloading single domain. Bot-traps are avoided by using credit systems.

Designing Facebook Newsfeed

Keep news feeds in memory in cache servers. Use fan-out on write for regular users, fan-out on load for celebrities. Partition by user id.

Designing Yelp

To efficiently search nearby locations, divide the map into cells of dynamic size so that each cell has not more than 500 objects. Use QuadTree to represent the tiles and nested tiles.

Partition by location id hash. Aggregation servers combine data from multiple index servers.

Designing Uber Backend

Different from Yelp because locations change frequently. The QuadTree have to be updated periodically, say every 20 seconds. HashTable contains oldLocation and newLocation for every driver.

Design Ticketmaster

Use SQL database with transactions. Use in-memory queues to hold linked list of in-progress reservations so they can be expired efficiently. Use in-memory waiting queues for shows on which there are users waiting for expired reservations from other users. Partition by show id.

Object-Oriented Programming

Abstraction - represent real-world entities with their abstraction i.e. classes in code.
Encapsulation - hide the implementation details and expose the interface.
Inheritance - build class hierarchy to represent subset/superset relationships in real world.
Polymorphism - write your code with interfaces, the implementation will be chosen at runtime.

Resources

Cracking The Coding Interview: https://www.amazon.com/Cracking-Coding-Interview-Programming-Questions/dp/0984782850
Operating Systems: http://pages.cs.wisc.edu/~remzi/OSTEP/
Algorithms Specialization on Coursera: https://www.coursera.org/specializations/algorithms
Socket Programming in Python: https://realpython.com/python-sockets/

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Technical Interview Cheat Sheet

Table of Contents

Interview Preparation Grid

Behavioral Questions

Tell Me About Yourself

What Questions to Ask?

Algorithms and Data Structures

Big-O

Amortized time

Divide and Conquer

Graphs and Trees

Dynamic Programming

Greedy Algorithms

NP-Completeness

Checklist for Solving Algorithm and Data Structures Problems

Python Cheat Sheet

Combinatorics

Operating Systems

Virtualization

CPU Virtualization (time sharing)

Memory Virtualization (space sharing)

Concurrency

Persistence

Network

Tracing System Calls

Scalability and System Design

DNS

Misc

Jeff Dean's Design Principles

The Google Stack

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Technical Interview Cheat Sheet

Table of Contents

Interview Preparation Grid

Behavioral Questions

Tell Me About Yourself

What Questions to Ask?

Algorithms and Data Structures

Big-O

Amortized time

Divide and Conquer

Graphs and Trees

Dynamic Programming

Greedy Algorithms

NP-Completeness

Checklist for Solving Algorithm and Data Structures Problems

Python Cheat Sheet

Combinatorics

Operating Systems

Virtualization

CPU Virtualization (time sharing)

Memory Virtualization (space sharing)

Concurrency

Persistence

Network

Tracing System Calls

Scalability and System Design

DNS

Misc

Jeff Dean's Design Principles

The Google Stack

Object-Oriented Programming

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