• A common language (for the two ends to understand)
• A way to address who do you want to communicate with
• A Connection (so the content of of the communication can reach the recipients)

A set of protocols that define how two or more devices can communicate with each other. To learn more about TCP/IP, read here

Ethernet simply refers to the most common type of Local Area Network (LAN) used today. A LAN—in contrast to a WAN (Wide Area Network), which spans a larger geographical area—is a connected network of computers in a small area, like your office, college campus, or even home.

A MAC address is a unique identification number or code used to identify individual devices on the network.

Packets that are sent on the ethernet are always coming from a MAC address and sent to a MAC address. If a network adapter is receiving a packet, it is comparing the packet’s destination MAC address to the adapter’s own MAC address.

When a device sends a packet to the broadcast MAC address (FF:FF:FF:FF:FF:FF​), it is delivered to all stations on the local network. Ethernet broadcasts are used to resolve IP addresses to MAC addresses (by ARP) at the datalink layer .

An Internet Protocol address (IP address) is a numerical label assigned to each device connected to a computer network that uses the Internet Protocol for communication.An IP address serves two main functions: host or network interface identification and location addressing.

A Subnet mask is a 32-bit number that masks an IP address, and divides the IP address into network address and host address. Subnet Mask is made by setting network bits to all "1"s and setting host bits to all "0"s. Within a given network, two host addresses are reserved for special purpose, and cannot be assigned to hosts. The "0" address is assigned a network address and "255" is assigned to a broadcast address, and they cannot be assigned to hosts.

For Example

| Address Class | No of Network Bits | No of Host Bits | Subnet mask     | CIDR notation |
| ------------- | ------------------ | --------------- | --------------- | ------------- |
| A             | 8                  | 24              | 255.0.0.0       | /8            |
| A             | 9                  | 23              | 255.128.0.0     | /9            |
| A             | 12                 | 20              | 255.240.0.0     | /12           |
| A             | 14                 | 18              | 255.252.0.0     | /14           |
| B             | 16                 | 16              | 255.255.0.0     | /16           |
| B             | 17                 | 15              | 255.255.128.0   | /17           |
| B             | 20                 | 12              | 255.255.240.0   | /20           |
| B             | 22                 | 10              | 255.255.252.0   | /22           |
| C             | 24                 | 8               | 255.255.255.0   | /24           |
| C             | 25                 | 7               | 255.255.255.128 | /25           |
| C             | 28                 | 4               | 255.255.255.240 | /28           |
| C             | 30                 | 2               | 255.255.255.252 | /30           |

• Application: user end (HTTP is here)
• Presentation: establishes context between application-layer entities (Encryption is here)
• Session: establishes, manages and terminates the connections
• Transport: transfers variable-length data sequences from a source to a destination host (TCP & UDP are here)
• Network: transfers datagrams from one network to another (IP is here)
• Data link: provides a link between two directly connected nodes (MAC is here)
• Physical: the electrical and physical spec the data connection (Bits are here)

You can read more about the OSI model in penguintutor.com

Unitcast: One to one communication where there is one sender and one receiver.

Broadcast: Sending a message to everyone in the network. The address ff:ff:ff:ff:ff:ff is used for broadcasting. Two common protocols which use broadcast are ARP and DHCP.

Multicast: Sending a message to a group of subscribers. It can be one-to-many or many-to-many.

CSMA/CD stands for Carrier Sense Multiple Access / Collision Detection. Its primarily focus it to manage access to shared medium/bus where only one host can transmit at a given point of time.

CSMA/CD algorithm:

1. Before sending a frame, it checks whether another host already transmitting a frame.
2. If no one transmitting, it starts transmitting the frame.
3. If two hosts transmitted at the same time, we have a collision.
4. Both hosts stop sending the frame and they send to everyone a 'jam signal' notifying everyone that a collision occurred
5. They are waiting for a random time before sending again
6. Once each host waited for a random time, they try to send the frame again and so the

A router is a physical or virtual appliance that passes information between two or more packet-switched computer networks. A router inspects a given data packet's destination Internet Protocol address (IP address), calculates the best way for it to reach its destination and then forwards it accordingly.

Network Address Translation (NAT) is a process in which one or more local IP address is translated into one or more Global IP address and vice versa in order to provide Internet access to the local hosts.

A proxy server acts as a gateway between you and the internet. It’s an intermediary server separating end users from the websites they browse.

If you’re using a proxy server, internet traffic flows through the proxy server on its way to the address you requested. The request then comes back through that same proxy server (there are exceptions to this rule), and then the proxy server forwards the data received from the website to you.

roxy servers provide varying levels of functionality, security, and privacy depending on your use case, needs, or company policy.

TCP 3-way handshake or three-way handshake is a process which is used in a TCP/IP network to make a connection between server and client.

A three-way handshake is primarily used to create a TCP socket connection. It works when:

• A client node sends a SYN data packet over an IP network to a server on the same or an external network. The objective of this packet is to ask/infer if the server is open for new connections.
• The target server must have open ports that can accept and initiate new connections. When the server receives the SYN packet from the client node, it responds and returns a confirmation receipt – the ACK packet or SYN/ACK packet.
• The client node receives the SYN/ACK from the server and responds with an ACK packet.

From wikipedia: "the length of time it takes for a signal to be sent plus the length of time it takes for an acknowledgement of that signal to be received"

Bonus question: what is the RTT of LAN?

TCP establishes a connection between the client and the server to guarantee the order of the packages, on the other hand, UDP does not establish a connection between client and server and doesn't handle package order. This makes UDP more lightweight than TCP and a perfect candidate for services like streaming.

Penguintutor.com provides a good explanation.

A default gateway serves as an access point or IP router that a networked computer uses to send information to a computer in another network or the internet.

ARP stands for Address Resolution Protocol. When you try to ping an IP address on your local network, say 192.168.1.1, your system has to turn the IP address 192.168.1.1 into a MAC address. This involves using ARP to resolve the address, hence its name.

Systems keep an ARP look-up table where they store information about what IP addresses are associated with what MAC addresses. When trying to send a packet to an IP address, the system will first consult this table to see if it already knows the MAC address. If there is a value cached, ARP is not used.

It stands for Dynamic Host Configuration Protocol, and allocates IP addresses, subnet masks and gateways to hosts. This is how it works:

• A host upon entering a network, broadcasts a message in search of a DHCP server (DHCP DISCOVER)
• An offer message is sent back by the DHCP server as a packet containing lease time, subnet mask, IP addresses, etc (DHCP OFFER)
• Depending on which offer accepted, the client sends back a reply broadcast letting all DHCP servers know (DHCP REQUEST)
• Server sends an acknowledgment (DHCP ACK)

Read more here

NAT stands for network address translation. It’s a way to map multiple local private addresses to a public one before transferring the information. Organizations that want multiple devices to employ a single IP address use NAT, as do most home routers. For example, your computer's private IP could be 192.168.1.100, but your router maps the traffic to it's public IP (e.g. 1.1.1.1). Any device on the internet would see the traffic coming from your public IP (1.1.1.1) instead of your private IP (192.168.1.100).

The control plane is the part of the network that decides how to route and forward packets to a different location.

The data plane is the part of the network that actually forwards the data/packets.

Refers to monitoring and management functions.

Control Plane.

Latency. To have a good latency, a search query should be forwarded to the closest datacenter.

Throughput. To have a good throughput, the upload stream should be routed to an underutilized link.

• Keep caches updated (which means the request could be forwarded not to the closest datacenter)

00110011110100011101

• Hypertext Transfer Protocol (HTTP) - used for the webpages on the internet
• Simple Mail Transfer Protocol (SMTP) - email transmission
• Telecommunications Network - (TELNET) - terminal emulation to allow client access to telnet server
• File Transfer Protocol (FTP) - facilitates transfer of files between any two machines
• Domain Name System (DNS) - domain name translation
• Dynamic Host Configuration Protocol (DHCP) - allocates IP addresses, subnet masks and gateways to hosts
• Simple Network Management Protocol (SNMP) - gathers data of devices on the network

• Internet Protocol (IP) - assists in routing packets from one machine to another
• Internet Control Message Protocol (ICMP) - lets one know what is going such as error messages and debugging information

Read more here

The internet refers to network of networks, transferring huge amounts of data around the globe.
The World Wide Web is an application running on millions of server, on top of the internet, accessed through what is know as the web browser

ISP (Internet Service Provider) is the local internet company provider.

Only you know :)

For example:

• Administration
• Troubleshooting & Debugging
• Storage
• Networking
• Development
• Deployments

The -r (or -R in some commands) flag allows the user to run a certain command recursively. For example, listing all the files under the following tree is possible when done recursively (ls -R):

/dir1/ dir2/ file1 file2 dir3/ file3

To list all the files, one can run ls -R /dir1

• file permissions, number of links, owner name, owner group, file size, timestamp of last modification and directory/file name

These are files directly not displayed after performing a standard ls direct listing. An example of these files are .bashrc which are used to execute some scripts. Some also store configuration about services on your host like .KUBECONFIG. The command used to list them is, ls -a

myProgram < input.txt > executionOutput.txt

• sed: a stream editor. Can be used for various purposes like replacing a word in a file: sed -i s/salad/burger/g
• grep: a search tool. Used to search, count or match a text in a file:
  • searching for any line that contains a word in a file: grep 'word' file.md
  • or displaying the total number of times a string appears in a file: grep -c 'This is a string' file.md
• cut: a tool for cutting out selected portions of each line of a file:
  • syntax: cut OPTION [FILE]
    • cutting first two bytes from a word in a file: cut -b 1-2 file.md, output: wo

Using the mv command.

• rm -rf dir
• cat or less
• chmod 777 /tmp/x
• cd ~
• sed -i s/good/great/g /tmp/y

• whereis
• which

The echo command receives two separate arguments in the first execution and in the second execution it gets one argument which is the string "hello world". The output will be the same.

Using a pipe in Linux, allows you to send the output of one command to the input of another command. For example: cat /etc/services | wc -l

sed 's/1/2/g' /tmp/myFile  # sed "s/1/2/g" is also fine
find . -iname "*.yaml" -exec sed -i "s/1/2/g" {} \;

history command or .bash_history file

1. Manually adding what you need to your $PATH PATH="$PATH":/user/bin:/..etc
2. You have your weird env variables backed up.
3. You would look for your distro default $PATH variable, copy paste using method #1

Note: There are many ways of getting errors like this: if bash_profile or any configuration file of your interpreter was wrongly modified; causing erratics behaviours, permissions issues, bad compiled software (if you compiled it by yourself)... there is no answer that will be true 100% of the time.

You can use the commands cron and at. With cron, tasks are scheduled using the following format:

*/30 * * * * bash myscript.sh Executes the script every 30 minutes.

The tasks are stored in a cron file, you can write in it using crontab -e

Alternatively if you are using a distro with systemd it's recommended to use systemd timers.

ls > ls_output.txt

yippiekaiyay 2> ls_output.txt

yippiekaiyay 1>&2

An output similar to: yippikaiyay: command not found...
The file die_hard will not be created

The root of the filesystem. The beginning of the tree.

• binaries
• configuration files
• home directories of the different users
• files that tend to change and be modified like logs
• temporary files

/tmp folder is cleaned automatically, usually upon reboot.

False. No one can create file in /proc directly (certain operations can lead to files being created in /proc by the kernel).

The command passed to the boot loader to run the kernel

Using the chmod command.

777 - You give the owner, group and other: Execute (1), Write (2) and Read (4); 4+2+1 = 7.
644 - Owner has Read (4), Write (2), 4+2 = 6; Group and Other have Read (4).
750 - Owner has x+r+w, Group has Read (4) and Execute (1); 4+1 = 5. Other have no permissions.

• setuid is a linux file permission that permits a user to run a file or program with the permissions of the owner of that file. This is possible by elevation of current user privileges.
• setgid is a process when executed will run as the group that owns the file.

• chmod - changes access permissions to files system objects
• chown - changes the owner of file system files and directories
• chgrp - changes the group associated with a file system object

True

• No more disk space
• No more inodes
• No permissions

A daemon is a program that runs in the background without direct control of the user, although the user can at any time talk to the daemon.

systemd has many features such as user processes control/tracking, snapshot support, inhibitor locks..

If we visualize the unix/linux system in layers, systemd would fall directly after the linux kernel.
Hardware -> Kernel -> Daemons, System Libraries, Server Display.

To start a service: systemctl start <service name> To stop a service: systemctl stop <service name>

systemctl status <service name>

journalctl

/var/log

tail -f

dstat -t is great for identifying network and disk issues. netstat -tnlaup can be used to see which processes are running on which ports. lsof -i -P can be used for the same purpose as netstat. ngrep -d any metafilter for matching regex against payloads of packets. tcpdump for capturing packets wireshark same concept as tcpdump but with GUI (optional).

dstat -t is great for identifying network and disk issues. opensnoop can be used to see which files are being opened on the system (in real time).

strace is great for understanding what your program does. It prints every system call your program executed.

top will show you how much CPU percentage each process consumes perf is a great choice for sampling profiler and in general, figuring out what your CPU cycles are "wasted" on flamegraphs is great for CPU consumption visualization (http://www.brendangregg.com/flamegraphs.html)

• Check with top for anything unusual
• Run dstat -t to check if it's related to disk or network.
• Check if it's network related with sar
• Check I/O stats with iostat

The kernel is part of the operating system and is responsible for tasks like:

• Allocating memory
• Schedule processes
• Control CPU

uname -a command

The operating system executes the kernel in protected memory to prevent anyone from changing (and risking it crashing). This is what is known as "Kernel space". "User space" is where users executes their commands or applications. It's important to create this separation since we can't rely on user applications to not tamper with the kernel, causing it to crash.

Applications can access system resources and indirectly the kernel space by making what is called "system calls".

• Build time (when it's compiled)
• Boot time (when it starts)
• Runtime (once it's already running)

Usually it will reside in /boot/config-<kernel version>.<os release>.<arch>

/proc/cmdline

sysctl -a

Yes, you might notice that in most systems, when running systctl -a with root, you'll get more runtime parameters compared to executing the same command with a regular user.

sudo sysctl net.ipv4.ip_forward=1

To make it persistent (applied after reboot for example): insert net.ipv4.ip_forward = 1 into /etc/sysctl.conf

Another way to is to run echo 1 | sudo tee /proc/sys/net/ipv4/ip_forward

If you strace the sysctl command you can see it does it by changing the file under /proc/sys/...

In the past it was done with sysctl system call, but it was deprecated at some point.

There is a service called systemd-sysctl that takes the content of /etc/sysctl.conf and applies it. This is how changes persist, even after reboot, when they are written in /etc/sysctl.conf

No. Containers have their own /proc filesystem so any change to kernel parameters inside a container, are not affecting the host or other containers running on that host.

Wikipedia Definition: "SSH or Secure Shell is a cryptographic network protocol for operating network services securely over an unsecured network."

Hostinger.com Definition: "SSH, or Secure Shell, is a remote administration protocol that allows users to control and modify their remote servers over the Internet."

An SSH server will have SSH daemon running. Depends on the distribution, you should be able to check whether the service is running (e.g. systemctl status sshd).

Telnet also allows you to connect to a remote host but as opposed to SSH where the communication is encrypted, in telnet, the data is sent in clear text, so it doesn't considered to be secured because anyone on the network can see what exactly is sent, including passwords.

It means that the key of the remote host was changed and doesn't match the one that stored on the machine (in ~/.ssh/known_hosts).

• The ? matches any single character
• The * matches zero or more characters

1. An IP address
2. The word "error" or "failure"
3. Lines which end with a number

lines 1 and 3.

An exit code (or return code) represents the code returned by a child process to its parent process.

0 is an exit code which represents success while anything higher than 1 represents error. Each number has different meaning, based on how the application was developed.

I consider this as a good blog post to read more about it: https://shapeshed.com/unix-exit-codes

Another way to ask this: what happens from the moment you turned on the server until you get a prompt

For each file (and directory) in Linux there is an inode, a data structure which stores meta data related to the file like its size, owner, permissions, etc.

File name (it's part of the directory file)

Run mount

cat /proc/mounts

Hard link is the same file, using the same inode. Soft link is a shortcut to another file, using a different inode.

False

True

True.

There are many answers for this question. One way is running df -T

du -sh

False. /tmp is cleared upon system boot while /var/tmp is cleared every a couple of days or not cleared at all (depends on distro).

One can use uptime or top

This article summarizes the load average topic in a great way

pidstat

iostat -xz 1

You can use the commands top and free

sar -n TCP,ETCP 1

ps -ef

You can achieve that by specifying & at the end of the command. As to why, since some commands/processes can take a lot of time to finish execution or run forever, you may want to run them in the background instead of waiting for them to finish before gaining control again in current session.

The default signal is SIGTERM (15). This signal kills
process gracefully which means it allows it to save current
state configuration.

SIGTERM - default signal for terminating a process SIGHUP - common usage is for reloading configuration SIGKILL - a signal which cannot caught or ignored

To view all available signals run kill -l

A background process. Most of these processes are waiting for requests or set of conditions to be met before actually running anything. Some examples: sshd, crond, rpcbind.

Running (R)
Uninterruptible Sleep (D) - The process is waiting for I/O
Interruptible Sleep (S)
Stopped (T)
Dead (x)
Zombie (z)

A process which has finished to run but has not exited.

One reason it happens is when a parent process is programmed incorrectly. Every parent process should execute wait() to get the exit code from the child process which finished to run. But when the parent isn't checking for the child exit code, the child process can still exists although it finished to run.

You can't kill a zombie process the regular way with kill -9 for example as it's already dead.

One way to kill zombie process is by sending SIGCHLD to the parent process telling it to terminate its child processes. This might not work if the parent process wasn't programmed properly. The invocation is kill -s SIGCHLD [parent_pid]

You can also try closing/terminating the parent process. This will make the zombie process a child of init (1) which does periodic cleanups and will at some point clean up the zombie process.

If you mention at any point ps command with arugments, be familiar with what these arguments does exactly.

find /some_dir -iname *.yml -print0 | xargs -0 -r sed -i "s/1/2/g"

The ls executable is built for an incompatible architecture.

You can use the split command this way: split -l 25 some_file

In Linux (and Unix) the first three file descriptors are:

• 0 - the default data stream for input
• 1 - the default data stream for output
• 2 - the default data stream for output related to errors

This is a great article on the topic: https://www.computerhope.com/jargon/f/file-descriptor.htm

ip link show

The loopback interface is a special, virtual network interface that your computer uses to communicate with itself. It is used mainly for diagnostics and troubleshooting, and to connect to servers running on the local machine.

One of the following would work:

netstat -tnlp | grep <port_number>
lsof -i -n -P | grep <port_number>

False

Technically, yes.

Telnet is a type of client-server protocol that can be used to open a command line on a remote computer, typically a server. By default, all the data sent and received via telnet is transmitted in clear/plain text, therefore it should not be used as it does not encrypt any data between the client and the server.

One way would be ping6 ff02::1

There a couple of modes:

• balance-rr: round robing bonding
• active-backup: a fault tolerance mode where only one is active
• balance-tlb: Adaptive transmit load balancing
• balance-alb: Adaptive load balancing

cat /etc/hostname

You can also run hostnamectl or hostname but that might print only a temporary hostname. The one in the file is the permanent one.

You can specify one or more of the following:

• dig
• host
• nslookup

This is the TTL. When you lookup for an address using a domain/host name, your OS is performing DNS resolution by contacting DNS name servers to get the IP address of the host/domain you are looking for.
When you get a reply, this reply in cached in your OS for a certain period of time. This is period of time is also known as TTL and this is the meaning of 3515 number - it will be cached for 3515 seconds before removed from the cache and during that period of time, you'll get the value from the cache instead of asking DNS name servers for the address again.

The answer depends on the distribution being used.

In Fedora/CentOS/RHEL/Rocky it can be done with rpm or dnf commands. In Ubuntu it can be done with the apt command.

Package managers allow you to manage packages lifecycle as in installing, removing and updating the packages.
In addition, you can specify in a spec how a certain package will be installed - where to copy the files, which commands to run prior to the installation, post the installation, etc.

From the repo:

"Dandified YUM (DNF) is the next upcoming major version of YUM. It does package management using RPM, libsolv and hawkey libraries."

Official docs

dnf provides /usr/bin/git

Depends on the init system.

Systemd: systemctl enable [service_name] System V: update-rc.d [service_name] and add this line id:5678:respawn:/bin/sh /path/to/app to /etc/inittab Upstart: add Upstart init script at /etc/init/service.conf

1. SSH server is not installed
2. SSH server is not running

Nginx, Apache httpd.

adduser user_name --shell=/bin/false --no-create-home You can also add a user and then edit /etc/passwd.

su command. Use su - to switch to root

Re-install the OS IS NOT the right answer :)

Using the last command.

useradd - Command for creating new users usermod - Modify the users setting whoami - Outputs, the username that we are currently logged in id - Prints the

The user you are using isn't defined locally but originates from services like LDAP.
You can verify with: getent passwd

/proc/cpuinfo

You can also use nproc for number of processors

dmidecoode

lsblk

True. Only in kernel space they have full access to hardware resources.

One way is using openssl this way:

openssl genrsa -aes256 -out ca-private-key.pem 4096

openssl req -new -x509 -days 730 -key [private key file name] -sha256 -out ca.pem

If using the private key from the previous question then the command would be:

openssl req -new -x509 -days 730 -key ca-private-key.pem -sha256 -out ca.pem

• Process ID namespaces: these namespaces include independent set of process IDs
• Mount namespaces: Isolation and control of mountpoints
• Network namespaces: Isolates system networking resources such as routing table, interfaces, ARP table, etc.
• UTS namespaces: Isolate host and domains
• IPC namespaces: Isolates interprocess communications
• User namespaces: Isolate user and group IDs
• Time namespaces: Isolates time machine

True. Inside the namespace it's PID 1 while to the parent namespace the PID is a different one.

False. The opposite is true. Parent PID namespace is aware and has visibility of processes in child PID namespace and child PID namespace has no visibility as to what is going on in the parent PID namespace.

False. Network namespace has its own interfaces and routing table. There is no way (without creating a bridge for example) for one network namespace to reach another.

True

False. In every child user namespace, it's possible to have a separate root user with uid of 0.

In time namespaces processes can use different system time.

• KVM
• XEN
• VirtualBox
• Linux-VServer
• User-mode Linux
• ...

Is an open source virtualization technology used to operate on x86 hardware.

From the official docs Recommended read:

• Red Hat Article - What is KVM?

It's an open source collection of software used to manage virtual machines. It can be used with: KVM, Xen, LXC and others. It's also called Libvirt Virtualization API.

From the official docs Hypervisor supported docs

From Wikipedia: "AWK is domain-specific language designed for text processing and typically used as a data extraction and reporting tool"

awk '{print $4}' file

awk 'length($0) > 79' file

Using system calls

• A program executes a trap instruction. The instruction jump into the kernel while raising the privileged level to kernel space.
• Once in kernel space, it can perform any privileged operation
• Once it's finished, it calls a "return-from-trap" instruction which returns to user space while reducing back the privilege level to user space.

fork() is used for creating a new process. It does so by cloning the calling process but the child process has its own PID and any memory locks, I/O operations and semaphores are not inherited.

• On success, the PID of the child process in parent and 0 in child process
• On error, -1 in the parent

Not enough memory to create a new process

wait() is used by a parent process to wait for the child process to finish execution. If wait is not used by a parent process then a child process might become a zombie process.

The kernel notifies the parent by sending the SIGCHLD to the parent.

The waitpid() is a non-blocking version of the wait() function.
It also supports using library routine (e.g. system()) to wait a child process without messing up with other children processes for which the process has not waited.

True in most cases though there are cases where wait() returns before the child exits.

It transforms the current running program into another program.
Given the name of an executable and some arguments, it loads the code and static data from the specified executable and overwrites its current code segment and current static code data. After initializing its memory space (like stack and heap) the OS runs the program passing any arguments as the argv of that process.

True
Since a succesful exec replace the current process, it can't return anything to the process that made the call.

fork(), exec() and the wait() system call is also included in this workflow.

Executes a program. The program is passed as a filename (or path) and must be a binary executable or a script.

Unix pipe implementation

"Pipes provide a unidirectional interprocess communication channel. A pipe has a read end and a write end. Data written to the write end of a pipe can be read from the read end of the pipe. A pipe is created using pipe(2), which returns two file descriptors, one referring to the read end of the pipe, the other referring to the write end."

• Shell reads the input using getline() which reads the input file stream and stores into a buffer as a string

• The buffer is broken down into tokens and stored in an array this way: {"ls", "-l", "NULL"}

• Shell checks if an expansion is required (in case of ls *.c)

• Once the program in memory, its execution starts. First by calling readdir()

Notes:

• getline() originates in GNU C library and used to read lines from input stream and stores those lines in the buffer

This way provides a lot of flexibility. It allows the shell for example, to run code after the call to fork() but before the call to exec(). Such code can be used to alter the environment of the program it about to run.

The shell figures out, using the PATH variable, where the executable of the command resides in the filesystem. It then calls fork() to create a new child process for running the command. Once the fork was executed successfully, it calls a variant of exec() to execute the command and finally, waits the command to finish using wait(). When the child completes, the shell returns from wait() and prints out the prompt again.

There are a couple of ways to do that:

• dd if=/dev/urandom of=new_file.txt bs=2MB count=1
• truncate -s 2M new_file.txt
• fallocate -l 2097152 new_file.txt

These system calls are reading the file /my/file and 5 is the file descriptor number.

From wikipedia: a context switch is the process of storing the state of a process or thread, so that it can be restored and resume execution at a later point

Another common way to task this questions is "what part of the tcp header does traceroute modify?"

This is a good article about the topic: https://ops.tips/blog/how-linux-creates-sockets

MemFree - The amount of unused physical RAM in your system MemAvailable - The amount of available memory for new workloads (without pushing system to use swap) based on MemFree, Active(file), Inactive(file), and SReclaimable.

• Kernel
• Utilities
• Services
• Software/Packages Management

echo $line | sed 's/.*\[//g;s/].*//g;s/:.*//g'

One way is to run the following: cat /proc/sys/kernel/random/uuid

• A collection of packages - kernel, GNU, third party apps, ...
• Sometimes distributions store some information on the distribution in /etc/*-release file
  • For example for Red Hat distribution it will be /etc/redhat-release and for Amazon it will be /etc/os-release
  • lsb_release is a common command you can use in multiple different distributions

ls, wc, dd, df, du, ps, ip, cp, cd ...

• touch new_file
• echo "" > new_file

$OLDPWD

• ls
• find .
• echo *

A good answer can be found here

X=2 for example. But this will persist to new shells. To have it in new shells as well, use export X=2

It's used in commands to mark the end of commands options. One common example is when used with git to discard local changes: git checkout -- some_file

/dev

GPL v2

From the book "Operating Systems: Three Easy Pieces":

"responsible for making it easy to run programs (even allowing you to seemingly run many at the same time), allowing programs to share memory, enabling programs to interact with devices, and other fun stuff like that".

A process is a running program. A program is one or more instructions and the program (or process) is executed by the operating system.

It would support the following:

• Create - allow to create new processes
• Delete - allow to remove/destroy processes
• State - allow to check the state of the process, whether it's running, stopped, waiting, etc.
• Stop - allow to stop a running process

• The OS is reading program's code and any additional relevant data
• Program's code is loaded into the memory or more specifically, into the address space of the process.
• Memory is allocated for program's stack (aka run-time stack). The stack also initialized by the OS with data like argv, argc and parameters to main()
• Memory is allocated for program's heap which is required for dynamically allocated data like the data structures linked lists and hash tables
• I/O initialization tasks are performed, like in Unix/Linux based systems where each process has 3 file descriptors (input, output and error)
• OS is running the program, starting from main()

False. It was true in the past but today's operating systems perform lazy loading which means only the relevant pieces required for the process to run are loaded first.

• Running - it's executing instructions
• Ready - it's ready to run but for different reasons it's on hold
• Blocked - it's waiting for some operation to complete. For example I/O disk request

• I/O operations (e.g. Reading from a disk)
• Waiting for a packet from a network

Even when using a system with one physical CPU, it's possible to allow multiple users to work on it and run programs. This is possible with time sharing where computing resources are shared in a way it seems to the user the system has multiple CPUs but in fact it's simply one CPU shared by applying multiprogramming and multi-tasking.

Somewhat the opposite of time sharing. While in time sharing a resource is used for a while by one entity and then the same resource can be used by another resource, in space sharing the space is shared by multiple entities but in a way where it's not being transferred between them.
It's used by one entity until this entity decides to get rid of it. Take for example storage. In storage, a file is yours until you decide to delete it.

CPU scheduler

The kernel is part of the operating system and is responsible for tasks like:

• Allocating memory
• Schedule processes
• Control CPU

True

Buffer: Reserved place in RAM which is used to hold data for temporary purposes Cache: Cache is usually used when processes reading and writing to the disk to make the process faster by making similar data used by different programs easily accessible.

Virtualization uses software to create an abstraction layer over computer hardware that allows the hardware elements of a single computer—processors, memory, storage and more - to be divided into multiple virtual computers, commonly called virtual machines (VMs).

Red Hat: "A hypervisor is software that creates and runs virtual machines (VMs). A hypervisor, sometimes called a virtual machine monitor (VMM), isolates the hypervisor operating system and resources from the virtual machines and enables the creation and management of those VMs."

Read more here

Hosted hypervisors and bare-metal hypervisors.

Due to having its own drivers and a direct access to hardware components, a baremetal hypervisor will often have better performances along with stability and scalability.

On the other hand, there will probably be some limitation regarding loading (any) drivers so a hosted hypervisor will usually benefit from having a better hardware compatibility.

Operating system virtualization Network functions virtualization Desktop virtualization

Yes, it's a operating-system-level virtualization, where the kernel is shared and allows to use multiple isolated user-spaces instances.

The introduction of virtual machines allowed companies to deploy multiple business applications on the same hardware while each application is separated from each other in secured way, where each is running on its own separate operating system.

1. It is a high level general purpose programming language created in 1991 by Guido Van Rosum.
2. The language is interpreted, being the CPython (Written in C) the most used/maintained implementation.
3. It is strongly typed. The typing discipline is duck typing and gradual.
4. Python focuses on readability and makes use of whitespaces/identation instead of brackets { }
5. The python package manager is called PIP "pip installs packages", having more than 200.000 available packages.
6. Python comes with pip installed and a big standard library that offers the programmer many precooked solutions.
7. In python **Everything** is an object.

List
Dictionary
Set
Numbers (int, float, ...)
String
Bool
Tuple
Frozenset

Mutability determines whether you can modify an object of specific type.

The mutable data types are:

List
Dictionary
Set

The immutable data types are:

Numbers (int, float, ...)
String
Bool
Tuple
Frozenset

• False
• True
• False
• TypeError

bool("") -> evaluates to False
bool(" ") -> evaluates to True

It evaluates to True.
The reason is that the two created empty list are different objects. x is y only evaluates to true when x and y are the same object.

0

True

Regex:

import re
if re.match("^[a-zA-Z]+.*", string):

string built-in:

if string and string[0].isalpha():

string.isdigit

string.rstrip('/')

• abcabcabc
• TypeError
• TypeError
• "abc"
• ""

char = input("Insert a character: ") # For readablity
if lower(char[0]) in "aieou": # Takes care of multiple characters and separate cases
    print("It's a vowel!")

OR

if lower(input("Insert a character: ")[0]) in "aieou": # Takes care of multiple characters and small/Capital cases
    print("It's a vowel!")

def sum(a, b):
    return (a + b)

In general, first class objects in programming languages are objects which can be assigned to variable, used as a return value and can be used as arguments or parameters.
In python you can treat functions this way. Let's say we have the following function

def my_function():
    return 5

You can then assign a function to a variables like this x = my_function or you can return functions as return values like this return my_function

• Code:

from typing import Union

def isNumberPalindrome(number: Union[int, str]) -> bool:
    if isinstance(number, int):
        number = str(number)
    return number == number[::-1]

print(isNumberPalindrome("12321"))

• Using Python3.10 that accepts using bitwise operator '|'.

def isNumberPalindrome(number: int | str) -> bool:
    if isinstance(number, int):
        number = str(number)
    return number == number[::-1]

print(isNumberPalindrome("12321"))

Note: Using slicing to reverse a list could be slower than other options like reversed that return an iterator.

• Result:

True

a
b
c

By definition inheritance is the mechanism where an object acts as a base of another object, retaining all its
properties.

So if Class B inherits from Class A, every characteristics from class A will be also available in class B.
Class A would be the 'Base class' and B class would be the 'derived class'.

This comes handy when you have several classes that share the same functionalities.

The basic syntax is:

class Base: pass

class Derived(Base): pass

A more forged example:

class Animal:
    def __init__(self):
        print("and I'm alive!")

    def eat(self, food):
        print("ñom ñom ñom", food)

class Human(Animal):
    def __init__(self, name):
        print('My name is ', name)
        super().__init__()

    def write_poem(self):
        print('Foo bar bar foo foo bar!')

class Dog(Animal):
    def __init__(self, name):
        print('My name is', name)
        super().__init__()

    def bark(self):
        print('woof woof')


michael = Human('Michael')
michael.eat('Spam')
michael.write_poem()

bruno = Dog('Bruno')
bruno.eat('bone')
bruno.bark()

>>> My name is  Michael
>>> and I'm alive!
>>> ñom ñom ñom Spam
>>> Foo bar bar foo foo bar!
>>> My name is Bruno
>>> and I'm alive!
>>> ñom ñom ñom bone
>>> woof woof

Calling super() calls the Base method, thus, calling super().__init__() we called the Animal __init__.

There is a more advanced python feature called MetaClasses that aid the programmer to directly control class creation.

In the following block of code x is a class attribute while self.y is a instance attribute

class MyClass(object):
    x = 1

    def __init__(self, y):
        self.y = y

#  Note that you generally don't need to know the compiling process but knowing where everything comes from
#  and giving complete answers shows that you truly know what you are talking about.

Generally, every compiling process have a two steps.
    - Analysis
    - Code Generation.

    Analysis can be broken into:
        1. Lexical analysis   (Tokenizes source code)
        2. Syntactic analysis (Check whether the tokens are legal or not, tldr, if syntax is correct)

               for i in 'foo'
                          ^
             SyntaxError: invalid syntax

        We missed ':'


        3. Semantic analysis  (Contextual analysis, legal syntax can still trigger errors, did you try to divide by 0,
          hash a mutable object or use an undeclared function?)

                 1/0
                ZeroDivisionError: division by zero

    These three analysis steps are the responsible for error handlings.

    The second step would be responsible for errors, mostly syntax errors, the most common error.
    The third step would be responsible for Exceptions.

    As we have seen, Exceptions are semantic errors, there are many builtin Exceptions:

        ImportError
        ValueError
        KeyError
        FileNotFoundError
        IndentationError
        IndexError
        ...

    You can also have user defined Exceptions that have to inherit from the `Exception` class, directly or indirectly.

    Basic example:

    class DividedBy2Error(Exception):
        def __init__(self, message):
            self.message = message


    def division(dividend,divisor):
        if divisor == 2:
            raise DividedBy2Error('I dont want you to divide by 2!')
        return dividend / divisor

    division(100, 2)

    >>> __main__.DividedBy2Error: I dont want you to divide by 2!

Exceptions: Errors detected during execution are called Exceptions.

Handling Exception: When an error occurs, or exception as we call it, Python will normally stop and generate an error message.
Exceptions can be handled using try and except statement in python.

Example: Following example asks the user for input until a valid integer has been entered.
If user enter a non-integer value it will raise exception and using except it will catch that exception and ask the user to enter valid integer again.

while True:
    try:
        a = int(input("please enter an integer value: "))
        break
    except ValueError:
        print("Ops! Please enter a valid integer value.")

For more details about errors and exceptions follow this https://docs.python.org/3/tutorial/errors.html

It is used to emulate callable objects. It allows a class instance to be called as a function.

• Example code:

class Foo:
    def __init__(self: object) ->  None:
        pass
    def __call__(self: object) -> None:
        print("Called!")

f = Foo()
f()

• Result:

Called!

1. Translation lookup in i18n
2. Hold the result of the last executed expression or statement in the interactive interpreter.
3. As a general purpose "throwaway" variable name. For example: x, y, _ = get_data() (x and y are used but since we don't care about third variable, we "threw it away").

A lambda expression is an 'anonymous' function, the difference from a normal defined function using the keyword `def`` is the syntax and usage.

The syntax is:

lambda[parameters]: [expresion]

Examples:

• A lambda function add 10 with any argument passed.

x = lambda a: a + 10
print(x(10))

• An addition function

addition = lambda x, y: x + y
print(addition(10, 20))

• Squaring function

square = lambda x : x ** 2
print(square(5))

Generally it is considered a bad practice under PEP 8 to assign a lambda expresion, they are meant to be used as parameters and inside of other defined functions.

x, y = y, x

First you ask the user for the amount of numbers that will be use. Use a while loop that runs until amount_of_numbers becomes 0 through subtracting amount_of_numbers by one each loop. In the while loop you want ask the user for a number which will be added a variable each time the loop runs.

def return_sum():
    amount_of_numbers = int(input("How many numbers? "))
    total_sum = 0
    while amount_of_numbers != 0:
        num = int(input("Input a number. "))
        total_sum += num
        amount_of_numbers -= 1
    return total_sum

li = [2, 5, 6]
print("{0:.3f}".format(sum(li)/len(li)))

A tuple is a built-in data type in Python. It's used for storing multiple items in a single variable.

List, as opposed to a tuple, is a mutable data type. It means we can modify it and at items to it.

x.append(2)

len(sone_list)

some_list[-1]

Don't use append unless you would like the list as one item.

my_list[0:3] = []

Maximum: max(some_list)
Minimum: min(some_list)

sorted(some_list, reverse=True)[:3]

Or

some_list.sort(reverse=True)
some_list[:3]

• One item:

numbers = [1, 2, 3, 4, 5]
numbers.insert(0, 0)
print(numbers)

• Multiple items or list:

numbers_1 = [2, 3, 4, 5]
numbers_2 = [0, 1]
numbers_1 = numbers_2 + numbers_1
print(numbers_1)

sorted_li = sorted(li, key=len)

Or without creating a new list:

li.sort(key=len)

• sorted(list) will return a new list (original list doesn't change)

• list.sort() will return None but the list is change in-place

• sorted() works on any iterable (Dictionaries, Strings, ...)

• list.sort() is faster than sorted(list) in case of Lists

nested_li = [['1', '2', '3'], ['4', '5', '6']]
[[int(x) for x in li] for li in nested_li]

sorted(li1 + li2)

Another way:

i, j = 0
merged_li = []

while i < len(li1) and j < len(li2):
    if li1[i] < li2[j]:
        merged_li.append(li1[i])
        i += 1
    else:
        merged_li.append(li2[j])
        j += 1

merged_li = merged_li + merged_li[i:] + merged_li[j:]

['hmm']
['hmm', 'hmm']
['hmm', 'hmm', 'hmm']

for item in some_list:
    print(item)

for i, item in enumerate(some_list):
    print(i)

Using range like this

for i in range(1, len(some_list)):
    some_list[i]

Another way is using slicing

for i in some_list[1:]:

Method 1

for i in reversed(li):
    ...

Method 2

n = len(li) - 1
while n > 0:
    ...
    n -= 1

li = [[1, 4], [2, 1], [3, 9], [4, 2], [4, 5]]

sorted(li, key=lambda l: l[1])

or

li.sort(key=lambda l: l[1)

nums = [1, 2, 3]
letters = ['x', 'y', 'z']

list(zip(nums, letters))

From Docs: "List comprehensions provide a concise way to create lists. Common applications are to make new lists where each element is the result of some operations applied to each member of another sequence or iterable, or to create a subsequence of those elements that satisfy a certain condition.".

It's better because they're compact, faster and have better readability.

• For loop:

number_lists = [[1, 7, 3, 1], [13, 93, 23, 12], [123, 423, 456, 653, 124]]
odd_numbers = []
for number_list in number_lists:
    for number in number_list:
        if number % 2 == 0:
            odd_numbers.append(number)
print(odd_numbers)

• List comprehesion:

number_lists = [[1, 7, 3, 1], [13, 93, 23, 12], [123, 423, 456, 653, 124]]
odd_numbers = [number for number_list in number_lists for number in number_list if number % 2 == 0]
print(odd_numbers)

brothers_menu =  \
[{'name': 'Mario', 'food': ['mushrooms', 'goombas']}, {'name': 'Luigi', 'food': ['mushrooms', 'turtles']}]

# "Classic" Way
def get_food(brothers_menu) -> set:
    temp = []

    for brother in brothers_menu:
        for food in brother['food']:
            temp.append(food)

    return set(temp)

# One liner way (Using list comprehension)
set([food for bro in x for food in bro['food']])

my_dict = dict(x=1, y=2) OR my_dict = {'x': 1, 'y': 2} OR my_dict = dict([('x', 1), ('y', 2)])

del my_dict['some_key'] you can also use my_dict.pop('some_key') which returns the value of the key.

{k: v for k, v in sorted(x.items(), key=lambda item: item[1])}

dict(sorted(some_dictionary.items()))

some_dict1.update(some_dict2)

output = {}
string = "a.b.c"
path = string.split('.')
target = reduce(lambda d, k: d.setdefault(k, {}), path[:-1], output)
target[path[-1]] = 1
print(output)

Solution

with open('file.txt', 'w') as file:
    file.write("My insightful comment")

import json

with open('file.json', 'w') as f:
    f.write(json.dumps(dict_var))

import os

print(os.getcwd())

import os

print(os.path.basename('/dir1/dir2/file1'))

# Another way
print(os.path.split('/dir1/dir2/file1')[1])

import os

Part 1.

os.path.dirname gives path removing the end component

dirpath = os.path.dirname('/dir1/dir2/file1') print(dirpath)

Part 2.

print(os.path.basename(dirpath))

Using the re module

While you iterate through the characters, store them in a dictionary and check for every character whether it's already in the dictionary.

def firstRepeatedCharacter(str):
    chars = {}
    for ch in str:
        if ch in chars:
            return ch
        else:
            chars[ch] = 0

x = "itssssssameeeemarioooooo"
y = ''.join(set(x))

def permute_string(string):

    if len(string) == 1:
        return [string]

    permutations = []
    for i in range(len(string)):
        swaps = permute_string(string[:i] + string[(i+1):])
        for swap in swaps:
            permutations.append(string[i] + swap)

    return permutations

print(permute_string("abc"))

Short way (but probably not acceptable in interviews):

from itertools import permutations

[''.join(p) for p in permutations("abc")]

Detailed answer can be found here: http://codingshell.com/python-all-string-permutations

>>> 'One, Two, Three'
>>> 'well done'
>>> 'cool'

It will reverse the string, so x would be equal to azzip.

mario

No output :)

One way is:

the_list.sort(key=lambda x: x[1])

pdb :D

[i for i in range(1, 10)]

The reason we need to implement in the first place, it's because a linked list isn't part of Python standard library.
To implement a linked list, we have to implement two structures: The linked list itself and a node which is used by the linked list.

Let's start with a node. A node has some value (the data it holds) and a pointer to the next node

class Node(object):
    def __init__(self, data):
        self.data = data
        self.next = None

Now the linked list. An empty linked list has nothing but an empty head.

class LinkedList(object):
    def __init__(self):
        self.head = None

Now we can start using the linked list

ll = Linkedlist()
ll.head = Node(1)
ll.head.next = Node(2)
ll.head.next.next = Node(3)

What we have is:

| 1 | -> | 2 | -> | 3 |

Usually, more methods are implemented, like a push_head() method where you insert a node at the beginning of the linked list

def push_head(self, value):
    new_node = Node(value)
    new_node.next = self.head
    self.head = new_node

def print_list(self): node = self.head while(node): print(node.data) node = node.next

Let's use the Floyd's Cycle-Finding algorithm:

def loop_exists(self):
    one_step_p = self.head
    two_steps_p = self.head
    while(one_step_p and two_steps_p and two_steps_p.next):
        one_step_p = self.head.next
        two_step_p = self.head.next.next
        if (one_step_p == two_steps_p):
            return True 
    return False

PEP8 is a list of coding conventions and style guidelines for Python

5 style guidelines:

1. Limit all lines to a maximum of 79 characters.
2. Surround top-level function and class definitions with two blank lines.
3. Use commas when making a tuple of one element
4. Use spaces (and not tabs) for indentation
5. Use 4 spaces per indentation level

There are multiple ways to map a URL with a function in Python.

• Decorator: you can use python decorators. In this case we're using app. This app decorator is the instance of the Flask class. And route it's a method of this class.

@app.route('/')
def home():
  return 'main website'

• add_url_rule method: This is a method of the Flask class. We can also use it for map the URL with a function.

def home():
  return 'main website'

app.add_url_rule('/', view_func=home)

[(1,), (2,), (3,)]

[(0, 0, 0), (1, 1, 1), (2, 2, 2), (3, 3, 3), (4, 4, 4)]
[]

Read about descriptors here

def add(num1, num2):
    return num1 + num2


def sub(num1, num2):
    return num1 - num2


def mul(num1, num2):
    return num1*num2


def div(num1, num2):
    return num1 / num2

operators = {
    '+': add,
    '-': sub,
    '*': mul,
    '/': div
}

if __name__ == '__main__':
    operator = str(input("Operator: "))
    num1 = int(input("1st number: "))
    num2 = int(input("2nd number: "))
    print(operators[operator](num1, num2))

This is a good reference https://docs.python.org/3/library/datatypes.html

def wee(word):
    return word

def oh(f):
    return f + "Ohh"

>>> oh(wee("Wee"))
<<< Wee Ohh

This allows us to control the before execution of any given function and if we added another function as wrapper, (a function receiving another function that receives a function as parameter) we could also control the after execution.

Sometimes we want to control the before-after execution of many functions and it would get tedious to write

f = function(function_1()) f = function(function_1(function_2(*args)))

every time, that's what decorators do, they introduce syntax to write all of this on the go, using the keyword '@'.

Simple decorator:

def deco(f):
    print(f"Hi I am the {f.__name__}() function!")
    return f

@deco
def hello_world():
    return "Hi, I'm in!"

a = hello_world()
print(a)

>>> Hi I am the hello_world() function!
    Hi, I'm in!

This is the simplest decorator version, it basically saves us from writting a = deco(hello_world()). But at this point we can only control the before execution, let's take on the after:

def deco(f):
    def wrapper(*args, **kwargs):
        print("Rick Sanchez!")
        func = f(*args, **kwargs)
        print("I'm in!")
        return func
    return wrapper

@deco
def f(word):
    print(word)

a = f("************")
>>> Rick Sanchez!
    ************
    I'm in!

deco receives a function -> f wrapper receives the arguments -> *args, **kwargs

wrapper returns the function plus the arguments -> f(*args, **kwargs) deco returns wrapper.

As you can see we conveniently do things before and after the execution of a given function.

For example, we could write a decorator that calculates the execution time of a function.

import time
def deco(f):
    def wrapper(*args, **kwargs):
        before = time.time()
        func = f(*args, **kwargs)
        after = time.time()
        print(after-before)
        return func
    return wrapper

@deco
def f():
    time.sleep(2)
    print("************")

a = f()
>>> 2.0008859634399414

Or create a decorator that executes a function n times.

def n_times(n):
    def wrapper(f):
        def inner(*args, **kwargs):
            for _ in range(n):
                func = f(*args, **kwargs)
            return func
        return inner
    return wrapper

@n_times(4)
def f():
    print("************")

a = f()

>>>************
   ************
   ************
   ************

class Car:
    def __init__(self, model, color):
        self.model = model
        self.color = color

    def __eq__(self, other):
        if not isinstance(other, Car):
            return NotImplemented
        return self.model == other.model and self.color == other.color

>> a = Car('model_1', 'red')
>> b = Car('model_2', 'green')
>> c = Car('model_1', 'red')
>> a == b
False
>> a == c
True

Google: "Monitoring is one of the primary means by which service owners keep track of a system’s health and availability".

This approach require from a human to always check why the value exceeded and how to handle it while today, it is more effective to notify people only when they need to take an actual action. If the issue doesn't require any human intervention, then the problem can be fixed by some processes running in the relevant environment.

Alerts
Tickets
Logging

From Prometheus documentation: "if you need 100% accuracy, such as for per-request billing".

Prometheus server is responsible for scraping and storing the data
Push gateway is used for short-lived jobs
Alert manager is responsible for alerts ;)

• Strong and static typing - the type of the variables can't be changed over time and they have to be defined at compile time
• Simplicity
• Fast compile times
• Built-in concurrency
• Garbage collected
• Platform independent
• Compile to standalone binary - anything you need to run your app will be compiled into one binary. Very useful for version management in run-time.

Go also has good community.

The result is the same, a variable with the value 2.

With var x int = 2 we are setting the variable type to integer while with x := 2 we are letting Go figure out by itself the type.

This should be answered based on your usage but some examples are:

• fmt - formatted I/O

It looks what unicode value is set at 101 and uses it for converting the integer to a string. If you want to get "101" you should use the package "strconv" and replace y = string(x) with y = strconv.Itoa(x)

Constants in Go can only be declared using constant expressions. But x, y and their sum is variable.
const initializer x + y is not a constant

Go's iota identifier is used in const declarations to simplify definitions of incrementing numbers. Because it can be used in expressions, it provides a generality beyond that of simple enumerations.
x and y in the first iota group, z in the second.
Iota page in Go Wiki

It avoids having to declare all the variables for the returns values. It is called the blank identifier.
answer in SO

Since the first iota is declared with the value 3 ( + 3), the next one has the value 4

Output: 2 1 3

Aritcle about sync/waitgroup

Golang package sync

Output:
1 [5 5 5 5]
1 [5 5 5 5]
2 [5 5 5 5 5]
2 [1 2 3 4]


In mod1 a is link, and when we're using a[i], we're changing s1 value to. But in mod2, append creats new slice, and we're changing only a value, not s2.

Aritcle about arrays, Blog post about append

Output: 3

Golang container/heap package

MongoDB advantages are as followings:

• Schemaless
• Easy to scale-out
• No complex joins
• Structure of a single object is clear

The main difference is that SQL databases are structured (data is stored in the form of tables with rows and columns - like an excel spreadsheet table) while NoSQL is unstructured, and the data storage can vary depending on how the NoSQL DB is set up, such as key-value pair, document-oriented, etc.

• Heterogeneous data which changes often
• Data consistency and integrity is not top priority
• Best if the database needs to scale rapidly

• mongoexport
• programming languages

SQL (Structured Query Language) is a standard language for relational databases (like MySQL, MariaDB, ...).
It's used for reading, updating, removing and creating data in a relational database.

The main difference is that SQL databases are structured (data is stored in the form of tables with rows and columns - like an excel spreadsheet table) while NoSQL is unstructured, and the data storage can vary depending on how the NoSQL DB is set up, such as key-value pair, document-oriented, etc.

SQL - Best used when data integrity is crucial. SQL is typically implemented with many businesses and areas within the finance field due to it's ACID compliance.

NoSQL - Great if you need to scale things quickly. NoSQL was designed with web applications in mind, so it works great if you need to quickly spread the same information around to multiple servers

Additionally, since NoSQL does not adhere to the strict table with columns and rows structure that Relational Databases require, you can store different data types together.

Select *
From Customers;

Select Items_in_cart
From Customers
Where Customer_Name = "John Smith";

Select SUM(Cash_spent_to_Date) as SUM_CASH
From Customers;

Select count(1) as Number_of_People_w_items
From Customers
where Items_in_cart > 0;

You would join them on the unique key. In this case, the unique key is Customer_ID in both the Customers table and Orders table

Select c.Customer_Name, o.Item
From Customers c
Left Join Orders o
On c.Customer_ID = o.Customer_ID;

with cat_food as (
Select Customer_ID, SUM(Price) as TOTAL_PRICE
From Orders
Where Item like "%Cat Food%"
Group by Customer_ID
)
Select Customer_name, TOTAL_PRICE
From Customers c
Inner JOIN cat_food f
ON c.Customer_ID = f.Customer_ID
where c.Customer_ID in (Select Customer_ID from cat_food);

Although this was a simple statement, the "with" clause really shines when a complex query needs to be run on a table before joining to another. With statements are nice, because you create a pseudo temp when running your query, instead of creating a whole new table.

The Sum of all the purchases of cat food weren't readily available, so we used a with statement to create the pseudo table to retrieve the sum of the prices spent by each customer, then join the table normally.

SELECT count(*)
FROM shawarma_purchases
WHERE
  purchased_at >= '2017-01-01' AND
  purchased_at <= '2017-31-12'

When you use a function (YEAR(purchased_at)) it has to scan the whole database as opposed to using indexes and basically the column as it is, in its natural state.

Microsoft Docs: "The Azure portal is a web-based, unified console that provides an alternative to command-line tools. With the Azure portal, you can manage your Azure subscription by using a graphical user interface."

Microsoft Docs: "Azure marketplace helps connect users with Microsoft partners, independent software vendors, and startups that are offering their solutions and services, which are optimized to run on Azure."

An availability set is a logical grouping of VMs that allows Azure to understand how your application is built to provide redundancy and availability. It is recommended that two or more VMs are created within an availability set to provide for a highly available application and to meet the 99.95% Azure SLA.

• Azure Virtual Machines
• Azure Batch
• Azure Service Fabric
• Azure Container Instances
• Azure Virtual Machine Scale Set?s

Windows or Linux virtual machines

Scaling Linux or Windows virtual machines used in Azure

Azure Functions is the serverless compute service of Azure.

Running containerized applications (without the need to provision virtual machines).

Running parallel and high-performance computing applications

It's a monitoring service that provides threat protection across all of the services in Azure. More specifically, it:

• Provides security recommendations based on your usage
• Monitors security settings and continuously all the services
• Analyzes and identifies potential inbound attacks
• Detects and blocks malware using machine learning

Azure AD is a cloud-based identity service. You can use it as a standalone service or integrate it with existing Active Directory service you already running.

startap-script

• It is the managed kubernetes service on GCP for deploying, managing and scaling containerised applications using Google infrastructure.

• Managed -> the customer does not need to worry about the underlying software intergrations, they just enable the API.
• application platform -> It consists of open source tools like K8s, Knative, Istio and Tekton
• Enterprises -> these are usually organisations with complex needs
• Consistency -> to have the same policies declaratively initiated to be run anywhere securely e.g on-prem, GCP or other-clouds (AWS or Azure)

fun fact: Anthos is flower in greek, they grow in the ground (earth) but need rain from the clouds to flourish.

• Infrastructure management - Google Kubernetes Engine (GKE)
• Cluster management - GKE, Ingress for Anthos
• Service management - Anthos Service Mesh
• Policy enforcement - Anthos Config Management, Anthos Enterprise Data Protection, Policy Controller
• Application deployment - CI/CD tools like Cloud Build, GitLab
• Application development - Cloud Code

• Google Kubernetes Engine (GKE)

On GCP the kubernetes api-server is the only control plane component exposed to customers whilst compute engine manages instances in the project.

• Networking load balancing for L4 and HTTP(S) Load Balancing for L7 which are both managed services that do not require additional configuration.
• Ingress for Anthos which allows the ability to deploy a load balancer that serves an application across multiple clusters on GKE

• Yes, Anthos on AWS is now GA. For more read here

• Control plane security - GCP manages and maintains the K8s control plane out of the box. The user can secure the api-server by using master authorized networks and private clusters. These allow the user to disable access on the public IP address by assigning a private IP address to the master.
• Node security - By default workloads are provisioned on Compute engine instances that use Google's Container Optimised OS. This operating system implements a locked-down firewall, limited user accounts with root disabled and a read-only filesystem. There is a further option to enable GKE Sandbox for stronger isolation in multi-tenant deployment scenarios.
• Network security - Within a created cluster VPC, Anthos GKE leverages a powerful software-defined network that enables simple Pod-to-Pod communications. Network policies allow locking down ingress and egress connections in a given namespace. Filtering can also be implemented to incoming load-balanced traffic for services that require external access, by supplying whitelisted CIDR IP ranges.
• Workload security - Running workloads run with limited privileges, default Docker AppArmor security policies are applied to all Kubernetes Pods. Workload identity for Anthos GKE aligns with the open source kubernetes service accounts with GCP service account permissions.
• Audit logging - Adminstrators are given a way to retain, query, process and alert on events of the deployed environments.

• Google Cloud Virtual Private Network (Cloud VPN) - this is for secure networking
• Google Cloud Key Management Service (Cloud KMS) - for key management

• This is when pods can directly talk to each other within a cluster, but cannot be reached from outside the cluster thus forming an "island" within the network that is not connected to the external network.

It is a core component of the Anthos stack which provides platform, service and security operators with a single, unified approach to multi-cluster management that spans both on-premises and cloud environments. It closely follows K8s best practices, favoring declarative approaches over imperative operations, and actively monitors cluster state and applies the desired state as defined in Git. It includes three key components as follows:

1. An importer that reads from a central Git repository
2. A component that synchronises stored configuration data into K8s objects
3. A component that monitors drift between desired and actual cluster configurations with a capability of reconciliation when need rises.

It follows common modern software development practices which makes cluster configuration, management and policy changes auditable, revertable, and versionable easily enforcing IT governance and unifying resource management in an organisation.

• It is a suite of tools that assist in monitoring and managing deployed services on Anthos of all shapes and sizes whether running in cloud, hybrid or multi-cloud environments. It leverages the APIs and core components from Istio, a highly configurable and open-source service mesh platform.

1. Data plane - it consists of a set of distributed proxies that mediate all inbound and outbound network traffic between individual services which are configured using a centralised control plane and an open API
2. Control plane - is a fully managed offering outside of Anthos GKE clusters to simplify management overhead and ensure highest possible availability.

1. Traffic Director - it is GCP's fully managed service mesh traffic control plane, responsible for translating Istio API objects into configuration information for the distributed proxies, as well as directing service mesh ingress and egress traffic
2. Managed CA - is a centralised certificate authority responsible for providing SSL certificates to each of the distributed proxies, authentication information and distributing secrets
3. Operations tooling - formerly stackdriver, provides a managed ingestion point for observability and telemetry, specifically monitoring, tracing and logging data generated by each of the proxies. This powers the observability dashboard for operators to visually inspect their services and service dependencies assisting in the implementation of SRE best practices for monitoring SLIs and establishing SLOs.

• Uniform observability - the data plane reports service to service communication back to the control plane generating a service dependency graph. Traffic inspection by the proxy inserts headers to facilitate distributed tracing, capturing and reporting service logs together with service-level metrics (i.e latency, errors, availability).
• Operational agility - fine-grained controls for managing the flow of inter-mesh (north-south) and intra-mesh (east-west) traffic are provided.
• Policy-driven security - policies can be enforced consistently across diverse protocols and runtimes as service communications are secured by default.

• Traffic splitting across differing service versions for canary or A/B testing
• Circuit breaking to prevent cascading failures
• Fault injection to help build resilient and fault-tolerant deployments
• HTTP header-based traffic steering between individual services or versions

It is part of the Anthos stack that brings a serverless container experience to Anthos, offering a high-level platform experience on top of K8s clusters. It is built with Knative, an open-source operator for K8s that brings serverless application serving and eventing capabilities.

Platform teams in organisations that wish to offer developers additional tools to test, deploy and run applications can use Knative to enhance this experience on Anthos as Cloud Run. Below are some of the benefits;

• Easy migration from K8s deployments - Without Cloud Run, platform engineers have to configure deployment, service, and HorizontalPodAutoscalers(HPA) objects to a loadbalancer and autoscaling. If application is already serving traffic it becomes hard to change configurations or roll back efficiently. Using Cloud Run all this is managed thus the Knative service manifest describes the application to be autoscaled and loadbalanced
• Autoscaling - a sudden traffic spike may cause application containers in K8s to crash due to overload thus an efficient automated autoscaling is executed to serve the high volume of traffic
• Networking - it has built-in load balancing capabilities and policies for traffic splitting between multiple versions of an application.
• Releases and rollouts - supports the notion of the Knatibe API's revisions which describe new versions or different configurations of your application and canary deployments by splitting traffic.
• Monitoring - observing and recording metrics such as latency, error rate and requests per second.

• Rapid, request-based autoscaling - default autoscalers monitor request metrics which allows Cloud Run for Anthos to handle spiky traffic patterns smoothly
• Concurrency controls - limits such as max in-flight requests per container are enforced to ensure the container does not become overloaded and crash. More containers are added to handle the spiky traffic, buffering the requests.
• Scale to zero - if an application is inactive for a while Cloud Run scales it down to zero to reduce its footprint. Alternatively one can turn off scale-to-zero to prevent cold starts.

As it does not support stateful applications or sticky sessions, it is suitable for running stateless applications such as:

• Machine learning model predictions e.g Tensorflow serving containers
• API gateways, API middleware, web front ends and Microservices
• Event handlers, ETL

• Nova - Manage virtual instances
• Cinder - Block Storage
• Keystone - Authentication service across the cloud

You can read about TripleO right here

• Used to provision and manage virtual instances
• It supports Multi-Tenancy in different levels - logging, end-user control, auditing, etc.
• Highly scalable
• Authentication can be done using internal system or LDAP
• Supports multiple types of block storage
• Tries to be hardware and hypervisor agnostice

• nova-api - the server which serves metadata and compute APIs
• the different Nova components communicate by using a queue (Rabbitmq usually) and a database
• a request for creating an instance is inspected by nova-scheduler which determines where the instance will be created and running
• nova-compute is the component responsible for communicating with the hypervisor for creating the instance and manage its lifecycle

• One of the core component of OpenStack and a standalone project
• Neutron focused on delivering networking as a service
• With Neutron, users can set up networks in the cloud and configure and manage a variety of network services
• Neutron interacts with:
  • Keystone - authorize API calls
  • Nova - nova communicates with neutron to plug NICs into a network
  • Horizon - supports networking entities in the dashboard and also provides topology view which includes networking details

• neutron-l3-agent - L3/NAT forwarding (provides external network access for VMs for example)
• neutron-dhcp-agent - DHCP services
• neutron-metering-agent - L3 traffic metering
• neutron-*-agtent - manages local vSwitch configuration on each compute (based on chosen plugin)
• neutron-server - exposes networking API and passes requests to other plugins if required

• Management Network - used for internal communication between OpenStack components. Any IP address in this network is accessible only within the datacetner
• Guest Network - used for communication between instances/VMs
• API Network - used for services API communication. Any IP address in this network is publicly accessible
• External Network - used for public communication. Any IP address in this network is accessible by anyone on the internet

• Port
• Subnet
• Router
• Network

There are many reasons for that. One for example: you can't remove router if there are active ports assigned to it.

• Glance is the OpenStack image service
• It handles requests related to instances disks and images
• Glance also used for creating snapshots for quick instances backups
• Users can use Glance to create new images or upload existing ones

• glance-api - responsible for handling image API calls such as retrieval and storage. It consists of two APIs: 1. registry-api - responsible for internal requests 2. user API - can be accessed publicly
• glance-registry - responsible for handling image metadata requests (e.g. size, type, etc). This component is private which means it's not available publicly
• metadata definition service - API for custom metadata
• database - for storing images metadata
• image repository - for storing images. This can be a filesystem, swift object storage, HTTP, etc.

• Swift is Object Store service and is an highly available, distributed and consistent store designed for storing a lot of data
• Swift is distributing data across multiple servers while writing it to multiple disks
• One can choose to add additional servers to scale the cluster. All while swift maintaining integrity of the information and data replications.

Not by default. Object Storage API limits the maximum to 5GB per object but it can be adjusted.

• Container - Defines a namespace for objects.
• Account - Defines a namespace for containers
• Object - Data content (e.g. image, document, ...)

False. Two objects can have the same name if they are in different containers.

• Cinder is OpenStack Block Storage service
• It basically provides used with storage resources they can consume with other services such as Nova
• One of the most used implementations of storage supported by Cinder is LVM
• From user perspective this is transparent which means the user doesn't know where, behind the scenes, the storage is located or what type of storage is used

• cinder-api - receives API requests
• cinder-volume - manages attached block devices
• cinder-scheduler - responsible for storing volumes

Using:

• Name
• ID number
• Type
• Description

• PublicURL - Publicly accessible through public internet
• InternalURL - Used for communication between services
• AdminURL - Used for administrative management

A list of services and their endpoints

• You can't have OpenStack deployed without Keystone
• It Provides identity, policy and token services
  • The authentication provided is for both users and services
  • The authorization supported is token-based and user-based.
• There is a policy defined based on RBAC stored in a JSON file and each line in that file defines the level of access to apply

• There is a service API and admin API through which Keystone gets requests
• Keystone has four backends:
  • Token Backend - Temporary Tokens for users and services
  • Policy Backend - Rules management and authorization
  • Identity Backend - users and groups (either standalone DB, LDAP, ...)
  • Catalog Backend - Endpoints
• It has pluggable environment where you can integrate with:
  • LDAP
  • KVS (Key Value Store)
  • SQL
  • PAM
  • Memcached

• Keystone gets a call/request and checks whether it's from an authorized user, using username, password and authURL
• Once confirmed, Keystone provides a token.
• A token contains a list of user's projects so there is no to authenticate every time and a token can submitted instead

• Nova-novncproxy - Access through VNC connections
• Nova-spicehtml5proxy - Access through SPICE
• Nova-xvpvncproxy - Access through a VNC connection

• Django-based project focusing on providing an OpenStack dashboard and the ability to create additional customized dashboards
• You can use it to access the different OpenStack services resources - instances, images, networks, ...
  • By accessing the dashboard, users can use it to list, create, remove and modify the different resources
• It's also highly customizable and you can modify or add to it based on your needs

• API is backward compatible
• There are three type of dashboards: user, system and settings
• It provides core support for all OpenStack core projects such as Neutron, Nova, etc. (out of the box, no need to install extra packages or plugins)
• Anyone can extend the dashboards and add new components
• Horizon provides templates and core classes from which one can build its own dashboard

The Elastic Stack consists of:

• Elasticsearch
• Kibana
• Logstash
• Beats
• Elastic Hadoop
• APM Server

Elasticserach, Logstash and Kibana are also known as the ELK stack.

From the official docs:

"Elasticsearch is a distributed document store. Instead of storing information as rows of columnar data, Elasticsearch stores complex data structures that have been serialized as JSON documents"

From the blog:

"Logstash is a powerful, flexible pipeline that collects, enriches and transports data. It works as an extract, transform & load (ETL) tool for collecting log messages."

Beats are lightweight data shippers. These data shippers installed on the client where the data resides. Examples of beats: Filebeat, Metricbeat, Auditbeat. There are much more.

From the official docs:

"Kibana is an open source analytics and visualization platform designed to work with Elasticsearch. You use Kibana to search, view, and interact with data stored in Elasticsearch indices. You can easily perform advanced data analysis and visualize your data in a variety of charts, tables, and maps."

The process may vary based on the chosen architecture and the processing you may want to apply to the logs. One possible workflow is:

1. The data logged by the application is picked by filebeat and sent to logstash
2. Logstash process the log based on the defined filters. Once done, the output is sent to Elasticsearch
3. Elasticsearch stores the document it got and the document is indexed for quick future access
4. The user creates visualizations in Kibana which based on the indexed data
5. The user creates a dashboard which composed out of the visualization created in the previous step

This is where data is stored and also where different processing takes place (e.g. when you search for a data).

Par of a master node responsibilites:

• Track the status of all the nodes in the cluster
• Verify replicas are working and the data is available from every data node.
• No hot nodes (no data node that works much harder than other nodes)

While there can be multiple master nodes in reality only of them is the elected master node.

A node which responsible for parsing the data. In case you don't use logstash then this node can recieve data from beats and parse it, similarly to how it can be parsed in Logstash.

A Coordinating node responsible for routing requests out and in to the cluser (data nodes).

• Data is stored in an index
• The index is spread across the cluster using shards

Index in Elastic is in most cases compared to a whole database from the SQL/NoSQL world.
You can choose to have one index to hold all the data of your app or have multiple indices where each index holds different type of your app (e.g. index for each service your app is running).

The official docs also offer a great explanation (in general, it's really good documentation, as every project should have):

"An index can be thought of as an optimized collection of documents and each document is a collection of fields, which are the key-value pairs that contain your data"

An index is split into shards and documents are hashed to a particular shard. Each shard may be on a different node in a cluster and each one of the shards is a self contained index.
This allows Elasticsearch to scale to an entire cluster of servers.

From the official docs:

"An inverted index lists every unique word that appears in any document and identifies all of the documents each word occurs in."

Continuing with the comparison to SQL/NoSQL a Document in Elastic is a row in table in the case of SQL or a document in a collection in the case of NoSQL. As in NoSQL a Document is a JSON object which holds data on a unit in your app. What is this unit depends on the your app. If your app related to book then each document describes a book. If you are app is about shirts then each document is a shirt.

Red means some data is unavailable. Yellow can be caused by running single node cluster instead of multi-node.

False. From the official docs:

"Each indexed field has a dedicated, optimized data structure. For example, text fields are stored in inverted indices, and numeric and geo fields are stored in BKD trees."

• _index
• _id
• _type

• You can optimize fields for partial matching
• You can define custom formats of known fields (e.g. date)
• You can perform language-specific analysis

In a network/cloud environment where failures can be expected any time, it is very useful and highly recommended to have a failover mechanism in case a shard/node somehow goes offline or disappears for whatever reason. To this end, Elasticsearch allows you to make one or more copies of your index’s shards into what are called replica shards, or replicas for short.

Term Frequency is how often a term appears in a given document and Document Frequency is how often a term appears in all documents. They both are used for determining the relevance of a term by calculating Term Frequency / Document Frequency.

"The index is actively being written to". More about the phases here

It creates customer index if it doesn't exists and adds a new document with the field name which is set to "John Dow". Also, if it's the first document it will get the ID 1.

1. If name value was different then it would update "name" to the new value
2. In any case, it bumps version field by one

Bulk API is used when you need to index multiple documents. For high number of documents it would be significantly faster to use rather than individual requests since there are less network roundtrips.

From the official docs:

"In the query context, a query clause answers the question “How well does this document match this query clause?” Besides deciding whether or not the document matches, the query clause also calculates a relevance score in the _score meta-field."

"In a filter context, a query clause answers the question “Does this document match this query clause?” The answer is a simple Yes or No — no scores are calculated. Filter context is mostly used for filtering structured data"

There are several possible answers for this question. One of them is as follows:

A small-scale architecture of elastic will consist of the elastic stack as it is. This means we will have beats, logstash, elastcsearch and kibana.
A production environment with large amounts of data can include some kind of buffering component (e.g. Reddis or RabbitMQ) and also security component such as Nginx.

• Input Plugins - how to collect data from different sources
• Filter Plugins - processing data
• Output Plugins - push data to different outputs/services/platforms

A logstash plugin which modifies information in one format and immerse it in another.

The raw data as it is stored in the index. You can search and filter it.

Total number of documents matching the search results. If not query used then simply the total number of documents.

"Visualize" is where you can create visual representations for your data (pie charts, graphs, ...)

Read here

False. One harvester harvests one file.

You can generate certificates with the provided elastic utils and change configuration to enable security using certificates model.

According to Martin Kleppmann:

"Many processes running on many machines...only message-passing via an unreliable network with variable delays, and the system may suffer from partial failures, unreliable clocks, and process pauses."

Another definition: "Systems that are physically separated, but logically connected"

• Network
• CPU
• Memory
• Disk

According to the CAP theorem, it's not possible for a distributed data store to provide more than two of the following at the same time:

• Availability: Every request receives a response (it doesn't has to be the most recent data)
• Consistency: Every request receives a response with the latest/most recent data
• Partition tolerance: Even if some the data is lost/dropped, the system keeps running

Ways to improve:

• Add another load balancer
• Use DNS A record for both load balancers
• Use message queue

It's an architecture in which data is and retrieved from a single, non-shared, source usually exclusively connected to one node as opposed to architectures where the request can get to one of many nodes and the data will be retrieved from one shared location (storage, memory, ...).

1. The browser searches for the record of the domain name IP address in the DNS in the following order:

• Browser cache
• Operating system cache
• The DNS server configured on the user's system (can be ISP DNS, public DNS, ...)

1. If it couldn't find a DNS record locally, a full DNS resolution is started.
2. It connects to the server using the TCP protocol
3. The browser sends an HTTP request to the server
4. The server sends an HTTP response back to the browser
5. The browser renders the response (e.g. HTML)
6. The browser then sends subsequent requests as needed to the server to get the embedded links, javascript, images in the HTML and then steps 3 to 5 are repeated.

TODO: add more details!

I like this definition from blog.christianposta.com:

"An explicitly and purposefully defined interface designed to be invoked over a network that enables software developers to get programmatic access to data and functionality within an organization in a controlled and comfortable way."

From swagger.io:

"An API specification provides a broad understanding of how an API behaves and how the API links with other APIs. It explains how the API functions and the results to expect when using the API"

False. From swagger.io:

"An API definition is similar to an API specification in that it provides an understanding of how an API is organized and how the API functions. But the API definition is aimed at machine consumption instead of human consumption of APIs."

Automation is the act of automating tasks to reduce human intervention or interaction in regards to IT technology and systems.
While automation focuses on a task level, Orchestration is the process of automating processes and/or workflows which consists of multiple tasks that usually across multiple systems.

• Authorization
• Logging
• Authentication
• Ordering
• Front-end
• Back-end ...

Data about data. Basically, it describes the type of information that an underlying data will hold.

I can't answer this for you :)

Domain Specific Language (DSLs) are used to create a customised language that represents the domain such that domain experts can easily interpret it.

Data serialization language used by many technologies today like Kubernetes, Ansible, etc.

True. Because YAML is superset of JSON.

someMultiLineString: |
  look mama
  I can write a multi-line string
  I love YAML

It's good for use cases like writing a shell script where each line of the script is a different command.

using > will make the multi-line string to fold into a single line

someMultiLineString: >
  This is actually
  a single line
  do not let appearances fool you

They allow you reference values instead of directly writing them and it is used like this:

username: {{ my.user_name }}

Using this: --- For Examples:

document_number: 1
---
document_number: 2

Wikipedia: "In computing, firmware is a specific class of computer software that provides the low-level control for a device's specific hardware. Firmware, such as the BIOS of a personal computer, may contain basic functions of a device, and may provide hardware abstraction services to higher-level software such as operating systems."

• Resolve host by request to DNS resolver
• Client SYN
• Server SYN+ACK
• Client SYN
• HTTP request
• HTTP response

False. It doesn't maintain state for incoming request.

It consists of:

• Request line - request type
• Headers - content info like length, enconding, etc.
• Body (not always included)

• GET
• POST
• HEAD
• PUT
• DELETE
• CONNECT
• OPTIONS
• TRACE

• 1xx - informational
• 2xx - Success
• 3xx - Redirect
• 4xx - Error, client fault
• 5xx - Error, server fault

HTTP is stateless. To share state, we can use Cookies.

TODO: explain what is actually a Cookie

The server didn't receive a response from another server it communicates with in a timely manner.

Wikipedia: "The X-Forwarded-For (XFF) HTTP header field is a common method for identifying the originating IP address of a client connecting to a web server through an HTTP proxy or load balancer."

A load balancer accepts (or denies) incoming network traffic from a client, and based on some criteria (application related, network, etc.) it distributes those communications out to servers (at least one).

• Scalability - using a load balancer, you can possibly add more servers in the backend to handle more requests/traffic from the clients, as opposed to using one server.
• Redundancy - if one server in the backend dies, the load balancer will keep forwarding the traffic/requests to the second server so users won't even notice one of the servers in the backend is down.

• Round Robin
• Weighted Round Robin
• Least Connection
• Weighted Least Connection
• Resource Based
• Fixed Weighting
• Weighted Response Time
• Source IP Hash
• URL Hash

• A simple round robin algorithm knows nothing about the load and the spec of each server it forwards the requests to. It is possible, that multiple heavy workloads requests will get to the same server while other servers will got only lightweight requests which will result in one server doing most of the work, maybe even crashing at some point because it unable to handle all the heavy workloads requests by its own.
• Each request from the client creates a whole new session. This might be a problem for certain scenarios where you would like to perform multiple operations where the server has to know about the result of operation so basically, being sort of aware of the history it has with the client. In round robin, first request might hit server X, while second request might hit server Y and ask to continue processing the data that was processed on server X already.

L4 and L7

Yes, you can use DNS for performing load balancing.

Recommended read:

• Red Hat Article

Cons:

• Can cause uneven load on instance (since requests routed to the same instances) Pros:
• Ensures in-proc sessions are not lost when a new request is created

You would like to make sure the user doesn't lose the current session data.

Cookies. There are application based cookies and duration based cookies.

• Generated by the application and/or the load balancer
• Usually allows to include custom data

• Generated by the load balancer
• Session is not sticky anymore once the duration elapsed

The maximum timeout value can be set between 1 and 3,600 seconds on both GCP and AWS.

In Copyleft, any derivative work must use the same licensing while in permissive licensing there are no such condition. GPL-3 is an example of copyleft license while BSD is an example of permissive license.

CPU cache. Source

SSH HTTP DHCP DNS ...

https://idiallo.com/blog/c10k-2016

• File
• Block
• Object

Be careful when asking this question - all companies, regardless of size, have some level of tech debt. Phrase the question in the light that all companies have the deal with this, but you want to see the current pain points they are dealing with

This is a great way to figure how managers deal with unplanned work, and how good they are at setting expectations with projects.

This can give you insights in some of the cool projects a company is working on, and if you would enjoy working on projects like these. This is also a good way to see if the managers are allowing employees to learn and grow with projects outside of the normal work you'd do.

Similar to the tech debt question, this helps you identify any pain points with the company. Additionally, it can be a great way to show how you'd be an asset to the team.

For Example, if they mention they have problem X, and you've solved that in the past, you can show how you'd be able to mitigate that problem.

Not only this will tell you what is expected from you, it will also provide big hint on the type of work you are going to do in the first months of your job.

• Data Storage: system to store data in tables
• SQL: programming language to manage relational databases
• Data Definition Language: a standard syntax to create, alter and delete tables

Sharding is a horizontal partitioning.

Are you able to explain what is it good for?

Not much information provided as to why it became a bottleneck and what is current architecture, so one general approach could be
to reduce the load on your database by moving frequently-accessed data to in-memory structure.

Connection Pool is a cache of database connections and the reason it's used is to avoid an overhead of establishing a connection for every query done to a database.

A connection leak is a situation where database connection isn't closed after being created and is no longer needed.

• Query for running queries and cancel the irrelevant queries
• Check for connection leaks (query for running connections and include their IP)
• Check for table locks and kill irrelevant locking sessions

"A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of organisation's decision-making process"

A database index is a data structure that improves the speed of operations in a table. Indexes can be created using one or more columns, providing the basis for both rapid random lookups and efficient ordering of access to records.

Data that is used multiple times in a database should be stored once and referenced with a foreign key.
This has the clear benefit of ease of maintenance where you need to change a value only in a single place to change it everywhere.

Primary Key: each row in every table should a unique identifier that represents the row.
Foreign Key: a reference to another table's primary key. This allows you to join table together to retrieve all the information you need without duplicating data.

• Primary data table: main data you care about
• Details table: includes a foreign key and has one to many relationship
• Lookup values table: can be one table per lookup or a table containing all the lookups and has one to many relationship
• Multi reference table

Wikipedia: "is a programming technique for converting data between incompatible type systems using object-oriented programming languages"

In regards to the relational databases:

• Database as code
• Database abstraction
• Encapsulates SQL complexity
• Enables code review process
• Enables usage as a native OOP structure

Wikipedia: "In the context of SQL, data definition or data description language (DDL) is a syntax for creating and modifying database objects such as tables, indices, and users."

Bonus: extract the last word of each line

• Not suitable for frequent code changes and the ability to deploy new features
• Not designed for today's infrastructure (like public clouds)
• Scaling a team to work monolithic architecture is more challenging

• Each of the services individually fail without escalating into an application-wide outage.
• Each service can be developed and maintained by a separate team and this team can choose its own tools and coding language

This article provides a great explanation.

The ability easily grow in size and capacity based on demand and usage.

The ability to grow but also to reduce based on what is required

Fault Tolerance - The ability to self-heal and return to normal capacity. Also the ability to withstand a failure and remain functional.

High Availability - Being able to access a resource (in some use cases, using different platforms)

wintellect.com: "High availability, simply put, is eliminating single points of failure and disaster recovery is the process of getting a system back to an operational state when a system is rendered inoperative. In essence, disaster recovery picks up when high availability fails, so HA first."

Vertical Scaling is the process of adding resources to increase power of existing servers. For example, adding more CPUs, adding more RAM, etc.

With vertical scaling alone, the component still remains a single point of failure. In addition, it has hardware limit where if you don't have more resources, you might not be able to scale vertically.

Databases, cache. It's common mostly for non-distributed systems.

Horizontal Scaling is the process of adding more resources that will be able handle requests as one unit

A load balancer. You can add more resources, but if you would like them to be part of the process, you have to serve them the requests/responses. Also, data inconsistency is a concern with horizontal scaling.

The load on the producers or consumers may be high which will then cause them to hang or crash.
Instead of working in "push mode", the consumers can pull tasks only when they are ready to handle them. It can be fixed by using a streaming platform like Kafka, Kinesis, etc. This platform will make sure to handle the high load/traffic and pass tasks/messages to consumers only when the ready to get them.

Take a look here

You can find a list here

Read about it here

You can mention:

roll-back & roll-forward cut over dress rehearsals DNS redirection

A central processing unit (CPU) performs basic arithmetic, logic, controlling, and input/output (I/O) operations specified by the instructions in the program. This contrasts with external components such as main memory and I/O circuitry, and specialized processors such as graphics processing units (GPUs).

RAM (Random Access Memory) is the hardware in a computing device where the operating system (OS), application programs and data in current use are kept so they can be quickly reached by the device's processor. RAM is the main memory in a computer. It is much faster to read from and write to than other kinds of storage, such as a hard disk drive (HDD), solid-state drive (SSD) or optical drive.

An embedded system is a computer system - a combination of a computer processor, computer memory, and input/output peripheral devices—that has a dedicated function within a larger mechanical or electronic system. It is embedded as part of a complete device often including electrical or electronic hardware and mechanical parts.

Raspberry Pi

As defined by Doug Laney:

• Volume: Extremely large volumes of data
• Velocity: Real time, batch, streams of data
• Variety: Various forms of data, structured, semi-structured and unstructured
• Veracity or Variability: Inconsistent, sometimes inaccurate, varying data

DataOps seeks to reduce the end-to-end cycle time of data analytics, from the origin of ideas to the literal creation of charts, graphs and models that create value. DataOps combines Agile development, DevOps and statistical process controls and applies them to data analytics.

An answer from talend.com:

"Data architecture is the process of standardizing how organizations collect, store, transform, distribute, and use data. The goal is to deliver relevant data to people who need it, when they need it, and help them make sense of it."

• Structured - data that has defined format and length (e.g. numbers, words)
• Semi-structured - Doesn't conform to a specific format but is self-describing (e.g. XML, SWIFT)
• Unstructured - does not follow a specific format (e.g. images, test messages)

Wikipedia's explanation on Data Warehouse Amazon's explanation on Data Warehouse

Data Lake - Wikipedia

Apache Hadoop - Wikipedia

Responsible for managing the compute resources in clusters and scheduling users' applications

A programming model for large-scale data processing

• Distributed file system providing high aggregate bandwidth across the cluster.
• For a user it looks like a regular file system structure but behind the scenes it's distributed across multiple machines in a cluster
• Typical file size is TB and it can scale and supports millions of files
• It's fault tolerant which means it provides automatic recovery from faults
• It's best suited for running long batch operations rather than live analysis

HDFS Architecture

• Master-slave architecture
• Namenode - master, Datanodes - slaves
• Files split into blocks
• Blocks stored on datanodes
• Namenode controls all metadata

• Object (RGW)
• Block (RBD)
• File (CephFS)

• Reliable Autonomic Distributed Object Storage
• Provides low-level data object storage service
• Strong Consistency
• Simplifies design and implementation of higher layers (block, file, object)

• Monitor
  • Central authority for authentication, data placement, policy
  • Coordination point for all other cluster components
  • Protect critical cluster state with Paxos
• Manager
  • Aggregates real-time metrics (throughput, disk usage, etc.)
  • Host for pluggable management functions
  • 1 active, 1+ standby per cluster
• OSD (Object Storage Daemon)
  • Stores data on an HDD or SSD
  • Services client IO requests

In general, Packer automates machine images creation. It allows you to focus on configuration prior to deployment while making the images. This allows you start the instances much faster in most cases.

A configuration->deployment which has some advantages like:

1. Deployment Speed - you configure once prior to deployment instead of configuring every time you deploy. This allows you to start instances/services much quicker.
2. More immutable infrastructure - with configuration->deployment it's not likely to have very different deployments since most of the configuration is done prior to the deployment. Issues like dependencies errors are handled/discovered prior to deployment in this model.