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 data link 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.

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

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

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

1. Router: a network device that connects multiple network segments together. It operates at the network layer (Layer 3) of the OSI model and uses routing protocols to direct data between networks. Routers use IP addresses to identify devices and route data packets to the correct destination.
2. Switch: a network device that connects multiple devices on a LAN. It operates at the data link layer (Layer 2) of the OSI model and uses MAC addresses to identify devices and direct data packets to the correct destination. Switches allow devices on the same network to communicate with each other more efficiently and can prevent data collisions that can occur when multiple devices send data simultaneously.
3. Hub: a network device that connects multiple devices through a single cable and is used to connect multiple devices without segmenting a network. However, unlike a switch, it operates at the physical layer (Layer 1) of the OSI model and simply broadcasts data packets to all devices connected to it, regardless of whether the device is the intended recipient or not. This means that data collisions can occur, and the network's efficiency can suffer as a result. Hubs are generally not used in modern network setups, as switches are more efficient and provide better network performance.

Three collision domains and one broadcast domain

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

• A client node sends an 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.

Bonus question: what is the RTT of LAN?

1. The client sends a Client Hello message to the server, which includes the client's version of the SSL/TLS protocol, a list of the cryptographic algorithms supported by the client, and a random value.
2. The server responds with a Server Hello message, which includes the server's version of the SSL/TLS protocol, a random value, and a session ID.
3. The server sends a Certificate message, which contains the server's certificate.
4. The server sends a Server Hello Done message, which indicates that the server is done sending messages for the Server Hello phase.
5. The client sends a Client Key Exchange message, which contains the client's public key.
6. The client sends a Change Cipher Spec message, which notifies the server that the client is about to send a message encrypted with the new cipher spec.
7. The client sends an Encrypted Handshake Message, which contains the pre-master secret encrypted with the server's public key.
8. The server sends a Change Cipher Spec message, which notifies the client that the server is about to send a message encrypted with the new cipher spec.
9. The server sends an Encrypted Handshake Message, which contains the pre-master secret encrypted with the client's public key.
10. The client and server can now exchange application data.

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.

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.

• TTL (Time to Live) is a value in an IP (Internet Protocol) packet that determines how many hops or routers a packet can travel before it is discarded. Each time a packet is forwarded by a router, the TTL value is decreased by one. When the TTL value reaches zero, the packet is dropped, and an ICMP (Internet Control Message Protocol) message is sent back to the sender indicating that the packet has expired.
• TTL is used to prevent packets from circulating indefinitely in the network, which can cause congestion and degrade network performance.
• It also helps to prevent packets from being trapped in routing loops, where packets continuously travel between the same set of routers without ever reaching their destination.
• In addition, TTL can be used to help detect and prevent IP spoofing attacks, where an attacker attempts to impersonate another device on the network by using a false or fake IP address. By limiting the number of hops that a packet can travel, TTL can help prevent packets from being routed to destinations that are not legitimate.

Read more here

• When two DHCP servers are configured on the same network, there is a risk that both servers will assign IP addresses and other network configuration settings to the same device, which can cause conflicts and connectivity issues. Additionally, if the DHCP servers are configured with different network settings or options, devices on the network may receive conflicting or inconsistent configuration settings.
• However, in some cases, it may be necessary to have two DHCP servers on the same network, such as in large networks where one DHCP server may not be able to handle all the requests. In such cases, DHCP servers can be configured to serve different IP address ranges or different subnets, so they do not interfere with each other.

1. A client initiates an SSL connection to a server, which involves a handshake process to establish the SSL session.
2. Once the SSL session is established, the client and server negotiate encryption parameters, such as the encryption algorithm and key length, then exchange digital certificates to authenticate each other.
3. The client then sends traffic through the SSL tunnel to the server, which decrypts the traffic and forwards it to its destination.
4. The server sends traffic back through the SSL tunnel to the client, which decrypts the traffic and forwards it to the application.

• A socket is a software endpoint that enables two-way communication between processes over a network. Sockets provide a standardized interface for network communication, allowing applications to send and receive data across a network. To view the list of open sockets on a Linux system:  netstat -an
• This command displays a list of all open sockets, along with their protocol, local address, foreign address, and state.

1. Address space: IPv4 has a limited address space, which has been exhausted in many parts of the world. IPv6 provides a much larger address space, allowing for trillions of unique IP addresses.
2. Security: IPv6 includes built-in support for IPsec, which provides end-to-end encryption and authentication for network traffic.
3. Performance: IPv6 includes features that can help to improve network performance, such as multicast routing, which allows a single packet to be sent to multiple destinations simultaneously.
4. Simplified network configuration: IPv6 includes features that can simplify network configuration, such as stateless autoconfiguration, which allows devices to automatically configure their own IPv6 addresses without the need for a DHCP server.
5. Better mobility support: IPv6 includes features that can improve mobility support, such as Mobile IPv6, which allows devices to maintain their IPv6 addresses as they move between different networks.

• A VLAN (Virtual Local Area Network) is a logical network that groups together a set of devices on a physical network, regardless of their physical location. VLANs are created by configuring network switches to assign a specific VLAN ID to frames sent by devices connected to a specific port or group of ports on the switch.

MTU stands for Maximum Transmission Unit. It's the size of the largest PDU (protocol Data Unit) that can be sent in a single transaction.

With IPv6 protocol, it issues an error to the user's computer.

False. Ping is actually using ICMP (Internet Control Message Protocol) which is a network protocol used to send diagnostic messages and control messages related to network communication.

• SDN stands for Software-Defined Networking. It is an approach to network management that emphasizes the centralization of network control, enabling administrators to manage network behavior through a software abstraction.
• In a traditional network, network devices such as routers, switches, and firewalls are configured and managed individually, using specialized software or command-line interfaces. In contrast, SDN separates the network control plane from the data plane, allowing administrators to manage network behavior through a centralized software controller.

1. Error reporting: ICMP messages are used to report errors that occur in the network, such as a packet that could not be delivered to its destination.
2. Ping: ICMP is used to send ping messages, which are used to test whether a host or network is reachable and to measure the round-trip time for packets.
3. Path MTU discovery: ICMP is used to discover the Maximum Transmission Unit (MTU) of a path, which is the largest packet size that can be transmitted without fragmentation.
4. Traceroute: ICMP is used by the traceroute utility to trace the path that packets take through the network.
5. Router discovery: ICMP is used to discover the routers in a network.

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 its 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).

• SSH - 22
• SMTP - 25
• HTTP - 80
• DNS - 53
• HTTPS - 443
• FTP - 21
• SFTP - 22

1. Bandwidth: The available bandwidth of a network connection can significantly impact its performance. Networks with limited bandwidth can experience slow data transfer rates, high latency, and poor responsiveness.
2. Latency: Latency refers to the delay that occurs when data is transmitted from one point in a network to another. High latency can result in slow network performance, especially for real-time applications like video conferencing and online gaming.
3. Network congestion: When too many devices are using a network at the same time, network congestion can occur, leading to slow data transfer rates and poor network performance.
4. Packet loss: Packet loss occurs when packets of data are dropped during transmission. This can result in slower network speeds and lower overall network performance.
5. Network topology: The physical layout of a network, including the placement of switches, routers, and other network devices, can impact network performance.
6. Network protocol: Different network protocols have different performance characteristics, which can impact network performance. For example, TCP is a reliable protocol that can guarantee the delivery of data, but it can also result in slower performance due to the overhead required for error checking and retransmission.
7. Network security: Security measures such as firewalls and encryption can impact network performance, especially if they require significant processing power or introduce additional latency.
8. Distance: The physical distance between devices on a network can impact network performance, especially for wireless networks where signal strength and interference can affect connectivity and data transfer rates.

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

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

It refers to monitoring and management functions.

Control Plane.

Learn more about OSPF router types: https://www.educba.com/ospf-router-types/

Latency is the time taken for information to reach its destination from the source.

Bandwidth is the capacity of a communication channel to measure how much data the latter can handle over a specific time period. More bandwidth would imply more traffic handling and thus more data transfer.

Throughput refers to the measurement of the real amount of data transferred over a certain period of time across any transmission channel.

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

Throughput. To have 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 data center)

The Spine & Leaf architecture has become increasingly popular in data centers due to its ability to handle the demands of modern cloud computing, virtualization, and big data applications, providing a scalable, high-performance, and reliable network infrastructure

Network congestion occurs when there is too much data to transmit on a network and it doesn't have enough capacity to handle the demand.
This can lead to increased latency and packet loss. The causes can be multiple, such as high network usage, large file transfers, malware, hardware issues, or network design problems.
To prevent network congestion, it's important to monitor your network usage and implement strategies to limit or manage the demand.

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 a client access to a telnet server
• File Transfer Protocol (FTP) - facilitates the 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 on 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](https://www.globalsign.com/en/blog/what-is-hsts-and-how-do-i-use-it#:~:text=HTTP%20Strict%20Transport%20Security%20(HSTS,and%20back%20to%20the%20browser.)

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

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

"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.

• 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

Inter-process communication (IPC) refers to the mechanisms provided by an operating system that allow processes to manage shared data.

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

Virtual memory combines your computer's RAM with temporary space on your hard disk. When RAM runs low, virtual memory helps to move data from RAM to a space called a paging file. Moving data to paging file can free up the RAM, so your computer can complete its work. In general, the more RAM your computer has, the faster the programs run. https://www.minitool.com/lib/virtual-memory.html

Demand paging is a memory management technique where pages are loaded into physical memory only when accessed by a process. It optimizes memory usage by loading pages on demand, reducing startup latency and space overhead. However, it introduces some latency when accessing pages for the first time. Overall, it’s a cost-effective approach for managing memory resources in operating systems.

1. If resources are shared between 2 or more entities (for example shared memory segments between 2 processes), the resources don't need to be copied for every entity, but rather every entity has a READ operation access permission on the shared resource. (the shared segments are marked as read-only) (Think of every entity having a pointer to the location of the shared resource, which can be dereferenced to read its value)
2. If one entity would perform a WRITE operation on a shared resource, a problem would arise, since the resource also would be permanently changed for ALL other entities sharing it. (Think of a process modifying some variables on the stack, or allocatingy some data dynamically on the heap, these changes to the shared resource would also apply for ALL other processes, this is definitely an undesirable behaviour)
3. As a solution only, if a WRITE operation is about to be performed on a shared resource, this resource gets COPIED first and then the changes are applied.

• Allocating memory
• Schedule processes
• Control CPU

True

POSIX (Portable Operating System Interface) is a set of standards that define the interface between a Unix-like operating system and application programs.

A semaphore is a synchronization primitive used in operating systems and concurrent programming to control access to shared resources. It's a variable or abstract data type that acts as a counter or a signaling mechanism for managing access to resources by multiple processes or threads.

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

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).

Read more here

Hosted hypervisors and bare-metal hypervisors.

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.

Yes, virtual machines are still relevant even in the age of containers. While containers provide a lightweight and portable alternative to virtual machines, they do have certain limitations. Virtual machines still matter because they offer isolation and security, can run different operating systems, and are good for legacy apps. Containers limitations for example are sharing the host kernel.

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

1. Prometheus Server: The Prometheus server is responsible for collecting and storing metrics data. It has a simple built-in storage layer that allows it to store time-series data in a time-ordered database.

2. Client Libraries: Prometheus provides a range of client libraries that enable applications to expose their metrics data in a format that can be ingested by the Prometheus server. These libraries are available for a range of programming languages, including Java, Python, and Go.

3. Exporters: Exporters are software components that expose existing metrics from third-party systems and make them available for ingestion by the Prometheus server. Prometheus provides exporters for a range of popular technologies, including MySQL, PostgreSQL, and Apache.

4. Alertmanager: The Alertmanager component is responsible for processing alerts generated by the Prometheus server. It can handle alerts from multiple sources and provides a range of features for deduplicating, grouping, and routing alerts to appropriate channels.

Overall, the Prometheus architecture is designed to be highly scalable and resilient. The server and client libraries can be deployed in a distributed fashion to support monitoring across large-scale, highly dynamic environments

Overall, the choice of a monitoring solution depends on the specific needs and requirements of the organization. While Prometheus is a great choice for large-scale monitoring and alerting, InfluxDB may be a better fit for smaller environments that require ease of use and simplicity. Nagios remains a solid choice for organizations that prioritize stability and reliability over advanced features.

In essence, an instance is an individual target that Prometheus collects metrics from, while a job is a collection of similar instances that can be managed as a group.

1. Counter: A monotonically increasing value used for tracking counts of events or samples. Examples include the number of requests processed or the total number of errors encountered.

2. Gauge: A value that can go up or down, such as CPU usage or memory usage. Unlike counters, gauge values can be arbitrary, meaning they can go up and down based on changes in the system being monitored.

3. Histogram: A set of observations or events that are divided into buckets based on their value. Histograms help in analyzing the distribution of a metric, such as request latencies or response sizes.

4. Summary: A summary is similar to a histogram, but instead of buckets, it provides a set of quantiles for the observed values. Summaries are useful for monitoring the distribution of request latencies or response sizes over time.

Prometheus also supports various functions and operators for aggregating and manipulating metrics, such as sum, max, min, and rate. These features make it a powerful tool for monitoring and alerting on system metrics.

Overall, exporters are a critical component of the Prometheus ecosystem, allowing for the monitoring of a wide range of systems and applications, and providing a high degree of flexibility and extensibility to the platform.

sum(rate(http_requests_total[1h]))

You can adjust the time range by changing the duration in the rate function. For example, if you wanted to get the total number of requests in the last day, you could change the function to rate(http_requests_total[1d]).

HA stands for High Availability. This means that the system is designed to be highly reliable and always available, even in the face of failures or other issues. In practice, this typically involves setting up multiple instances of Prometheus and ensuring that they are all synchronized and able to work together seamlessly. This can be achieved through a variety of techniques, such as load balancing, replication, and failover mechanisms. By implementing HA in Prometheus, users can ensure that their monitoring data is always available and up-to-date, even in the face of hardware or software failures, network issues, or other problems that might otherwise cause downtime or data loss.

sum_series(
  join(
    on(service, instance) request_count_total,
    on(service, instance) error_count_total,
  )
)

In this example, the join() function combines the request_count_total and error_count_total time series based on their service and instance label values. The sum_series() function then calculates the sum of the resulting time series

label_values(http_requests_total, method)

This will return a list of all the values for the method label in the http_requests_total metric. You can then use this list in further queries or to filter your data.

Go also has good community.

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.

False. We can't redeclare variables but yes, we must used declared variables.

• fmt - formatted I/O

package main

import "fmt"

func main() {
    var x int = 101
    var y string
    y = string(x)
    fmt.Println(y)
}

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)

package main

func main() {
    var x = 2
    var y = 3
    const someConst = x + y
}

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

package main

import "fmt"

const (
	x = iota
	y = iota
)
const z = iota

func main() {
	fmt.Printf("%v\n", x)
	fmt.Printf("%v\n", y)
	fmt.Printf("%v\n", z)
}

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

package main

import "fmt"

const (
	_ = iota + 3
	x
)

func main() {
	fmt.Printf("%v\n", x)
}

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

package main

import (
	"fmt"
	"sync"
	"time"
)

func main() {
	var wg sync.WaitGroup

	wg.Add(1)
	go func() {
		time.Sleep(time.Second * 2)
		fmt.Println("1")
		wg.Done()
	}()

	go func() {
		fmt.Println("2")
	}()

	wg.Wait()
	fmt.Println("3")
}

Golang package sync

package main

import (
	"fmt"
)

func mod1(a []int) {
	for i := range a {
		a[i] = 5
	}

	fmt.Println("1:", a)
}

func mod2(a []int) {
	a = append(a, 125) // !

	for i := range a {
		a[i] = 5
	}

	fmt.Println("2:", a)
}

func main() {
	s1 := []int{1, 2, 3, 4}
	mod1(s1)
	fmt.Println("1:", s1)

	s2 := []int{1, 2, 3, 4}
	mod2(s2)
	fmt.Println("2:", s2)
}

Aritcle about arrays, Blog post about append

package main

import (
	"container/heap"
	"fmt"
)

// An IntHeap is a min-heap of ints.
type IntHeap []int

func (h IntHeap) Len() int           { return len(h) }
func (h IntHeap) Less(i, j int) bool { return h[i] < h[j] }
func (h IntHeap) Swap(i, j int)      { h[i], h[j] = h[j], h[i] }

func (h *IntHeap) Push(x interface{}) {
	// Push and Pop use pointer receivers because they modify the slice's length,
	// not just its contents.
	*h = append(*h, x.(int))
}

func (h *IntHeap) Pop() interface{} {
	old := *h
	n := len(old)
	x := old[n-1]
	*h = old[0 : n-1]
	return x
}

func main() {
	h := &IntHeap{4, 8, 3, 6}
	heap.Init(h)
	heap.Push(h, 7)

  fmt.Println((*h)[0])
}

Golang container/heap package

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

• A document is a record in MongoDB, which is stored in BSON (Binary JSON) format and is the basic unit of data in MongoDB.
• A collection is a group of related documents stored in a single database in MongoDB.

• An aggregator is a framework in MongoDB that performs operations on a set of data to return a single computed result.

• There is no definitive answer to which is better, it depends on the specific use case and requirements. Some explanations : Embedded documents provide atomic updates, while referenced documents allow for better normalization.

• Some ways to optimize data retrieval in MongoDB are: indexing, proper schema design, query optimization and database load balancing.

• find() returns all documents that match the query conditions.
• find_one() returns only one document that matches the query conditions (or null if no match is found).

• 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.

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

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

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.

• Nova - Manage virtual instances
• Neutron - Manage networking by providing Network as a service (NaaS)
• Cinder - Block Storage
• Glance - Manage images for virtual machines and containers (search, get and register)
• Keystone - Authentication service across the cloud

• Glance - Images Service. Also used for copying or snapshot instances
• Horizon - GUI for viewing and modifying resources
• Cinder - Block Storage
• Nova - Manage virtual instances

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

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

• Role - A list of rights and privileges determining what a user or a project can perform
• Tenant/Project - Logical representation of a group of resources isolated from other groups of resources. It can be an account, organization, ...
• Service - An endpoint which the user can use for accessing different resources
• Endpoint - a network address which can be used to access a certain OpenStack service
• Token - Used for access resources while describing which resources can be accessed by using a scope

• 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

• Swift - highly available, distributed, eventually consistent object/blob store
• Sahara - Manage Hadoop Clusters
• Ironic - Bare Metal Provisioning
• Trove - Database as a service that runs on OpenStack
• Aodh - Alarms Service
• Ceilometer - Track and monitor usage

• Database as a service which runs on OpenStack - Trove
• Bare Metal Provisioning - Ironic
• Track and monitor usage - Ceilometer
• Alarms Service - Aodh
• Manage Hadoop Clusters
• Manage Hadoop Clusters - Sahara
• highly available, distributed, eventually consistent object/blob store - Swift

• 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-api - responsible for managing requests/calls
• nova-compute - responsible for managing instance lifecycle
• nova-conductor - Mediates between nova-compute and the database so nova-compute doesn't access it directly

• 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

• Puppet is a configuration management tool ensuring that all systems are configured to a desired and predictable state.

• Puppet has a primary-secondary node architecture. The clients are distributed across the network and communicate with the primary-secondary environment where Puppet modules are present. The client agent sends a certificate with its ID to the server; the server then signs that certificate and sends it back to the client. This authentication allows for secure and verifiable communication between the client and the master.

• Puppet is often compared to other configuration management tools like Chef, Ansible, SaltStack, and cfengine. The choice to use Puppet often depends on an organization's needs, such as ease of use, scalability, and community support.

• Modules - are a collection of manifests, templates, and files
• Manifests - are the actual codes for configuring the clients
• Node - allows you to assign specific configurations to specific nodes

• Facter is a standalone tool in Puppet that collects information about a system and its configuration, such as the operating system, IP addresses, memory, and network interfaces. This information can be used in Puppet manifests to make decisions about how resources should be managed, and to customize the behavior of Puppet based on the characteristics of the system. Facter is integrated into Puppet, and its facts can be used within Puppet manifests to make decisions about resource management.

• MCollective is a middleware system that integrates with Puppet to provide orchestration, remote execution, and parallel job execution capabilities.

• Hiera is a hierarchical data store in Puppet that is used to separate data from code, allowing data to be more easily separated, managed, and reused.

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

"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"

"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.

"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."

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).

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

A node which responsible for processing the data according to ingest pipeline. In case you don't need to use logstash then this node can receive data from beats and process it, similarly to how it can be processed in Logstash.

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

"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.

"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 Elasticsearch 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 in your cluster. Some shards of your indices are unassigned. There are some other states for the cluster. Yellow means that you have unassigned shards in the cluster. You can be in this state if you have single node and your indices have replicas. Green means that all shards in the cluster are assigned to nodes and your cluster is healthy.

"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.

"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"

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, ...)

Filebeat is used to monitor the logging directories inside of VMs or mounted as a sidecar if exporting logs from containers, and then forward these logs onward for further processing, usually to logstash.

Filebeat is a typical component of the ELK stack, since it was developed by Elastic to work with the other products (Logstash and Kibana). It's possible to send logs directly to logstash, though this often requires coding changes for the application. Particularly for legacy applications with little test coverage, it might be a better option to use filebeat, since you don't need to make any changes to the application code.

Read here

False. One harvester harvests one file.

These are pre-configured modules for specific types of logging locations (eg, Traefik, Fargate, HAProxy) to make it easy to configure forwarding logs using filebeat. They have different configurations based on where you're collecting logs from.

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

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

• Network
• CPU
• Memory
• Disk

• 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

What are the problems with the following design? How to improve it?

• 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, ...).

TODO: add more details!

"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."

"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"

"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."

By using an API gateway, organizations can simplify the management of their APIs, ensure consistent security and governance, and improve the performance and scalability of their backend services. They are also commonly used in microservices architectures, where there are many small, independent services that need to be accessed by different clients.

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.

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."

Avinetworks: HTTP stands for Hypertext Transfer Protocol. HTTP uses TCP port 80 to enable internet communication. It is part of the Application Layer (L7) in OSI Model.

• 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.

• Request line - request type
• Headers - content info like length, encoding, 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

Learn more: https://www.cloudflare.com/learning/ssl/why-is-http-not-secure/

TODO: explain what is actually a Cookie

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

• Proxies can be used for load balancing, distributing traffic across multiple servers to help prevent server overload and improve website or application performance. They can also be used for data analysis, as they can log requests and traffic, providing useful insights into user behavior and preferences.

• They're commonly used to improve web server performance, provide high availability and fault tolerance, and enhance security by preventing direct access to the back-end server. They are often used in large-scale web applications and high-traffic websites to manage and distribute requests to multiple servers, resulting in improved scalability and reliability.

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.

• 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

More information: https://creativecommons.org/licenses/

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

The leaks can lead to a variety of problems, including system crashes, performance degradation, and instability. Usually occurring after failed maintenance on older systems and compatibility with new components over time.

SSH HTTP DHCP DNS ...

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

• File
• Block
• Object

• Data is divided to self-contained objects
• Objects can contain metadata

• Usually with object storage, you pay for what you use as opposed to other storage types where you pay for the storage space you allocate
• Scalable storage: Object storage mostly based on a model where what you use, is what you get and you can add storage as need Cons:
• Usually performs slower than other types of storage
• No granular modification: to change an object, you have re-create it

• File Storage used for storing data in files, in a hierarchical structure
• Some of the devices for file storage: hard drive, flash drive, cloud-based file storage
• Files usually organized in directories

• Users have full control of their own files and can run variety of operations on the files: delete, read, write and move.
• Security mechanism allows for users to have a better control at things such as file locking

Local filesystem Dropbox Google Drive

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.

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.

The main objective of unit tests are to verify each function is producing proper outputs given a set of inputs.

• "\d+"

• "^\w+" Bonus: extract the last word of each line

• "\w+(?=\W*$)" (in most cases, depends on line formatting)

• "\b(?:\d{1,3}\ .){3}\d{1,3}\b" IPV4:(This format looks for 1 to 3 digit sequence 3 times)

• "\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\ .[A-Za-z]{2,}\b"

CDN (Content Delivery Network) responsible for distributing content geographically. Part of it, is what is known as edge locations, aka cache proxies, that allows users to get their content quickly due to cache features and geographical distribution.

In single CDN, the whole content is originated from content delivery network.
In multi-CDN, content is distributed across multiple different CDNs, each might be on a completely different provider/cloud.

• Resiliency: Relying on one CDN means no redundancy. With multiple CDNs you don't need to worry about your CDN being down
• Flexibility in Costs: Using one CDN enforces you to specific rates of that CDN. With multiple CDNs you can take into consideration using less expensive CDNs to deliver the content.
• Performance: With Multi-CDN there is bigger potential in choosing better locations which more close to the client asking the content
• Scale: With multiple CDNs, you can scale services to support more extreme conditions

• Simpler to manage
• Different teams and developers can work on different parts of the project independently, making parallel development easier. CONS (Multi-repo):
• Code duplication
• Integration challenges

• 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
• If a single component in this architecture fails, then the entire application fails.

• 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

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

Disaster recovery is the process of restoring critical business systems and data after a disruptive event. The goal is to minimize the impact and resume normal business activities quickly. This involves creating a plan, testing it, backing up critical data, and storing it in safe locations. In case of a disaster, the plan is then executed, backups are restored, and systems are hopefully brought back online. The recovery process may take hours or days depending on the damages of infrastructure. This makes business planning important, as a well-designed and tested disaster recovery plan can minimize the impact of a disaster and keep operations going.

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.

Take a look here

You can find a list here

Read about it here

On the other hand, databases are optimized for storing and managing persistent data. Databases are designed to handle concurrent read and write operations, enforce consistency and integrity constraints, and provide features such as indexing and querying.

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.

There are several types of storage, including hard disk drives (HDDs), solid-state drives (SSDs), and optical drives (CD/DVD/Blu-ray). Other types of storage include USB flash drives, memory cards, and network-attached storage (NAS).

Choosing the right DevOps hardware is essential for ensuring streamlined CI/CD pipelines, timely feedback loops, and consistent service availability. Here's a distilled guide on what DevOps teams should consider:

1. Understanding Workloads:

   • CPU: Consider the need for multi-core or high-frequency CPUs based on your tasks.
   • RAM: Enough memory is vital for activities like large-scale coding or intensive automation.
   • Storage: Evaluate storage speed and capacity. SSDs might be preferable for swift operations.

2. Expandability:

   • Horizontal Growth: Check if you can boost capacity by adding more devices.
   • Vertical Growth: Determine if upgrades (like RAM, CPU) to individual machines are feasible.

3. Connectivity Considerations:

   • Data Transfer: Ensure high-speed network connections for activities like code retrieval and data transfers.
   • Speed: Aim for low-latency networks, particularly important for distributed tasks.
   • Backup Routes: Think about having backup network routes to avoid downtimes.

4. Consistent Uptime:

   • Plan for hardware backups like RAID configurations, backup power sources, or alternate network connections to ensure continuous service.

5. System Compatibility:

   • Make sure your hardware aligns with your software, operating system, and intended platforms.

6. Power Efficiency:

   • Hardware that uses energy efficiently can reduce costs in long-term, especially in large setups.

7. Safety Measures:

   • Explore hardware-level security features, such as TPM, to enhance protection.

8. Overseeing & Control:

   • Tools like ILOM can be beneficial for remote handling.
   • Make sure the hardware can be seamlessly monitored for health and performance.

9. Budgeting:

   • Consider both initial expenses and long-term costs when budgeting.

10. Support & Community:

    • Choose hardware from reputable vendors known for reliable support.
    • Check for available drivers, updates, and community discussions around the hardware.

11. Planning Ahead:

    • Opt for hardware that can cater to both present and upcoming requirements.

12. Operational Environment:

    • Temperature Control: Ensure cooling systems to manage heat from high-performance units.
    • Space Management: Assess hardware size considering available rack space.
    • Reliable Power: Factor in consistent and backup power sources.

13. Cloud Coordination:

    • If you're leaning towards a hybrid cloud setup, focus on how local hardware will mesh with cloud resources.

14. Life Span of Hardware:

    • Be aware of the hardware's expected duration and when you might need replacements or upgrades.

15. Optimized for Virtualization:

    • If utilizing virtual machines or containers, ensure the hardware is compatible and optimized for such workloads.

16. Adaptability:

    • Modular hardware allows individual component replacements, offering more flexibility.

17. Avoiding Single Vendor Dependency:

    • Try to prevent reliance on a single vendor unless there are clear advantages.

18. Eco-Friendly Choices:

    • Prioritize sustainably produced hardware that's energy-efficient and environmentally responsible.

In essence, DevOps teams should choose hardware that is compatible with their tasks, versatile, gives good performance, and stays within their budget. Furthermore, long-term considerations such as maintenance, potential upgrades, and compatibility with impending technological shifts must be prioritized.

Hardware is critical in disaster recovery (DR) solutions. While the broader scope of DR includes things like standard procedures, norms, and human roles, it's the hardware that keeps business processes running smoothly. Here's an outline of how hardware works with DR:

1. Storing Data and Ensuring Its Duplication:

   • Backup Equipment: Devices like tape storage, backup servers, and external HDDs keep essential data stored safely at a different location.
   • Disk Arrays: Systems such as RAID offer a safety net. If one disk crashes, the others compensate.

2. Alternate Systems for Recovery:

   • Backup Servers: These step in when the main servers falter, maintaining service flow.
   • Traffic Distributors: Devices like load balancers share traffic across servers. If a server crashes, they reroute users to operational ones.

3. Alternate Operation Hubs:

   • Ready-to-use Centers: Locations equipped and primed to take charge immediately when the main center fails.
   • Basic Facilities: Locations with necessary equipment but lacking recent data, taking longer to activate.
   • Semi-prepped Facilities: Locations somewhat prepared with select systems and data, taking a moderate duration to activate.

4. Power Backup Mechanisms:

   • Instant Power Backup: Devices like UPS offer power during brief outages, ensuring no abrupt shutdowns.
   • Long-term Power Solutions: Generators keep vital systems operational during extended power losses.

5. Networking Equipment:

   • Backup Internet Connections: Having alternatives ensures connectivity even if one provider faces issues.
   • Secure Connection Tools: Devices ensuring safe remote access, especially crucial during DR situations.

6. On-site Physical Setup:

   • Organized Housing: Structures like racks to neatly store and manage hardware.
   • Emergency Temperature Control: Backup cooling mechanisms to counter server overheating in HVAC malfunctions.

7. Alternate Communication Channels:

   • Orbit-based Phones: Handy when regular communication methods falter.
   • Direct Communication Devices: Devices like radios useful when primary systems are down.

8. Protection Mechanisms:

   • Electronic Barriers & Alert Systems: Devices like firewalls and intrusion detection keep DR systems safeguarded.
   • Physical Entry Control: Systems controlling entry and monitoring, ensuring only cleared personnel have access.

9. Uniformity and Compatibility in Hardware:

   • It's simpler to manage and replace equipment in emergencies if hardware configurations are consistent and compatible.

10. Equipment for Trials and Upkeep:

    • DR drills might use specific equipment to ensure the primary systems remain unaffected. This verifies the equipment's readiness and capacity to manage real crises.

In summary, while software and human interventions are important in disaster recovery operations, it is the hardware that provides the underlying support. It is critical for efficient disaster recovery plans to keep this hardware resilient, duplicated, and routinely assessed.

Direct memory access (DMA) is a feature of computer systems that allows certain hardware subsystems to access main system memory independently of the central processing unit (CPU).DMA enables devices to share and receive data from the main memory in a computer. It does this while still allowing the CPU to perform other tasks.

A real-time operating system (RTOS) is an operating system (OS) for real-time computing applications that processes data and events that have critically defined time constraints. An RTOS is distinct from a time-sharing operating system, such as Unix, which manages the sharing of system resources with a scheduler, data buffers, or fixed task prioritization in a multitasking or multiprogramming environment. Processing time requirements need to be fully understood and bound rather than just kept as a minimum. All processing must occur within the defined constraints. Real-time operating systems are event-driven and preemptive, meaning the OS can monitor the relevant priority of competing tasks, and make changes to the task priority. Event-driven systems switch between tasks based on their priorities, while time-sharing systems switch the task based on clock interrupts.

• External
• Machine check
• I/O
• Program
• Restart
• Supervisor call (SVC)

• 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.

"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

• 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.

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.