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How to manage docker container volumes.

Volumes are the preferred mechanism for persisting data generated by and used by Docker containers. A volume allows data to persist, even when a container is deleted. Volumes can be used to share data between the host and the container(s).

By mounting a volume you can achive to

• Pull data from a container.
• Push data to a container.
• Share data between containers.

Please note that, arguments prefixed with @(at) in below commands must be replaced with actual values.

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FROM nginx:alpine

COPY nginx-custom.conf /etc/nginx/conf.d/default.conf

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FROM — specifies the base (parent) image.
LABEL —provides metadata. Good place to include maintainer info.
ENV — sets a persistent environment variable.
RUN —runs a command and creates an image layer. Can be used to install packages into containers or run specific commands(i.e mkdir and etc.).
COPY — copies files and directories to the container.
ADD — copies files and directories to the container. Can upack local .tar files.
CMD — provides a command and arguments for an executing container. Parameters can be overridden. There can be only one CMD.
WORKDIR — sets the working directory for the instructions that follow.
ARG — defines a variable to pass to Docker at build-time.
ENTRYPOINT — provides command and arguments for an executing container. Arguments persist.\

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   public static void Main(string[] args)
   {
       Func<int, int> doubleCall(Func<int, int> func) => x => func(func(x));
       static int plusN(int n) => n + 3;

       Console.WriteLine(doubleCall(plusN)(5));
   }

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CAP theorem NoSQL database types

NoSQL (non-relational) databases are ideal for distributed network applications. Unlike their vertically scalable SQL (relational) counterparts, NoSQL databases are horizontally scalable and distributed by design—they can rapidly scale across a growing network consisting of multiple interconnected nodes.

The ‘CAP’ in the CAP theorem, explained

Let’s take a detailed look at the three distributed system characteristics to which the CAP theorem refers.

Consistency Consistency means that all clients see the same data at the same time, no matter which node they connect to. For this to happen, whenever data is written to one node, it must be instantly forwarded or replicated to all the other nodes in the system before the write is deemed ‘successful.’

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Flows

The authentication flows, dictate the process on how a client application can receive an access token from the authorization server. Each flow is appropriate for different scenarios, not all flows are appropriate for all kinds of applications. There are two types of redirect flows and two types of credential exchange type of flows.

Redirect flows

• Implicit grant flow. This is one of the most popular approaches, the resource owner is redirected to the authorization server and logins there. After a successful login, user is redirected back to the client application, where they get their access token.
• Authorization code flow. This is similar approach to the above, with one twist. Instead of getting an access token when redirected back to the website, we simply get an authorization code, which can be used to trade for an access token. This mechanism adds an extra layer of security, mitigating MIM attacks.

Credential flows

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###############################################################################

Helpful Docker commands and code snippets

###############################################################################

CONTAINERS

#stop ALL containers
docker stop $(docker ps -a -q)
# remove ALL containers
docker rm -f $(docker ps -a -q) 

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###############################################################################
# Helpful Docker commands and code snippets
###############################################################################

### CONTAINERS ###
docker stop $(docker ps -a -q) #stop ALL containers
docker rm -f $(docker ps -a -q) # remove ALL containers
docker rm -f $(sudo docker ps --before="container_id_here" -q) # can also filter

# exec into container

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How to simplify your codebase with map(), reduce(), and filter() in JavaScript

1. Remove duplicates from an array of numbers/strings

Well, this is the only one not about map/reduce/filter, but it’s so compact that it was hard not to put it in the list. Plus we’ll use it in a few examples too.

const values = [3, 1, 3, 5, 2, 4, 4, 4];
const uniqueValues = [...new Set(values)];

// uniqueValues is [3, 1, 5, 2, 4]

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#!/bin/sh

git filter-branch --env-filter '

OLD_EMAIL="aaa@company.com"
CORRECT_NAME="bbb_name"
CORRECT_EMAIL="bbb@company.com"

if [ "$GIT_COMMITTER_EMAIL" = "$OLD_EMAIL" ]
then