The core for asynchronous programming are the objects Task and Task<T>. Both of them are compatible with the keywords async and await.

First of all we need to identify if the code’s I/O-bound or CPU-bound.

• the code’s limited for external operations and waits for something a lot of time. Examples of this are DDBB calls, or a server’s response. In this case we have to use async/await to free the thread while we wait
• the code does a CPU-intensive operation. Then we move the work to another thread using Task.Run() so we don’t block the main thread.

async code vs parallel code

(!) Asynchronous code is not the same as parallel code (!)

• In async code you are trying to make your threads do as little work as possible. This will keep your app responsibe, capable to serve many requests at once and scale well.
• In parallel code you do the opposite. You use and keep a hold on a thread to do CPU-intensive calculations

async code

The importante of async programming is that you choose when to wait on a task. This way, you can start other tasks concurrently

In async code, one single thread can start the next task concurrently before the previous one completes.
(!) async code doesn’t cause additional threads to be created because an async method doesn’t run on its own thread. (!) It runs on the current synchronization context and uses time on the thread only when the method is active.

parallel code

For parallelism you need multiple threads where each thread executes a task, and all of those tasks are executed at the same time

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Data generated and used by Docker containers does not persist after restarts. We use Docker volumes to manage data to solve this issue. We use it to persist data in a container or share data between containers.

Volumes are the preferred mechanism for persisting data generated and user by Docker containers. Volumes are easier to back up or migrate.

Volumes are often a better choice than persisting data in a container’s writable layer, because a volume does not increase the size of the containers using it. The volume’s contents exist outside the lifecycle of a given container.

We have 3 volume types:

Anonymous volumes

Helpful to persist data temporarily. If we restart our container data is still visible. It doesn’t persist when we remove the container. Not accessible by other containers. They’re created inside /var/lib/docker/volume.

Example file:

version: '3.8'  
services:  
  db:  
    image: mysql  
    restart: always  
    environment:  
      MYSQL_ROOT_PASSWORD: root  
      MYSQL_DATABASE: test_db  
    ports:  
      - "3306:3306"  
    volumes:  
      - /var/lib/mysql

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Steps to go from a docker-compose file, to build an Image out of the file, upload it to DockerHub, and run it with Kubernetes (minikube).

Set up

Install Kompose

# linux
curl -L https://github.com/kubernetes/kompose/releases/download/v1.22.0/kompose-linux-amd64 -o kompose
chmod +x kompose
sudo mv ./kompose /usr/local/bin/kompose

Clone App and Host It into DockerHub

Clone the files, go to the directory where your Dockerfile is and run:

# mariocodes is my DockerHub username
# kubernetes-custom-java-maven-app is the name of the repo I created at DockerHub
docker build -f Dockerfile -t mariocodes/kubernetes-custom-java-maven-app .

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(Oficial Doc)

Technology to quickly set up a Kubernetes cluster locally to learn how to use Kubernetes.

Installation

How to install on a fresh Ubuntu installation:

Install prerrequisites and kubectl

sudo apt-get update && sudo apt-get install -y apt-transport-https curl

curl -s https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -

echo "deb https://apt.kubernetes.io/ kubernetes-xenial main" | sudo tee -a /etc/apt/sources.list.d/kubernetes.list

sudo apt-get update
sudo apt-get install -y kubectl

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STATUS CrashLoopBackOff

CrashLoopBackOff and no log appears. This happens as the container has no command to run so it start up and then immediately exits.

Give your image a command.

more here

apiVersion: v1
kind: Pod
metadata:
  name: twocontainers
spec:
  containers:
  - name: container1
    image: python:3.6-alpine
	command: ['sh', '-c', 'echo cont1 > index.html && python -m http.server 8082']
  - name: container2
    image: python:3.6-alpine
	command: ['sh', '-c', 'echo cont1 > index.html && python -m http.server 8083']

Relational

SQL

SQL Cheat Sheet
SQL Triggers & Stored Procedures
SQL Views & Materialized views
SQL UNION ALL

PgSQL Functions

PgSQL Functions (Stored Procedure)

MySQL

MySQL Administration
MySQL User Privileges

No-relational

Mongo

MongoDB

ElasticSearch

ElasticSearch

i18n is just a nomenclature for Internationalization. i18n involves, among other things, the ability to display translated content. It prepares a digital product for localization by for example, separate the content into strings so they are ready to be translated and delivered.

The same goes for L10n. This is a nomenclature for Localization. L10n involves translating content, adapting graphics and finalizing the producto for each regional market.

Reference(s)

https://www.oneskyapp.com/blog/i18n-need-to-know-internationalization/

Indexes are a basic structure type that apply to one or multiple columns in order to improve performance and speed up queries that: filter, sort or join data for a table.

this may improve performance for a query that uses last_name in a WHERE clause or an ORDER BY

CREATE INDEX idx_last_name ON employees (last_name);

you can also create composite indexes

CREATE INDEX idx_composite ON employees (last_name, first_name)

and also composite index for only active employees

CREATE INDEX idx_active_employees ON employees (status) WHERE status = 'active';

When to use indexes

Frequent filters WHERE - if you usually filter by a specific column (last_name for example), an index should improve performance.

JOIN for big tables - when you use JOIN with big tables through PKs or FKs.

ORDER BY or GROUP BY - queries that search for / group by a specific column also benefit from indexes.

Best practices

Don’t create indexes in every column. This slows things down on insert, delete or update operations.

Index are best used for big domain fields such as ids, names, surnames. Don’t use them for male/female (or boolean) fields.

Keep indexes optimized: operations where you mass update or mass delete items in your tables may fragment your indexes. You may need to periodically check them and REINDEX them.

ALWAYS MEASURE PERFORMANCE TIME before and after the creation of that index. If your index doesn’t improve performance, remove it as it causes overhead.

Reference(s)

https://stackoverflow.com/questions/7744038/decision-when-to-create-index-on-table-column-in-database
https://stackoverflow.com/questions/52444912/how-to-find-out-fragmented-indexes-and-defragment-them-in-postgresql
https://chatgpt.com/

UNION

The union sentence is used to accumulate results for two SELECT sentences.

SELECT column1, column2 FROM table1
UNION
SELECT column1, column2 FROM table2

We have the following tables

company1

company2

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Find an exact match of a nested field (note that .keyword has to be added to string fields)

{
  "query": {
    "bool": {
      "must": [
        {
          "term": {
            "metadata.title.keyword": "File test.pdf"
          }
        }
      ]
    }
  },
  "size": 200
}

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Azure CosmosDB query examples
Azure Functions - Introduction