This guide gathers the basic commands every Docker user will frequently need. Personally, I keep getting back to it from time to time when my Docker knowledge gets rusty. Hope it helps you too!
docker images
package main | |
import ( | |
"fmt" | |
"net/http" | |
"os" | |
"strings" | |
"github.com/aws/aws-sdk-go/aws" | |
"github.com/aws/aws-sdk-go/service/dynamodb" |
<!-- | |
This page shows a form using Semantic UI library. | |
The form is used to upload reports SQL scripts to system in order to show | |
report content in reports/view page. | |
The form uses codemirror library which is a code-supported text area. It is | |
used by <text-codemirror>, a tag created in a separate file (text-codemirror.tag) | |
--> | |
<reports-form> |
sprintf = require('sprintf').sprintf; | |
/* Tables their INSERT commands will be written to CSV file | |
'articles' and 'range_test' are sample tables to test the streamer. | |
*/ | |
var tables = { | |
'articles': ['id', 'title', 'body', 'author', 'publish_date'], | |
'range_test': ['hash_id', 'range_id', 'item', 'quantity'], | |
}; |
<?php | |
class Vote | |
{ | |
private $username = "root"; | |
private $password = ""; | |
private $database = ""; | |
private $barWidth = 200; | |
private $barHeight = 10; | |
private $showtablewidth = "280"; | |
private $showAddForm = 400; |
## | |
# | |
# This is a game where 4 players have 6 dices each. They throw the dices each time. | |
# Dices with 6 will be removed. Dices with 1 will be added to player's dices in the right. | |
# | |
## | |
class DiceGame | |
private |
This article introduces Databricks Delta Lake. A revolutionary storage layer that brings reliability and improve performance of data lakes using Apache Spark.
First, we'll go through the dry parts which explain what Apache Spark and data lakes are and it explains the issues faced with data lakes. Then it talks about Delta lake and how it solved these issues with a practical, easy-to-apply tutorial.
If you don't know what Spark is, Apache Spark is a large-scale data processing and unified analytics engine for big data and machine learning. It was originally developed at UC Berkeley in 2009. Apache Spark is 100% open source, hosted at the vendor-independent Apache Software Foundation.