Justice  Reilly

Justice Reilly

1594437540

Docker AWS Beta Just Announced: DevOps and Docker Live Show (Ep 84)

Docker just released a new Beta with built-in AWS deployment support. Let’s check it out!

Support this show on Patreon! It’s the #1 way to support me interviewing DevOps and container experts, and doing this Live Q&A. https://patreon.com/BretFisher

#devops #docker #aws #just

What is GEEK

Buddha Community

Docker AWS Beta Just Announced: DevOps and Docker Live Show (Ep 84)
Callum Slater

Callum Slater

1653465344

PySpark Cheat Sheet: Spark DataFrames in Python

This PySpark SQL cheat sheet is your handy companion to Apache Spark DataFrames in Python and includes code samples.

You'll probably already know about Apache Spark, the fast, general and open-source engine for big data processing; It has built-in modules for streaming, SQL, machine learning and graph processing. Spark allows you to speed analytic applications up to 100 times faster compared to other technologies on the market today. Interfacing Spark with Python is easy with PySpark: this Spark Python API exposes the Spark programming model to Python. 

Now, it's time to tackle the Spark SQL module, which is meant for structured data processing, and the DataFrame API, which is not only available in Python, but also in Scala, Java, and R.

Without further ado, here's the cheat sheet:

PySpark SQL cheat sheet

This PySpark SQL cheat sheet covers the basics of working with the Apache Spark DataFrames in Python: from initializing the SparkSession to creating DataFrames, inspecting the data, handling duplicate values, querying, adding, updating or removing columns, grouping, filtering or sorting data. You'll also see that this cheat sheet also on how to run SQL Queries programmatically, how to save your data to parquet and JSON files, and how to stop your SparkSession.

Spark SGlL is Apache Spark's module for working with structured data.

Initializing SparkSession 
 

A SparkSession can be used create DataFrame, register DataFrame as tables, execute SGL over tables, cache tables, and read parquet files.

>>> from pyspark.sql import SparkSession
>>> spark a SparkSession \
     .builder\
     .appName("Python Spark SQL basic example") \
     .config("spark.some.config.option", "some-value") \
     .getOrCreate()

Creating DataFrames
 

Fromm RDDs

>>> from pyspark.sql.types import*

Infer Schema

>>> sc = spark.sparkContext
>>> lines = sc.textFile(''people.txt'')
>>> parts = lines.map(lambda l: l.split(","))
>>> people = parts.map(lambda p: Row(nameap[0],ageaint(p[l])))
>>> peopledf = spark.createDataFrame(people)

Specify Schema

>>> people = parts.map(lambda p: Row(name=p[0],
               age=int(p[1].strip())))
>>>  schemaString = "name age"
>>> fields = [StructField(field_name, StringType(), True) for field_name in schemaString.split()]
>>> schema = StructType(fields)
>>> spark.createDataFrame(people, schema).show()

 

From Spark Data Sources
JSON

>>>  df = spark.read.json("customer.json")
>>> df.show()

>>>  df2 = spark.read.load("people.json", format="json")

Parquet files

>>> df3 = spark.read.load("users.parquet")

TXT files

>>> df4 = spark.read.text("people.txt")

Filter 

#Filter entries of age, only keep those records of which the values are >24
>>> df.filter(df["age"]>24).show()

Duplicate Values 

>>> df = df.dropDuplicates()

Queries 
 

>>> from pyspark.sql import functions as F

Select

>>> df.select("firstName").show() #Show all entries in firstName column
>>> df.select("firstName","lastName") \
      .show()
>>> df.select("firstName", #Show all entries in firstName, age and type
              "age",
              explode("phoneNumber") \
              .alias("contactInfo")) \
      .select("contactInfo.type",
              "firstName",
              "age") \
      .show()
>>> df.select(df["firstName"],df["age"]+ 1) #Show all entries in firstName and age, .show() add 1 to the entries of age
>>> df.select(df['age'] > 24).show() #Show all entries where age >24

When

>>> df.select("firstName", #Show firstName and 0 or 1 depending on age >30
               F.when(df.age > 30, 1) \
              .otherwise(0)) \
      .show()
>>> df[df.firstName.isin("Jane","Boris")] #Show firstName if in the given options
.collect()

Like 

>>> df.select("firstName", #Show firstName, and lastName is TRUE if lastName is like Smith
              df.lastName.like("Smith")) \
     .show()

Startswith - Endswith 

>>> df.select("firstName", #Show firstName, and TRUE if lastName starts with Sm
              df.lastName \
                .startswith("Sm")) \
      .show()
>>> df.select(df.lastName.endswith("th"))\ #Show last names ending in th
      .show()

Substring 

>>> df.select(df.firstName.substr(1, 3) \ #Return substrings of firstName
                          .alias("name")) \
        .collect()

Between 

>>> df.select(df.age.between(22, 24)) \ #Show age: values are TRUE if between 22 and 24
          .show()

Add, Update & Remove Columns 

Adding Columns

 >>> df = df.withColumn('city',df.address.city) \
            .withColumn('postalCode',df.address.postalCode) \
            .withColumn('state',df.address.state) \
            .withColumn('streetAddress',df.address.streetAddress) \
            .withColumn('telePhoneNumber', explode(df.phoneNumber.number)) \
            .withColumn('telePhoneType', explode(df.phoneNumber.type)) 

Updating Columns

>>> df = df.withColumnRenamed('telePhoneNumber', 'phoneNumber')

Removing Columns

  >>> df = df.drop("address", "phoneNumber")
 >>> df = df.drop(df.address).drop(df.phoneNumber)
 

Missing & Replacing Values 
 

>>> df.na.fill(50).show() #Replace null values
 >>> df.na.drop().show() #Return new df omitting rows with null values
 >>> df.na \ #Return new df replacing one value with another
       .replace(10, 20) \
       .show()

GroupBy 

>>> df.groupBy("age")\ #Group by age, count the members in the groups
      .count() \
      .show()

Sort 
 

>>> peopledf.sort(peopledf.age.desc()).collect()
>>> df.sort("age", ascending=False).collect()
>>> df.orderBy(["age","city"],ascending=[0,1])\
     .collect()

Repartitioning 

>>> df.repartition(10)\ #df with 10 partitions
      .rdd \
      .getNumPartitions()
>>> df.coalesce(1).rdd.getNumPartitions() #df with 1 partition

Running Queries Programmatically 
 

Registering DataFrames as Views

>>> peopledf.createGlobalTempView("people")
>>> df.createTempView("customer")
>>> df.createOrReplaceTempView("customer")

Query Views

>>> df5 = spark.sql("SELECT * FROM customer").show()
>>> peopledf2 = spark.sql("SELECT * FROM global_temp.people")\
               .show()

Inspect Data 
 

>>> df.dtypes #Return df column names and data types
>>> df.show() #Display the content of df
>>> df.head() #Return first n rows
>>> df.first() #Return first row
>>> df.take(2) #Return the first n rows >>> df.schema Return the schema of df
>>> df.describe().show() #Compute summary statistics >>> df.columns Return the columns of df
>>> df.count() #Count the number of rows in df
>>> df.distinct().count() #Count the number of distinct rows in df
>>> df.printSchema() #Print the schema of df
>>> df.explain() #Print the (logical and physical) plans

Output

Data Structures 
 

 >>> rdd1 = df.rdd #Convert df into an RDD
 >>> df.toJSON().first() #Convert df into a RDD of string
 >>> df.toPandas() #Return the contents of df as Pandas DataFrame

Write & Save to Files 

>>> df.select("firstName", "city")\
       .write \
       .save("nameAndCity.parquet")
 >>> df.select("firstName", "age") \
       .write \
       .save("namesAndAges.json",format="json")

Stopping SparkSession 

>>> spark.stop()

Have this Cheat Sheet at your fingertips

Original article source at https://www.datacamp.com

#pyspark #cheatsheet #spark #dataframes #python #bigdata

Justice  Reilly

Justice Reilly

1594437540

Docker AWS Beta Just Announced: DevOps and Docker Live Show (Ep 84)

Docker just released a new Beta with built-in AWS deployment support. Let’s check it out!

Support this show on Patreon! It’s the #1 way to support me interviewing DevOps and container experts, and doing this Live Q&A. https://patreon.com/BretFisher

#devops #docker #aws #just

Jeevi Academy

1672143538

7 Best Chrome Extensions for UI/UX Designers | Jeevisoft |

#chromeextension #chrome #extension #ux #uxbook #contentmarketing #design #principles #gooddesign ##ui #userinterface #services #academy #userflow #userjourney #devops #automation #designer #gestalt #ux #designer #skills #interviewquestions #aws #docker#interviewquestions #interview #aws #scenario #cheatsheet #solutionarchitect #azure #ansibleinterview #questions #Devops #interview #guideline #Terraform #cheatsheet #interview #steps #localbusiness #business #videocreating #containor #devops #interview #opportunities #findabestway #certification #top #digitalmarketing #seo #mail #ppc #socialmediamarketing #shorts #technology #frontend #developer #youtube#programming #learn #tech #technology #trending #beginners #worldnews #creative #knowledge #academy #shorts #youtubeshorts #youtube #aws #docker #ui #website #webdesign #development #developer 

How to Create a Responsive Dropdown Menu Bar with Search Field using HTML & CSS

In this guide you’ll learn how to create a Responsive Dropdown Menu Bar with Search Field using only HTML & CSS.

To create a responsive dropdown menu bar with search field using only HTML & CSS . First, you need to create two Files one HTML File and another one is CSS File.

1: First, create an HTML file with the name of index.html

<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <meta http-equiv="X-UA-Compatible" content="ie=edge">
  <title>Dropdown Menu with Search Box | Codequs</title>
  <link rel="stylesheet" href="style.css">
  <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.3/css/all.min.css"/>
</head>
<body>
  <div class="wrapper">
    <nav>
      <input type="checkbox" id="show-search">
      <input type="checkbox" id="show-menu">
      <label for="show-menu" class="menu-icon"><i class="fas fa-bars"></i></label>
      <div class="content">
      <div class="logo"><a href="#">CodingNepal</a></div>
        <ul class="links">
          <li><a href="#">Home</a></li>
          <li><a href="#">About</a></li>
          <li>
            <a href="#" class="desktop-link">Features</a>
            <input type="checkbox" id="show-features">
            <label for="show-features">Features</label>
            <ul>
              <li><a href="#">Drop Menu 1</a></li>
              <li><a href="#">Drop Menu 2</a></li>
              <li><a href="#">Drop Menu 3</a></li>
              <li><a href="#">Drop Menu 4</a></li>
            </ul>
          </li>
          <li>
            <a href="#" class="desktop-link">Services</a>
            <input type="checkbox" id="show-services">
            <label for="show-services">Services</label>
            <ul>
              <li><a href="#">Drop Menu 1</a></li>
              <li><a href="#">Drop Menu 2</a></li>
              <li><a href="#">Drop Menu 3</a></li>
              <li>
                <a href="#" class="desktop-link">More Items</a>
                <input type="checkbox" id="show-items">
                <label for="show-items">More Items</label>
                <ul>
                  <li><a href="#">Sub Menu 1</a></li>
                  <li><a href="#">Sub Menu 2</a></li>
                  <li><a href="#">Sub Menu 3</a></li>
                </ul>
              </li>
            </ul>
          </li>
          <li><a href="#">Feedback</a></li>
        </ul>
      </div>
      <label for="show-search" class="search-icon"><i class="fas fa-search"></i></label>
      <form action="#" class="search-box">
        <input type="text" placeholder="Type Something to Search..." required>
        <button type="submit" class="go-icon"><i class="fas fa-long-arrow-alt-right"></i></button>
      </form>
    </nav>
  </div>
  <div class="dummy-text">
    <h2>Responsive Dropdown Menu Bar with Searchbox</h2>
    <h2>using only HTML & CSS - Flexbox</h2>
  </div>
</body>
</html>

2: Second, create a CSS file with the name of style.css

@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@200;300;400;500;600;700&display=swap');
*{
  margin: 0;
  padding: 0;
  box-sizing: border-box;
  text-decoration: none;
  font-family: 'Poppins', sans-serif;
}
.wrapper{
  background: #171c24;
  position: fixed;
  width: 100%;
}
.wrapper nav{
  position: relative;
  display: flex;
  max-width: calc(100% - 200px);
  margin: 0 auto;
  height: 70px;
  align-items: center;
  justify-content: space-between;
}
nav .content{
  display: flex;
  align-items: center;
}
nav .content .links{
  margin-left: 80px;
  display: flex;
}
.content .logo a{
  color: #fff;
  font-size: 30px;
  font-weight: 600;
}
.content .links li{
  list-style: none;
  line-height: 70px;
}
.content .links li a,
.content .links li label{
  color: #fff;
  font-size: 18px;
  font-weight: 500;
  padding: 9px 17px;
  border-radius: 5px;
  transition: all 0.3s ease;
}
.content .links li label{
  display: none;
}
.content .links li a:hover,
.content .links li label:hover{
  background: #323c4e;
}
.wrapper .search-icon,
.wrapper .menu-icon{
  color: #fff;
  font-size: 18px;
  cursor: pointer;
  line-height: 70px;
  width: 70px;
  text-align: center;
}
.wrapper .menu-icon{
  display: none;
}
.wrapper #show-search:checked ~ .search-icon i::before{
  content: "\f00d";
}
.wrapper .search-box{
  position: absolute;
  height: 100%;
  max-width: calc(100% - 50px);
  width: 100%;
  opacity: 0;
  pointer-events: none;
  transition: all 0.3s ease;
}
.wrapper #show-search:checked ~ .search-box{
  opacity: 1;
  pointer-events: auto;
}
.search-box input{
  width: 100%;
  height: 100%;
  border: none;
  outline: none;
  font-size: 17px;
  color: #fff;
  background: #171c24;
  padding: 0 100px 0 15px;
}
.search-box input::placeholder{
  color: #f2f2f2;
}
.search-box .go-icon{
  position: absolute;
  right: 10px;
  top: 50%;
  transform: translateY(-50%);
  line-height: 60px;
  width: 70px;
  background: #171c24;
  border: none;
  outline: none;
  color: #fff;
  font-size: 20px;
  cursor: pointer;
}
.wrapper input[type="checkbox"]{
  display: none;
}
/* Dropdown Menu code start */
.content .links ul{
  position: absolute;
  background: #171c24;
  top: 80px;
  z-index: -1;
  opacity: 0;
  visibility: hidden;
}
.content .links li:hover > ul{
  top: 70px;
  opacity: 1;
  visibility: visible;
  transition: all 0.3s ease;
}
.content .links ul li a{
  display: block;
  width: 100%;
  line-height: 30px;
  border-radius: 0px!important;
}
.content .links ul ul{
  position: absolute;
  top: 0;
  right: calc(-100% + 8px);
}
.content .links ul li{
  position: relative;
}
.content .links ul li:hover ul{
  top: 0;
}
/* Responsive code start */
@media screen and (max-width: 1250px){
  .wrapper nav{
    max-width: 100%;
    padding: 0 20px;
  }
  nav .content .links{
    margin-left: 30px;
  }
  .content .links li a{
    padding: 8px 13px;
  }
  .wrapper .search-box{
    max-width: calc(100% - 100px);
  }
  .wrapper .search-box input{
    padding: 0 100px 0 15px;
  }
}
@media screen and (max-width: 900px){
  .wrapper .menu-icon{
    display: block;
  }
  .wrapper #show-menu:checked ~ .menu-icon i::before{
    content: "\f00d";
  }
  nav .content .links{
    display: block;
    position: fixed;
    background: #14181f;
    height: 100%;
    width: 100%;
    top: 70px;
    left: -100%;
    margin-left: 0;
    max-width: 350px;
    overflow-y: auto;
    padding-bottom: 100px;
    transition: all 0.3s ease;
  }
  nav #show-menu:checked ~ .content .links{
    left: 0%;
  }
  .content .links li{
    margin: 15px 20px;
  }
  .content .links li a,
  .content .links li label{
    line-height: 40px;
    font-size: 20px;
    display: block;
    padding: 8px 18px;
    cursor: pointer;
  }
  .content .links li a.desktop-link{
    display: none;
  }
  /* dropdown responsive code start */
  .content .links ul,
  .content .links ul ul{
    position: static;
    opacity: 1;
    visibility: visible;
    background: none;
    max-height: 0px;
    overflow: hidden;
  }
  .content .links #show-features:checked ~ ul,
  .content .links #show-services:checked ~ ul,
  .content .links #show-items:checked ~ ul{
    max-height: 100vh;
  }
  .content .links ul li{
    margin: 7px 20px;
  }
  .content .links ul li a{
    font-size: 18px;
    line-height: 30px;
    border-radius: 5px!important;
  }
}
@media screen and (max-width: 400px){
  .wrapper nav{
    padding: 0 10px;
  }
  .content .logo a{
    font-size: 27px;
  }
  .wrapper .search-box{
    max-width: calc(100% - 70px);
  }
  .wrapper .search-box .go-icon{
    width: 30px;
    right: 0;
  }
  .wrapper .search-box input{
    padding-right: 30px;
  }
}
.dummy-text{
  position: absolute;
  top: 50%;
  left: 50%;
  width: 100%;
  z-index: -1;
  padding: 0 20px;
  text-align: center;
  transform: translate(-50%, -50%);
}
.dummy-text h2{
  font-size: 45px;
  margin: 5px 0;
}

Now you’ve successfully created a Responsive Dropdown Menu Bar with Search Field using only HTML & CSS.

Mikel  Okuneva

Mikel Okuneva

1602317778

Ever Wondered Why We Use Containers In DevOps?

At some point we’ve all said the words, “But it works on my machine.” It usually happens during testing or when you’re trying to get a new project set up. Sometimes it happens when you pull down changes from an updated branch.

Every machine has different underlying states depending on the operating system, other installed programs, and permissions. Getting a project to run locally could take hours or even days because of weird system issues.

The worst part is that this can also happen in production. If the server is configured differently than what you’re running locally, your changes might not work as you expect and cause problems for users. There’s a way around all of these common issues using containers.

What is a container

A container is a piece of software that packages code and its dependencies so that the application can run in any computing environment. They basically create a little unit that you can put on any operating system and reliably and consistently run the application. You don’t have to worry about any of those underlying system issues creeping in later.

Although containers were already used in Linux for years, they became more popular in recent years. Most of the time when people are talking about containers, they’re referring to Docker containers. These containers are built from images that include all of the dependencies needed to run an application.

When you think of containers, virtual machines might also come to mind. They are very similar, but the big difference is that containers virtualize the operating system instead of the hardware. That’s what makes them so easy to run on all of the operating systems consistently.

What containers have to do with DevOps

Since we know how odd happenings occur when you move code from one computing environment to another, this is also a common issue with moving code to the different environments in our DevOps process. You don’t want to have to deal with system differences between staging and production. That would require more work than it should.

Once you have an artifact built, you should be able to use it in any environment from local to production. That’s the reason we use containers in DevOps. It’s also invaluable when you’re working with microservices. Docker containers used with something like Kubernetes will make it easier for you to handle larger systems with more moving pieces.

#devops #containers #containers-devops #devops-containers #devops-tools #devops-docker #docker #docker-image