How to use PHP functions to handle big data analysis

In today's information age, with the popularity of the Internet and the development of technology, more and more data are collected, stored and processed. How to analyze these data, discover patterns and explore value from them is a challenging and practical topic. As a popular programming language, PHP has a rich function library and flexible usage, which can help us handle big data analysis efficiently and accurately. This article will introduce how to use PHP functions to handle big data analysis to better achieve this goal.

I. Data preparation

Before conducting big data analysis, we need to prepare the data to be analyzed. This includes steps such as retrieving data from a data source, storing the data into a database, etc. After this, we can use PHP's database connection functions (such as mysqli_connect, PDO) to connect to the database to operate and query it.

II. Commonly used PHP functions

1. Query statements

For database queries, we can use some functions in PHP to achieve it. The most commonly used is the mysqli_query function (mysql_query has been deprecated). The syntax is as follows:

mysqli_query(connection,query,resultmode)

Parameter description:

• connection: database connection object.
• query: SQL statement to be executed.
• resultmode: Optional parameter, used to specify the type of returned result (such as MYSQLI_STORE_RESULT). The default is MYSQLI_STORE_RESULT.
• Return value: If the execution is successful, a result object is returned, otherwise FALSE is returned.

For example, we can use the following code to query data in the database:

$conn=mysqli_connect('localhost','root','123456','test' );
if($conn){

$sql="SELECT * FROM data";
$result=mysqli_query($conn,$sql);
if($result){
    while($row=mysqli_fetch_array($result)){
        echo $row['name']." ".$row['age']." ".$row['level']."<br/>";
    }
    mysqli_free_result($result);
}else{
    echo "查询出错！";
}

}else{

echo "数据库连接失败！";

}
mysqli_close($conn);

In the above code, we first Use the mysqli_connect function to connect to the database, then execute the SQL statement and return the result object through the mysqli_query function, and further use the mysqli_fetch_array function to obtain the result data and perform operations.

2. Statistical functions

In big data analysis, statistical functions are an indispensable part. PHP provides some commonly used functions to implement such statistical operations. For example, the summation function we often use can use array_sum, such as:

$data=array(3,2,1,4,5,6);
echo array_sum($data);

The output result is 21.

In addition, you can also use the array_count_values ​​function to count the number of occurrences of each value in the array, such as:

$data=array("Tom","Jack","Tom","Rose" ,"Tom");
print_r(array_count_values($data));

The output result is:

Array ( [Tom] => 3 [Jack] => 1 [Rose] => 1)

3. Array function

The array function in PHP is also an indispensable tool in big data analysis. For example, we can use the array_unique function to get the unique value in the array, such as:

$data=array(3,2,1,4,5,6,4,5);
print_r( array_unique($data));

The output result is:

Array ( [0] => 3 [1] => 2 [2] => 1 [3] = > 4 [4] => 5 [5] => 6 )

In addition, the array_filter function is also very useful and can be used to filter useless elements in the array, such as:

$ data=array(3,2,0,4,5,0,6,0);
print_r(array_filter($data));

The output result is:

Array ( [0] => 3 [1] => 2 [3] => 4 [4] => 5 [6] => 6 )

4. Time function

In big data processing, the time function is also very important. We can use PHP's time function to count and convert time. For example, use the date function to convert the timestamp into a readable time format, such as:

echo date("Y-m-d H:i:s");
echo date("Y-m-d H:i:s" ,time() - 3600);

In the above code, the first date function outputs the current time, and the second date function outputs the time one hour ago.

III. Practical Case

Finally, let’s look at a practical case to help you better understand how to use PHP functions for big data analysis. Suppose we want to analyze a sales data, this data contains the following fields: date, sales volume, price and salesperson. We need to collect statistics and analyze data to discover patterns and find optimization strategies.

First, we need to query the data from the database and store it into an array, as follows:

$conn=mysqli_connect('localhost','root','123456', 'test');
if($conn){

$sql="SELECT date,sell_count,price,seller FROM sales";
$result=mysqli_query($conn,$sql);
if($result){
    while($row=mysqli_fetch_assoc($result)){
        $data[]=$row;
    }
    mysqli_free_result($result);
}else{
    echo "查询出错！";
}

}else{

echo "数据库连接失败！";

}
mysqli_close($conn);

Next, We can use the array_column function to reorganize the data in the $data array with date as the key and sales volume as the value, such as:

$sell_count=array_column($data,"sell_count","date");

Then use the array_column function to reorganize the data in the $data array with date as the key and price as the value, such as:

$price=array_column($data,"price"," date");

Then, we can use the array_sum function to find the total daily sales amount and total sales volume, such as:

$total_count=array_sum($sell_count);
$total_price=array_sum($price);

For each salesperson’s sales, we can achieve it through the array_reduce function, such as:

$seller_sell_count=array_reduce($data,function( $result,$value){

if(!isset($result[$value['seller']])){
    $result[$value['seller']]=0;
}
$result[$value['seller']]+=$value['sell_count'];
return $result;

});

Finally, we can also use PHP's time function to convert dates to days of the week to better analyze sales trends, such as:

$week=array("日","一", "二","三","四","五","六");
$week_day=date("w",strtotime("2021-08-07"));
echo " 2021-08-07 is the week".$week[$week_day];

Summary:

This article introduces how to use PHP functions to process big data analysis, including data preparation, common functions and practical combat case. I believe that through learning, everyone will be able to master the basic methods and techniques of using PHP for big data analysis, and provide more efficient and accurate solutions for data analysis and mining.

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