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今回はDataTablesを使用したテーブル生成方法とサーバーサイド連携方法をシェアしたいと思います。
サーバーサイドはLaravel/PHPを使用する想定です。

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SCSS (Sassy CSS) is the superset, it’s the more advanced version of CSS. We can add additional functionalities to CSS such as variables, nesting , loops, indentations and more by using SCSS. All these additional functionalities can make writing CSS much easier and faster as compared to writing the traditional CSS.

Nesting

Nesting has many benefits:

Variables

 SCSS offers variables in order to declare re-used properties in one place. Using traditional CSS, we would need to repeat the same code over and over, but with SASS we can store these values as variables.

Mathematical functions

In SASS, we can also use mathematical operations like +, -, *, /, or %. This allows us to influence size specifications, for example.

Indentations

The original SASS works with indentations and line breaks to structure the code. We don’t need parentheses to display nesting or semicolons to mark line ends.

By Tsuki

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Flutterには画像を用意しなくても表示できるアイコンがあります。
Icons というクラスを使用し、Icons.{アイコン名}で取得する事ができます。

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Today I would like to share about differences between SQL and NoSQL. Let’s take a look.

SQL databases are commonly referred to as relational database management systems (RDBMS). Traditional RDBMS uses SQL syntax as these systems utilize row-based database structures that connect related data objects between tables. Examples of RDBMS SQL databases include Backendless, Microsoft Access, MySQL, Microsoft SQL Server, SQLite, Oracle Database, IBM DB2, etc.

NoSQL databases, on the other hand, are databases without any structured tables fixed for holding data. Technically, all non-relational databases can be called NoSQL databases. Because a NoSQL database is not a relational database, it can be set up very quickly and with minimal pre-planning. Examples of NoSQL databases include MongoDB, DynamoDB, SimpleDB, CouchDB, CouchBase, Orient DB, Infinite Graph, Neo4j, FlockDB, Cassandra, HBase, etc. 

Querying Language & Database Schema

SQL

SQL database use structured query language. They have pre-defined schema for data structure. SQL querying languages have been around for long time and have evolved greatly , even providing with great libraries one can use to ease querying. It is perfect for complex data structures and queries. But SQL has strict data structure. SQL query language is declarative and lightweight. Significant research has to be done before creating and implementing any RDB since it is very difficult to change schema and data structure once project is deployed.

NoSQL

NoSQL have dynamic schema and hence data can be created quickly without defining the structure. Every document can have its own structure and syntax, and there is flexibility to use column-oriented, document-oriented, graph-oriented or Key-value pairs! Each NoSQL can have a different query language, adding to the complexity of learning more languages in order to work with different database. NoSQL query language is non-declarative.

Data Structure

SQL

Data is stored in tables with pre-defined columns. Each entry is a new row essentially that we are creating or accessing.

NoSQL

Data can be stored as a JSON, graph, key-value pairs or tables with dynamic columns.

Database Scaling

SQL

They are Vertically scalable. Load handling capabilities of a single server can be increased by adding more RAM, CPU or SSD capacity. This is also called ‘scale-up’.

NoSQL

They are Horizontally scalable. Data traffic can be increased by sharding, simply by adding more servers. They are better from scalability perspective, and preferred for large and frequently changed data.

Conclusion

Both SQL and NoSQL databases are used in specific needs. Depending on the goals and data environment of an organization, their specific pros and cons could be amplified.

When we decide between SQL and NoSQL, we have to focus on 3 core concepts for our database that suits our project.

Structure: Every project needs to store and retrieve data differently. Structure needs to be chose that requires least amount of work and easy scalability.

Speed and Scale: Data modelling can help in deciding best route to get optimum speed. Some databases are designed for optimized read-heavy app while others write-heavy solutions. Selecting right database for project’s I/O is important.

Size: It depends on maximum amount of data we can store and process, before impacting the database. It can vary from combination of data structure stored, partitioned data across multiple filesystems and servers, and also vendor specifics.

This is all for now.

Hope you enjoy that.

By Asahi

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When we are supposed to work with large csv file with 500K or 1mil records, there is always a thing to be careful about the memory usage. If our program consumes lot of memory , its not good for the physical server we are using. Beside from that our program should also performed well. Today I would like to share some tips working with laravel csv file.

Configuration

Firstly , we have to make sure our PHP setting is configured. Please check the below settings and configure as you need. But keep in mind, don’t enlarge the memory_limit value if it’s not required.

- memory_limit
- post_max_size
- upload_max_filesize
- max_execution_time

Code

Once our PHP configuration is done, you might to restart the server or PHP itself. The next step is our code. We have to write a proper way not to run out of memory.

Normally we used to read the csv files like this.

$handle = fopen($filepath, "r"); //getting our csv file
while ($csv = fgetcsv($handle, 1000, ",")) { //looping through each records
//making csv rows validation
// inserting to database
// etc.
}

The above code might be ok for a few records like 1000 to 5000 and so on. But if you are working with 100K 500K records , the while loop will consume lot of memory. So we have to chunk and separate the loop to get some rest time for our program.

$handle = fopen($filepath, "r"); //getting our csv file
$data = []; 
while ($csv = fgetcsv($handle, 1000, ",")) { //looping through each records
   $data[] = $csv;// you can customize the array as you want
   //we will only collect each 1000 records and do the operations
   if(count($data) >= 1000){
　 // do the operations here
   // inserting to database (If you already prepared the array in above, can directly add to db, no need loops)
   // etc.
   
   //resetting the data array
   $data = [];
   }

   //if there is any rows less than 1000, keep going for it
   if(count($data) > 0){
      // do the operations here
   }
}

Above one is a simple protype to run the program not to run out of the memory, our program will get rest time for each 1000records.

Here is an another way using array_chuck and file function

$csvArray = file($filepath); //this will output array of our csv file
//chunking array by 1000 records
$chunks = array_chunk($csvArray,1000);

// Then lets store the chunked data files in somewhere
foreach ($chunks as $key => $chunk) {
   file_put_contents($path,$chunk);
}

//get the files we have stored and can loop through it
files = glob('path/path'."/*.csv");

foreach ($files as $key => $file) {
  $filer = fopen($file, "r");
  while ($csv = fgetcsv($filer, 1000, ",")) {
     // do the operations here
  }

  //delete the file back
  unlink($file);
}

Please don’t forget to close the files back fclose if you have done the file operations.

Looping content

One more thing to keep in mind is we have to take care of the codes we put inside loops. If there is any

• Database calls or
• Third party API calls,

it will surely slow down the performance and consume the memory more. So if you can put these calls outside of the loops, our program will be much more efficient.

I am sure there might also be some other work arounds or some packages to handle about this issue behind the scence.

Yuuma

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