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In recent years, with the rapid development of mobile Internet and the vigorous development of cloud computing, Internet of Things, artificial intelligence and other fields, the efficiency of data processing has become one of the concerns of major enterprises. In web development, processing json data has become an indispensable link. PHP, as the main force in web development, is also faced with the problem of how to process json data efficiently. In this context, this article will explore the efficiency of converting json to array in PHP and how to optimize it.
In PHP, the operation of converting json data to an array is very simple, just use the json_decode() function. The definition of this function is as follows:
mixed json_decode ( string $json [, bool $assoc = FALSE [, int $depth = 512 [, int $options = 0 ]]] )
Among them, the $json parameter indicates the json string to be decoded, the $assoc parameter indicates whether to convert the json object into an associative array, and the $depth parameter indicates the maximum number of supported decoding layers ( The default is 512), and the $options parameter indicates the behavior options during decoding.
The following is a simple example:
$json_str = '{"name":"张三","age":18}'; $arr = json_decode($json_str, true); print_r($arr);
The output result is:
Array ( [name] => 张三 [age] => 18 )
It can be seen that converting a json string into an array is very simple, and the processing efficiency is also relatively high High, supports conversion of associative arrays and ordinary arrays at the same time.
Although converting json to array is very simple, there will be a performance bottleneck when processing a large amount of json data. First of all, decoding json strings requires certain system resources, such as memory, CPU, etc. Secondly, when converting a json string into an array, it needs to be parsed and converted layer by layer according to the structure of the json string. This process also requires a lot of time and resources.
To illustrate this problem, the following is an example of performance testing. The tested json data contains 10,000 objects, each object has 10 attributes, and all attributes are string types. The total size is about 10MB. .
<?php $start_time = microtime(true); $json = file_get_contents('test.json'); $arr = json_decode($json, true); echo count($arr) . " "; echo (microtime(true) - $start_time) . " ";
The test result is:
10000 0.785
It can be seen that it takes 0.785 seconds to decode and convert 10MB of json data. This time may be used in some applications with high concurrency and real-time requirements. will become a bottleneck. Therefore, how to improve the efficiency of converting json to array has become a very important issue.
A variety of optimization solutions can be adopted to address the performance bottleneck of converting json to array.
3.1. Compress json data
First of all, you can compress json data to reduce the size of the json string, thereby shortening the decoding and conversion time. Common compression methods include gzip and LZ4. For example, using gzip to compress the json data in the above example can reduce the data size to about 1.56MB, thus shortening the decoding and conversion time.
3.2. Use streaming parsing method
Secondly, you can use streaming parsing method to gradually parse json data to avoid loading the entire json string into memory at one time. You can use the json_stream_decode() function in php to implement streaming parsing. For example:
<?php $handle = fopen('test.json', 'r'); $arr = []; $parser = json_parser(); while (($line = fgets($handle)) !== false) { $result = $parser->write($line); if ($result !== null) { array_push($arr, $result); } } fclose($handle); echo count($arr) . " "; function json_parser() { $parser = new JsonStreamingParser_Parser(new class() implements JsonStreamingParser_Listener { private $current_obj; private $stack; public function start_document() { $this->stack = []; } public function start_object() { $this->current_obj = []; } public function end_object() { $obj = array_pop($this->stack); if (!empty($this->stack)) { $current_key = end($this->stack); $current_key[] = $obj; } else { array_push($this->current_obj, $obj); } } public function start_array() { $this->stack[] = []; } public function end_array() { $arr = array_pop($this->stack); if (!empty($this->stack)) { $current_key = end($this->stack); $current_key[] = $arr; } else { array_push($this->current_obj, $arr); } } public function key($key) { array_push($this->stack, [$key]); } public function value($value) { $current_key = end($this->stack); $current_key[] = $value; } public function whitespace($whitespace) { } }); return $parser; }
In this example, the third-party library JsonStreamingParser is used to implement streaming parsing. The basic principle is to disassemble json data into multiple small pieces, parse each small piece, and generate corresponding array elements. In this way, you can avoid loading the entire json string into memory at once, thus greatly improving efficiency.
3.3. Use binary protocol
Finally, you can use binary protocol to replace json string and convert it into binary data for transmission and processing. Because binary data is usually more compact than literal data, it can be parsed faster. In php, you can use the MessagePack extension to implement binary protocol processing. For example:
$msgpack = new MessagePack(); $packed = $msgpack->pack($arr); $unpacked = $msgpack->unpack($packed);
In this example, the array is converted to binary format, and the pack() and unpack() methods in the MessagePack class are used for mutual conversion. It can be seen that using binary protocols can greatly improve the efficiency of decoding and conversion.
In actual development, json is a very commonly used data format and is also one of the standard data formats of Web API. Therefore, it is very important to optimize the efficiency of converting json to array in php. In this article, three common optimization solutions are introduced, including using compression algorithms, using streaming parsing, and using binary protocols. Each optimization solution has its own advantages and applicable scenarios, and developers can choose the appropriate solution based on actual needs.
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