When doing big data analysis, I encountered a common but difficult problem: how to interact with the ClickHouse database efficiently. Traditional database connection and query methods cannot meet the needs of high concurrency and large data volumes, resulting in slow response and even crashes of the program. After some exploration, I found the powerful PHP library smi2/phpclickhouse, which greatly improved my data processing efficiency.

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smi2/phpclickhouse is a lightweight PHP library designed for ClickHouse databases. It supports PHP 7.1 and above and does not need to rely on other libraries, just Curl. This makes it very easy to install and use, just run the following command:

 <code class="bash">composer require smi2/phpclickhouse</code>

Then initialize in PHP code:

 <code class="php">// vendor autoload $db = new ClickHouseDB\Client(['config_array']); if (!$db->ping()) echo 'Error connect';</code>

This library provides multiple features to improve interaction efficiency with ClickHouse. Here are a few key usage scenarios:

1. Parallel query : Use the selectAsync method to execute multiple queries in parallel, greatly improving the speed of data query. For example:

    $state1 = $db->selectAsync('SELECT 1 as ping'); $state2 = $db->selectAsync('SELECT 2 as ping'); // run $db->executeAsync(); // result print_r($state1->rows()); print_r($state2->fetchOne('ping'));

2. Batch Insert : Through the insertBatchFiles method, you can batch insert data from multiple CSV files in parallel, improving the efficiency of data import:

    $file_data_names = [ '/tmp/clickHouseDB_test.1.data', '/tmp/clickHouseDB_test.2.data', //... ]; // insert all files $stat = $db->insertBatchFiles( 'summing_url_views', $file_data_names, ['event_time', 'site_key', 'site_id', 'views', 'v_00', 'v_55'] );

3. HTTP Compression : By enabling HTTP compression, you can reduce the burden of network transmission when inserting large amounts of data:

    $db->settings()->max_execution_time(200); $db->enableHttpCompression(true); $result_insert = $db->insertBatchFiles('summing_url_views', $file_data_names, [...]);

4. Streaming processing : Using streamWrite and streamRead methods, data streaming processing can be implemented, suitable for processing large-scale data:

    $streamWrite=new ClickHouseDB\Transport\StreamWrite($stream); $client->streamWrite( $streamWrite, // StreamWrite Class 'INSERT INTO {table_name} FORMAT JSONEachRow', // SQL Query ['table_name'=>'_phpCh_SteamTest'] // Binds );

After using the smi2/phpclickhouse library, my data processing efficiency has been significantly improved. Parallel query and batch insert functions greatly reduce processing time, while HTTP compression and streaming reduce network and memory burden. Overall, this library not only solves the performance bottlenecks I encountered, but also brings more possibilities and flexibility to my big data analysis project. If you are facing similar data processing challenges, you might as well try this library.

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