PHP implements open source Kylin multidimensional data analysis engine

With the continuous development of the big data era and Internet technology, data analysis has become an important part of corporate decision-making. Kylin multidimensional data analysis engine is an open source software that can easily process PB-level data and respond to complex multidimensional analysis queries in real time. This article will introduce you to the working principle and usage of Kylin, as well as how to implement Kylin in PHP.

1. Working Principle of Kylin

The core principle of Kylin is columnar storage and multi-dimensional data cube technology based on Hadoop cluster architecture. Kylin extracts data from various tables in the Hadoop cluster, preprocesses the data and stores it in a multidimensional data framework. It can quickly respond to multidimensional analysis queries and provide analysis functions similar to OLAP. Let’s learn more about how Kylin works.

1. Data preprocessing

Kylin first needs to preprocess the data. Kylin generates different data sets for each row of data according to different granularities. For example, aggregation based on time granularity will create different data sets based on time granularity of days, weeks, months, quarters, years, etc. Then perform data skew processing for each data set. In this process, Kylin will maximize the use of distributed and parallel computing based on massive data, process and clean the data, and then perform multi-dimensional pre-calculation to generate multi-dimensional cube data.

2. Query acceleration

Query acceleration is Kylin’s biggest advantage. When performing multi-dimensional analysis queries, Kylin can quickly respond to query requests, achieving a second-level response. It also supports a huge amount of query concurrency, and good service quality is guaranteed. Kylin's query acceleration is achieved through pre-computation and file storage. Kylin will store multi-dimensional data cubes in HBase, and you can directly access the data in HBase when querying, giving full play to the advantages of the Hadoop cluster.

2. How to use Kylin

The installation and use of Kylin is relatively complicated. Here is a brief introduction to how to use Kylin.

1. Install Kylin

The installation of Kylin needs to be carried out in a Hadoop cluster environment. You need to bring your own server with a deployed Hadoop cluster or rent a configured cloud host. Kylin's installation guide can be found on GitHub.

2. Import data

Kylin’s data source can be a variety of different data, such as Hive tables, HBase tables, local data files, etc. You must import data into Kylin through the corresponding ETL tool. Kylin can handle all tables sorted in chronological order, and can handle index columns and partition columns.

3. Establish a data model

Kylin supports data modeling based on multi-dimensional data cubes. Kylin has advanced scalability and flexibility for different models. First, you need to create a data cube. Kylin will import the data into the cube and define all dimensions and indicators for each cube table. Next, you need to create metric aggregations to further define how the data is presented and processed so that it can be retrieved and processed efficiently.

4. Query Analysis

After completing the data import and model establishment, you can use Kylin’s analysis function to explore and study the data. In Kylin, you can perform time-based analysis, statistical analysis, enterprise-level data analysis, data interaction between different organizations, and more. Kylin provides a rich interface to support multi-dimensional data analysis and query, including web-based interface and API interface.

3. PHP implements Kylin multidimensional data analysis engine

PHP is a popular development language and is widely used in the development of Web applications. If you have implemented a big data system based on Hadoop cluster and want to use PHP to implement Kylin's functions, you can follow the following steps:

1. Install Kylin's dependencies

Kylin needs to be integrated with the Hadoop cluster and HBase, so you need to adapt Kylin's dependent libraries to the Hadoop cluster. You can refer to Kylin's official documentation to complete this step.

2. Write PHP script

In the PHP script, you need to use the RESTful API interface provided by Kylin to interact with Kylin. The RESTful API provided by Kylin can easily call various functions of Kylin. You can build Kylin's multidimensional data queries by writing code that requests the API in a PHP script.

3. Implement query acceleration

Kylin’s query acceleration is achieved through multi-dimensional precomputation and file storage, so you need to implement this function in a PHP script. This function can be achieved by using PHP's preprocessing engine to avoid repeated calculations and queries and improve the response speed of data queries.

4. Summary

Kylin multidimensional data analysis engine is an open source software that provides multidimensional data analysis functions by importing data into multidimensional cubes. Kylin supports multiple data sources, including Hive tables, HBase tables, local data files, etc. Kylin's query speed is very fast, with huge query concurrency and good service quality. Implementing Kylin through PHP can improve query speed and enable more efficient multi-dimensional data analysis queries.

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