Split into data blocks,In this format, efficient reading can be realized,Avoid unnecessary data reading operations.

Overview

image dataTear

  • 切换至:中文文档

  • knowledge base

  • dataTear

    Split into data fragments for data management. In this format, efficient reading can be achieved to avoid unnecessary data reading operations.

  • MAVEN dependent

    Maven repository url: https://s01.oss.sonatype.org/content/repositories/snapshots

    <repositories>
        <repository>
            <id>a</id>
            <name>sonatype</name>
            <url>https://s01.oss.sonatype.org/content/repositories/snapshots</url>
        </repository>
    </repositories>
    
    <dependencies>
        <dependency>
            <groupId>io.github.BeardedManZhao</groupId>
            <artifactId>dataTear</artifactId>
            <version>1.4-SNAPSHOT</version>
        </dependency>
    </dependencies>
    
  • Example of use

    The following is an example of reading and writing the dataTear file. The master is the data output component, and the Reader is the data reading component. The hyper-interfaces of these two components are the same, which is more flexible! The parameters can be set in chain mode. Of course, you can also set them step by step, providing strong flexibility. Please refer to the following main function code document for specific usage!

  • Full API Example

The API calls here are relatively complete, and the functions used are relatively comprehensive. You can use the following API calls for integrated development.

public static void main(String[] args) throws IOException {
        BasicConfigurator.configure();
        Date date = new Date();
        // Instantiate "Master" through RW, When instantiating, external data components can be integrated into this class, or the RW interface can be directly called to extract data components from the algorithm library
        DTMaster dtMaster = new DTMaster(s -> RW.getDT_UDF_Stream(DT_builtIn_UDF.LOCAL_GZIP).writeStream(s)) 
                .ReadFormat(DataSourceFormat.built_in).WriterFormat(DataOutputFormat.UDT) // Set data input and output mode
                .setUseSynchronization(true) // Whether to use synchronous data writing and wait for data output to complete
                .setIn_FilePath("D:\\InternetInformation.txt") // Set the read file path
                .setOUT_FilePath("C:\\Users\\4\\Desktop\\out") // Set the directory to which DataTear data is output
                .setSplitrex(",") // Set the column separator for data input
                .setOutSplit(",") // Set the column separator for data output
                .setPrimaryNum(0) // Set the primary key index in the data table. The data of the index column will be used as part of the nameManager
                .setFragmentationNum(2); // Set the number of output data fragments
        // Running components
        runRW(dtMaster);

        System.err.println("ok !  Time consuming to write data:" + (new Date().getTime() - date.getTime()) + "");

        /*TODO 数据组件分割 */

        Date date2 = new Date();
        // Instantiate "DTRead" through RW, When instantiating, external data components can be integrated into this class, or the RW interface can be directly called to extract data components from the algorithm library
        Reader dtRead = new DTRead(s -> RW.getDT_UDF_Stream(DT_builtIn_UDF.LOCAL_GZIP).readStream(s))
                .setPrimaryCharacteristic(data -> true) // Set the data primary key description. The data fragment of the primary key meeting this condition will be read
                .setUseMultithreading(true) // Set whether to use synchronous read
                .setMaxOutTimeMS(10000) // Set the maximum timeout (ms) for data reading. If the timeout is exceeded, data reading will be stopped immediately
                .setIn_FilePath("C:\\Users\\4\\Desktop\\out\\NameManager.NDT"); // Set the read NameManager path
        // Running components
        runRW(dtRead);

        System.err.println("ok !  Time consuming to read data:" + (new Date().getTime() - date2.getTime()) + "millisecond");
        System.err.println("source file:" + dtRead.getSrcFile() + "\tCreation time:" + new Date(dtRead.getCreateDateMS()).toLocaleString());
        System.err.println("Number of data rows:" + dtRead.getDataString().split("\n").length);
    }

    /**
     * 运行一个rw组件
     */
    public static boolean runRW(RW rw) throws IOException {
        return rw.openStream() && rw.op_Data() && rw.closeStream();
}
  • The simplest API example

If your customization requirements for functions are not so strong, this API call will be more suitable for you. It sets the necessary parameters. Note the data output mode here. If you need to call custom components or data components in the algorithm library, you need to change this mode to "UDT".

public static void main(String[] args) throws IOException {
    BasicConfigurator.configure();
    // Configure Data Output Class
    DTMaster dtMaster = new DTMaster(null)
            .WriterFormat(DataOutputFormat.built_in) // be careful!!! If UDF is not set here, data will be automatically written in "LOCAL_TEXT" mode
            .setPrimaryNum(0)
            .setIn_FilePath("C:\\Users\\4\\Desktop\\mathematicalModeling\\Attached documents\\test.txt") // Set the path of the converted file
            .setOUT_FilePath("C:\\Users\\4\\Desktop\\mathematicalModeling\\out") // Set the storage path of NM and other files after conversion
            .setSplitrex("\\s+");
    dtMaster.openStream();
    dtMaster.op_Data();
    dtMaster.closeStream();

    // Configure Data Reading Class
    Reader reader = new DTRead(InPath -> RW.getDT_UDF_Stream(DT_builtIn_UDF.LOCAL_TEXT).readStream(InPath))
            .setPrimaryCharacteristic((data) -> true)
            .setIn_FilePath("C:\\Users\\4\\Desktop\\mathematicalModeling\\out\\NameManager.NDT"); // Set the NM path of the read file
    reader.openStream();
    reader.op_Data();
    reader.closeStream();

    System.out.println(reader.getDataString());
}
  • DTMaster component output mode

image

  • built_in

The mode of using the built-in data output component is equivalent to the data output component of "LOCAL_TEXT". In this mode, you do not need to load the data output component. However, the disadvantage is that when using the built-in data component output mode, you can only use "LOCAL_TEXT" for data output. The setting method is shown in the following figure.

image

  • UDF

UDF is also a user-defined data output mode. In this mode, you must load a data output component when instantiating the DTMaster. The loading of this data component can be extracted from the algorithm library or directly implemented by yourself. The following is an example of calling the local GZIP data output component in the algorithm library. image

directory structure

KnowledgeDocument

The knowledge base file archive is used for the storage task of DataTear series knowledge documents. You can access it directly through the documents on the home page. There is no need to enter here directly. It is messy internally.

src_code

The source code storage directory of DataTear, where you can view the relevant source code of DataTear. Of course, here is the latest source code, which you can use to compile, so that you can obtain the latest version.

Note: The latest version is often unstable, so we recommend you to use the version that has been released for a long time!

README-Chinese.md

The Chinese version of the DaraTear homepage document. You can switch languages on the homepage default page to access this file.

README.md

The default version of the DaraTear homepage document. You can directly access this file on the homepage!

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Comments
  • Bump commons-io from 2.6 to 2.7 in /src_code1.4

    Bump commons-io from 2.6 to 2.7 in /src_code1.4

    Bumps commons-io from 2.6 to 2.7.

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Releases(v1.4.1)
  • v1.4.1(Sep 28, 2022)

    • introduce Thank you for using. This is my packed jar,You can directly import the jar into the project!
    • Contact me phone: +86 17802240898 postbox:[email protected]
    • Update content Optimize the Maven dependency structure, and upgrade the Maven dependency version to avoid hidden vulnerabilities to the greatest extent.
    • Use jar The jar contains the most basic operation requirements, which can simplify the dependent configuration. If you have the requirement to rely on customization, please refer to Maven Dependent Import. 3feeef21f8e32d0ab1f72452a561a99
    • Maven Dependent Import This is the dependency configuration of the framework pulled from the central repository. If you have imported the jar package, you do not need to configure the following maven dependencies.
    <repositories>
        <repository>
            <id>a</id>
            <name>sonatype</name>
            <url>https://s01.oss.sonatype.org/content/repositories/snapshots</url>
        </repository>
    </repositories>
    
    <dependencies>
        <dependency>
            <groupId>io.github.BeardedManZhao</groupId>
            <artifactId>dataTear</artifactId>
            <version>1.4.1-SNAPSHOT</version>
        </dependency>
    </dependencies>
    
    Source code(tar.gz)
    Source code(zip)
    DataTear-1.4.1.jar(4.74 MB)
  • v1.4(Sep 21, 2022)

Owner
LingYuZhao
LingYuZhao
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