Failed to load: com/amazon/deequ/checks/Check

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英文:

Failed to load : com/amazon/deequ/checks/Check

问题

我正在构建一个Spark应用程序，用于加载两个JSON文件，比较它们并打印出差异。我还尝试使用Amazon的aws deequ库来验证这些文件，但我遇到了以下异常：

警告：使用--illegal-access=warn来启用进一步非法反射访问操作的警告
警告：所有非法访问操作将在将来的版本中被拒绝
20/08/07 11:56:33 WARN NativeCodeLoader: 无法加载适用于您的平台的本地Hadoop库...在适用的情况下使用内置的Java类
错误：无法加载com.deeq.CompareDataFrames：com/amazon/deequ/checks/Check
log4j:警告：找不到附加程序，用于记录器（org.apache.spark.util.ShutdownHookManager）。
log4j:警告：请

当我提交作业给Spark时：

./spark-submit --class com.deeq.CompareDataFrames--master 
spark://saif-VirtualBox:7077 ~/Downloads/deeq-trial-1.0-SNAPSHOT.jar

我正在使用Ubuntu来托管Spark，之前在没有添加deequ来运行一些验证之前，它是正常工作的。我想知道是否在部署过程中漏掉了什么。这个错误似乎不是互联网上众所周知的错误。

代码：

import com.amazon.deequ.VerificationResult;
import com.amazon.deequ.VerificationSuite;
import com.amazon.deequ.checks.Check;
import com.amazon.deequ.checks.CheckLevel;
import com.amazon.deequ.checks.CheckStatus;
import com.amazon.deequ.constraints.Constraint;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;
import scala.Option;
import scala.Tuple2;
import scala.collection.mutable.ArraySeq;
import scala.collection.mutable.Seq;

public class CompareDataFrames {

    public static void main(String[] args) {
        SparkSession session = SparkSession.builder().appName("CompareDataFrames").getOrCreate();
        session.sparkContext().setLogLevel("ALL");

        StructType schema = DataTypes.createStructType(new StructField[]{
                DataTypes.createStructField("CUST_ID", DataTypes.StringType, true),
                DataTypes.createStructField("RECORD_LOCATOR_ID", DataTypes.StringType, true),
                DataTypes.createStructField("EVNT_ID", DataTypes.StringType, true)
        });

        Dataset<Row> first = session.read().option("multiline", "true").schema(schema).json("/home/saif/Downloads/FILE_DEV1.json");
        System.out.println("======= DataSet 1 =======");
        first.printSchema();
        first.show(false);

        Dataset<Row> second = session.read().option("multiline", "true").schema(schema).json("/home/saif/Downloads/FILE_DEV2.json");
        System.out.println("======= DataSet 2 =======");
        second.printSchema();
        second.show(false);

        // This will show all the rows which are present in the first dataset
        // but not present in the second dataset. But the comparison is at row
        // level and not at column level.
        System.out.println("======= Expect =======");
        first.except(second).show();
        StructType one = first.schema();
        JavaPairRDD<String, Row> pair1 = first.toJavaRDD().mapToPair((PairFunction<Row, String, Row>)
                row -> new Tuple2<>(row.getString(1), row));
        JavaPairRDD<String, Row> pair2 = second.toJavaRDD().mapToPair((PairFunction<Row, String, Row>)
                row -> new Tuple2<>(row.getString(1), row));
        System.out.println("======= Pair1 & Pair2 were created =======");
        JavaPairRDD<String, Row> subs = pair1.subtractByKey(pair2);
        JavaRDD<Row> rdd = subs.values();
        Dataset<Row> diff = session.createDataFrame(rdd, one);
        System.out.println("======= Diff Show =======");
        diff.show();

        Seq<Constraint> cons = new ArraySeq<>(0);
        VerificationResult vr = new VerificationSuite().onData(first)
                .addCheck(new Check(CheckLevel.Error(), "unit test", cons)
                        .isComplete("EVNT_ID", Option.empty())
                )
                .run();
        Seq<Check> checkSeq = new ArraySeq<>(0);
        if (vr.status() != CheckStatus.Success()) {
            Dataset<Row> vrr = vr.checkResultsAsDataFrame(session, vr, checkSeq);
            vrr.show(false);
        }
    }
}

Maven依赖：

<dependencies>
    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-core_2.12</artifactId>
        <version>3.0.0</version>
    </dependency>
    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-streaming_2.12</artifactId>
        <version>3.0.0</version>
        <scope>provided</scope>
    </dependency>
    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-sql_2.12</artifactId>
        <version>3.0.0</version>
    </dependency>
    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-catalyst_2.12</artifactId>
        <version>3.0.0</version>
    </dependency>
    <dependency>
        <groupId>com.amazon.deequ</groupId>
        <artifactId>deequ</artifactId>
        <version>1.0.4</version>
    </dependency>
    <dependency>
        <groupId>org.apache.logging.log4j</groupId>
        <artifactId>log4j-core</artifactId>
        <version>2.13.3</version>
    </dependency>
    <dependency>
        <groupId>org.scala-lang.modules</groupId>
        <artifactId>scala-java8-compat_2.13</artifactId>
        <version>0.9.1</version>
    </dependency>
</dependencies>

请注意，这只是代码和依赖项的翻译。如果您需要任何进一步的帮助或解释，请随时提问。

英文:

I'm building a spark application to load two json files, compare them, and print the differences. I also try to validate these files using amazon library aws deequ , but I'm getting the below exception:

WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations
WARNING: All illegal access operations will be denied in a future release
20/08/07 11:56:33 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Error: Failed to load com.deeq.CompareDataFrames: com/amazon/deequ/checks/Check
log4j:WARN No appenders could be found for logger (org.apache.spark.util.ShutdownHookManager).
log4j:WARN Please

when I submit the job to spark:

./spark-submit --class com.deeq.CompareDataFrames--master 
spark://saif-VirtualBox:7077 ~/Downloads/deeq-trial-1.0-SNAPSHOT.jar

I'm using Ubuntu to host spark, it was working without any issues before I added deequ to run some validation. I wonder if I'm missing something in the deployment process. It doesn't seem like this error is a well-know one on the internet.

Code :

<!-- begin snippet: java hide: false console: true babel: false -->

<!-- language: lang-java -->

import com.amazon.deequ.VerificationResult;
import com.amazon.deequ.VerificationSuite;
import com.amazon.deequ.checks.Check;
import com.amazon.deequ.checks.CheckLevel;
import com.amazon.deequ.checks.CheckStatus;
import com.amazon.deequ.constraints.Constraint;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;
import scala.Option;
import scala.Tuple2;
import scala.collection.mutable.ArraySeq;
import scala.collection.mutable.Seq;
public class CompareDataFrames {
public static void main(String[] args) {
SparkSession session = SparkSession.builder().appName(&quot;CompareDataFrames&quot;).getOrCreate();
session.sparkContext().setLogLevel(&quot;ALL&quot;);
StructType schema = DataTypes.createStructType(new StructField[]{
DataTypes.createStructField(&quot;CUST_ID&quot;, DataTypes.StringType, true),
DataTypes.createStructField(&quot;RECORD_LOCATOR_ID&quot;, DataTypes.StringType, true),
DataTypes.createStructField(&quot;EVNT_ID&quot;, DataTypes.StringType, true)
});
Dataset&lt;Row&gt; first = session.read().option(&quot;multiline&quot;, &quot;true&quot;).schema(schema).json(&quot;/home/saif/Downloads/FILE_DEV1.json&quot;);
System.out.println(&quot;======= DataSet 1 =======&quot;);
first.printSchema();
first.show(false);
Dataset&lt;Row&gt; second = session.read().option(&quot;multiline&quot;, &quot;true&quot;).schema(schema).json(&quot;/home/saif/Downloads/FILE_DEV2.json&quot;);
System.out.println(&quot;======= DataSet 2 =======&quot;);
second.printSchema();
second.show(false);
// This will show all the rows which are present in the first dataset
// but not present in the second dataset. But the comparison is at row
// level and not at column level.
System.out.println(&quot;======= Expect =======&quot;);
first.except(second).show();
StructType one = first.schema();
JavaPairRDD&lt;String, Row&gt; pair1 = first.toJavaRDD().mapToPair((PairFunction&lt;Row, String, Row&gt;)
row -&gt; new Tuple2&lt;&gt;(row.getString(1), row));
JavaPairRDD&lt;String, Row&gt; pair2 = second.toJavaRDD().mapToPair((PairFunction&lt;Row, String, Row&gt;)
row -&gt; new Tuple2&lt;&gt;(row.getString(1), row));
System.out.println(&quot;======= Pair1 &amp; Pair2 were created =======&quot;);
JavaPairRDD&lt;String, Row&gt; subs = pair1.subtractByKey(pair2);
JavaRDD&lt;Row&gt; rdd = subs.values();
Dataset&lt;Row&gt; diff = session.createDataFrame(rdd, one);
System.out.println(&quot;======= Diff Show =======&quot;);
diff.show();
Seq&lt;Constraint&gt; cons = new ArraySeq&lt;&gt;(0);
VerificationResult vr = new VerificationSuite().onData(first)
.addCheck(new Check(CheckLevel.Error(), &quot;unit test&quot;, cons)
.isComplete(&quot;EVNT_ID&quot;, Option.empty())
)
.run();
Seq&lt;Check&gt; checkSeq = new ArraySeq&lt;&gt;(0);
if (vr.status() != CheckStatus.Success()) {
Dataset&lt;Row&gt; vrr = vr.checkResultsAsDataFrame(session, vr, checkSeq);
vrr.show(false);
}
}
}

<!-- end snippet -->

**Maven: **

<!-- begin snippet: xml hide: false console: true babel: false -->

<!-- language: lang-xml -->

 &lt;dependencies&gt;
&lt;dependency&gt;
&lt;groupId&gt;org.apache.spark&lt;/groupId&gt;
&lt;artifactId&gt;spark-core_2.12&lt;/artifactId&gt;
&lt;version&gt;3.0.0&lt;/version&gt;
&lt;/dependency&gt;
&lt;dependency&gt;
&lt;groupId&gt;org.apache.spark&lt;/groupId&gt;
&lt;artifactId&gt;spark-streaming_2.12&lt;/artifactId&gt;
&lt;version&gt;3.0.0&lt;/version&gt;
&lt;scope&gt;provided&lt;/scope&gt;
&lt;/dependency&gt;
&lt;dependency&gt;
&lt;groupId&gt;org.apache.spark&lt;/groupId&gt;
&lt;artifactId&gt;spark-sql_2.12&lt;/artifactId&gt;
&lt;version&gt;3.0.0&lt;/version&gt;
&lt;/dependency&gt;
&lt;dependency&gt;
&lt;groupId&gt;org.apache.spark&lt;/groupId&gt;
&lt;artifactId&gt;spark-catalyst_2.12&lt;/artifactId&gt;
&lt;version&gt;3.0.0&lt;/version&gt;
&lt;/dependency&gt;
&lt;dependency&gt;
&lt;groupId&gt;com.amazon.deequ&lt;/groupId&gt;
&lt;artifactId&gt;deequ&lt;/artifactId&gt;
&lt;version&gt;1.0.4&lt;/version&gt;
&lt;/dependency&gt;
&lt;dependency&gt;
&lt;groupId&gt;org.apache.logging.log4j&lt;/groupId&gt;
&lt;artifactId&gt;log4j-core&lt;/artifactId&gt;
&lt;version&gt;2.13.3&lt;/version&gt;
&lt;/dependency&gt;
&lt;dependency&gt;
&lt;groupId&gt;org.scala-lang.modules&lt;/groupId&gt;
&lt;artifactId&gt;scala-java8-compat_2.13&lt;/artifactId&gt;
&lt;version&gt;0.9.1&lt;/version&gt;
&lt;/dependency&gt;

<!-- end snippet -->

答案1

得分: 1

请按照以下方法解决您的问题。

方法 1.

使用 --jars 选项提交 Spark 作业，
从以下 Maven 仓库下载 JAR 文件到您的机器上，链接为 https://mvnrepository.com/artifact/com.amazon.deequ/deequ/1.0.4，保存到 ~/Downloads/deequ-1.0.4.jar。

./spark-submit --class com.deeq.CompareDataFrames --master 
spark://saif-VirtualBox:7077 --jars ~/Downloads/deequ-1.0.4.jar ~/Downloads/deeq-trial-1.0-SNAPSHOT.jar

方法 2.

使用 --packages 选项提交 Spark 作业，

./spark-submit --class com.deeq.CompareDataFrames --master 
spark://saif-VirtualBox:7077 --packages com.amazon.deequ:deequ:1.0.4 ~/Downloads/deeq-trial-1.0-SNAPSHOT.jar

注：

  1. 只有在需要引用自定义仓库时才需要使用 --repositories 选项，默认情况下，如果未提供 --repositories 选项，则使用 Maven 中央仓库。
  2. 当指定 --packages 选项时，提交操作会尝试在 ~/.ivy2/cache、~/.ivy2/jars 和 ~/.m2/repository 目录中查找包及其依赖项。如果找不到，则会使用 Ivy 从 Maven 中央仓库下载并存储在 ~/.ivy2 目录下。

编辑 1：

方法 3：

如果上述方法 1 和 2 不起作用，请使用 maven-shade-plugin 构建 uber jar，然后使用 spark-submit 进行部署。
使用以下 pom.xml 文件来使用 maven-shade-plugin 构建 uber jar。添加下面的 POM 配置并重新构建您的 JAR 文件，然后使用以下命令进行部署：

spark-submit --class com.deeq.CompareDataFrames --master 
spark://saif-VirtualBox:7077 ~/Downloads/deeq-trial-1.0-SNAPSHOT.jar

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
	xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
	<modelVersion>4.0.0</modelVersion>
	<groupId>com.deeq</groupId>
	<artifactId>deeq-trial-1.0-SNAPSHOT</artifactId>
	<version>1.0</version>
	<name>Spark-3.0  Spark  Application</name>
	<url>https://maven.apache.org</url>
	<repositories>
		<repository>
			<id>codelds</id>
			<url>https://code.lds.org/nexus/content/groups/main-repo</url>
		</repository>
		<repository>
			<id>central</id>
			<name>Maven Repository Switchboard</name>
			<layout>default</layout>
			<url>https://repo1.maven.org/maven2</url>
			<snapshots>
				<enabled>false</enabled>
			</snapshots>
		</repository>
	</repositories>
	<properties>
		<maven.compiler.source>1.8</maven.compiler.source>
		<maven.compiler.target>1.8</maven.compiler.target>
		<encoding>UTF-8</encoding>
		<scala.version>2.12.8</scala.version>
		<java.version>1.8</java.version>
		<CodeCacheSize>512m</CodeCacheSize>
		<es.version>2.4.6</es.version>
	</properties>
	<dependencies>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.12</artifactId>
            <version>3.0.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_2.12</artifactId>
            <version>3.0.0</version>
            <scope>provided</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.12</artifactId>
            <version>3.0.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-catalyst_2.12</artifactId>
            <version>3.0.0</version>
        </dependency>
        <dependency>
            <groupId>com.amazon.deequ</groupId>
            <artifactId>deequ</artifactId>
            <version>1.0.4</version>
        </dependency>
        <dependency>
            <groupId>org.apache.logging.log4j</groupId>
            <artifactId>log4j-core</artifactId>
            <version>2.13.3</version>
        </dependency>
        <dependency>
            <groupId>org.scala-lang.modules</groupId>
            <artifactId>scala-java8-compat_2.13</artifactId>
            <version>0.9.1</version>
        </dependency>
	</dependencies>
	<build>
		<resources>
			<resource>
				<directory>src/main/resources</directory>
			</resource>
		</resources>
		<plugins>
			<plugin>
				<groupId>net.alchim31.maven</groupId>
				<artifactId>scala-maven-plugin</artifactId>
				<version>3.2.2</version>
				<executions>
					<execution>
						<id>eclipse-add-source</id>
						<goals>
							<goal>add-source</goal>
						</goals>
					</execution>
					<execution>
						<id>scala-compile-first</id>
						<phase>process-resources</phase>
						<goals>
							<goal>compile</goal>
						</goals>
					</execution>
					<execution>
						<id>scala-test-compile-first</id>
						<phase>process-test-resources</phase>
						<goals>
							<goal>testCompile</goal>
						</goals>
					</execution>
					<execution>
						<id>attach-scaladocs</id>
						<phase

<details>
<summary>英文:</summary>


Please follow the below approaches to resolve your problem.


**Approach 1.** 

spark submit with `--jars` option,
Download the jar from the following Maven Repo to your machine, https://mvnrepository.com/artifact/com.amazon.deequ/deequ/1.0.4 to `~/Downloads/deequ-1.0.4.jar`

./spark-submit --class com.deeq.CompareDataFrames --master
spark://saif-VirtualBox:7077 --jars ~/Downloads/deequ-1.0.4.jar ~/Downloads/deeq-trial-1.0-SNAPSHOT.jar


**Approach 2.** 
spark submit with `--packages` option,

./spark-submit --class com.deeq.CompareDataFrames --master
spark://saif-VirtualBox:7077 --packages com.amazon.deequ:deequ:1.0.4 ~/Downloads/deeq-trial-1.0-SNAPSHOT.jar

**Notes:**
1. The `--repositories` option is required only if some custom repository has to be referenced
By default the maven central repository is used if the `--repositories` option is not provided
When `--packages` option is specified, the submit operation tries to look for the packages and their dependencies in the `~/.ivy2/cache`, `~/.ivy2/jars`, `~/.m2/repository` directories.
If they are not found, then they are downloaded from maven central using ivy and stored under the `~/.ivy2` directory.
**Edit 1:** 
**Approach 3:** 
If the above solutions 1 &amp; 2 is not working then use `maven-shade-plugin` to build the `uber jar` and proceed with the `spark-submit`.
use the below `pom.xml` file for building uber jar using `maven-shade-plugin`. adding the below pom and rebuild your jar and deploy it with `spark-submit`.

spark-submit --class com.deeq.CompareDataFrames --master
spark://saif-VirtualBox:7077 ~/Downloads/deeq-trial-1.0-SNAPSHOT.jar

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd&quot;>
<modelVersion>4.0.0</modelVersion>
<groupId>com.deeq</groupId>
<artifactId>deeq-trial-1.0-SNAPSHOT</artifactId>
<version>1.0</version>
<name>Spark-3.0 Spark Application</name>
<url>https://maven.apache.org</url>
<repositories>
<repository>
<id>codelds</id>
<url>https://code.lds.org/nexus/content/groups/main-repo</url>
</repository>
<repository>
<id>central</id>
<name>Maven Repository Switchboard</name>
<layout>default</layout>
<url>https://repo1.maven.org/maven2</url>
<snapshots>
<enabled>false</enabled>
</snapshots>
</repository>
</repositories>
<properties>
<maven.compiler.source>1.8</maven.compiler.source>
<maven.compiler.target>1.8</maven.compiler.target>
<encoding>UTF-8</encoding>
<scala.version>2.12.8</scala.version>
<java.version>1.8</java.version>
<CodeCacheSize>512m</CodeCacheSize>
<es.version>2.4.6</es.version>
</properties>
<dependencies>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.12</artifactId>
<version>3.0.0</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming_2.12</artifactId>
<version>3.0.0</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_2.12</artifactId>
<version>3.0.0</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-catalyst_2.12</artifactId>
<version>3.0.0</version>
</dependency>
<dependency>
<groupId>com.amazon.deequ</groupId>
<artifactId>deequ</artifactId>
<version>1.0.4</version>
</dependency>
<dependency>
<groupId>org.apache.logging.log4j</groupId>
<artifactId>log4j-core</artifactId>
<version>2.13.3</version>
</dependency>
<dependency>
<groupId>org.scala-lang.modules</groupId>
<artifactId>scala-java8-compat_2.13</artifactId>
<version>0.9.1</version>
</dependency>
</dependencies>
<build>
<resources>
<resource>
<directory>src/main/resources</directory>
</resource>
</resources>
<plugins>
<plugin>
<groupId>net.alchim31.maven</groupId>
<artifactId>scala-maven-plugin</artifactId>
<version>3.2.2</version>
<executions>
<execution>
<id>eclipse-add-source</id>
<goals>
<goal>add-source</goal>
</goals>
</execution>
<execution>
<id>scala-compile-first</id>
<phase>process-resources</phase>
<goals>
<goal>compile</goal>
</goals>
</execution>
<execution>
<id>scala-test-compile-first</id>
<phase>process-test-resources</phase>
<goals>
<goal>testCompile</goal>
</goals>
</execution>
<execution>
<id>attach-scaladocs</id>
<phase>verify</phase>
<goals>
<goal>doc-jar</goal>
</goals>
</execution>
</executions>
<configuration>
<scalaVersion>${scala.version}</scalaVersion>
<recompileMode>incremental</recompileMode>
<useZincServer>true</useZincServer>
<args>
<arg>-unchecked</arg>
<arg>-deprecation</arg>
<arg>-feature</arg>
</args>
<jvmArgs>
<jvmArg>-Xms1024m</jvmArg>
<jvmArg>-Xmx1024m</jvmArg>
<jvmArg>-XX:ReservedCodeCacheSize=${CodeCacheSize}</jvmArg>
</jvmArgs>
<javacArgs>
<javacArg>-source</javacArg>
<javacArg>${java.version}</javacArg>
<javacArg>-target</javacArg>
<javacArg>${java.version}</javacArg>
<javacArg>-Xlint:all,-serial,-path</javacArg>
</javacArgs>
</configuration>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-shade-plugin</artifactId>
<executions>
<execution>
<phase>package</phase>
<goals>
<goal>shade</goal>
</goals>
<configuration>
<artifactSet>
<excludes>
<exclude>org.xerial.snappy</exclude>
<exclude>org.scala-lang.modules</exclude>
<exclude>org.scala-lang</exclude>
</excludes>
</artifactSet>
<filters>
<filter>
<artifact>:</artifact>
<excludes>
<exclude>META-INF/.SF</exclude>
<exclude>META-INF/.DSA</exclude>
<exclude>META-INF/*.RSA</exclude>
</excludes>
</filter>
</filters>
<relocations>
<relocation>
<pattern>com.google.common</pattern>
<shadedPattern>shaded.com.google.common</shadedPattern>
</relocation>
</relocations>
</configuration>
</execution>
</executions>
</plugin>
</plugins>
</build>
</project>


</details>

huangapple
  • 本文由 发表于 2020年8月8日 03:01:07
  • 转载请务必保留本文链接：https://go.coder-hub.com/63307771.html
  • amazon-deequ
  • apache-spark
  • java
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