我正在构建一个spark应用程序来加载两个json文件，比较它们，并打印差异。我还尝试使用amazon库验证这些文件 aws deequ ，但我得到以下例外：

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

当我把工作交给spark时：

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

我使用ubuntu来托管spark，在我添加deequ来运行一些验证之前，它没有任何问题。我想知道在部署过程中是否遗漏了什么。这似乎不是一个众所周知的错误在互联网上。
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：

<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>