scala – 如何在Mesos集群上使用Spark时预先打包外部库

发布时间：2020-12-16 09:25:51 所属栏目：安全来源：网络整理

导读：根据 Spark on Mesos docs,需要设置指向Spark发行版的spark.executor.uri： val conf = new SparkConf() .setMaster("mesos://HOST:5050") .setAppName("My app") .set("spark.executor.uri","path to spark-1.4.1.tar.gz uploaded above") 文档还指出,可以

根据 Spark on Mesos docs,需要设置指向Spark发行版的spark.executor.uri：

val conf = new SparkConf()
  .setMaster("mesos://HOST:5050")
  .setAppName("My app")
  .set("spark.executor.uri","<path to spark-1.4.1.tar.gz uploaded above>")

文档还指出,可以构建Spark发行版的自定义版本.

我现在的问题是是否可能/希望预先打包外部库,例如

> spark-streaming-kafka
> elasticsearch-spark
> spark-csv

这将用于我将通过spark-submit提交的大部分工作罐

>减少sbt组装需要包装脂肪罐的时间
>减少需要提交的脂肪罐的大小

如果是这样,怎么能实现呢？一般来说,是否有一些提示可以加快工作提交过程中脂肪罐的生成速度？

背景是我想为Spark作业运行一些代码生成,并立即提交这些代码并在浏览器前端异步显示结果.前端部分不应该太复杂,但我想知道后端部分是如何实现的.

解决方法

使用所有依赖项创建示例maven项目,然后使用maven插件maven-shade-plugin.它将在目标文件夹中创建一个阴影罐.

这是样品pom

<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</groupId>
    <artifactId>test</artifactId>
    <version>0.0.1</version>
    <properties>
        <java.version>1.7</java.version>
        <hadoop.version>2.4.1</hadoop.version>
        <spark.version>1.4.0</spark.version>
        <version.spark-csv_2.10>1.1.0</version.spark-csv_2.10>
        <version.spark-avro_2.10>1.0.0</version.spark-avro_2.10>
    </properties>
    <build>
        <plugins>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <version>3.1</version>
                <configuration>
                    <source>${java.version}</source>
                    <target>${java.version}</target>
                </configuration>
            </plugin>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-shade-plugin</artifactId>
                <version>2.3</version>
                <executions>
                    <execution>
                        <phase>package</phase>
                        <goals>
                            <goal>shade</goal>
                        </goals>
                    </execution>
                </executions>
                <configuration>
                    <!-- <minimizeJar>true</minimizeJar> -->
                    <filters>
                        <filter>
                            <artifact>*:*</artifact>
                            <excludes>
                                <exclude>META-INF/*.SF</exclude>
                                <exclude>META-INF/*.DSA</exclude>
                                <exclude>META-INF/*.RSA</exclude>
                                <exclude>org/bdbizviz/**</exclude>
                            </excludes>
                        </filter>
                    </filters>
                    <finalName>spark-${project.version}</finalName>
                </configuration>
            </plugin>
        </plugins>
    </build>
    <dependencies>
        <dependency> <!-- Hadoop dependency -->
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>${hadoop.version}</version>
            <exclusions>
                <exclusion>
                    <artifactId>servlet-api</artifactId>
                    <groupId>javax.servlet</groupId>
                </exclusion>
                <exclusion>
                    <artifactId>guava</artifactId>
                    <groupId>com.google.guava</groupId>
                </exclusion>
            </exclusions>
        </dependency>
        <dependency>
            <groupId>joda-time</groupId>
            <artifactId>joda-time</artifactId>
            <version>2.4</version>
        </dependency>

        <dependency> <!-- Spark Core -->
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.10</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency> <!-- Spark SQL -->
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.10</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency> <!-- Spark CSV -->
            <groupId>com.databricks</groupId>
            <artifactId>spark-csv_2.10</artifactId>
            <version>${version.spark-csv_2.10}</version>
        </dependency>
        <dependency> <!-- Spark Avro -->
            <groupId>com.databricks</groupId>
            <artifactId>spark-avro_2.10</artifactId>
            <version>${version.spark-avro_2.10}</version>
        </dependency>
        <dependency> <!-- Spark Hive -->
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-hive_2.10</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency> <!-- Spark Hive thriftserver -->
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-hive-thriftserver_2.10</artifactId>
            <version>${spark.version}</version>
        </dependency>
    </dependencies>
</project>

（编辑：李大同）

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