【Spark 实战】基于spark3.4.2+iceberg1.6.1搭建本地调试环境
基于spark3.4.2+iceberg1.6.1搭建本地调试环境
环境准备
- IntelliJ IDEA 2024.1.2 (Ultimate Edition)
- JDK 1.8
- Spark 3.4.2
- Iceberg 1.6.1
使用maven构建sparksql
pom文件
<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.donny.demo</groupId>
<artifactId>iceberg-demo</artifactId>
<version>1.0-SNAPSHOT</version>
<packaging>jar</packaging>
<name>iceberg-demo</name>
<url>http://maven.apache.org</url>
<properties>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<spark.version>3.4.2</spark.version>
<iceberg.version>1.6.1</iceberg.version>
<parquet.version>1.13.1</parquet.version>
<avro.version>1.11.3</avro.version>
<parquet.hadoop.bundle.version>1.8.1</parquet.hadoop.bundle.version>
</properties>
<dependencies>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.12</artifactId>
<version>${spark.version}</version>
<exclusions>
<exclusion>
<groupId>org.apache.avro</groupId>
<artifactId>avro</artifactId>
</exclusion>
</exclusions>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_2.12</artifactId>
<version>${spark.version}</version>
<exclusions>
<exclusion>
<groupId>org.apache.parquet</groupId>
<artifactId>parquet-column</artifactId>
</exclusion>
<exclusion>
<groupId>org.apache.parquet</groupId>
<artifactId>parquet-hadoop-bundle</artifactId>
</exclusion>
<exclusion>
<groupId>org.apache.parquet</groupId>
<artifactId>parquet-hadoop</artifactId>
</exclusion>
</exclusions>
</dependency>
<dependency>
<groupId>org.apache.iceberg</groupId>
<artifactId>iceberg-core</artifactId>
<version>${iceberg.version}</version>
</dependency>
<dependency>
<groupId>org.apache.iceberg</groupId>
<artifactId>iceberg-spark-3.4_2.12</artifactId>
<version>${iceberg.version}</version>
</dependency>
<dependency>
<groupId>org.apache.iceberg</groupId>
<artifactId>iceberg-spark-extensions-3.4_2.12</artifactId>
<version>${iceberg.version}</version>
<exclusions>
<exclusion>
<groupId>org.antlr</groupId>
<artifactId>antlr4</artifactId>
</exclusion>
<exclusion>
<groupId>org.antlr</groupId>
<artifactId>antlr4-runtime</artifactId>
</exclusion>
</exclusions>
</dependency>
<dependency>
<groupId>org.apache.parquet</groupId>
<artifactId>parquet-column</artifactId>
<version>${parquet.version}</version>
</dependency>
<dependency>
<groupId>org.apache.parquet</groupId>
<artifactId>parquet-hadoop</artifactId>
<version>${parquet.version}</version>
</dependency>
<dependency>
<groupId>org.apache.parquet</groupId>
<artifactId>parquet-hadoop-bundle</artifactId>
<version>${parquet.hadoop.bundle.version}</version>
</dependency>
<dependency>
<groupId>org.apache.avro</groupId>
<artifactId>avro</artifactId>
<version>${avro.version}</version>
</dependency>
<dependency>
<groupId>junit</groupId>
<artifactId>junit</artifactId>
<version>3.8.1</version>
<scope>test</scope>
</dependency>
</dependencies>
</project>
在 idea 中 直接使用iceberg 生成好的 runtime jar，无法attach 不上 iceberg 的源码，为了解决这个问题把maven 依赖改成上面的pom文件上的iceberg依赖。
<dependency>
<groupId>org.apache.iceberg</groupId>
<artifactId>iceberg-spark-runtime-3.4_2.12</artifactId>
<version>1.6.1</version>
</dependency>
编辑SparkSQL简单任务
- 指定了 catalog 类型为 hadoop。可以方便简单的本地调试。
- 创建非分区的iceberg原生表
- 插入数据
- 查询数据（展示数据）
package com.donny.demo;
import org.apache.iceberg.expressions.Expressions;
import org.apache.iceberg.spark.Spark3Util;
import org.apache.iceberg.spark.actions.SparkActions;
import org.apache.spark.api.java.function.FilterFunction;
import org.apache.spark.sql.AnalysisException;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.catalog.Table;
import java.util.Objects;
/**
* @author 1792998761@qq.com
* @version 1.0
* @since 2024年09月26日
*/
public class IcebergSparkDemo {
public static void main(String[] args) throws AnalysisException {
SparkSession spark = SparkSession
.builder()
.master("local")
.appName("Iceberg spark example")
.config("spark.sql.extensions", "org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions")
.config("spark.sql.catalog.local", "org.apache.iceberg.spark.SparkCatalog")
.config("spark.sql.catalog.local.type", "hadoop") //指定catalog 类型
.config("spark.sql.catalog.local.warehouse", "iceberg_warehouse")
.getOrCreate();
spark.sql("create database iceberg_db");
spark.sql("CREATE TABLE local.iceberg_db.table (id bigint, data string) USING iceberg ");
spark.sql("INSERT INTO local.iceberg_db.table VALUES (1, 'a'), (2, 'b'), (3, 'c')");
Dataset<Row> result = spark.sql("select * from local.iceberg_db.table order by data");
result.show();
spark.close();
}
}
附录A iceberg术语
- Schema – 表中的字段名称和类型
- Partition spec – 定义如何从数据字段导出分区值。
- Partition tuple – 分区元组是存储在每个数据文件中的分区数据的元组或结构体。
- Snapshot – 表在某个时间点的状态，包括所有数据文件的集合。
- Snapshot log – 快照日志是记录表当前快照随时间变化情况的元数据日志。该日志是一个时间戳和ID对的列表:当前快照发生变化的时间和当前快照发生变化的ID。
- Manifest list – 列出清单文件的文件；每个快照一个。
- Manifest – 列出数据或删除文件的文件；快照的子集。
- Data file – 包含表行的文件。
- Delete file – 对表格中按位置或数据值删除的行进行编码的文件。
参考
原文地址：https://blog.csdn.net/weixin_43820556/article/details/142631269
免责声明：本站文章内容转载自网络资源，如本站内容侵犯了原著者的合法权益，可联系本站删除。更多内容请关注自学内容网（zxcms.com）！