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集成Spark开发
Spark编程读写示例
通过IDE如Idea编程实质上和前面的spark-shell和spark-sql相似，其他都是Spark编程的知识，下面以scala语言为示例，idea新建scala的maven项目
pom文件添加如下依赖
<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/maven-v4_0_0.xsd"> <modelVersion>4.0.0</modelVersion> <groupId>cn.itxs</groupId> <artifactId>hoodie-spark-demo</artifactId> <version>1.0</version> <properties> <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding> <scala.version>2.12.10</scala.version> <scala.binary.version>2.12</scala.binary.version> <spark.version>3.3.0</spark.version> <hoodie.version>0.12.1</hoodie.version> <hadoop.version>3.3.4</hadoop.version> </properties> <dependencies> <dependency> <groupId>org.scala-lang</groupId> <artifactId>scala-library</artifactId> <version>${scala.version}</version> </dependency> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-core_${scala.binary.version}</artifactId> <version>${spark.version}</version> <scope>provided</scope> </dependency> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-sql_${scala.binary.version}</artifactId> <version>${spark.version}</version> <scope>provided</scope> </dependency> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-hive_${scala.binary.version}</artifactId> <version>${spark.version}</version> <scope>provided</scope> </dependency> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-client</artifactId> <version>${hadoop.version}</version> <scope>provided</scope> </dependency> <dependency> <groupId>org.apache.hudi</groupId> <artifactId>hudi-spark3.3-bundle_${scala.binary.version}</artifactId> <version>${hoodie.version}</version> <scope>provided</scope> </dependency> </dependencies> <build> <plugins> <plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-compiler-plugin</artifactId> <version>3.10.1</version> <configuration> <source>1.8</source> <target>1.8</target> <encoding>${project.build.sourceEncoding}</encoding> </configuration> </plugin> <plugin> <groupId>org.scala-tools</groupId> <artifactId>maven-scala-plugin</artifactId> <version>2.15.2</version> <executions> <execution> <goals> <goal>compile</goal> <goal>testCompile</goal> </goals> </execution> </executions> </plugin> <plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-shade-plugin</artifactId> <version>3.2.4</version> <executions> <execution> <phase>package</phase> <goals> <goal>shade</goal> </goals> <configuration> <filters> <filter> <artifact>*:*</artifact> <excludes> <exclude>META-INF/*.SF</exclude> <exclude>META-INF/*.DSA</exclude> <exclude>META-INF/*.RSA</exclude> </excludes> </filter> </filters> </configuration> </execution> </executions> </plugin> </plugins> </build> </project> 创建常量对象
object Constant { val HUDI_STORAGE_PATH = "hdfs://192.168.5.53:9000/tmp/" } 插入hudi数据
package cn.itxs import org.apache.spark.sql.SparkSession import org.apache.spark.SparkConf import org.apache.hudi.QuickstartUtils._ import scala.collection.JavaConversions._ import org.apache.spark.sql.SaveMode._ import org.apache.hudi.DataSourceWriteOptions._ import org.apache.hudi.config.HoodieWriteConfig._ object InsertDemo { def main(args: Array[String]): Unit = { val sparkConf = new SparkConf() .setAppName(this.getClass.getSimpleName) .setMaster("local[*]") .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer") val sparkSession = SparkSession.builder() .config(sparkConf) .enableHiveSupport() .getOrCreate() val tableName = "hudi_trips_cow_idea" val basePath = Constant.HUDI_STORAGE_PATH+tableName val dataGen = new DataGenerator val inserts = convertToStringList(dataGen.generateInserts(10)) val df = sparkSession.read.json(sparkSession.sparkContext.parallelize(inserts,2)) df.write.format("hudi"). options(getQuickstartWriteConfigs). option(PRECOMBINE_FIELD.key(), "ts"). option(RECORDKEY_FIELD.key(), "uuid"). option(PARTITIONPATH_FIELD.key(), "partitionpath"). option(TBL_NAME.key(), tableName). mode(Overwrite). save(basePath) sparkSession.close() } } 由于依赖中scope是配置为provided，因此运行配置中勾选下面这项
运行InsertDemo程序写入hudi数据
运行ReadDemo程序读取hudi数据
通过mvn clean package打包后上传运行
spark-submit --class cn.itxs.ReadDemo /home/commons/spark-3.3.0-bin-hadoop3/appjars/hoodie-spark-demo-1.0.jar DeltaStreamer
HoodieDeltaStreamer实用程序(hudi-utilities-bundle的一部分)提供了从不同源(如DFS或Kafka)中获取的方法，具有以下功能。
- 从Kafka的新事件，从Sqoop的增量导入或输出HiveIncrementalPuller或DFS文件夹下的文件。
- 支持json, avro或自定义记录类型的传入数据。
- 管理检查点、回滚和恢复。
- 利用来自DFS或Confluent模式注册中心的Avro模式。
- 支持插入转换。
# 拷贝hudi-utilities-bundle_2.12-0.12.1.jar到spark的jars目录 cp /home/commons/hudi-release-0.12.1/packaging/hudi-utilities-bundle/target/hudi-utilities-bundle_2.12-0.12.1.jar jars/ # 查看帮助文档，参数非常多，可以在有需要使用的时候查阅 spark-submit --class org.apache.hudi.utilities.deltastreamer.HoodieDeltaStreamer /home/commons/spark-3.3.0-bin-hadoop3/jars/hudi-utilities-bundle_2.12-0.12.1.jar --help
该工具采用层次结构组成的属性文件，并具有提取数据、密钥生成和提供模式的可插入接口。在hudi-下提供了从kafka和dfs中摄取的示例配置
接下里以File Based Schema Provider和JsonKafkaSoiurce为示例演示如何使用
# 创建topic bin/kafka-topics.sh --zookeeper zk1:2181,zk2:2181,zk3:2181 --create --partitions 1 --replication-factor 1 --topic data_test 然后编写demo程序持续向这个kafka的topic发送消息
# 创建一个配置文件目录 mkdir /home/commons/hudi-properties # 拷贝示例配置文件 cp hudi-utilities/src/test/resources/delta-streamer-config/kafka-source.properties /home/commons/hudi-properties/ cp hudi-utilities/src/test/resources/delta-streamer-config/base.properties /home/commons/hudi-properties/ 定义avro所需的schema文件包括source和target,创建source文件 vim source-json-schema.avsc
{ "type" : "record", "name" : "Profiles", "fields" : [ { "name" : "id", "type" : "long" }, { "name" : "name", "type" : "string" }, { "name" : "age", "type" : "int" }, { "name" : "partitions", "type" : "int" } ] } 拷贝一份为target文件
cp source-json-schema.avsc target-json-schema.avsc 修改kafka-source.properties的配置如下
include=hdfs://hadoop2:9000/hudi-properties/base.properties # Key fields, for kafka example hoodie.datasource.write.recordkey.field=id hoodie.datasource.write.partitionpath.field=partitions # schema provider configs #hoodie.deltastreamer.schemaprovider.registry.url=http://localhost:8081/subjects/impressions-value/versions/latest hoodie.deltastreamer.schemaprovider.source.schema.file=hdfs://hadoop2:9000/hudi-properties/source-json-schema.avsc hoodie.deltastreamer.schemaprovider.target.schema.file=hdfs://hadoop2:9000/hudi-properties/target-json-schema.avsc # Kafka Source #hoodie.deltastreamer.source.kafka.topic=uber_trips hoodie.deltastreamer.source.kafka.topic=data_test #Kafka props bootstrap.servers=kafka1:9092,kafka2:9092,kafka3:9092 auto.offset.reset=earliest #schema.registry.url=http://localhost:8081 group.id=mygroup 将本地hudi-properties文件夹上传到HDFS
cd .. hdfs dfs -put hudi-properties/ /
# 运行导入命令 spark-submit --class org.apache.hudi.utilities.deltastreamer.HoodieDeltaStreamer /home/commons/spark-3.3.0-bin-hadoop3/jars/hudi-utilities-bundle_2.12-0.12.1.jar --props hdfs://hadoop2:9000/hudi-properties/kafka-source.properties --schemaprovider-class org.apache.hudi.utilities.schema.FilebasedSchemaProvider --source-class org.apache.hudi.utilities.sources.JsonKafkaSource --source-ordering-field id --target-base-path hdfs://hadoop2:9000/tmp/hudi/user_test --target-table user_test --op BULK_INSERT --table-type MERGE_ON_READ
查看hdfs目录已经有表目录和分区目录
通过spark-sql查询从kafka摄取的数据
use hudi_spark; create table user_test using hudi location 'hdfs://hadoop2:9000/tmp/hudi/user_test'; select * from user_test limit 10;
集成Flink
环境准备
# 解压进入flink目录，这里我就用之前flink的环境，详细可以查看之前关于flink的文章 cd /home/commons/flink-1.15.1 # 拷贝编译好的jar到flink的lib目录 cp /home/commons/hudi-release-0.12.1/packaging/hudi-flink-bundle/target/hudi-flink1.15-bundle-0.12.1.jar lib/
# 拷贝guava包，解决依赖冲突 cp /home/commons/hadoop/share/hadoop/common/lib/guava-27.0-jre.jar lib/ # 配置hadoop环境变量和启动hadoop export HADOOP_CLASSPATH=`$HADOOP_HOME/bin/hadoop classpath` sql-clent使用
启动
修改配置文件 vi conf/flink-conf.yaml
classloader.check-leaked-classloader: false taskmanager.numberOfTaskSlots: 4 state.backend: rocksdb state.checkpoints.dir: hdfs://hadoop2:9000/checkpoints/flink state.backend.incremental: true execution.checkpointing.interval: 5min - local 模式
修改workers文件，也可以多配制几个（伪分布式或完全分布式），官方提供示例是4个
localhost localhost localhost # 在本机上启动三个TaskManagerRunner和一个Standalone伪分布式集群 ./bin/start-cluster.sh # 查看进程确认 jps -l
# 启动内嵌的flink sql客户端 ./bin/sql-client.sh embedded show databases; show tables;
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yarn-session 模式
- 解决依赖冲突问题
# 拷贝jar到flink的lib目录 cp /home/commons/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-client-core-3.3.4.jar lib/- 启动yarn-session
# 先停止上面启动Standalone伪分布式集群 ./bin/stop-cluster.sh # 启动yarn-session分布式集群 ./bin/yarn-session.sh --detached
查看yarn上已经有一个Flink session集群job， ID为application_1669357770610_0015
查看Flink的Web UI可用TaskSlots为0，可确认已切换为yarn管理资源非分配
- 启动sql-client
# 由于使用内嵌模式管理元数据，元数据是保存在内存中，关闭sql-client后则元数据也会消失，生产环境建议使用如Hive元数据管理方式，后面再做配置 ./bin/sql-client.sh embedded -s yarn-session show databases; show tables;
插入数据
CREATE TABLE t1( uuid VARCHAR(20), name VARCHAR(10), age INT, ts TIMESTAMP(3), `partition` VARCHAR(20), PRIMARY KEY(uuid) NOT ENFORCED ) PARTITIONED BY (`partition`) WITH ( 'connector' = 'hudi', 'path' = 'hdfs://hadoop1:9000/tmp/hudi_flink/t1', 'table.type' = 'MERGE_ON_READ' -- 创建一个MERGE_ON_READ表，默认情况下是COPY_ON_WRITE表 ); -- 插入数据 INSERT INTO t1 VALUES ('id1','Danny',23,TIMESTAMP '2022-11-25 00:00:01','par1'), ('id2','Stephen',33,TIMESTAMP '2022-11-25 00:00:02','par1'), ('id3','Julian',53,TIMESTAMP '2022-11-25 00:00:03','par2'), ('id4','Fabian',31,TIMESTAMP '2022-11-25 00:00:04','par2'), ('id5','Sophia',18,TIMESTAMP '2022-11-25 00:00:05','par3'), ('id6','Emma',20,TIMESTAMP '2022-11-25 00:00:06','par3'), ('id7','Bob',44,TIMESTAMP '2022-11-25 00:00:07','par4'), ('id8','Han',56,TIMESTAMP '2022-11-25 00:00:08','par4');
查看Flink Web UI Job的信息
# 查询数据 select * from t1;
# 更新数据 INSERT INTO t1 VALUES ('id1','Danny',28,TIMESTAMP '2022-11-25 00:00:01','par1'); # 查询数据 select * from t1;
流式读取
-- 设置结果模式为tableau，在CLI中直接显示结果;另外还有table和changelog；changelog模式可以获取+I,-U之类动作数据； set 'sql-client.execution.result-mode' = 'tableau'; CREATE TABLE sourceT ( uuid varchar(20), name varchar(10), age int, ts timestamp(3), `partition` varchar(20), PRIMARY KEY(uuid) NOT ENFORCED ) WITH ( 'connector' = 'datagen', 'rows-per-second' = '1' ); CREATE TABLE t2 ( uuid varchar(20), name varchar(10), age int, ts timestamp(3), `partition` varchar(20), PRIMARY KEY(uuid) NOT ENFORCED ) WITH ( 'connector' = 'hudi', 'path' = 'hdfs://hadoop1:9000/tmp/hudi_flink/t2', 'table.type' = 'MERGE_ON_READ', 'read.streaming.enabled' = 'true', 'read.streaming.check-interval' = '4' ); insert into t2 select * from sourceT; select * from t2;
Bucket索引
在0.11.0增加了一种高效、轻量级的索引类型bucket index，其为字节贡献回馈给hudi社区。
- Bucket Index是一种Hash分配方式，根据指定的索引字段，计算hash值，然后结合Bucket个数，均匀分配到具体的文件中。Bucket Index支持大数据量场景下的更新，Bucket Index也可以对数据进行分桶存储，但是对于桶数的计算是需要根据当前数据量的大小进行评估的，如果后续需要re-hash的话成本也会比较高。在这里我们预计通过建立Extensible Hash Index来提高哈希索引的可扩展能力。
- 要使用此索引，请将索引类型设置为BUCKET并设置hoodie.storage.layout.partitioner.class为
org.apache.hudi.table.action.commit.SparkBucketIndexPartitioner。对于 Flink，设置index.type=BUCKET. - 该方式相比于BloomIndex在元素定位性能高很多，缺点是Bucket个数无法动态扩展。另外Bucket不适合于COW表，否则会导致写放大更严重。
- 实时入湖写入的性能要求高的场景建议采用Bucket索引。
Hudi Catalog
前面基于内容管理hudi元数据的方式每次重启sql客户端就丢掉了，Hudi Catalog则是可以持久化元数据；Hudi Catalog支持多种模式，包括dfs和hms,hudi还可以直接集群hive使用，后续再一步步演示，现在先简单看下dfs模式的Hudi Catalog，先添加启动sql文件，vim conf/sql-client-init.sql
create catalog hudi_catalog with( 'type' = 'hudi', 'mode' = 'dfs', 'catalog.path'='/tmp/hudi_catalog' ); use catalog hudi_catalog; create catalog hudi_catalog with( 'type' = 'hudi', 'mode' = 'hms', 'hive.conf.dir'='/etc/hive/conf' ); 创建目录并启动，建表测试
hdfs dfs -mkdir /tmp/hudi_catalog ./bin/sql-client.sh embedded -i conf/sql-client-init.sql -s yarn-session
查看hdfs的数据如下，退出客户端后重新登录客户端还可以查到上面的hudi_catalog及其库和表的数据。
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